Depression and suicidality as evolved credible signals of need in social conflicts

Michael R. Gaffney (Department of Anthropology, Washington State University) , Kai H. Adams (UC Berkeley Haas School of Business) , Kristen L. Syme (Department of Experimental and Applied Psychology, Vrije Universiteit Amsterdam) , Edward H. Hagen (Department of Anthropology, Washington State University)
January 19, 2022

Abstract

Mental health professionals generally view major depression and suicidality as pathological responses to stress that elicit aversive responses from others. An alternative hypothesis grounded in evolutionary theory contends that depression and suicidality are honest signals of need in response to adversity that can increase support from reluctant others when there are conflicts of interest. To test this hypothesis, we examined responses to emotional signals in a preregistered experimental vignette study involving claims of substantial need in the presence of conflicts of interest and private information about the signaler’s true level of need. In a sample of 1,240 participants recruited from Amazon Mechanical Turk, costlier signals like depression and suicidality resulted in greater perceptions of need, reduced perceptions of manipulativeness, and increased likelihood of support compared to simple verbal requests and crying without further symptoms. Additionally, as predicted, the effect of signaling on likelihood of support was largely mediated by the effect of signaling on participants’ belief that the signaler was genuinely in need. Our results support the hypothesis that depression and suicidality, apparent human universals, are credible signals of need that elicit more support than verbal requests, sad expressions, and crying when there are conflicts of interest.

Introduction

In a classic study, Coyne (1976) found that depression alienates others, a result that was subsequently confirmed in numerous studies (Segrin, 2000; Segrin & Dillard, 1992). (For DSM-5 depression symptoms, see Table 1.) In interactions with spouses and others, depressed individuals express anger and aggression, make frequent demands for help, self-disclose personally relevant negative issues at inappropriate times, and view such topics as more appropriate for discussion than do the non-depressed (Segrin, 2000; Segrin & Abramson, 1994). Such self-disclosures have been shown to be a key ingredient in the rejection of depressed persons by others, and “may appropriately be understood as an attempt to elicit social support from targets” (Segrin & Abramson, 1994, p. 657). Excessive reassurance seeking – repeatedly requesting reassurance that one is lovable and worthy despite previous attempts by others to provide such reassurance – is another factor implicated in the rejection of the depressed by others (Joiner & Metalsky, 1995; Joiner, Metalsky, Katz, & Beach, 1999; Starr & Davila, 2008).

Table 1: DSM-5 criteria for a Major Depressive Episode include five or more of the criteria, at least one of which is criteria 1 or 2. The symptom must persist most of the day, daily, for at least 2 weeks in a row. For more details, see American Psychiatric Association (2013).
Symptom
  1. Depressed mood—indicated by subjective report or observation by others (in children and adolescents, can be irritable mood).
  1. Loss of interest or pleasure in almost all activities—indicated by subjective report or observation by others.
  1. Significant (more than 5 percent in a month) unintentional weight loss/gain or decrease/increase in appetite (in children, failure to make expected weight gains).
  1. Sleep disturbance (insomnia or hypersomnia).
  1. Psychomotor changes (agitation or retardation) severe enough to be observable by others.
  1. Tiredness, fatigue, or low energy, or decreased efficiency with which routine tasks are completed.
  1. A sense of worthlessness or excessive, inappropriate, or delusional guilt (not merely self-reproach or guilt about being sick).
  1. Impaired ability to think, concentrate, or make decisions—indicated by subjective report or observation by others.
  1. Recurrent thoughts of death (not just fear of dying), suicidal ideation, or suicide attempts.

Negative social responses to depression are widely interpreted as evidence of impaired social functioning in the depressed (Evraire & Dozois, 2011; Gadassi & Rafaeli, 2015; Gotlib & Lee, 1989; Hames, Hagan, & Joiner, 2013; Hirschfeld et al., 2000; Kupferberg, Bicks, & Hasler, 2016; Weightman, Knight, & Baune, 2019). This interpretation is reinforced by the equally widespread view that depressed individuals have impaired, negative perceptions of themselves and their environments (Beck, 1963; Joiner, 2007; Nolen-Hoeksema, 1991). Together with the costs of depression, such as profound loss of interest in virtually all activities and suicide, these facts are the basis of the mainstream claim that depression is a psychopathology.

In contrast to these views, we will argue that the depressed have suffered genuinely severe forms of adversity and are therefore genuinely in need, but often have conflicts with their social partners. In these circumstances, costly and putatively dysfunctional depressive behaviors can instead be understood as aversive but credible and adaptive signals of need that elicit more support than verbal requests, sad expressions, and crying when there are conflicts of interest. We follow Smith & Harper (2003) in defining a signal as (p. 15):

An act or structure that alters the behaviour of another organism, which evolved because of that effect, and which is effective because the receiver’s response has also evolved.

Depression is caused by genuine adversity

All individuals suffer adversity, such as injury or loss of material or social resources, at some point in their lives, with over 70% of participants in a global survey reporting exposure to a traumatic event (Benjet et al., 2016). Psychological pain, such as sadness and low mood, probably evolved to motivate victims of adversity to shift their attention to the causes of adversity so as to mitigate its negative fitness consequences and to learn to avoid future such adverse events (Andrews & Thomson, 2010; Del Giudice, 2018; Nesse, 1990; Thornhill & Thornhill, 1989). Over human evolution, social partners could have often helped victims, and therefore signals of psychological pain, such as sad expressions and crying, probably evolved to indicate need (Balsters, Krahmer, Swerts, & Vingerhoets, 2013; Bowlby, 1980; e.g., Darwin, 1872; Reed & DeScioli, 2017).

Contrary to the view that the depressed have a distorted perception of their environment, there is strong evidence that most cases of depression are caused by genuinely severe negative life events, such as physical assault and death of a loved one (Devries et al., 2013, 2011; Ellsberg et al., 2008; Hammen, 2005; Mazure, 1998). Compared to non-depressed individuals, those with depression report about twice as many negative events (Mazure, 1998) and more negative events than those with schizophrenia and bipolar depression across multiple studies (Paykel, 1994). Longitudinal studies indicate that depression onsets soon after a negative event (Han et al., 2019; Kendler, Karkowski, & Prescott, 1999; Lewinsohn, Hoberman, & Rosenbaum, 1988; Rich, Gidycz, Warkentin, Loh, & Weiland, 2005; Sen et al., 2010) or coincides with periods where adversity is likely to increase prior to it (e.g., depression starting before a divorce rather than after, Blekesaune, 2008; Metsä-Simola & Martikainen, 2013; Rosenström et al., 2017). Although the relationship between negative events and depression is likely bidirectional (i.e., depression probably also causes adversity, Wichers et al., 2012), negative events predict depression even when considering only events outside of one’s control, indicating that the connection is unlikely to be driven solely by individuals who are already depressed selecting into situations where negative events are likely to be common (Hammen, 2005; Kendler et al., 1999). Furthermore, twin studies have shown that one’s history of negative events remains a strong predictor of major depression when controlling for genetic similarity, and that part of the heritability of depression stems from the heritability of negative events like divorce and family conflict (Kendler & Baker, 2007; Kendler et al., 1999).

For these and other reasons, we and others argue that most cases of depression are probably functional instances of psychological pain, i.e., the severe end of a spectrum of adaptive low mood, sadness, and grief, and not mental dysfunctions (Andrews & Thomson, 2010; Dowrick & Frances, 2013; Frances, 2013; Hagen, 2003; Hagen & Syme, 2021; Horwitz & Wakefield, 2007). For reviews of other evolutionary theories of depression, see Hagen (2011) and Durisko, Mulsant, & Andrews (2015).

Depression, anger, and conflict

One might expect that victims of adversity who become depressed would receive positive responses from family, friends, colleagues, and perhaps even strangers. Indeed, beneficial responses to depressed individuals have also been reported, such as increased caretaking (Hokanson, Loewenstein, Hedeen, & Howes, 1986), more offers of advice and support (Stephens, Hokanson, & Welker, 1987), and reduced aggression within families (Dadds, Sanders, Morrison, & Rebgetz, 1992; Hops et al., 1987; Sheeber, Hops, & Davis, 2001). Why, though, would these positive responses often be accompanied by negative ones?

The missing piece of the puzzle is that depression is closely associated with anger and conflict (Cassiello-Robbins & Barlow, 2016). Of the adversity-related risk factors for depression, those that involve conflict tend to be the strongest (Hammen, 2005; Mazure, 1998). Marital problems, bullying, and abusive relationships are all common risk factors for depression (Kendler et al., 1999, 1995; Klomek et al., 2019; Klomek, Marrocco, Kleinman, Schonfeld, & Gould, 2007), with sexual and non-sexual assault, in particular, greatly increasing one’s risk of depression (Kendler et al., 1999, 1995). This holds true even in a small-scale, non-Western society: among the Tsimane, Amazonian horticulturalists, depression is also associated with conflict, especially conflict involving non-kin (Stieglitz, Schniter, von Rueden, Kaplan, & Gurven, 2015). See Hagen & Syme (2021) for a review of the association of depression with anger and conflict.

Other notable depression risk factors, like loss of a loved one or severe or prolonged illness, might seem less related to conflict. In these situations, however, the fitness costs that stem from reduced access to resources could be mitigated with help from social partners (Sugiyama & Sugiyama, 2003). However, social partners might not be able, or want, to provide more investment than they already are. Therefore, problems whose solutions require substantial investment or other changes on the part of social partners will often involve social conflict even if they did not start that way (Hagen, 2003). Indeed, there is increasing evidence that loss of a loved one is often followed by increased family conflict (see Hagen & Syme, 2021 for a brief review).

When need is private information and there are conflicts with social partners, “cheap” signals of need, such as sad expressions and crying, may often not be believed when providing support is costly. We argue next that in this common situation, some of the most harmful and mysterious symptoms of depression – profound loss of interest in virtually all activities, and suicidal ideation and behaviors – serve as credible and adaptive signals of need.

Bargaining: Credibly signaling need during conflicts

With a cooperative species like our own, ubiquitous conflicts of interests means that there will always be disagreement over the levels of investment in a cooperative endeavor and the division of the resulting benefits, even among closely related individuals. According to partner choice models, individuals who are dissatisfied with the terms of cooperation can switch partners (Hammerstein & Noë, 2016), e.g., workers unhappy with their pay can look for a better job. In many cases, however, it is difficult or impossible to switch partners. Spouses who are dissatisfied with their partner’s investment in their new infant, for example, cannot easily find a different partner to invest in that infant (Hagen, 1999). Similarly, an adolescent who is dissatisfied with her parent’s investment in her cannot easily find other parents who were willing to invest more, nor could parents easily produce another adolescent. In these latter examples, and many cooperative endeavors central to human biological fitness, all parties have monopoly power over the benefits they bring to the endeavor – no one is easily replaced (Hagen, 2002, 2003). Such interdependence is increasingly recognized to be important to the evolution of cooperation in humans and other animals (Aktipis et al., 2018; Balliet, Tybur, & Van Lange, 2017; Roberts, 2005; Tomasello et al., 2012).

Hagen (2003) proposed that physical aggression and core depression symptoms like loss of interest in virtually all activities were complementary strategies to resolve conflicts in interdependent relationships. Sell, Tooby, & Cosmides (2009) found that physically formidable individuals were more prone to anger, prevailed more in conflicts of interest, and considered themselves entitled to better treatment. Physically or socially weaker individuals, though, are not without options to resolve conflicts in their favor. An individual with monopoly power over the benefits she contributes to a critical cooperative endeavor can withhold those benefits, or put them at risk, until her partners change their behaviors in ways that benefit her. As depression often involves a profound loss of interest in virtually all activities that can jeopardize one’s productivity (American Psychiatric Association, 2013), it might therefore be an evolved bargaining strategy for relatively powerless individuals in the wake of adversity and social conflict (Hagen, 2003; Hagen & Syme, 2021; see also Watson & Andrews, 2002).

Bargaining models assume that delaying cooperation is costly so that there is an incentive to quickly agree on a division of benefits, especially for those who highly value the fruits of the cooperative endeavor. In a classic non-cooperative game theory model of bargaining, Rubinstein (1982) showed that two parties can come to an immediate agreement over division of benefits despite conflicts of interest if the parties’ valuations of cooperation are not private information: in this case, each party knows exactly what division of benefits the other will accept, and can therefore make that offer immediately, avoiding the cost of delay.

If valuations are private information, however, costly delays might be unavoidable because each party has an incentive to deceptively request more than their actual valuation, and to reject the likely inflated requests from partners, leading to multiple rounds of bargaining. Models of bargaining with private information have a close relationship to models of credible signaling. When there are conflicts of interest, there are incentives to send deceptive signals. A credible signal is one that the receiver can believe despite the signaler’s incentive to deceive. A willingness to delay (i.e., refuse offers) credibly reveals one’s low valuation of the endeavor, and therefore genuine need – the benefit of waiting for a better offer outweighs the low cost of delay. Eagerness to reach a deal, on the other hand, credibly reveals a high valuation – the benefit of waiting for a better offer does not outweigh the higher cost of delay. Once valuations are known, the game reduces to the one analyzed by Rubinstein (1982), and the parties can reach an agreement on the division of benefits (Kennan & Wilson, 1993). Delays also typically require that additional factors come into play (Feinberg & Skrzypacz, 2005 and references therein).

Hagen (2003) proposed that whereas crying is a “cheap,” mostly short-term signal of need that might be deceptive (e.g., crocodile tears), the substantial, long-term reduction in productivity that characterizes many cases of depression corresponds to a willingness to delay, and is therefore a credible signal of low valuation and need. In terms of classic costly signaling theory, reduced productivity is relatively less costly for signalers whose efforts are currently not yielding many fitness benefits (i.e., the needy) than it would be for signalers whose efforts are yielding substantial benefits (the non-needy). Hence, the benefits of signaling outweigh the costs for needy individuals, who therefore send the signal, whereas the costs outweigh the benefits for non-needy individuals, who therefore do not send the signal.

Suicidality

Theoretical models of depression must account for suicidality. Suicidal ideation is one of nine diagnostic criteria for a major depressive episode (MDE) (American Psychiatric Association, 2013) and is associated with depression across cultures (Haroz et al., 2017); depression is a major risk factor for suicidal behavior (Hawton, Comabella, Haw, & Saunders, 2013); and suicidality is a major justification for the claim that depression is a brain dysfunction (e.g., Pies, 2014).

Anthropology, in contrast, has long viewed suicidality as largely the result of social problems. Early in the field’s history, anthropologists reported on suicide attempts and deaths in the small-scale societies that serve as models for the types of societies in which humans evolved. Suicide, they found, was commonly a form of protest, revenge, and/or appeal (Firth, 1936, 1961; Malinowski, 1932; Niehaus, 2012). Some ethnographers emphasized suicide as a form of anger or social pressure (Giddens, 1964; Hezel, 1987), whereas others emphasized the powerlessness of suicide victims (Counts, 1980).

Common to almost all theoretical and empirical investigations of suicide in anthropology and other disciplines is a focus on completed suicides, i.e., suicide deaths. The vast majority of suicidal behavior, however, does not result in death. In young adult women in the US, for example, there are hundreds of attempts for every death (see Figure 1). Syme, Garfield, & Hagen (2016) therefore argued that the theoretical focus should be on suicide ideation and suicide attempts.

US Suicidality non-fatal injury and death rates by age and sex (2001-2019). Data from @CDC2021.

Figure 1: US Suicidality non-fatal injury and death rates by age and sex (2001-2019). Data from CDC (2021).

Raymond Firth, an anthropologist who worked in the southwestern Pacific, was one of the first to view suicidality as a gamble to improve one’s circumstances in the here and now. Based on observations that suicide attempts often followed loss or conflict and varied substantially in their likelihood of death, he argued that a sizable subset of the suicide attempts among the Tikopia were not meant to end in death but instead were a means to elicit aid, status, or immediate reintegration into the community following negative events (Firth, 1936, 1961).

In the bargaining framework, suicidality, and perhaps also non-suicidal self-injury (Hagen, Watson, & Hammerstein, 2008), is conceptualized as putting all future contributions to cooperative endeavors with social partners at risk with some low but non-zero probability, credibly signaling low valuation of current circumstances. On this view, most suicides deaths, especially in young, physically healthy individuals, would therefore be the inevitable consequence that some individuals lose this gamble.

As with depression, there are negative social responses to suicidal behavior. Most studies examining responses to those who have survived suicide attempts have focused on stigmatization. In these studies, perceptions of survivors as being weak, selfish, mentally ill, and antisocial are commonly reported (Batterham, Calear, & Christensen, 2013; Tzeng & Lipson, 2004), with stigmatization often being found within and outside one’s social network (Frey, Hans, & Cerel, 2016; Scocco, Castriotta, Toffol, & Preti, 2012).

Despite this potential for stigmatization, increased social support and beneficial changes to important relationships have been reported to follow suicide attempts with some indication that these effects may hold long term (Stengel, 1956). For example, a study of 100 women who survived suicide attempts found that individuals gained identifiable benefits through the attempt in 75 cases, with 41 individuals benefiting from reconciliations with others (Lukianowicz, 1971). Unlike many Western countries, where suicide is often viewed as pathological (Hidaka, 2012), members of traditional societies have been reported to view suicide attempts as cries for help rather than mental illness (Shostak, 1981), with attempts having been described as ways of escaping unwanted marriage arrangements, persistent abuse, or a lack of support in obtaining mates by those engaging in the behavior and observers (Gutiérrez de Pineda & Muirden, 1948; Hilger, 1957; Karsten, 1935; Tessmann, 1930; Wilson, 1960). Although findings that individuals view suicide attempts as cries for help is not necessarily evidence for their ability to lead to beneficial responses, 30 out of 84 examples of suicidal behavior included in the HRAF resulted in positive changes for the survivor (Syme et al., 2016).

Aversiveness is a feature of depression, not a bug

Under the bargaining model, aversive responses to depressive and suicidal bargaining are expected throughout the process, encouraging beneficial concessions by interdependent social partners with whom one is in conflict, who in turn signal the costliness of increasing their support (Hagen, 2003; Hagen & Syme, 2021). This predicted pattern is quite similar to anger, which is aversive to targeted social partners, yet is probably an adaptation that exploits advantages in physical or social formidability to force beneficial concessions from them (Sell et al., 2009).

We argue that among those who lack better options, aversive depression symptoms that put one’s value to others at risk, such as loss of interest and suicidality, credibly signal low valuation of the current efforts of social partners and motivate them to provide more support so as to end the aversive depressive behaviors (for similar views, see Andrews, 2006; Farberow & Shneidman, 1961; Firth, 1936, 1961; Hagen et al., 2008; Nock, 2008; Rosenthal, 1993; Stengel, 1956).

Study aims and predictions

The prevailing view is that depression involves impaired social abilities that lead to rejection by social partners (Coyne, 1976; Gadassi & Rafaeli, 2015; Hames et al., 2013; Joiner et al., 1999; Segrin, 2000; Weightman et al., 2019). The aim of this study was to test an alternative hypothesis that when there are conflicts of interest, depressive and suicidal behaviors benefit victims of adversity by increasing belief that they are telling the truth and consequently increasing willingness to help them.

Most of the limited literature on social responses to depression and suicidality comprises observational studies of depressed individuals interacting with family, friends, or roommates (Dadds et al., 1992; Hops et al., 1987; Joiner & Metalsky, 1995; Sheeber et al., 2001; Starr & Davila, 2008). These have ecological validity, but cannot easily determine causal relationships. Some studies, though, have employed an experimental design in which participants were randomized into conditions in which they listened to, watched, or interacted with either a depressed or non-depressed person, where in some cases the depressed person was a non-depressed confederate enacting a depressed role (Marcus & Nardone, 1992). These designs can demonstrate causation but the transient, inconsequential relationships and laboratory settings lack ecological validity.

Experimental vignette studies, which employ a short, carefully constructed description of a person, object, or situation, aim to approach the ecological validity of observational studies by presenting participants with rich, real-world scenarios, while at the same time allowing researchers to randomize participants into conditions in which theoretically relevant dimensions of the vignettes are systematically manipulated, thus enabling robust causal inferences (Atzmüller & Steiner, 2010). Experimental vignette studies are conducted in a broad range of disciplines, including psychology, economics, sociology, management studies, political science, and education (Aguinis & Bradley, 2014; Atzmüller & Steiner, 2010).

In the bargaining framework, one’s “willingness to delay” is a credible signal of one’s valuation of current cooperative arrangements, with a greater willingness to delay indicating a lower valuation. Here, we investigated responses to emotional signals that varied in the extent to which they reduced productivity or put future productivity at risk, which we refer to as costs, in an experimental vignette study in which a possible victim of adversity asks for help from the participant, but has incentives to exaggerate her need. As signal cost increased, we predicted that participants would report (1) increased belief in the signaler’s claims and (2) increased likelihood of providing help, with (3) the increased likelihood of providing help mediated by the increased belief in the signaler’s need.

Materials and methods

Design

This study utilized a between-subjects pretest-posttest design to examine how four different emotional signals (treatments) would influence (1) the degree participants believed a fictional character to be in need (Belief) and (2) the likelihood they would provide help (Action) relative to a simple Verbal request without additional signaling (the control condition), in four different vignettes, for a total of 20 conditions. In this design, the outcomes are measured at pre-treatment (T1). Participants are then randomized into either a control group or a treatment group, i.e., one of the emotional signals, and the outcomes are measured again (T2). Regression models (described later) are used to determine the effect of the treatment conditions on the posttest outcome variables, relative to the control condition, controlling for pretest levels of the outcome variables (we also explored within-subjects effects of the signal on outcomes at T2 compared to T1).

In principle, pretest-posttest designs, by controlling for pretest variation in the outcome, increase the precision of the estimate of the treatment effect on the outcome (Dimitrov & Rumrill Jr, 2003). In survey experiments, however, researchers often favor posttest-only designs over pretest-posttest designs. The common concern is that the pre-treatment measurement of the outcome will influence the treatment effect on the outcome (i.e., the effects of asking the same question twice) due to, e.g., demand effects, in which participants try to conform to experimenter expectations, or to consistency pressures, in which participants try to provide consistent responses regardless of treatments (Clifford, Sheagley, & Piston, 2021). In a study with six experiments that randomly assigned respondents to alternative designs (e.g., pretest-posttest, posttest only) Clifford et al. (2021) found these concerns to be overblown. In all cases, the pretest-posttest design had substantially greater precision than the posttest-only design, with little evidence that pretest measurement altered the treatment effect.

Lessons learned from two pilot studies

The current study is a refinement of a large MTurk experimental vignette pilot study (N=1636) that used a different vignette but very similar signals and outcomes (see below for more details on MTurk samples), and a much smaller pilot study posted to reddit.com/r/SampleSize/ (N=28) that used draft versions of three vignettes used in the current study, along with the same signals and outcomes. One major goal of the MTurk pilot study was to determine if believability and willingness to help were simply artifacts of the fictional victim’s psychiatric distress. We therefore included a “signaling” condition in which the victim exhibited schizophrenic symptoms. As predicted, believability and willingness to help in this condition were dramatically lower than in any other condition (see Figure 9), ruling out this alternative explanation. We consequently did not include the schizophrenic condition in the current study. See the SI for more details on the pilot studies.

A second lesson was that participants in the pilot study tended to believe the fictional victim prior to her signaling need, which made it difficult to determine if the signals increased her believability. The vignettes for this study were therefore written to undermine the victim’s credibility by making her seem manipulative at T1.

Power analysis

We used the MTurk pilot data to estimate the sample sizes needed to detect an effect of the Mild depression signal vs. Verbal request control on Belief in the victim’s need. Power was about 80% for a sample size of about 95, and was about 90% for a sample size of about 130. See Figure 10. Given our $1500 USD budget, we aimed for a sample size of 120-130 for treatment plus control conditions, and 1200-1300 for all conditions in the study. For more details, see the SI.

Sampling

Participants for this study were recruited from Amazon Mechanical Turk, a crowdsourcing platform that allows for the creation of Human Intelligence Tasks (HITs) that workers can complete for pay. As Amazon provides the infrastructure, it allows for a relatively low-cost way of collecting data for academic research, with the disadvantage that the data are not representative of any real population (Thomas & Clifford, 2017). Despite this limitation, MTurk samples have a wider range of ages and incomes than most university samples, and therefore might be more informative about the general population (J. Dworkin, Hessel, Gliske, & Rudi, 2016; Kennedy et al., 2020; Thomas & Clifford, 2017). US MTurk samples do differ from the general US population, though, mainly in being younger, more educated, and lower income (Boas, Christenson, & Glick, 2020; Ross, Zaldivar, Irani, & Tomlinson, 2010).

To test emotional signals in an arranged marriage vignette, we recruited an Indian MTurk sample. Indian samples are also likely to be younger, more educated, and have higher income than the general Indian population, and are more likely to come from regions with good internet access (Boas et al., 2020).

Overall, the quality of data provided by MTurk workers tends to resemble that of university sample pools (Necka, Cacioppo, Norman, & Cacioppo, 2016; Robinson, Rosenzweig, Moss, & Litman, 2019; Thomas & Clifford, 2017), with some studies reporting that MTurk samples are more attentive than samples of university students (Hauser & Schwarz, 2016). In vignette studies, MTurk data quality also compares favorably to that from much more expensive population-based samples (Weinberg, Freese, & McElhattan, 2014). For these reasons, concerns about data quality come primarily from the threat of bot use or respondents faking their location to take surveys in a language they do not understand well, with there being little evidence of the former (Kennedy et al., 2020) and the risk of the latter able to be minimized through well designed attention checks, timed responses, and good study design (Aguinis, Villamor, & Ramani, 2020; Huang, Bowling, Liu, & Li, 2015; Kennedy et al., 2020; Thomas & Clifford, 2017).

Participants

All participants were over 18, located in the United States or India, and had high quality MTurk metrics (completed at least 100 HITs with a HIT approval rate of over 98%, Kennedy et al., 2020). Participants were excluded from the study if (1) they read the vignette too quickly (one-third of the time it took MG to read it), and (2) they failed clearly labeled attention checks. The first attention check was shown immediately after the consent form and provided participants with a random word and asked them to enter the vowels in the order in which they are found in the word. The second attention check followed the vignette and involved asking three questions about the story that were easy to answer for anyone paying attention.

Ethics

All participants provided informed consent, and the consent form warned that some content might involve sexual assault. We estimated the study would take 4-8 minutes to complete for participants who did not take breaks (MTurkers commonly multitask, or leave the survey page and return later, Necka et al., 2016). All participants who passed the attention checks were paid $1 for their time, for an estimated rate of $7.50/hr to $15/hr (75% of US participants completed in 8.4 minutes; 75% of Indian participants completed in 25 minutes). This study was certified exempt by the Washington State University Human Research Protection Program.

Survey

Four vignettes were used in this study that involved (1) a female’s claim of severe adversity that was private information, (2) conflicts of interest between the victim and the participant that would undermine the believability of her claims and make her seem manipulative, and (3) her emotional signals. The vignette scenarios involved potentially severe types of adversity, such as sexual and non-sexual assault and thwarted marriage, that often precede cases of depression and suicidality in the ethnographic and clinical record (Brown, 1986; Kendler et al., 1999, 1995; Syme et al., 2016). See Table 2.

Time 1: Claim of need in a conflictual relationship

At Time 1 (T1) participants in the US sample were randomly assigned to either the “basketball coach,” “romantic partner,” or “brother-in-law” vignettes, and the Indian sample was assigned to the “thwarted marriage” vignette.

Basketball coach vignette: Participants were asked to imagine that they are a university athletic director. The star player on the women’s basketball team comes to the participant and claims she was sexually assaulted by her head coach, a physically powerful man. However, there is a history of conflict between the star player and the coach over playing time, and police are unable to find evidence to corroborate her claims.

Brother-in-law vignette: Participants were asked to imagine that they let their sister, brother-in-law, and niece move in with them after their sister’s family lost their house in a fire. During this time, the participant’s 15 year old daughter becomes jealous of the niece, who appears to be a social competitor. A few weeks after claiming the niece was trying to steal her boyfriend, the participant’s daughter accuses the brother-in-law of sexually assaulting her.

Romantic partner vignette: Participants were asked to imagine that they found a highly desirable romantic partner after years of being single. However, the participant’s 13-year-old daughter, who has a history of interfering with the participant’s past relationships, is clearly unhappy with the new partner. After a period of sustained conflict with both the participant and the romantic partner, the daughter accuses the romantic partner of physically assaulting her, but cannot produce any evidence.

Thwarted marriage vignette: Indian sample only. Participants were asked to imagine that their family was trying to arrange a dowry for their older daughter (the signaler in this vignette) so she can marry a man she already loves, while still saving enough money for their younger daughter’s dowry. After the man’s family demands more money, the participant’s family tries to find a second man, who the older daughter claims to find unattractive. Any increase in the dowry will come at the younger daughter’s expense. The participant therefore proceeds to arrange a marriage to the second man as the first man’s family makes arrangements with a different woman.

The full vignettes are available in the SI.

Table 2: The cooperative endeavor, conflict of interest, and private information in each vignette
Vignette Cooperative Endeavor Conflict of interest Private information
Thwarted marriage Inclusive fitness (parent-offspring) Parental investment in sibling Value of arranged marriage with second man
Basketball coach Winning the championship Coach’s investment in other players; keeping the coach Did sexual assault happen?
Romantic partner Inclusive fitness (parent-offspring) Investment in offspring vs. romantic partner Did physical assault happen?
Brother-in-law Inclusive fitness (parent-offspring) Investment in child vs. investment in adult sibling and niece Did sexual assault happen?

Baseline measures (T1)

After reading the vignettes, participants rated their belief that the signaler was telling the truth (T1 Belief: 0-100) and the likelihood of them helping the signaler, as requested (T1 Action: 0-100). With the thwarted marriage vignette we also asked how they would split the money they had saved for the dowry between their daughters (T1 Divide: 0-100; 50 is equal split). In every instance, the order of the questions was randomized to avoid order effects (Krosnick & Alwin, 1987).

Responses were recorded with sliders due to the fact they allow for finer grained changes than categorical scales (Klimek et al., 2017). Based on findings that a slider’s starting position may bias results (Liu & Conrad, 2019), we had concerns that participants would be less likely to move away from intermediate starting values than they would be in reality. For this reason, we set each T1 slider to start fully to the left (0). For the exact wording of the question and the labels on the sliders, see Table 4.

We also asked which emotions participants felt the signaler was experiencing using a multiple choice question in which they could select as many options as they would like. This included emotions directly related to the signals (e.g., sad, depressed, and suicidal), states which suggest genuine need (traumatized and violated), states which suggest deception (e.g., deviousness or jealousy), and if the victim was mentally ill. The complete list can be found in the SI. To further explore the effect of signaling on participants’ inferences of the signaler’s emotional state, we created two new variables: Low mood was the sum of the binary variables Depressed, Distressed and Sad; and Manipulative was the sum of the binary variables Devious and Jealous.

Time 2: Signals

After rating their beliefs and actions, and which emotions they thought the potential victim was experiencing, participants were randomized into either the control condition or one of four emotional signals by the victim (in order of increasing signal cost): (1) control condition: a verbal request without additional signaling; (2) crying; (3) mild-depression; (4) depression; and (5) a suicide attempt. The signals involved the participant encountering the victim some time after the adverse event and observing, e.g., crying; sad expressions; reduced effort, fatigue, and poor personal hygiene; and suicidal self-injury. These descriptions did not use the terms depression, depressed, suicidal, or mental health.

The signals were cumulative: crying can be an important feature of depression (for discussion on the relationship between crying and depression see Bylsma, Gračanin, & Vingerhoets, 2020), and depression is a major risk factor for suicide (Bostwick & Pankratz, 2000; Kessler, 2012). Accordingly, components of less-costly signals were included in more-costly signals. Although we use the term ‘signals’ throughout the paper for brevity, we expect that the hypothesized signals of need, like many signals, would also provide information to others in the form of cues (for discussion on the evolution of signals from cues see: Biernaskie, Perry, & Grafen, 2018; Steinkopf, 2015; Tiokhin, 2016). The complete texts of the signals are available in the SI.

Post-treatment measures (T2-T3)

After reading the signaling text, participants answered questions identical to those asked at T1 as the main post-treatment variables of interest (T2). For the Belief, Action, and Divide variables, the position of the slider starting where participants placed it at T1. T2 emotion multiple-choice questions were identical to those used in T1, as were the composite variables Low mood and Manipulative.

As both a validity check and a way to understand the degree participants would be willing to help if they believed the participant completely, at T3 we presented participants with strong evidence that the claims were true. In the US sample, this involved telling participants there was video evidence of the event in question occurring or a similar event after the fact. With the thwarted marriage vignette, this involved the participant seeing the man their daughter wants to marry trash-talk their daughter and their family. Participants were then asked to rate their likelihood of acting (T3 Action).

Demographic Questions

The final part of the survey was a brief demographic questionnaire which asked for the (1) age, (2) sex, (3) number of siblings, (4) number of sons, (5) number of daughters, (6) current relationship status, (7) highest level of education, and (8) the annual household income of each participant.

Statistical analyses: Preregistered and modified

Our intervention was signal, an ordinal variable with the following preregistered rank order: verbal request (control), crying, mild depression, depression, suicide attempt. We coded this 5-level ordinal factor variable using default 4th-order orthogonal polynomial contrasts. We preregistered a test of our hypothesis that used ordinary least squares (OLS) regression models with the following form:

\[ \begin{aligned} \operatorname{Belief}_{T2} &= \beta_{0} + \beta_{1}(\operatorname{Belief}_{T1}) + \beta_{2}(\operatorname{signal}_{\operatorname{.L}}) + \beta_{3}(\operatorname{signal}_{\operatorname{.Q}})\ + \\ &\quad \beta_{4}(\operatorname{signal}_{\operatorname{.C}}) + \beta_{5}(\operatorname{signal}_{\operatorname{\text{^}4}}) \end{aligned} \]

\[ \begin{aligned} \operatorname{Action}_{T2} &= \beta_{0} + \beta_{1}(\operatorname{Action}_{T1}) + \beta_{2}(\operatorname{signal}_{\operatorname{.L}}) + \beta_{3}(\operatorname{signal}_{\operatorname{.Q}})\ + \\ &\quad \beta_{4}(\operatorname{signal}_{\operatorname{.C}}) + \beta_{5}(\operatorname{signal}_{\operatorname{\text{^}4}}) \end{aligned} \]

We predicted that there would be a statistically significant monotonically increasing effect of the signal on Belief and Action.

We decided to fit generalized linear regression models instead of OLS, however, for the following reasons. Our pre-test and post-test measures, T1 & T2 Belief and T1 & T2 Action, were all measured on a 0-100 point scale. A substantial number of participants rated their beliefs and actions as exactly 0 or exactly 100 at either T1 or T2. OLS linear regression models are not suitable for a closed and bounded distribution with so many values on the boundary because the residuals would not be normally distributed or have constant variance. For further discussion, see the SI, where we also report the preregistered OLS models.

To test our preregistered hypothesis that the likelihood of acting to help the victim would be largely mediated by a signal’s positive impact on the participant’s belief in the victim’s need, we used the mediation package (Tingley, Yamamoto, Hirose, Keele, & Imai, 2014) to fit a mediation model for Depression treatment vs. Verbal request control. We did the same for Suicide attempt. See Figure 2.

Possible causal effects of the signal on helping behavior. A credible signal of need increases belief that the victim is telling the truth and needs help, which increase the likelihood of helping. The direct path from the signal to action represents other causal effects of the signal on observers, such as perceptions of the victim's emotional state (and other factors not measured in this study) that might alter observer behavior.

Figure 2: Possible causal effects of the signal on helping behavior. A credible signal of need increases belief that the victim is telling the truth and needs help, which increase the likelihood of helping. The direct path from the signal to action represents other causal effects of the signal on observers, such as perceptions of the victim’s emotional state (and other factors not measured in this study) that might alter observer behavior.

For specifications of all regression models, see the SI. Our preregistration is here: https://osf.io/g3s6n

Data availability

The data are available at http://doi.org/10.5281/zenodo.4637904

Results

The study was started by N=1950 participants who clicked the link to Qualtrics (1213 US and 737 India). After removing participants who did not finish the survey, failed attention checks, or moved through the study at an unrealistic pace (N=710, 36%), our final sample was N = 1240 (937 US and 303 India), with 759 males and 479 females, 609 of whom were married or in a long-term relationship, 205 who were divorced, 414 who were single, and 11 who were widowed. The median number of participants per condition was 61 (min=58, max=67). For the number of participants in each condition, see Table 5. For summary statistics, see Table 3. For the distributions of participants by age, income, and nationality, see Figure 11.

Table 3: Summary statistics for study variables. Values that were on a 0-100 scale were rescaled to 0-1. Indian participants reported income in rupees, which we converted to USD at the current exchange rate (1 rupee = 0.014 USD).
Variable N Range Mean (SD)
Age (years) 1240 18-81 37 (12)
Income (USD) 1235 0-840000 53000 (60000)
Education (years) 1238 11-24 16 (2.1)
Number of children 1236 0-11 1 (1.3)
Time to complete (minutes) 1240 1.6-1700 13 (50)
T1 Belief 1240 0-1 0.38 (0.3)
T1 Action 1240 0-1 0.37 (0.32)
T1 Division 303 0.14-1 0.58 (0.15)
T1 Low mood 1240 0-3 1.3 (1.1)
T1 Manipulative 1240 0-2 1 (0.74)
T2 Belief 1240 0-1 0.48 (0.33)
T2 Action 1240 0-1 0.49 (0.35)
T2 Division 303 0.14-1 0.62 (0.16)
T2 Low mood 1240 0-3 1.7 (1.1)
T2 Manipulative 1240 0-2 0.53 (0.7)
T3 Action 1240 0-1 0.88 (0.22)

Distributions of beliefs and actions at Time 1

Across the four vignettes, mean belief of the victim (after rescaling original 0-100 values to 0-1) was relatively low at baseline (T1), Mean = 0.38, albeit with wide variation, SD = 0.3; 118 participants (9.5%) rated their belief = 0, and 38 participants (3.1%) rated it as = 1. The distribution of likelihood of helping the victim (action) was similar, Mean = 0.37, SD = 0.32, with 168 participants (14%) rating their action = 0, and 57 participants (4.6%) rated action as = 1. Although not a pre-registered prediction, T1 Belief and T1 Action were highly correlated across the four vignettes, consistent with help being worth providing if the signaler’s claims were true (see Figure 3).

The distributions of *T1 Belief* vs.*T1 Action*, by vignette. Each dot is one participant. Original 0-100 values rescaled to 0-1. Lines fit by linear regression. Dot size indicates the number of overlapping points. Black dot is the mean of each variable.

Figure 3: The distributions of T1 Belief vs.T1 Action, by vignette. Each dot is one participant. Original 0-100 values rescaled to 0-1. Lines fit by linear regression. Dot size indicates the number of overlapping points. Black dot is the mean of each variable.

Confirmatory: Signals increase beliefs and actions in the predicted rank order

As predicted, there was a strong statistically significant positive causal effect of the ordinal signal on T2 Belief (LR \(\chi^2(4)=221\), \(p=1.3 \times 10^{-46}\)) and T2 Action (LR \(\chi^2(4)=259\), \(p=7.3 \times 10^{-55}\)), controlling for T1 Belief and T1 Action, respectively, with the effect of each signal increasing in the predicted rank order (verbal request, crying, mild depression, depression, suicide attempt). See Figure 4 and models m1 and m3 in Tables 7 and 8. Results for the preregistered OLS models were very similar; see Figures 29 and 30, and Tables 9 and 10. The within-subject effects of each signal are depicted in Figure 15.

The mean effects of the signals on *T2 Belief* and *T2 Action*, controlling for *T1 Belief* and *T1 Action*, respectively (original 0-100 scale rescaled to 0-1). Effects plotted for T1 values set to their median value (*T1 Belief*=0.34, *T1 Action*=0.3). Fit using generalized linear regression models with the quasibinomial family. Bars are 95% CIs. Dots are *T2 Belief* and *T2 Action* values; dot size = number of overlapping data points. For coefficients, p-values, and other statistics, see models m1 and m3 in Tables \@ref(tab:regressiontables) and \@ref(tab:anovatables).

Figure 4: The mean effects of the signals on T2 Belief and T2 Action, controlling for T1 Belief and T1 Action, respectively (original 0-100 scale rescaled to 0-1). Effects plotted for T1 values set to their median value (T1 Belief=0.34, T1 Action=0.3). Fit using generalized linear regression models with the quasibinomial family. Bars are 95% CIs. Dots are T2 Belief and T2 Action values; dot size = number of overlapping data points. For coefficients, p-values, and other statistics, see models m1 and m3 in Tables 7 and 8.

The estimated marginal mean between-subjects effect of the high cost Suicide attempt vs. the Verbal request control on T2 Belief (averaging over all four vignettes and all values of T1 Belief) was an increase of 21 (95% CI: 18-25) points on the original 0-100 point scale. The equivalent increase for T2 Action was 27 (95% CI: 23-30) points. Given that the standard deviations of T2 Belief and Action are 33 and 35, respectively, these represent increases of 0.65 and 0.81 standard deviations, respectively.

Confirmatory: The effect of signals on action is largely mediated by belief

According to our theoretical model, emotional signals that put cooperative benefits at risk increase observers’ belief that the victim is genuinely in need. Increased belief then increases the likelihood of helping the victim (action). See Figure 2.

We fit the mediation and outcome models using GLM’s with the binomial family as the mediation package does not support the quasibinomial family. The total effect of the Suicide attempt signal (treatment) vs. Verbal request (control) on the likelihood of Action was to increase it by 23 points from T1 to T2 (on the original 100-point scale). Of this increase, 69% was mediated by the increased Belief that the victim was telling the truth. The total effect of the Depression signal (treatment) vs. Verbal request (control) on the likelihood of Action was to increase it by 16 points from T1 to T2. Of this increase, 74% was mediated by the increased Belief that the victim was telling the truth. See Figure 5.

The effect of each signal (treatment) vs. Verbal request (control) on Action. The x-axis is the change in Action on the [0, 1] scale. The effects of the signals on Action are largely mediated by Belief (percent mediated on the right). The mediation model controlled for *T1 Belief*, and the outcome model controlled for *T1 Belief* and *T1 Action*. Both models were GLMs with the binomial family (the mediation package does not support the quasibinomial family). ACME: Average causal mediation effect. ADE: Average direct effect. Total: Total effect (ADE + ACME). Bars are 95% CIs.

Figure 5: The effect of each signal (treatment) vs. Verbal request (control) on Action. The x-axis is the change in Action on the [0, 1] scale. The effects of the signals on Action are largely mediated by Belief (percent mediated on the right). The mediation model controlled for T1 Belief, and the outcome model controlled for T1 Belief and T1 Action. Both models were GLMs with the binomial family (the mediation package does not support the quasibinomial family). ACME: Average causal mediation effect. ADE: Average direct effect. Total: Total effect (ADE + ACME). Bars are 95% CIs.

Exploratory: Costlier signals decrease perceived manipulation and increase perceived low mood

The mean change in the Low mood and Manipulative variables from T1 to T2 for each signal in each vignette reveals a large decrease in inferred Manipulation for costly signals, and a large increase in inferred Low mood (with the exception of the Thwarted marriage vignette, in which changes are small). See Figure 6.

The within-subjects effect of the signals on participants' inferred *Manipulative* and *LowMood* emotional states of the victim, from T1 (arrow base) to T2 (arrow head), for each signal in each of the four vignettes. Low mood: higher values indicate lower perceived mood. Manipulative: higher values indicate higher perceived manipulativeness. Thick arrows: mean change. Thin arrows: means of 500 bootstrap resamples.

Figure 6: The within-subjects effect of the signals on participants’ inferred Manipulative and LowMood emotional states of the victim, from T1 (arrow base) to T2 (arrow head), for each signal in each of the four vignettes. Low mood: higher values indicate lower perceived mood. Manipulative: higher values indicate higher perceived manipulativeness. Thick arrows: mean change. Thin arrows: means of 500 bootstrap resamples.

The proportion of participants inferring each emotion in each vignette at T1 and after each signal at T2 is depicted in Figure 16, and the change in proportions from T1 to T2 in Figure 17. The increase in inferred depression in the Depression and Suicide attempt conditions, but not the Verbal request and Crying conditions, helps validate these signals, as does the increase in inferred suicidality in the Suicide attempt condition.

At baseline (T1), 16% of participants thought the victim was mentally ill, which decreased slightly at T2 in the Verbal request and Crying conditions (13%), and then increased with signal cost to 20% in Mild depression, 31% in Depression, and 47% in Suicide attempt, with some variation by vignette. See Figures 18 and 19. Perceived mental illness at T2 was associated with lower T2 Belief and Action in the US sample, but this effect was mainly evident in the Verbal request and Crying conditions. See Figures 20 and 21.

Exploratory: Signal effects differ by vignette

The effects of the signals on rated beliefs and actions differed substantially by vignette, which we display in three ways for each signal in each vignette: estimated cumulative distribution function plots, which show the entire distributions of rated beliefs and actions, including which conditions have high fractions of 0’s and 1’s (Figures 7); regression estimates of the mean effects of the signals on beliefs and actions by vignette (Figure 8); and between- and within-subjects signal effect sizes (Cohen’s d) by vignette (Figures 12 and 13).

Empirical cumulative distribution functions for Time 2 Belief (top) and Action (bottom) compared to their Time 1 baseline values (red), by signal and vignette. Y-values indicate the fraction of all ratings equal to or less than a given x-value. Ratings rescaled to [0, 1]. Dots indicate median values of Belief and Action, by signal and vignette.

Figure 7: Empirical cumulative distribution functions for Time 2 Belief (top) and Action (bottom) compared to their Time 1 baseline values (red), by signal and vignette. Y-values indicate the fraction of all ratings equal to or less than a given x-value. Ratings rescaled to [0, 1]. Dots indicate median values of Belief and Action, by signal and vignette.

The effect of the signals on *T2 Belief* and *T2 Action* in each vignette, controlling for T1 values of Belief and Action, respectively. Ratings rescaled to [0, 1]. Effects plotted for T1 values set to their median value (*T1 Belief*=0.34, *T1 Action*=0.3). Fit using generalized linear regression models with the quasibinomial family. Bars are 95% CIs. For coefficients, p-values, and other statistics, see models m2 and m4 in Tables \@ref(tab:regressiontables) and \@ref(tab:anovatables).

Figure 8: The effect of the signals on T2 Belief and T2 Action in each vignette, controlling for T1 values of Belief and Action, respectively. Ratings rescaled to [0, 1]. Effects plotted for T1 values set to their median value (T1 Belief=0.34, T1 Action=0.3). Fit using generalized linear regression models with the quasibinomial family. Bars are 95% CIs. For coefficients, p-values, and other statistics, see models m2 and m4 in Tables 7 and 8.

We note the following patterns in Figure 7, which we will return to in the Discussion section. First, as we intended, the distributions of Belief and Action in the Verbal request control condition were very similar to their distributions at T1 baseline across vignettes. In the “Basketball coach” and “Brother-in-law” vignettes, though, their distributions in the Verbal request condition were shifted to somewhat lower values relative to baseline (i.e., the verbal request slightly reduced belief and action in those vignettes). Second, in the “Romantic partner” and “Brother-in-law” vignettes, the effect of Crying differed little from Verbal request, but in the “Basketball coach” vignette, it differed little from the Depression and Suicide attempt signals. Third, the effect of the Suicide attempt signal on Belief was similar to that of Depression across all vignettes, but had a noticeably greater effect on Action in the “Romantic partner” and “Brother-in-law” vignettes. Fourth, there was little difference between the effect of the Mild depression vs. Depression signals.

The largest between-subjects effect was Suicide attempt vs. Verbal request control on Belief in the “Brother-in-law” vignette, Cohen’s d = 1.7, and the smallest was the effect of Crying on Belief in the “Romantic partner” vignette, Cohen’s d =0.085. The largest within-subjects effect was Suicide attempt on Action at T2 vs. T1 in the “Brother-in-law” vignette, Cohen’s d = 1.5, and the smallest was the negative effect of Verbal request on Action at T2 vs. T1 in the “Brother-in-law” vignette, Cohen’s d =-0.25.

Exploratory: Sociodemographic associations

The sociodemographic variables were strongly confounded with nationality (Indian participants were younger, lower income, with more years of education than US participants; see Figure 11), which, in turn, were confounded with vignette (responses in the Indian Thwarted marriage vignette differed substantially from those in the US vignettes). We therefore conducted our exploration of the sociodemographic variables separately by nationality.

In the US sample, female participants were more likely to believe and help the victim than male participants, and younger participants were more likely to believe and help the victim than older participants. We found no significant effects of Income, Education, relationship status (e.g., married, single), or number of sons or daughters on Belief or Action. See Figure 24.

In the Indian sample and vignette, in contrast, males were more likely to help the victim than females, those with more years of education were less likely to believe the victim, and there was a marked increase in likelihood of helping among older individuals in the suicide signal condition. We found no significant associations with age or income. See Figure 25.

Validity check: large T3 increase in Action with proof that victim was telling the truth

As a partial check on the validity of our methods and results, at T3 we presented participants with strong evidence that the victim was telling the truth, and asked them to again rate their likelihood of acting to help the victim (T3 Action). Compared to the mean T2 Action (M=0.49), mean T3 Action increased substantially, Mean diff. = 0.389, t(1239) = 35.2, \(p<2.2 \times 10^{-16}\). Across the US vignettes, likelihood of acting at T3 increased to near-ceiling (M=0.96), regardless of participants’ T2 Action values, indicating that propensity to act was indeed contingent on believing the victim. In the Indian Thwarted marriage vignette, however, there was little change in T3 Action compared to T2 Action (Figure 26), perhaps because Indian participants’ beliefs and actions were relatively insensitive to the signals to begin with (Figures 7 and 8).

Discussion

As predicted, in vignettes involving conflicts of interest and private information about the need for help, costly signals of need increased participants’ belief in the victim and their likelihood of helping her, with the increase in perceived need and likelihood of helping increasing monotonically with signal cost. As predicted, the increase in likelihood of helping was largely mediated by the increase in belief in the victim. In an exploratory analysis, costlier signals also decreased perceptions that the victim was manipulative. These results provide evidence that, contrary to the influential “interpersonal” view that depressive behaviors are socially dysfunctional (reviewed in Hames et al., 2013), they in fact outperform verbal requests, sad expressions, and crying in providing benefits to victims when there are conflicts of interest.

Signal effects were largest in the “brother-in-law” and “romantic partner” vignettes, both of which involved claims of assault against participants’ imagined daughters, and smaller in the “basketball coach” and the “thwarted marriage” vignettes. The smaller effect in the “basketball coach” vignette might have been because in the role of athletic director, participants did not value their relationship with the star player as much as we anticipated (e.g., due to lack of relatedness), or how participants weighted the costs of suspending the coach vs. punishing a potentially innocent person (for discussion of suicidal signaling to kin vs. nonkin, see Syme & Hagen, 2018). The US vignettes also had different degrees of evidence against the victim beyond just denial by the accused, ranging from strong evidence in the basketball coach vignette (a negative police report) to moderate evidence in the romantic partner vignette (no physical injuries) to weak evidence in the brother-in-law vignette (nothing beyond denial by the brother-in-law), raising the possibility that credible signals are more effective when negative evidence is lacking (Dylan Tweed, personal communication).

The small signal effect in the “thwarted marriage” vignette, which involved the Indian sample, could indicate that our results do not generalize across cultures, undermining our adaptationist hypothesis. It could also reflect our poor understanding of contemporary Indian culture regarding dowry (the effect was larger in older participants). Baseline belief in the older daughter, and likelihood of helping her, was relatively high at baseline (58%) compared to victims in the other vignettes. Private information and conflict therefore probably played a smaller role and thus costly signals were less necessary. We observed a similar pattern in our pilot study, in which baseline belief in the victim’s need was high, and costly signals had smaller effects than they did in the current study. Additionally, supporting the older daughter came at the cost of one’s younger daughter, which may also help explain the relatively small signal effects. A final consideration is that data from the Indian sample appeared to be of lower quality, limiting our confidence in any of these interpretations (see the Limitations section for more information).

In the “brother-in-law” and “romantic partner” scenarios, Crying had little effect on the magnitude of pro-victim responses relative to Verbal request, suggesting it was not costly enough to serve as a reliable signal in times of substantial conflicts of interests. In contrast, both Depression conditions increased support, albeit to similar degrees. One potential reason for the similar effects of the Depression conditions is the increase in costs from Mild depression to Depression was small (e.g., grades dropping from As to Bs in Mild depression vs. Cs in Depression). Such small changes may be less impactful in vignettes than in real-life, where the effects of signaling may increase in severity as they persist over time.

The effect of Suicide attempt on T2 Belief was similar to the Depression conditions across vignettes, but it resulted in greater T2 Action. One interpretation is that although some participants did not believe the victim’s story, her signal nevertheless convinced them that she needed help. For example, maybe the brother-in-law did not assault her, but the presence of his family in her home was causing genuine distress. Support for this interpretation comes from our mediation analyses, which showed that the likelihood of help was largely, but not entirely, mediated by signal’s effect on belief in need.

There were minor associations of age and sex with T2 Belief and T2 Action in the US participants, with both being higher among females and younger individuals. The US vignettes all involved assaults against young women, which might have been more salient to female and younger participants. In the Indian sample, T2 Belief and T2 Action were somewhat lower among those with more education and among females, respectively. Costlier signals, suicidality in particular, had a larger effect among older individuals, perhaps because older individuals were more likely to have children of marriageable age, like the victim in the vignette.

Contrary to our adaptationist hypothesis, and supporting the mainstream view that depression is a psychopathology, participants’ perceptions that the victim was mentally ill increased with signal cost. However, there have been extensive media campaigns to convince the public that depression is a mental illness with the laudable goal of reducing stigma (Corrigan, 2012; Rüsch, Angermeyer, & Corrigan, 2005). Even so, in the Depression conditions across vignettes, no more than 25% of participants thought the victim was mentally ill, and in the Suicide attempt condition the proportion of participants perceiving mental illness exceeded 50% only in the Basketball coach vignette. Although perceived mental illness was associated with somewhat lower T2 Belief and T2 Action, this effect was mainly evident in the Verbal request and Crying conditions.

Finally, after the T3 evidence that the victim was telling the truth, likelihood of helping by the US participants increased to near ceiling, an effect that helped validate our vignettes. Among Indian participants, in contrast, participants only slightly increased their likelihood of helping from their T2 level. One interpretation of the latter is that Indian participants tended to believe the older daughter anyway, so their decision to help was not changed by additional information.

Limitations

This study has less ecological validity than real-world observations of depressed individuals interacting with their social partners, which might have biased results in a pro-signaler direction if the lack of real costs of helping made support feel less costly or if there was a social desirability bias toward helping (Grimm, 2010). It may have also biased participants against helping if they could not fully imagine the characters in the story as kin or interdependent partners, and the survey’s short duration may have weakened the strength of the costlier signals as bargaining tools.

Our design did not include vignettes with male signalers. For this reason, we have no data on the possibility of sex differences in the effectiveness of the signaling strategies examined. Although not predicted theoretically, such differences are possible if the costs of signaling vary between the sexes due to differential access to alternative bargaining strategies (Hagen & Rosenström, 2016) or if one sex tends to suffer greater negative reputational effects when displaying the emotions and behaviors in the vignettes. It is also possible the costliness of the situations presented in the vignettes differ by sex. This study is therefore most clearly demonstrated the effectiveness of costly signals of need by females, leaving open the question of the effectiveness of costly signals of need by males.

Compared to the US sample, far more Indian participants failed our attention checks, which is consistent with botting, unfamiliarity with English, or low-effort responses (Kennedy et al., 2020). If this high failure rate indicates lower-quality responses among those who passed the attention checks, the weak signal effect in the thwarted marriage vignette may simply be due to greater noise rather than differences in the scenario or the effectiveness of the signals compared to those in the US. Another concern relevant to all vignettes is that our decision to anchor the T1 sliders at 0 may have resulted in participants being more likely to report extreme values.

Finally, we adopted game theory models of bargaining with incomplete information as our theoretical framework, but there are many other models of credible signaling (e.g., Számadó, 2011), including for need (Számadó, Czégel, & Zachar, 2019) and suicidality (Rosenthal, 1993). If depression and suicidality involve signaling, they might be better explained by a different model.

Conclusion

Depression is costly and sometimes leads to death by suicide. Our results indicate that these costs, which mainstream theories take as evidence of brain dysfunction, instead function to help victims of adversity elicit support when their true level of need is private information and they have conflicts with social partners. Our findings align with real-world evidence that depression and suicide simultaneously elicit positive and negative responses from social partners (for review, see Hagen & Syme, 2021). In particular, sexual assault, which appeared in two of our four vignettes, is the biggest risk factor for a suicide attempt (E. R. Dworkin, Menon, Bystrynski, & Allen, 2017; Husky, Guignard, Beck, & Michel, 2013). Our results strongly suggest that a major reason for this pattern is that the victim’s social partners are skeptical that she is telling the truth. If the bargaining model is correct, depression and suicidality are “rational” responses to adversity and conflict (Hagen, 2003; Syme & Hagen, 2020).

Acknowledgements

This study was funded through a Washington State University, Vancouver Research Mini Grant. We thank Anne Pisor, Caroline Smith, Tiffany Alvarez, Aaron Lightner, Luke Martello, and Darcy Bird for numerous helpful comments. We thank Arpita Sinha for feedback on the Thwarted marriage vignette.

Supplementary information

Pilot studies

We conducted two pilot studies, one on MTurk with a large sample size (N=1636), and one on reddit.com/r/SampleSize with a small sample size (N=28). Both studies used the experimental pre-test, post-test design used in the current study.

In the MTurk pilot study, participants read a vignette that put them in situations in which an imagined sister was requesting a loan of $50,000 from the participant that was originally saved for the participant’s imagined daughter’s college education. The sister claims the money is needed for medical treatment for her own child, but she has incentives to deceive. The manipulations were signals very similar to those used in this study, and the outcomes were belief that the sister really needed the money and intention to loan her the money. See XX for the full write-up. See https://osf.io/3rg8b for preregistration of the pilot study. See http://doi.org/10.5281/zenodo.4637883 for pilot data.

Pilot study effect of the signals. A. Effect of signal on Belief at T2, controlling for Belief at T1. B: Proportion of participants who inferred the victim was mentally ill. Fit with a generalized linear model with the quasibinomial family.

Figure 9: Pilot study effect of the signals. A. Effect of signal on Belief at T2, controlling for Belief at T1. B: Proportion of participants who inferred the victim was mentally ill. Fit with a generalized linear model with the quasibinomial family.

In the small reddit.com study, participants viewed and responded to two vignettes used here, each with a different signal.

We learned six major lessons from the MTurk pilot study that influenced the design of the current study. First, as predicted, belief in the sister’s need was highest in the Depression condition and lowest in the Verbal request condition, giving us confidence to continue this research (see Figure 9). Second, to rule out that believability and willingness to help were simply artifacts of the sister’s psychiatric distress, we included a “signaling” condition in which the sister exhibited schizophrenic symptoms. As predicted, believability and willingness to help in this condition were dramatically lower than in any other condition, ruling out this alternative explanation. We therefore did not include the schizophrenic condition in the current study. Third, baseline (T1) believability was very high (mean = 69 and median = 76 on a 0-100 point scale), leaving little scope for signals to increase believability. In the current study we therefore endeavored to provide details in the vignettes that would undermine the victim’s believability at baseline. Fourth, contrary to our hypothesis, the Suicide threat and Suicide attempt signals reduced believability relative to Depression. We surmised that the sister’s adversity – a sick child – was not sufficient to justify a suicide attempt, thus undermining her believability. In the current study the signaler herself is therefore the victim, and the forms of adversity – physical and sexual assault and thwarted and forced marriage – are those that are frequently associated with suicidality. Fifth, the content of the vignette matters, so instead of a single vignette we decided to use four different vignettes to help determine the extent to which signaling effects were sensitive to seemingly irrelevant details of the vignettes. Sixth, in a pre-test post-test design, the control condition should be identical to the treatment condition in every respect except for the factor being tested, which in this study was signal cost. Post hoc, we realized that the control condition in the pilot study differed substantially from the treatment condition beyond signal cost: in the treatment conditions, participants encountered the sister twice: once when she requests help, and a second time when she signals. In our faulty control, participants only encountered the sister once, when she requests help, with no second encounter. In this study we therefore ensured that the control condition (Verbal request) was identical to the treatment conditions except for signal cost.

In the reddit.com pilot study, participants viewed two vignettes (with different signals in each one), and responded with lower belief to the second vignette compared to the first regardless of the vignette or signal. To avoid such order effects, which are a common problem in vignette studies that use multiple vignettes (Su & Steiner, 2020), we decided to present each participant in the current study with a single vignette and signal.

Power from MTurk pilot study

We estimated power using our MTurk pilot study data to compare the effect of the Mild depression signal vs. the Verbal request control on Belief (that the sister needed the money) at T2 controlling for Belief at T1 in a linear regression model. Specifically, we resampled the pilot data with replacement, fit the linear regression model, and extracted the p-value for the signal coefficient, repeating this 2000 times for each sample size ranging from N=20 to N=200. Power was computed as the proportion of p-values < 0.05. Power was about 80% for a sample size of about 95, and was about 90% for a sample size of about 130. See Figure 10.

Power curve for between-subjects effect of Mild depression vs. Verbal request signal on T2 Belief, controlling for T1 Belief.

Figure 10: Power curve for between-subjects effect of Mild depression vs. Verbal request signal on T2 Belief, controlling for T1 Belief.

Vignettes of the current study (main bodies and signals)

Basketball coach

Imagine yourself in the following scenario:

You are a college athletic director at a university with a long history of success in women’s basketball. Unfortunately, despite quality players, the team struggled for the first three years of your leadership. You came close to losing your job but were given two more years to turn things around. You immediately hired a new head coach who you had spotted coaching a low-ranked college team that punched above its weight. Two years later, your team is one of the best in the country and has attracted a top recruit who has become the star player on your team and is probably the best player in the country. You have been told that if the team makes it to the final rounds of the playoff tournament, the university will offer you a high paying contract extension.

Your star player is hard to deal with, however. She is constantly jealous of other players, and often comes to your office to complain that the coach unfairly limits her playing time, claiming this will ruin her chances of going professional. The star player has also posted self-serving accounts of your discussions with her on social media.

A few days ago, after the coach started a new player in one game, the star player went to the police and accused the coach, a physically powerful man, of cornering her in the locker room one night and forcibly kissing her and grabbing her body. The coach strongly denies the allegation, and the police said they do not have any evidence to corroborate the player’s accusation.

You reached out to other players on the team, all of whom say that the relationship between the coach and star player has always seemed professional, and they are shocked by the allegation. None of the other players have any complaints about the coach. On the contrary, the other players praise the coach for leading everyone into the playoffs. They mention that the star player is a prima donna, but they are worried that if either the coach or the star player miss the playoffs, it will hurt everyone’s chances of making it to the championships.

You find out that the star player then told the university president that she feared the coach might attack her again, and asked her to replace the coach with one of his assistants, who you know wants the star player to have more playing time. The president refused, saying there was no evidence of wrongdoing.

Signals

Verbal request

A week later the coach informs you that the star player’s performance in games is excellent, helping the team’s performance and her reputation with pro scouts. Later that day, there is a formal luncheon at the athlete dorm to introduce the coaches and members of each team to the alumni. The alumni are especially interested in meeting the basketball coach and members of the basketball team because they are in the playoffs. You notice your star player is not there, but you see her roommate, a member of the swim team. She tells you that you should talk to your star player in person, and she leads you to their dorm room.

When you arrive, the two of you find her sitting on her bed. She appears fit, well-rested, and her room is clean. She looks calm with her hands folded as she asks you to replace the coach, saying she can’t attend the luncheon if he is there.

Crying

A week later the coach informs you that the star player’s performance in games is excellent, helping the team’s performance and her reputation with pro scouts. Later that day, there is a formal luncheon at the athlete dorm to introduce the coaches and members of each team to the alumni. The alumni are especially interested in meeting the basketball coach and members of the basketball team because they are in the playoffs. You notice your star player is not there, but you see her roommate, a member of the swim team. She tells you that you should talk to your star player in person, and she leads you to their dorm room.

When you arrive, the two of you find her sitting on her bed. She appears fit and well-rested, and her room is clean. She looks sad and begins sobbing as she asks you to replace the coach, saying she can’t attend the luncheon if he is there.

Mild-depression (crying with behavioral changes)

A week later the coach informs you that the star player’s performance in games is OK and not harming the team’s performance and her reputation with pro scouts. Later that day, there is a formal luncheon at the athlete dorm to introduce the coaches and members of each team to the alumni. The alumni are especially interested in meeting the basketball coach and members of the basketball team because they are in the playoffs. You notice your star player is not there, but you see her roommate, a member of the swim team. She tells you that you should talk to your star player in person, and she leads you to their dorm room.

When you arrive, the two of you find her sitting on her bed. Her room is a little messy and she appears a little less fit than you remember, while also seeming slightly tired. She looks sad and begins sobbing as she asks you to replace the coach, saying she can’t attend the luncheon if he is there.

Depression

A week later the coach informs you that the star player’s performance in games is poor, harming the team’s performance and her reputation with pro scouts. Later that day, there is a formal luncheon at the athlete dorm to introduce the coaches and members of each team to the alumni. The alumni are especially interested in meeting the basketball coach and members of the basketball team because they are in the playoffs. You notice your star player is not there, but you see her roommate, a member of the swim team. She tells you that you should talk to your star player in person, and she leads you to their dorm room.

When you arrive, the two of you find her slouched on her bed. Her room is a mess, and you notice she has lost weight and looks fatigued. She looks sad and begins sobbing and wringing her hands as she asks you to replace the coach, saying she can’t attend the luncheon if he is there.

Suicide attempt

A week later the coach informs you that the star player’s performance in games is poor, harming the team’s performance and her reputation with pro scouts. Later that day, there is a formal luncheon at the athlete dorm to introduce the coaches and members of each team to the alumni. The alumni are especially interested in meeting the basketball coach and members of the basketball team because they are in the playoffs. You notice your star player is not there, but you see her roommate, a member of the swim team. She tells you that you should talk to your star player in person, and she leads you to their dorm room.

When you arrive, the two of you find her passed out on her bed. The room is a mess, and you notice she has lost weight. Her roommate tries to wake her up, but she doesn’t respond. You see an empty bottle of OxyContin painkillers on the table and immediately call 911. That evening you visit her in the hospital. Her doctor tells you she almost died, and then leaves the two of you alone. The star player begins sobbing as she asks you to replace the coach.

Brother-in-law

Imagine yourself in the following scenario:

You are the single parent of a 15-year-old girl. Your relationship with her has always been full of conflict. She is very self-centered, and the two of you fight frequently.

Recently, your sister lost her house in a fire and was having difficulty finding a place for her family to live because many other families also lost their homes. You are very close with your sister and brother-in-law, who helped you escape your abusive ex-partner. You have a large house with several extra bedrooms, so you decide to let your sister, your brother-in-law, and your teenage niece move in while they look for more permanent housing.

(page break)

Unfortunately, your daughter and your niece are not getting along. They have to share a bathroom, and your daughter complains constantly that she is in there too long. Your daughter is also jealous that her cousin has expensive clothes, a designer handbag, and a new iPhone, all of which are nicer than your daughter’s.

One day your daughter had a party at your house with many of her friends. You noticed that your niece was very popular and got a lot of attention, especially from your daughter’s friends and boyfriend. You could tell that your daughter was upset. After the party, your daughter tells you that her cousin is a bitch who is trying to steal her boyfriend. However, you saw that your niece was trying to avoid interacting with the boyfriend.

(page break)

Later, your daughter complained that your brother-in-law was looking at her body. A few days later, while grocery shopping, you got a text from your daughter saying that your brother-in-law tried to get into the bathroom while she was showering and came into her room while she was changing. She says that she does not feel safe and wants your sister and her family to leave. However, you know your daughter will exaggerate to get her way.

The next week, your daughter comes to you saying your brother-in-law groped her as they passed in the hallway and that she wants your sister’s family to leave immediately. You confront your brother-in-law due to the severity of your daughter’s allegation. He strongly denies it.

That night, you call your mother who says your daughter has long been jealous of her cousin. She reminds you that you’ve taken your daughter’s side before and been burned when you found out your daughter wasn’t telling the whole truth. She reminds you of the difficult situation your sister’s family is in.

Signals

Verbal request

Your daughter has appeared normal since her claims about your brother-in-law. She has still looked fit and put the same amount of effort into her appearance, always wearing clean clothes and spending time on her hair. She has also slept about the same amount of time as normal and remained active throughout the day.

Her demeanor has also remained normal. She has generally appeared calm and has been able to maintain her concentration. She has also done her homework on time and maintained an A in all her classes as she always has in the past.

When the two of you talk about your brother-in-law, she remains calm and asks you to kick him out.

Crying

Your daughter has appeared normal since her claims about your brother-in-law. She has still looked fit and put the same amount of effort into her appearance, always wearing clean clothes and spending time on her hair. She has also slept about the same amount of time as normal and remained active throughout the day.

Her demeanor has also remained normal. She has generally appeared calm and has been able to maintain her concentration. She has also done her homework on time and maintained an A in all her classes as she always has in the past.

When the two of you talk about your brother-in-law, she starts sobbing and asks you to kick him out.

Mild-depression (crying with behavioral changes)

Your daughter has appeared sad since her claims about your brother-in-law. She has looked a little less fit and has put less effort into her appearance, occasionally wearing dirty clothes and sometimes not doing her hair. She has also sometimes woken up late and has seemed a little less active than normal.

Her demeanor has also changed slightly. She has occasionally gotten distressed and has seemed distracted. She has also missed a few assignments and her grades dropped from straight A’s to a B average for the first time in her life.

When the two of you talk about your brother-in-law, she starts sobbing and asks you to kick him out.

Depression

Your daughter started to appear noticeably different since her claims about your brother-in-law. She has lost weight and has been putting no effort into her appearance, often wearing dirty clothes and rarely doing her hair. She has also slept more than normal but still seemed tired and inactive.

Her demeanor also changed drastically. She has often gotten distressed and has had difficulty maintaining her concentration. She has also missed multiple assignments, and her grades dropped from straight A’s to a C average for the first time in her life.

When the two of you talk about your brother-in-law, she starts sobbing and asks you to kick him out.

Suicide attempt

Your daughter started to appear noticeably different since her claims about your brother-in-law. She has lost weight and has been putting no effort into her appearance, often wearing dirty clothes and rarely doing her hair. She has also slept more than normal but still seemed tired and inactive.

Her demeanor also changed drastically. She has often gotten distressed and has had difficulty maintaining her concentration. She has also missed multiple assignments, and her grades dropped from straight A’s to a C average for the first time in her life.

When the two of you talk about your brother-in-law, she starts sobbing and asks you to kick him out. Then, this past week, you and your daughter went through a traumatic experience when your daughter attempted suicide. You rushed her to the hospital, where she was placed in critical but stable condition.

Romantic Partner (pronouns match sex participants reported primary attraction to)

Imagine yourself in the following scenario:

You are a single parent of a 13-year-old girl. The two of you have lived alone together for the past five years since your breakup with her [father/mother]. During this time, finding potential partners has been difficult.

Recently, you have gotten closer with a new [man/woman]. [He/She] is very attractive, has a good job, and has treated you better than anyone before. Confident that you will marry soon, the two of you decide to move in together.

Your daughter, who wants you to get back together with her [dad/mom], is not happy about this. She frequently complains whenever your boyfriend comes over and is extremely rude and avoidant. In previous relationships, such behavior has driven others away. However, your partner says [he/she] is committed to this relationship and to establishing a bond with your daughter.

After a month of living together with your partner, things have only gotten worse. Your daughter is almost always upset and has treated you partner worse than others in the past.

One night, she accused your partner of hitting her multiple times. You ask if she has any bruises or injuries, but she says she doesn’t. Despite this, she maintains that she was hit and says she doesn’t know why there aren’t any marks on her.

You bring up the accusation with your partner, and [he/she] denies it, saying that [he/she] would never hurt her and that this is just a way for her to split the two of you up.

Measuring Beliefs and Actions

Table 4: Outcome variables in each vignette.
Variable Vignette Wording Coding
T1 Belief Basketball How much do you believe the star player is telling the truth about the coach? 0: Not at all; 100: Completely
T1 Belief Brother-in-law How much do you believe your daughter is telling the truth about your brother-in-law? 0: Not at all; 100: Completely
T1 Belief Romantic partner How much do you believe your daughter is telling the truth about your partner? 0: Not at all; 100: Completely
T1 Belief Thwarted Marriage How much do you believe your older(bold) daughter is telling the truth about the second man not being a good match for her? 0: Not at all; 100: Completely
T1 Action Basketball How likely are you to recommend that the president of the university should suspend the coach? 0: Very unlikely; 100: Very likely
T1 Action Brother-in-law How likely are you to kick your brother-in-law out? 0: Very unlikely; 100: Very likely
T1 Action Romantic partner How likely are you to kick your partner out? 0: Very unlikely; 100: Very likely
T1 Action Thwarted Marriage How likely are you to provide the large dowry so your older(bold) daughter can marry the man she is in love with? 0: Very unlikely; 100: Very likely
T1 Divide Thwarted Marriage How would you plan on dividing the money between your daughters? 0: Much more for the younger daughter; 50: Even division; 100: Much more for the older daughter

Signals

Verbal request

Your daughter has appeared normal since her claims about your partner. She has still looked fit and put the same amount of effort into her appearance, always wearing clean clothes and spending time on her hair. She has also slept about the same amount of time as normal and remained active throughout the day.

Her demeanor has also remained normal. She has generally appeared calm and has been able to maintain her concentration. She has also done her homework on time and maintained an A in all her classes as she always has in the past.

When the two of you talk about your partner, she remains calm and asks you to kick your partner out.

Crying

Your daughter has appeared normal since her claims about your partner. She has still looked fit and put the same amount of effort into her appearance, always wearing clean clothes and spending time on her hair. She has also slept about the same amount of time as normal and remained active throughout the day.

Her demeanor has also remained normal. She has generally appeared calm and has been able to maintain her concentration. She has also done her homework on time and maintained an A in all her classes as she always has in the past.

When the two of you talk about your partner, she starts sobbing and asks you to kick your partner out.

Mild-depression (crying with behavioral changes)

Your daughter has appeared sad since her claims about your partner. She has looked a little less fit and has put less effort into her appearance, occasionally wearing dirty clothes and sometimes not doing her hair. She has also sometimes woken up late and has seemed a little less active than normal.

Her demeanor has also changed slightly. She has occasionally gotten distressed and has seemed distracted. She has also missed a few assignments and her grades dropped from straight A’s to a B average for the first time in her life.

When the two of you talk about your partner, she starts sobbing and asks you to kick your partner out.

Depression

Your daughter started to appear noticeably different since her claims about your partner. She has lost weight and has been putting no effort into her appearance, often wearing dirty clothes and rarely doing her hair. She has also slept more than normal but still seemed tired and inactive.

Her demeanor also changed drastically. She has often gotten distressed and has had difficulty maintaining her concentration. She has also missed multiple assignments, and her grades dropped from straight A’s to a C average for the first time in her life.

When the two of you talk about your partner, she starts sobbing and asks you to kick your partner out.

Suicide attempt

Your daughter started to appear noticeably different since her claims about your partner. She has lost weight and has been putting no effort into her appearance, often wearing dirty clothes and rarely doing her hair. She has also slept more than normal but still seemed tired and inactive.

Her demeanor also changed drastically. She has often gotten distressed and has had difficulty maintaining her concentration. She has also missed multiple assignments, and her grades dropped from straight A’s to a C average for the first time in her life.

When the two of you talk about your partner, she starts sobbing and asks you to kick your partner out. Then, this past week, you and your daughter went through a traumatic experience when your daughter attempted suicide. You rushed her to the hospital, where she was placed in critical but stable condition.

Thwarted Marriage

Imagine you are the parent of two daughters, ages 19 and 21, and you have no sons.

Your younger daughter is very sweet. Your older daughter is hard to deal with, however. She is very jealous of her younger sister, and constantly accuses you of favoring her, even when your older daughter receives more.

You live in a community where marriages are arranged and dowries are paid. Recently, you and your partner have been attempting to arrange a marriage for your older daughter with a man she is already in love with. The man, who comes from a good family and is in the same caste as yours, appears to be equally interested in your daughter, and his family seems receptive.

(page break)

As your younger daughter is also not far from marriage, you and your partner decide to divide the money you have saved for dowries in half, with equal amounts for both daughters. At first, an equal split of your savings seemed possible. However, the family of the man your older daughter wants to marry is getting offers of marriage from other families, and it now appears that his family is expecting a much larger dowry.

Since providing a large enough dowry to compete with the other offers would leave little savings for your younger daughter’s dowry, you decide to look for another husband for your older daughter.

(page break)

After a few months of searching, you are confident you found a good man for your older daughter. He is very devout, kind, and comes from a good family in the same caste as yours. He is a bit younger than your daughter, and has less money than typical for your caste, but his family appears to be willing to accept a smaller dowry that would leave a fair amount for your younger daughter.

You ask your older daughter what she thinks of the man, and she is immediately disappointed. She complains that she already loves the man you originally intended her to marry. She says the second man is younger than she is and not attractive. You explain that she might not be able to marry the man she loves because it would require a much larger dowry, leaving too little for her sister.

(page break)

You encourage your daughter to spend some time with the second man, and she begrudgingly agrees. After each date, she becomes more insistent that there is no chemistry between them, he is immature, has no sense of humor, and just isn’t right for her. She also complains that the family of the man she loves is seeking other marriage partners and that she is afraid he will become interested in his other marriage options.

Your partner is not sure if your daughter really thinks the other man is a poor choice, or if she just wants to get her way as usual.

(page break)

Your family talks to the first man’s family again but the amount they want is almost all you have saved. Your partner tells your older daughter that it’s just not fair to spend so much on her because saving enough for your younger daughter’s dowry would be almost impossible in the next two or three years. For this reason, your family begins to hold more serious discussions with the family of the second man despite your older daughter’s objections.

Two days later, your family receives word that the first man’s family is finalizing an agreement for him to marry another woman.

Signals

Verbal request

Your older daughter has appeared normal since your attempts at finding another potential husband for her. She has still looked fit and put the same amount of effort into her appearance, always wearing clean clothes and spending time on her hair. She has also slept about the same amount of time as normal and remained active throughout the day.

Her demeanor has also remained normal. She has generally appeared calm and has been able to maintain her concentration. She has also done her homework on time and maintained an A in all her classes as she always has in the past.

When the two of you talk about the arranged marriage, she remains calm and asks you to pay the amount the family of the man she loves wants.

Crying

Your older daughter has appeared normal since your attempts at finding another potential husband for her. She has still looked fit and put the same amount of effort into her appearance, always wearing clean clothes and spending time on her hair. She has also slept about the same amount of time as normal and remained active throughout the day.

Her demeanor has also remained normal. She has generally appeared calm and has been able to maintain her concentration. She has also done her homework on time and maintained an A in all her classes as she always has in the past.

When the two of you talk about the arranged marriage, she starts sobbing and asks you to pay the amount the family of the man she loves wants.

Mild-depression (crying with behavioral changes)

Your older daughter has appeared sad since your attempts at finding another potential husband for her. She has looked a little less fit and has put less effort into her appearance, occasionally wearing dirty clothes and sometimes not doing her hair. She has also sometimes woken up late and has seemed a little less active than normal.

Her demeanor has also changed slightly. She has occasionally gotten distressed and has seemed distracted. She has also missed a few assignments and her grades dropped from straight A’s to a B average for the first time in her life.

When the two of you talk about the arranged marriage, she starts sobbing and asks you to pay the amount the family of the man she loves wants.

Depression

Your older daughter started to appear noticeably different since your attempts at finding another potential husband for her. She has lost weight and has been putting no effort into her appearance, often wearing dirty clothes and rarely doing her hair. She has also slept more than normal but still seemed tired and inactive.

Her demeanor also changed drastically. She has often gotten distressed and has had difficulty maintaining her concentration. She has also missed multiple assignments, and her grades dropped from straight A’s to a C average for the first time in her life.

When the two of you talk about the arranged marriage, she starts sobbing and asks you to pay the amount the family of the man she loves wants.

Suicide attempt

Your older daughter started to appear noticeably different since your attempts at finding another potential husband for her. She has lost weight and has been putting no effort into her appearance, often wearing dirty clothes and rarely doing her hair. She has also slept more than normal but still seemed tired and inactive.

Her demeanor also changed drastically. She has often gotten distressed and has had difficulty maintaining her concentration. She has also missed multiple assignments, and her grades dropped from straight A’s to a C average for the first time in her life.

When the two of you talk about the arranged marriage, she starts sobbing and asks you to pay the amount the family of the man she loves wants. Then, this past week, you and your daughter went through a traumatic experience when your daughter attempted suicide. You rushed her to the hospital, where she was placed in critical but stable condition.

Emotion Checklist

How do you think [the star player / your daughter] feels? Click all that apply.

T3 Evidence

Basketball Coach

Since your last response, it was discovered that a security camera captured the assault happening, just as the star player had described.

Brother-in-law

Since your last response, your daughter has come to you with a video she secretly took which showed your brother-in-law trying to grope her as she had described previously.

Romantic partner

Since your last response, your daughter has come to you with a video she secretly took which showed your partner hitting her as she had described previously.

Thwarted marriage

Just now in the market, you saw the man whose family is willing to accept a smaller dowry (the man your daughter does not want to marry). You heard him insult your daughter and your family.

Preregistered regression models

We preregistered four OLS linear regression models and one mediation model (https://osf.io/g3s6n). However, in the main text we report GLM models instead, for the following reasons. Our pre-test and post-test measures, T1 & T2 Belief and T1 & T2 Action, were all measured on a 0-100 point scale. A substantial number of participants rated their beliefs and actions as exactly 0 or exactly 100 at either T1 or T2. OLS linear regression models are not suitable for a closed and bounded distribution with so many values on the boundary because the residuals would not be normally distributed or have constant variance. Beta regression, a possible alternative, is only defined on the open interval, (0, 1), whereas our data had many values on the boundaries (a closed interval). A variant of beta regression, zero-one inflated beta, is inappropriate because it assumes different processes for the generation of data on the boundaries vs. the interior, and we had no reason to believe that our zeros and ones were generated by a different process than the rest of our observations.

We therefore fit models of our two outcome variables with the preregistered OLS models (reported here in the SI) and four variants of logistic regression (for which we rescaled the 0-100 outcomes to the continuous interval [0, 1]). First, we fit fractional regression models, introduced in a seminal paper by Papke & Wooldridge (1996) for continuous outcomes on the interval [0, 1], which, in brief, involve fitting a quasi-likelihood version of logistic regression with robust standard errors. This method is implemented in the frm package (Ramalho, 2019). Second, we fit GLM logistic regression models, computing 95% CI’s via bootstrapping, using the glmmboot package created to model continuous outcomes on [0, 1] (Humphrey, 2020). Third, we fit GLM models with the quasibinomial family and logit link using the glm function in R stats library, which can fit continuous outcomes on [0, 1]. Fourth, we computed robust standard errors and 95% confidence intervals from the quasibinomial models using the sandwich package (Zeileis, 2006). The estimated coefficients, standard errors, and confidence intervals produced by these four methods were very similar, and often identical (see Figure 31. We therefore proceeded to fit and report models in the main text using glm with the quasibinomial family and logit link because model objects produced by this function are compatible with numerous other R packages for summarizing and plotting effects.

Two of our four preregistered OLS regression models included interaction terms between signal and T1 Belief and Action, with the rationale that the signals might have reduced effect for individuals who indicate high levels of belief and helping at baseline. This rationale was less convincing for quasibinomial models using a logit link because the expected interaction – reduced effect of signals in individuals with high baseline belief – is inherent to logistic regression: the effect of a predictor on the outcome necessarily diminishes as the outcome approaches 1. Our theory does not predict an interaction beyond that already present in a logistic regression model, so we did not include an interaction term in the GLM models (but we still report OLS models with the interaction term).

The first two OLS regression models tested the effects of our ordinal signal variable on Time 2 Belief and Action, controlling for T1 Belief and Action, respectively (a pre-test/post-test design):

\[ \begin{aligned} \operatorname{T2Belief} &= \beta_{0} + \beta_{1}(\operatorname{T1Belief}) + \beta_{2}(\operatorname{signal}_{\operatorname{.L}}) + \beta_{3}(\operatorname{signal}_{\operatorname{.Q}})\ + \\ &\quad \beta_{4}(\operatorname{signal}_{\operatorname{.C}}) + \beta_{5}(\operatorname{signal}_{\operatorname{\text{^}4}}) + \epsilon \end{aligned} \] \[ \begin{aligned} \operatorname{T2Action} &= \beta_{0} + \beta_{1}(\operatorname{T1Action}) + \beta_{2}(\operatorname{signal}_{\operatorname{.L}}) + \beta_{3}(\operatorname{signal}_{\operatorname{.Q}})\ + \\ &\quad \beta_{4}(\operatorname{signal}_{\operatorname{.C}}) + \beta_{5}(\operatorname{signal}_{\operatorname{\text{^}4}}) + \epsilon \end{aligned} \]

We also preregistered models with an interaction term, for reasons explained in the main text:

\[ \begin{aligned} \operatorname{T2Belief} &= \beta_{0} + \beta_{1}(\operatorname{T1Belief}) + \beta_{2}(\operatorname{signal}_{\operatorname{.L}}) + \beta_{3}(\operatorname{signal}_{\operatorname{.Q}})\ + \\ &\quad \beta_{4}(\operatorname{signal}_{\operatorname{.C}}) + \beta_{5}(\operatorname{signal}_{\operatorname{\text{^}4}}) + \beta_{6}(\operatorname{T1Belief} \times \operatorname{signal}_{\operatorname{.L}}) + \beta_{7}(\operatorname{T1Belief} \times \operatorname{signal}_{\operatorname{.Q}})\ + \\ &\quad \beta_{8}(\operatorname{T1Belief} \times \operatorname{signal}_{\operatorname{.C}}) + \beta_{9}(\operatorname{T1Belief} \times \operatorname{signal}_{\operatorname{\text{^}4}}) + \epsilon \end{aligned} \] \[ \begin{aligned} \operatorname{T2Action} &= \beta_{0} + \beta_{1}(\operatorname{T1Action}) + \beta_{2}(\operatorname{signal}_{\operatorname{.L}}) + \beta_{3}(\operatorname{signal}_{\operatorname{.Q}})\ + \\ &\quad \beta_{4}(\operatorname{signal}_{\operatorname{.C}}) + \beta_{5}(\operatorname{signal}_{\operatorname{\text{^}4}}) + \beta_{6}(\operatorname{T1Action} \times \operatorname{signal}_{\operatorname{.L}}) + \beta_{7}(\operatorname{T1Action} \times \operatorname{signal}_{\operatorname{.Q}})\ + \\ &\quad \beta_{8}(\operatorname{T1Action} \times \operatorname{signal}_{\operatorname{.C}}) + \beta_{9}(\operatorname{T1Action} \times \operatorname{signal}_{\operatorname{\text{^}4}}) + \epsilon \end{aligned} \]

The glm models we report in the main text:

\[ \begin{aligned} \log\left[ \frac { E( \operatorname{T2Belief} ) }{ 1 - E( \operatorname{T2Belief} ) } \right] &= \beta_{0} + \beta_{1}(\operatorname{T1Belief}) + \beta_{2}(\operatorname{signal}_{\operatorname{.L}}) + \beta_{3}(\operatorname{signal}_{\operatorname{.Q}})\ + \\ &\quad \beta_{4}(\operatorname{signal}_{\operatorname{.C}}) + \beta_{5}(\operatorname{signal}_{\operatorname{\text{^}4}}) \end{aligned} \] \[ \begin{aligned} \log\left[ \frac { E( \operatorname{T2Action} ) }{ 1 - E( \operatorname{T2Action} ) } \right] &= \beta_{0} + \beta_{1}(\operatorname{T1Action}) + \beta_{2}(\operatorname{signal}_{\operatorname{.L}}) + \beta_{3}(\operatorname{signal}_{\operatorname{.Q}})\ + \\ &\quad \beta_{4}(\operatorname{signal}_{\operatorname{.C}}) + \beta_{5}(\operatorname{signal}_{\operatorname{\text{^}4}}) \end{aligned} \]

The preregistered mediation and outcome models, respectively, were:

\[ \begin{aligned} \operatorname{T2Belief} &= \beta_{0} + \beta_{1}(\operatorname{signal}_{\operatorname{Depression}}) + \beta_{2}(\operatorname{T1Belief}) + \epsilon \end{aligned} \] \[ \begin{aligned} \operatorname{T2Action} &= \beta_{0} + \beta_{1}(\operatorname{signal}_{\operatorname{Depression}}) + \beta_{2}(\operatorname{T1Belief}) + \beta_{3}(\operatorname{T1Action})\ + \\ &\quad \beta_{4}(\operatorname{T2Belief}) + \epsilon \end{aligned} \]

The mediation and outcome models, respectively, that we report in the main text were:

\[ \begin{aligned} \log\left[ \frac { P( \operatorname{T2Belief} = \operatorname{1} ) }{ 1 - P( \operatorname{T2Belief} = \operatorname{1} ) } \right] &= \beta_{0} + \beta_{1}(\operatorname{signal}_{\operatorname{Depression}}) + \beta_{2}(\operatorname{T1Belief}) \end{aligned} \] \[ \begin{aligned} \log\left[ \frac { P( \operatorname{T2Action} = \operatorname{1} ) }{ 1 - P( \operatorname{T2Action} = \operatorname{1} ) } \right] &= \beta_{0} + \beta_{1}(\operatorname{signal}_{\operatorname{Depression}}) + \beta_{2}(\operatorname{T1Belief}) + \beta_{3}(\operatorname{T1Action})\ + \\ &\quad \beta_{4}(\operatorname{T2Belief}) \end{aligned} \]

Fitted model parameters are reported below.

Sample size in each condition in the current study

Table 5: Sample size in each condition.
Thwarted marriage Basketball coach Romantic partner Brother-in-law
Verbal request 61 60 66 60
Crying 58 60 67 60
Mild depression 60 62 66 62
Depression 62 61 66 59
Suicide attempt 62 60 67 61

Socioeconomic distribution

Distribution of participants by age, log annual income (USD), and nationality. Each dot is one participant. A small number of participants who did not report annual income, or reported values < $10 USD, were removed from this plot. A small amount of jitter was added to reveal overlapping points.

Figure 11: Distribution of participants by age, log annual income (USD), and nationality. Each dot is one participant. A small number of participants who did not report annual income, or reported values < $10 USD, were removed from this plot. A small amount of jitter was added to reveal overlapping points.

Signal effect sizes

Between-subjects Cohen's d (95% CI) for the effect of each signal on Belief and Action in each vignette relative to the Verbal request control condtion, ranked by effect size.

Figure 12: Between-subjects Cohen’s d (95% CI) for the effect of each signal on Belief and Action in each vignette relative to the Verbal request control condtion, ranked by effect size.

Within-subjects Cohen's d (95% CI) for the effect of each signal on *T2 Belief* and *T2 Action* in each vignette relative to baseline *T1 Belief* and *T1 Action*, respectively, ranked by effect size.

Figure 13: Within-subjects Cohen’s d (95% CI) for the effect of each signal on T2 Belief and T2 Action in each vignette relative to baseline T1 Belief and T1 Action, respectively, ranked by effect size.

Mediation of Suicide attempt relative to Depression signals

The total effect of the Suicide attempt signal (treatment) vs. Depression (control) on the likelihood of Action was to increase it by 6.78 points from T1 to T2 (on the original 100-point scale). Of this increase, 42% was mediated by the increased Belief that the victim was telling the truth. See Figure 14.

The effect of a Suicide attempt (treatment) vs. Depression (control) on Action, [0-1 scale], is only partially mediated by Belief. ACME: Average causal mediation effect. ADE: Average direct effect. The mediation model controlled for *T1 Belief*, and the outcome model controlled for *T1 Belief* and *T1 Action*. Both models were GLMs with the binomial family (the mediation package does not support the quasibinomial family).

Figure 14: The effect of a Suicide attempt (treatment) vs. Depression (control) on Action, [0-1 scale], is only partially mediated by Belief. ACME: Average causal mediation effect. ADE: Average direct effect. The mediation model controlled for T1 Belief, and the outcome model controlled for T1 Belief and T1 Action. Both models were GLMs with the binomial family (the mediation package does not support the quasibinomial family).

Change in beliefs and actions from T1 to T2

Change in beliefs and actions from T1 (arrow base) to T2 arrow head) for participants in each condition. Each grey arrow is one participant. Red arrows are the mean change.

Figure 15: Change in beliefs and actions from T1 (arrow base) to T2 arrow head) for participants in each condition. Each grey arrow is one participant. Red arrows are the mean change.

Emotions in vignette (T1) and signal (T2) conditions

Each cell represents the proportion of participants who inferred that emotion. **Time 1 emotions**: inference of victim's emotional state in each vignette at time 1. **Time 2 emotions**: inference of victim's emotional state following the signal at time 2. Participants could check multiple emotions from the list. Rows ordered by mean proportion across vignettes.

Figure 16: Each cell represents the proportion of participants who inferred that emotion. Time 1 emotions: inference of victim’s emotional state in each vignette at time 1. Time 2 emotions: inference of victim’s emotional state following the signal at time 2. Participants could check multiple emotions from the list. Rows ordered by mean proportion across vignettes.

Change in emotions T1 to T2 by signal

Change in inference of victim's emotional state From pre- to post-signal. Each cell represents the difference in proportions of participants who inferred that emotion (T2 - T1). Participants could check multiple emotions from the list. Rows and columns ordered with the PCA angle method in the seriation package.

Figure 17: Change in inference of victim’s emotional state From pre- to post-signal. Each cell represents the difference in proportions of participants who inferred that emotion (T2 - T1). Participants could check multiple emotions from the list. Rows and columns ordered with the PCA angle method in the seriation package.

Mentally ill

Proportion of participants in each vignette who thought that the victim was mentally ill at T1. Means estimated with a generalized linear regression model with the quasibinomial family.

Figure 18: Proportion of participants in each vignette who thought that the victim was mentally ill at T1. Means estimated with a generalized linear regression model with the quasibinomial family.

Proportion of participants in each condition who thought that the victim was mentally ill at T2, controlling for perceptions of mental illness at T1. Means estimated with a generalized linear regression model with the quasibinomial family, and displayed for participants who did not perceive mental illness at T1.

Figure 19: Proportion of participants in each condition who thought that the victim was mentally ill at T2, controlling for perceptions of mental illness at T1. Means estimated with a generalized linear regression model with the quasibinomial family, and displayed for participants who did not perceive mental illness at T1.

Association of T2 Perceived Mental Illness (Yes/No) with T2 Belief in the US sample, by signal, controlling for T1 Perceived Mental Illness (Yes/No), T1 Belief, and vignette. Means estimated with a generalized linear regression model with the quasibinomial family.

Figure 20: Association of T2 Perceived Mental Illness (Yes/No) with T2 Belief in the US sample, by signal, controlling for T1 Perceived Mental Illness (Yes/No), T1 Belief, and vignette. Means estimated with a generalized linear regression model with the quasibinomial family.

Association of T2 Perceived Mental Illness (Yes/No) with T2 Action in the US sample, by signal, controlling for T1 Perceived Mental Illness (Yes/No), T1 Action, and vignette. Means estimated with a generalized linear regression model with the quasibinomial family.

Figure 21: Association of T2 Perceived Mental Illness (Yes/No) with T2 Action in the US sample, by signal, controlling for T1 Perceived Mental Illness (Yes/No), T1 Action, and vignette. Means estimated with a generalized linear regression model with the quasibinomial family.

The effect of signals on T2 division in thwarted marriage vignette

Effect of signals on division of dowry between the daughters in the thwarted marriage vignette, with an interaction between T1 Division and signal. Division > 0.50 favored older daughter. Means estimated with a generalized linear regression model with the quasibinomial family.

Figure 22: Effect of signals on division of dowry between the daughters in the thwarted marriage vignette, with an interaction between T1 Division and signal. Division > 0.50 favored older daughter. Means estimated with a generalized linear regression model with the quasibinomial family.

Interaction of age with signals on *T2 Division* of dowry between the daughters in the thwarted marriage vignette. Division > 0.50 favored older daughter.

Figure 23: Interaction of age with signals on T2 Division of dowry between the daughters in the thwarted marriage vignette. Division > 0.50 favored older daughter.

Exploratory demographic associations

Sociodemographic associations with *T2 Belief* (top) and *T2 Action* (bottom) in US participants. Each model controlled for *T1 Belief* or *T1 Action*, vignette, Education, and Income. Significant associations displayed here. Estimated using generalized linear regression models with the quasibinomial family. Grey bars are 95% CIs.

Figure 24: Sociodemographic associations with T2 Belief (top) and T2 Action (bottom) in US participants. Each model controlled for T1 Belief or T1 Action, vignette, Education, and Income. Significant associations displayed here. Estimated using generalized linear regression models with the quasibinomial family. Grey bars are 95% CIs.

Sociodemographic associations with *T2 Belief* and *T2 Action* in Indian participants (Thwarted marriage vignette). Each model included *T1 Belief* or *T1 Action*, Income, Age, Sex, Years of education, and signal. Significant associations displayed here. Estimated using generalized linear regression models with the quasibinomial family. Grey bars are 95% CIs.

Figure 25: Sociodemographic associations with T2 Belief and T2 Action in Indian participants (Thwarted marriage vignette). Each model included T1 Belief or T1 Action, Income, Age, Sex, Years of education, and signal. Significant associations displayed here. Estimated using generalized linear regression models with the quasibinomial family. Grey bars are 95% CIs.

Validity check: T3 Action vs. T2 Action

The effect of post-signal (T3) information that the victim was telling the truth on the likelihood of acting to help her (*T3 Action*), controlling for *T2 Action*, with a term for the interaction of *T2 Action* and vignette.

Figure 26: The effect of post-signal (T3) information that the victim was telling the truth on the likelihood of acting to help her (T3 Action), controlling for T2 Action, with a term for the interaction of T2 Action and vignette.

Feedback distribution by vignette

At the end of the study, participants could optionally provide written feedback about study. We classified all feedback into two categories: Generic for a few short words, such as ‘nice study,’ or ‘thanks’; and Informative for one or two sentences commenting on specific aspects of the survey. See Figure 27.

The distribution of optional feedback provided at the end of the study. None: no feedback provided (includes, e.g., 'none', 'no', 'N/A'). Generic: A few short words, such as 'nice study', or 'thanks'. Informative: one or two sentences commenting on specific aspects of the survey. Numbers are the number of participants in that category.

Figure 27: The distribution of optional feedback provided at the end of the study. None: no feedback provided (includes, e.g., ‘none,’ ‘no,’ ‘N/A’). Generic: A few short words, such as ‘nice study,’ or ‘thanks.’ Informative: one or two sentences commenting on specific aspects of the survey. Numbers are the number of participants in that category.

We then further classified the Informative feedback into six categories: the vignette corresponds to real-life experiences; comments on the vignette (e.g., other ways to respond); what the participant thought about the T3 information that the victim was telling the truth; criticisms of the study (e.g., the attention checks); comments on the study; and an explanation of why the participant acted or failed to act. See Figure 28. The full texts of all comments are in Table 6.

The distribution of informative feedback provided at the end of the study. See Figure \@ref(fig:feedbackplot) for other types of feedback. See Table \@ref(tab:informativetab) for the text of all informative statements. Numbers are the number of participants in that category.

Figure 28: The distribution of informative feedback provided at the end of the study. See Figure 27 for other types of feedback. See Table 6 for the text of all informative statements. Numbers are the number of participants in that category.

Table 6: Informative feedback provided by participants after T3 (the end of the study). Categorization required some judgement calls.
Informative vignette Feedback_original
Decision explanation Thwarted marriage Actually my decision is the man who really loves the woman does not expect money from her except her pure love .. so it seems unfair to me that getting dowry for marriages. I strongly denied that concept in my culture.
Decision explanation Basketball coach I questioned the student’s honest throughout because she has exaggerated before. It’s tough to judge a person as honest when they have not been honest before.
Decision explanation Basketball coach Unfortunate scenario with the star player, but to me, people are innocent until proven guilty.
Decision explanation Basketball coach This was interesting, as I thought I was more confident standing by women who accuses others or rape, but this story shows otherwise. I should’ve stood by her at the beginning but chose not to because of her attitude and behavior. This study reminded me that that should not matter when it comes to sexual assaults accusations.
Decision explanation Basketball coach I made my decision by clear evidence. Seriously, I concerned this study
Decision explanation Basketball coach This definitely was a bit challenging! However, I err on the side of victims, because bad things can still happen to bad people, and I have never really seen someone putting out a rape accusation when it could harm their own career unless the situation is serious – usually it’s the exact opposite that occurs.
Decision explanation Basketball coach I know we are always supposed to believe the victim, but without any other information that what I had on hand, I’d make the same decision again.
Decision explanation Basketball coach Without evidence it hard to believe someone….
Decision explanation Basketball coach The behavior and personality of the star athlete made it hard to believe her allegation.
Decision explanation Romantic partner I know that some children would lie to get what they wanted. If my daughter came to me and said that someone I was with hit her. There would be a little doubt in my mind but I would pack his stuff immediately and kick him out. I don’t think my daughter would lie about an issue like that, and I definitely wouldn’t take any chances.
Decision explanation Romantic partner I would want to believe my daughter and my partner, but with the evidence the daughter took, I’d believe her over him and most likely kick him out to protect her.
Decision explanation Brother-in-law Interesting HIT, thanks. I’d always err on the side of caution and in the initial scenario, even if I wasn’t fully sure she was telling the truth, I’d kick the brother in law out. I’d rather be wrong than let him stay if he were actually molesting her.
Decision explanation Brother-in-law despite of how passive aggressive my daughter is, any allegation of that nature would always warrant a reaction of my part. I believe there is always truth behind bold allegation so that nature. I would definitely kicked my brother in law only from the beginning, reason why I put the slider at 72. I would have asked my sister and niece to stay. there should be a explanation box as to why the rating one gave. to further explain.
Decision explanation Brother-in-law Considering the daughter’s past incidences of lying, the video evidence would be necessary for me to believe her.
Decision explanation Brother-in-law yes thank you my sister and her daughter could stay they be welcome her husband on the other hand would have been long gone he could have went to my mothers if she though he was telling the truth also. In that circumstance I rather believe my daughter
Study comment Thwarted marriage the survey is very usefull
Study comment Thwarted marriage It is easy to understand the story to answer the attention question and it was very interesting
Study comment Thwarted marriage Very Difficult Survey
Study comment Basketball coach I think you should add the following question in future surveys” How many Granddaughters/Grandsons do you have.
Study comment Basketball coach I hope I didn’t miss anything on the AC’s, thank you for letting me take this!
Study comment Romantic partner THE QUESTONS ARE TOO SHORT. IT WAS GOOD.
Study comment Romantic partner Thank you for this meaningful study!
Study comment Romantic partner Attention checking questions are easy to answer.
Study comment Romantic partner Thank you for including the most accurate demographic descriptor of my current relationship than I have seen in any other surveys. So frustrating to just be classified as simply “divorced” when in a long-term unmarried relationship after a divorce. Awesome job!
Study comment Brother-in-law I think I may have missed an attention check toward the end, I am not sure.
Study comment Brother-in-law Good study on how we feel about evidence.
Study comment Brother-in-law I accidentally skipped an attention check because my internet is going so slow, I noticed it but it was too late to stop the next page loading
Study comment Brother-in-law Very interesting study overall. Thanks for conducting research on important topics such as this. Stay safe during this Corona pandemic and keep up the great research.
Study comment Brother-in-law The survey is clear and interesting. There are different options that could be considered if one thinks that the brother in law should not be kicked out. The options include having a talk with the brother in law, threatening to kick him out if it happens again, etc. What option would participants select?
Study criticism Thwarted marriage Please increase the pay.
Study criticism Thwarted marriage The questionnaires seemed to be repetitive.
Study criticism Thwarted marriage The sliders are not resetting on every page that made me a little confused.
Study criticism Basketball coach If there were more details then I’d be able to make a better decision before having to see video evidence.
Study criticism Basketball coach there is no reason to ask how many sons or daughters one has. “do you have any children?” “yes,” “no” that is all that needs to be asked.
Study criticism Basketball coach Hello, just wanted to say that I did not miss that attention check at the end of the survey before demographics. The slider however seemed to be broken and I couldn’t move it! I tried refreshing the page and using the keyboard instead of the mouse but it seemed stuck. I hope this does not cause a rejection. Please check the slider for future turkers.
Study criticism Basketball coach Just a minor comment, but you may receive some false positives with regards to the last attention check question. Qualtrics sometimes fails to register a response and reusing the last the question for the check may prompt participants to think that their response was not recorded at first glance at first glance, leading them to try clicking again. I think changing the question text may be a better filter as it would still require careful reading but it does not leave the possibility of a participant thinking that there was a Qualtrics error.
Study criticism Romantic partner People don’t like being treated like children. Cut your “attention checks” down significantly. You just come across as extremely paranoid.
Study criticism Romantic partner This was kind of depressing, so you might want to add that little warning you normally do for content warnings about things about abuse, or if it’s there, I don’t think it was displayed by itself enough to notice.
Study criticism Brother-in-law I found it odd that my theoretical niece had fancy belongings after a house fire.
Study criticism Brother-in-law Can’t imagine how this teenage girl could possibly have a video of bro-in-law groping her, seems implausible, but the text stated that she did have it, so I changed my view accordingly. - thanks
Study criticism Brother-in-law Those attention checks were ridiculous.
Study criticism Brother-in-law The last attention check seemed unnecessary.
Study criticism Brother-in-law Scenario was somewhat difficult to understand.
T3 reaction Basketball coach That was a surprising twist to the story. I thought the star player was lying until the video.
T3 reaction Basketball coach Okay, now I would want to beat the snot out of that coach for all he put her through and for all the stress he put me through.
T3 reaction Basketball coach that last part was surprising
T3 reaction Romantic partner I feel bad about not believing my fictional daughter.
T3 reaction Romantic partner Guess I should have believed her!
T3 reaction Romantic partner I feel bad because I chose love over the child in the beginning
T3 reaction Romantic partner If there was video proof of my girlfriend hitting my daughter than I would almost certainly break up with her.
T3 reaction Romantic partner I feel very guilty on not believing my hypothetical daughter the first time. If this ever happens to me I will side with my child first. Thank you for the eye opener.
T3 reaction Brother-in-law Had me fooled for a little bit.
T3 reaction Brother-in-law well now I feel awful for not believing my fake daughter!
T3 reaction Brother-in-law That twist in the scenario really messed with my head. I feel bad about not believing the girl that was my daughter in the scenario.
Vignette comment Thwarted marriage Dowry system needs to be abolished. Women should be made self-dependent, they should not be using their parents money. If they want to get settled with the person they love, they should mutually share all the expenses.
Vignette comment Thwarted marriage Man who wants more money really wants the money not the daughter.
Vignette comment Basketball coach The beginning of the survey said the event didn’t happen, but then there’s video that it did?
Vignette comment Romantic partner The moment my daughter’s grades dropped I would start considering getting rid of the partner. No partner is worth ruining a person’s life.
Vignette comment Romantic partner This was an interesting scenario overall. I honestly do not know how I would react if my partner started hitting out daughter.
Vignette comment Romantic partner This would depend more on you and your daughter’s relationship. My real daughter would never lie about this, so I would have believed her from the beginning.
Vignette comment Brother-in-law I would probably have set up a hidden camera in this situation to see what is going on when I am not around.
Vignette comment Brother-in-law Very disturbing to imagine myself in this situation!!
Vignette comment Brother-in-law I would have probably have been keeping a closer eye on the brother-in-law and my daughter, even having her sleep in the same room as me while they were there at her first accusation even if I was doubting she was telling the truth. I wouldn’t risk someone potentially harming her, and I know this has occurred many times before when strange men are brought into a home with other children.
Vignette comment Brother-in-law I’d ask my mom to take my daughter for a bit
Vignette comment Brother-in-law I am not sure hat I would throw him out right away, but I would investigate 100%! Also, I would put cams in every room of the home without letting him know so that I could see. If he did do anything and I can get it on cam, I would also have him arrested. I would also explain to my daughter that, crying wolf can make it so that people are concerned about whether you can be trusted later, too. I would also tell her to come get me immediately, take video, etc. to help me research.
Vignette endorsement Thwarted marriage IT IS LIKE A CURRENT SITUATION IN EVERY FAMILY
Vignette endorsement Thwarted marriage REAL LIFE
Vignette endorsement Thwarted marriage THIS SURVEY MAKES SENSE WITH THE PLACE OF THE WORD
Vignette endorsement Thwarted marriage THIS SURVEY MAKES SENSE WITH ME
Vignette endorsement Thwarted marriage The Study is very good and describes an even which is happening frequently in India.
Vignette endorsement Thwarted marriage this survey just like my family two daughters are same as this survey like
Vignette endorsement Thwarted marriage Many family ,members have this type of troubles
Vignette endorsement Thwarted marriage Dowry is a major problems to women, and this situation happened to many family
Vignette endorsement Romantic partner I think this was a great study. I actually kicked my boyfriend out because of my daughters accusations. She did get depressed and I will always believe my child.
Vignette endorsement Romantic partner This kind of thing did happen to my daughter and I. It was horrible.
Vignette endorsement Romantic partner I went through this exact scenario as a kid. I wound up homeless.
Vignette endorsement Brother-in-law That hit really close to home. I tried to imagine myself as the person in the scenario outlined, but I think a lot of me got into it and that affected my feelings and decision-making.
Vignette endorsement Brother-in-law Thanks for asking challenging, thoughtful questions. Sexual abuse in families is more prevalent than many people think.
Vignette endorsement Brother-in-law I was in this situation growing up. No one believed me, not even social workers but I was very much telling the truth and it destroyed me. I never got trusted professional help with it and for it. Today I am a barely functioning adult with depression and other emotional problems.

Regression tables

Table 7: Regression coefficients
Estimate Std.Err Statistic P-value Lower 95% CI Upper 95% CI
m11
(Intercept) −1.68 0.0524 −32.0 3.9 × 10−164 −1.78 −1.57
T1Belief 4.25 0.118 36.1 3.1 × 10−195 4.02 4.48
signal.L 0.966 0.0689 14.0 1.3 × 10−41 0.832 1.10
signal.Q −0.163 0.0668 −2.44 1.5 × 10−2 −0.294 −0.0320
signal.C −0.0672 0.0674 −0.997 3.2 × 10−1 −0.200 0.0649
signal^4 0.199 0.0660 3.02 2.6 × 10−3 0.0699 0.329
m22
(Intercept) −2.06 0.0908 −22.7 2.5 × 10−95 −2.24 −1.88
T1Belief 4.52 0.128 35.3 1.0 × 10−188 4.27 4.77
signal.L 0.161 0.127 1.27 2.0 × 10−1 −0.0868 0.409
signal.Q 0.00448 0.126 0.0356 9.7 × 10−1 −0.242 0.252
signal.C −0.0382 0.128 −0.299 7.7 × 10−1 −0.289 0.212
signal^4 0.0579 0.126 0.459 6.5 × 10−1 −0.189 0.306
vignetteBasketball coach 0.231 0.0916 2.52 1.2 × 10−2 0.0516 0.411
vignetteRomantic partner 0.214 0.0843 2.54 1.1 × 10−2 0.0492 0.380
vignetteBrother-in-law 0.653 0.0873 7.47 1.5 × 10−13 0.482 0.824
signal.L:vignetteBasketball coach 0.547 0.191 2.87 4.2 × 10−3 0.175 0.922
signal.Q:vignetteBasketball coach −0.381 0.186 −2.05 4.1 × 10−2 −0.747 −0.0174
signal.C:vignetteBasketball coach 0.209 0.185 1.13 2.6 × 10−1 −0.155 0.572
signal^4:vignetteBasketball coach −0.146 0.180 −0.810 4.2 × 10−1 −0.498 0.207
signal.L:vignetteRomantic partner 1.03 0.180 5.74 1.2 × 10−8 0.681 1.39
signal.Q:vignetteRomantic partner −0.195 0.177 −1.10 2.7 × 10−1 −0.543 0.152
signal.C:vignetteRomantic partner −0.223 0.181 −1.23 2.2 × 10−1 −0.578 0.131
signal^4:vignetteRomantic partner 0.423 0.176 2.40 1.7 × 10−2 0.0773 0.769
signal.L:vignetteBrother-in-law 1.76 0.188 9.37 3.5 × 10−20 1.39 2.13
signal.Q:vignetteBrother-in-law −0.174 0.184 −0.948 3.4 × 10−1 −0.534 0.186
signal.C:vignetteBrother-in-law −0.0759 0.185 −0.410 6.8 × 10−1 −0.439 0.287
signal^4:vignetteBrother-in-law 0.283 0.181 1.57 1.2 × 10−1 −0.0708 0.638
m33
(Intercept) −1.61 0.0562 −28.7 2.4 × 10−139 −1.72 −1.50
T1Action 4.27 0.128 33.4 4.9 × 10−175 4.03 4.53
signal.L 1.21 0.0790 15.3 2.3 × 10−48 1.05 1.36
signal.Q 0.00107 0.0760 0.0141 9.9 × 10−1 −0.148 0.150
signal.C 0.00906 0.0764 0.119 9.1 × 10−1 −0.141 0.159
signal^4 0.231 0.0752 3.07 2.2 × 10−3 0.0835 0.378
m44
(Intercept) −1.87 0.0937 −19.9 8.7 × 10−77 −2.05 −1.69
T1Action 4.36 0.135 32.3 3.1 × 10−166 4.10 4.63
signal.L 0.424 0.144 2.95 3.2 × 10−3 0.143 0.706
signal.Q 0.228 0.144 1.59 1.1 × 10−1 −0.0528 0.510
signal.C 0.0996 0.144 0.692 4.9 × 10−1 −0.183 0.382
signal^4 0.135 0.143 0.940 3.5 × 10−1 −0.146 0.417
vignetteBasketball coach −0.0786 0.102 −0.770 4.4 × 10−1 −0.279 0.122
vignetteRomantic partner 0.303 0.0936 3.24 1.2 × 10−3 0.120 0.487
vignetteBrother-in-law 0.664 0.0973 6.83 1.3 × 10−11 0.474 0.856
signal.L:vignetteBasketball coach 0.335 0.220 1.52 1.3 × 10−1 −0.0952 0.768
signal.Q:vignetteBasketball coach −0.507 0.216 −2.35 1.9 × 10−2 −0.931 −0.0861
signal.C:vignetteBasketball coach −0.0730 0.213 −0.343 7.3 × 10−1 −0.490 0.344
signal^4:vignetteBasketball coach −0.224 0.207 −1.08 2.8 × 10−1 −0.631 0.183
signal.L:vignetteRomantic partner 1.30 0.204 6.36 2.8 × 10−10 0.897 1.70
signal.Q:vignetteRomantic partner −0.0732 0.201 −0.364 7.2 × 10−1 −0.467 0.320
signal.C:vignetteRomantic partner −0.199 0.202 −0.982 3.3 × 10−1 −0.595 0.198
signal^4:vignetteRomantic partner 0.254 0.199 1.27 2.0 × 10−1 −0.137 0.645
signal.L:vignetteBrother-in-law 1.69 0.218 7.78 1.5 × 10−14 1.27 2.12
signal.Q:vignetteBrother-in-law −0.339 0.213 −1.59 1.1 × 10−1 −0.757 0.0798
signal.C:vignetteBrother-in-law −0.0247 0.211 −0.117 9.1 × 10−1 −0.439 0.389
signal^4:vignetteBrother-in-law 0.357 0.207 1.72 8.5 × 10−2 −0.0486 0.764
m245
(Intercept) −1.58 0.385 −4.10 4.5 × 10−5 −2.33 −0.823
T1Belief 4.35 0.128 33.9 5.2 × 10−178 4.10 4.60
signal.L 0.988 0.0676 14.6 1.2 × 10−44 0.856 1.12
signal.Q −0.159 0.0656 −2.43 1.5 × 10−2 −0.288 −0.0309
signal.C −0.0672 0.0665 −1.01 3.1 × 10−1 −0.198 0.0630
signal^4 0.192 0.0649 2.96 3.2 × 10−3 0.0649 0.319
SexMale −0.280 0.0611 −4.57 5.3 × 10−6 −0.400 −0.160
years_education −0.0295 0.0149 −1.98 4.8 × 10−2 −0.0588 −0.000250
Income −0.000000317 0.000000551 −0.574 5.7 × 10−1 −0.00000141 0.000000760
vignetteBasketball coach 1.19 0.356 3.36 8.1 × 10−4 0.499 1.89
vignetteRomantic partner 0.991 0.340 2.91 3.6 × 10−3 0.325 1.66
vignetteBrother-in-law 1.13 0.346 3.26 1.1 × 10−3 0.452 1.81
Age 0.0112 0.00857 1.31 1.9 × 10−1 −0.00559 0.0280
vignetteBasketball coach:Age −0.0309 0.0102 −3.03 2.5 × 10−3 −0.0509 −0.0109
vignetteRomantic partner:Age −0.0249 0.00991 −2.51 1.2 × 10−2 −0.0443 −0.00547
vignetteBrother-in-law:Age −0.0174 0.00992 −1.75 8.0 × 10−2 −0.0369 0.00204
m256
(Intercept) −2.12 0.440 −4.81 1.7 × 10−6 −2.98 −1.26
T1Action 4.22 0.136 31.0 4.9 × 10−156 3.96 4.49
signal.L 1.25 0.0784 16.0 2.8 × 10−52 1.10 1.41
signal.Q 0.00332 0.0750 0.0443 9.6 × 10−1 −0.144 0.150
signal.C 0.0140 0.0758 0.185 8.5 × 10−1 −0.135 0.162
signal^4 0.221 0.0742 2.97 3.0 × 10−3 0.0753 0.366
SexMale −0.181 0.0701 −2.59 9.8 × 10−3 −0.319 −0.0439
years_education −0.0152 0.0171 −0.888 3.7 × 10−1 −0.0487 0.0183
Income −0.000000461 0.000000629 −0.733 4.6 × 10−1 −0.00000172 0.000000751
vignetteBasketball coach 1.27 0.409 3.10 2.0 × 10−3 0.471 2.08
vignetteRomantic partner 1.30 0.383 3.39 7.2 × 10−4 0.550 2.05
vignetteBrother-in-law 1.43 0.395 3.62 3.1 × 10−4 0.656 2.20
Age 0.0237 0.00982 2.42 1.6 × 10−2 0.00454 0.0431
vignetteBasketball coach:Age −0.0423 0.0118 −3.59 3.5 × 10−4 −0.0655 −0.0193
vignetteRomantic partner:Age −0.0315 0.0112 −2.81 5.0 × 10−3 −0.0535 −0.00960
vignetteBrother-in-law:Age −0.0260 0.0114 −2.29 2.2 × 10−2 −0.0483 −0.00375
m217
(Intercept) −2.40 0.202 −11.9 6.7 × 10−31 −2.82 −2.02
T1MentallyIll 3.26 0.226 14.4 1.3 × 10−43 2.83 3.72
signal.L 1.41 0.397 3.56 3.8 × 10−4 0.648 2.21
signal.Q 0.374 0.386 0.967 3.3 × 10−1 −0.383 1.14
signal.C −0.219 0.434 −0.504 6.1 × 10−1 −1.09 0.621
signal^4 0.754 0.420 1.79 7.3 × 10−2 −0.0702 1.59
vignetteBasketball coach 0.898 0.243 3.69 2.3 × 10−4 0.426 1.38
vignetteRomantic partner 0.249 0.285 0.874 3.8 × 10−1 −0.330 0.798
vignetteBrother-in-law 0.197 0.258 0.763 4.5 × 10−1 −0.309 0.705
signal.L:vignetteBasketball coach 0.921 0.526 1.75 8.0 × 10−2 −0.110 1.96
signal.Q:vignetteBasketball coach 0.130 0.528 0.247 8.1 × 10−1 −0.910 1.17
signal.C:vignetteBasketball coach 0.790 0.559 1.41 1.6 × 10−1 −0.298 1.90
signal^4:vignetteBasketball coach −1.06 0.555 −1.91 5.6 × 10−2 −2.16 0.0214
signal.L:vignetteRomantic partner 1.75 0.670 2.61 9.3 × 10−3 0.501 3.19
signal.Q:vignetteRomantic partner −0.574 0.634 −0.905 3.7 × 10−1 −1.91 0.625
signal.C:vignetteRomantic partner 0.138 0.632 0.219 8.3 × 10−1 −1.11 1.39
signal^4:vignetteRomantic partner −0.653 0.581 −1.12 2.6 × 10−1 −1.80 0.489
signal.L:vignetteBrother-in-law 0.375 0.549 0.682 5.0 × 10−1 −0.698 1.46
signal.Q:vignetteBrother-in-law 0.582 0.558 1.04 3.0 × 10−1 −0.512 1.68
signal.C:vignetteBrother-in-law −0.344 0.607 −0.566 5.7 × 10−1 −1.54 0.847
signal^4:vignetteBrother-in-law −0.813 0.598 −1.36 1.7 × 10−1 −1.99 0.357

1 N=1240; Null deviance=652 on 1240 df; Residual deviance=263 on 1230 df

2 N=1240; Null deviance=652 on 1240 df; Residual deviance=231 on 1220 df

3 N=1240; Null deviance=751 on 1240 df; Residual deviance=318 on 1230 df

4 N=1240; Null deviance=751 on 1240 df; Residual deviance=277 on 1220 df

5 N=1230; Null deviance=645 on 1230 df; Residual deviance=242 on 1220 df

6 N=1230; Null deviance=744 on 1230 df; Residual deviance=291 on 1220 df

7 N=1240; Null deviance=1390 on 1240 df; Residual deviance=956 on 1220 df

ANOVA tables

ANOVA tables for generalized linear regression models (quasibinomial family).

Table 8: ANOVA tables
LR Chisq df p.value
m1
T1Belief 1,773.2 1 0.0
signal 220.7 4 1.3 × 10−46
m2
T1Belief 1,722.6 1 0.0
signal 1.9 4 7.5 × 10−1
vignette 61.7 3 2.6 × 10−13
signal:vignette 119.7 12 7.1 × 10−20
m3
T1Action 1,570.5 1 0.0
signal 259.1 4 7.3 × 10−55
m4
T1Action 1,472.2 1 4.3 × 10−322
signal 12.7 4 1.3 × 10−2
vignette 73.1 3 9.4 × 10−16
signal:vignette 102.1 12 2.1 × 10−16
m24
T1Belief 1,548.3 1 0.0
signal 238.1 4 2.4 × 10−50
Sex 21.0 1 4.7 × 10−6
years_education 3.9 1 4.8 × 10−2
Income 0.3 1 5.7 × 10−1
vignette 13.6 3 3.4 × 10−3
Age 1.7 1 1.9 × 10−1
vignette:Age 10.4 3 1.6 × 10−2
m25
T1Action 1,331.1 1 2.0 × 10−291
signal 282.9 4 5.2 × 10−60
Sex 6.7 1 9.7 × 10−3
years_education 0.8 1 3.7 × 10−1
Income 0.5 1 4.6 × 10−1
vignette 15.4 3 1.5 × 10−3
Age 5.9 1 1.5 × 10−2
vignette:Age 13.6 3 3.5 × 10−3
m21
T1MentallyIll 280.5 1 6.0 × 10−63
signal 17.2 4 1.7 × 10−3
vignette 16.0 3 1.1 × 10−3
signal:vignette 19.4 12 7.8 × 10−2

Preregistered OLS models

The effect of the signals on *T2 Belief* (top) and Action (bottom) controlling for T1 values of Belief and Action, respectively. Estimated using OLS linear regression models (gaussian).

Figure 29: The effect of the signals on T2 Belief (top) and Action (bottom) controlling for T1 values of Belief and Action, respectively. Estimated using OLS linear regression models (gaussian).

The effect of the signals on *T2 Belief* (top) and Action (bottom) controlling for T1 values of Belief and Action, respectively, with an interaction between T1 values and the signal. Estimated using OLS linear regression models (gaussian).

Figure 30: The effect of the signals on T2 Belief (top) and Action (bottom) controlling for T1 values of Belief and Action, respectively, with an interaction between T1 values and the signal. Estimated using OLS linear regression models (gaussian).

OLS regression tables

Table 9: OLS regression coefficients for preregistered linear regression models.
Estimate Std.Err Statistic P-value Lower 95% CI Upper 95% CI
m11
(Intercept) 0.164 0.00875 18.7 6.9 × 10−69 0.146 0.181
T1Belief 0.836 0.0182 45.9 2.1 × 10−269 0.801 0.872
signal.L 0.171 0.0122 14.1 9.4 × 10−42 0.147 0.195
signal.Q −0.0292 0.0121 −2.40 1.6 × 10−2 −0.0530 −0.00534
signal.C −0.0119 0.0122 −0.974 3.3 × 10−1 −0.0358 0.0120
signal^4 0.0369 0.0122 3.04 2.4 × 10−3 0.0131 0.0608
m1b2
(Intercept) 0.163 0.00872 18.7 1.2 × 10−68 0.146 0.180
T1Belief 0.839 0.0182 46.2 1.1 × 10−270 0.803 0.875
signal.L 0.217 0.0192 11.3 3.5 × 10−28 0.179 0.254
signal.Q 0.000375 0.0194 0.0193 9.8 × 10−1 −0.0377 0.0384
signal.C −0.0135 0.0196 −0.687 4.9 × 10−1 −0.0520 0.0250
signal^4 0.0309 0.0198 1.56 1.2 × 10−1 −0.00792 0.0698
T1Belief:signal.L −0.121 0.0402 −3.02 2.6 × 10−3 −0.200 −0.0424
T1Belief:signal.Q −0.0759 0.0411 −1.85 6.5 × 10−2 −0.156 0.00468
T1Belief:signal.C 0.00159 0.0398 0.0398 9.7 × 10−1 −0.0766 0.0797
T1Belief:signal^4 0.0186 0.0415 0.449 6.5 × 10−1 −0.0628 0.100
m33
(Intercept) 0.182 0.00912 19.9 8.9 × 10−77 0.164 0.200
T1Action 0.821 0.0187 43.9 3.9 × 10−254 0.784 0.858
signal.L 0.209 0.0132 15.8 1.7 × 10−51 0.183 0.235
signal.Q 0.00336 0.0132 0.254 8.0 × 10−1 −0.0226 0.0293
signal.C 0.00419 0.0133 0.316 7.5 × 10−1 −0.0218 0.0302
signal^4 0.0434 0.0132 3.28 1.1 × 10−3 0.0174 0.0693
m3b4
(Intercept) 0.182 0.00902 20.1 4.2 × 10−78 0.164 0.199
T1Action 0.819 0.0185 44.2 2.4 × 10−256 0.783 0.856
signal.L 0.286 0.0199 14.4 2.7 × 10−43 0.247 0.325
signal.Q 0.0413 0.0200 2.06 4.0 × 10−2 0.00195 0.0806
signal.C −0.0105 0.0204 −0.516 6.1 × 10−1 −0.0505 0.0295
signal^4 0.0511 0.0204 2.51 1.2 × 10−2 0.0111 0.0911
T1Action:signal.L −0.208 0.0418 −4.99 7.0 × 10−7 −0.290 −0.126
T1Action:signal.Q −0.0971 0.0421 −2.31 2.1 × 10−2 −0.180 −0.0146
T1Action:signal.C 0.0284 0.0405 0.703 4.8 × 10−1 −0.0509 0.108
T1Action:signal^4 −0.0142 0.0415 −0.342 7.3 × 10−1 −0.0955 0.0672

1 N=1240; Rsq=0.657; Adj.Rsq=0.655; F(5,1230)=472; p=1.59e-283

2 N=1240; Rsq=0.66; Adj.Rsq=0.658; F(9,1230)=266; p=4.11e-281

3 N=1240; Rsq=0.641; Adj.Rsq=0.639; F(5,1230)=440; p=3.39e-271

4 N=1240; Rsq=0.65; Adj.Rsq=0.647; F(9,1230)=253; p=9.60999999999999e-273

OLS Anova tables

Table 10: OLS ANOVA tables
sumsq df LR Chisq p.value
m1
(Intercept) 12.8 1 349.6 6.9 × 10−69
T1Belief 77.3 1 2,110.1 2.1 × 10−269
signal 7.8 4 53.3 1.7 × 10−41
Residuals 45.2 1234
m1b
(Intercept) 12.7 1 348.4 1.2 × 10−68
T1Belief 77.4 1 2,130.2 1.1 × 10−270
signal 4.7 4 32.4 1.0 × 10−25
T1Belief:signal 0.5 4 3.2 1.2 × 10−2
Residuals 44.7 1230
m3
(Intercept) 17.2 1 396.8 8.9 × 10−77
T1Action 83.4 1 1,925.0 3.9 × 10−254
signal 11.3 4 65.3 3.7 × 10−50
Residuals 53.4 1234
m3b
(Intercept) 17.2 1 405.1 4.2 × 10−78
T1Action 82.8 1 1,954.5 2.4 × 10−256
signal 9.0 4 53.2 2.1 × 10−41
T1Action:signal 1.3 4 7.8 3.2 × 10−6
Residuals 52.1 1230

Comparison of estimates and confidence intervals of GLM, Fractional, and bootstrapped models

Comparison of the coefficients and 95% CIs of the quasibinomial glm, fractional, and bootstrapped logistic regression models of *T2 Belief* and *T2 Action* (for details, see the Statistical methods section).

Figure 31: Comparison of the coefficients and 95% CIs of the quasibinomial glm, fractional, and bootstrapped logistic regression models of T2 Belief and T2 Action (for details, see the Statistical methods section).

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