Identifying Causal Estimands for Time-Varying Treatments Measured with Time-Varying (Age or Grade-Based) Instruments

This commentary discusses causal estimands of same-age and same-grade comparisons for assessing grade-retention effects on student ability and performance. Using potential outcomes notation, we show that same-age and same-grade comparisons refer to different retention–promotion contrasts and therefore assess different causal questions. We also comment on deleting versus censoring records of students who dropped out of the study or do not belong to the treatment regimes under investigation. Whereas deleting entire student records potentially induces collider bias, censoring circumvents bias if censoring is ignorable given the observed pretreatment covariates.     

A Bayesian Theory of Sequential Causal Learning and Abstract Transfer

Two key research issues in the field of causal learning are how people acquire causal knowledge when observing data that are presented sequentially, and the level of abstraction at which learning takes place. Does sequential causal learning solely involve the acquisition of specific cause‐effect links, or do learners also acquire knowledge about abstract causal constraints? Recent empirical studies have revealed that experience with one set of causal cues can dramatically alter subsequent learning and performance with entirely different cues, suggesting that learning involves abstract transfer, and such transfer effects involve sequential presentation of distinct sets of causal cues. It has been demonstrated that pre‐training (or even post‐training) can modulate classic causal learning phenomena such as forward and backward blocking. To account for these effects, we propose a Bayesian theory of sequential causal learning. The theory assumes that humans are able to consider and use several alternative causal generative models, each instantiating a different causal integration rule. Model selection is used to decide which integration rule to use in a given learning environment in order to infer causal knowledge from sequential data. Detailed computer simulations demonstrate that humans rely on the abstract characteristics of outcome variables (e.g., binary vs. continuous) to select a causal integration rule, which in turn alters causal learning in a variety of blocking and overshadowing paradigms. When the nature of the outcome variable is ambiguous, humans select the model that yields the best fit with the recent environment, and then apply it to subsequent learning tasks. Based on sequential patterns of cue‐outcome co‐occurrence, the theory can account for a range of phenomena in sequential causal learning, including various blocking effects, primacy effects in some experimental conditions, and apparently abstract transfer of causal knowledge.     

The Invisible Hand: Toddlers Connect Probabilistic Events With Agentive Causes

Children posit unobserved causes when events appear to occur spontaneously (e.g., Gelman & Gottfried, 1996). What about when events appear to occur probabilistically? Here toddlers (M = 20.1 months) saw arbitrary causal relationships (Cause A generated Effect A; Cause B generated Effect B) in a fixed, alternating order. The relationships were then changed in one of two ways. In the Deterministic condition, the event order changed (Event B preceded Event A); in the Probabilistic condition, the causal relationships changed (Cause A generated Effect B; Cause B generated Effect A). As intended, toddlers looked equally long at both changes (Experiment 1). We then introduced a previously unseen candidate cause. Toddlers looked longer at the appearance of a hand (Experiment 2) and novel agent (Experiment 3) in the Deterministic than the Probabilistic conditions, but looked equally long at novel non‐agents (Experiment 4), suggesting that by 2 years of age, toddlers connect probabilistic events with unobserved agents.     

Two metamodels of causal effects

Two metamodels, termed Model S and Model V, are proposed for definition, measurement, and generalization of quantitative causal effects. The effect is defined as a part change in score in Model S and as a part change in variance in Model V. Two additional changes, total and remainder change, are defined. The latter is due to all other factors or variables than the cause, while total change is the sum of remainder and effect change. Furthermore, it is shown how contrafactual concepts, which imply that some parts of the study situation are supposed to be otherwise, enter into the metamodels. Casual effects are defined and measured in terms of non-contrafactual concepts, except that statistical induction includes contrafactual as well as non-contrafactual inferences. Non-statistical generalization involves both kinds of inferences. Contrafactual definitions are considered inadequate, and a contrafactual interpretation of statistical adjustment is unnecessary and should be replaced by a non-contrafactual one.     

Children's Ideas About What Can Really Happen: The Impact of Age and Religious Background

Five- to 11-year-old U.S. children, from either a religious or secular background, judged whether story events could really happen. There were four different types of stories: magical stories violating ordinary causal regularities; religious stories also violating ordinary causal regularities but via a divine agent; unusual stories not violating ordinary causal regularities but with an improbable event; and realistic stories not violating ordinary causal regularities and with no improbable event. Overall, children were less likely to judge that religious and magical stories could really happen than unusual and realistic stories although religious children were more likely than secular children to judge that religious stories could really happen. Irrespective of background, children frequently invoked causal regularities in justifying their judgments. Thus, in justifying their conclusion that a story could really happen, children often invoked a causal regularity, whereas in justifying their conclusion that a story could not really happen, they often pointed to the violation of causal regularity. Overall, the findings show that children appraise the likelihood of story events actually happening in light of their beliefs about causal regularities. A religious upbringing does not impact the frequency with which children invoke causal regularities in judging what can happen, even if it does impact the type of causal factors that children endorse.     

Motivated Reasoning in an Explore-Exploit Task

The current research investigates how prior preferences affect causal learning. Participants were tasked with repeatedly choosing policies (e.g., increase vs. decrease border security funding) in order to maximize the economic output of an imaginary country and inferred the influence of the policies on the economy. The task was challenging and ambiguous, allowing participants to interpret the relations between the policies and the economy in multiple ways. In three studies, we found evidence of motivated reasoning despite financial incentives for accuracy. For example, participants who believed that border security funding should be increased were more likely to conclude that increasing border security funding actually caused a better economy in the task. In Study 2, we hypothesized that having neutral preferences (e.g., preferring neither increased nor decreased spending on border security) would lead to more accurate assessments overall, compared to having a strong initial preference; however, we did not find evidence for such an effect. In Study 3, we tested whether providing participants with possible functional forms of the policies (e.g., the policy takes some time to work or initially has a negative influence but eventually a positive influence) would lead to a smaller influence of motivated reasoning but found little evidence for this effect. This research advances the field of causal learning by studying the role of prior preferences, and in doing so, integrates the fields of causal learning and motivated reasoning using a novel explore-exploit task.     

The heterogeneous processes of cheating: Attention evidence from two eye tracking experiments

Dishonesty erodes society. Although much is known about dishonesty, the process leading up to the decision of whether to be honest or dishonest is often assumed to be homogenous and is not well understood. In this paper, we take a more nuanced approach and explore more closely the process of deciding whether to cheat or be honest when an opportunity to cheat arises. We do this in two laboratory eye tracking experiments. In our first experiment (n = 193), we identify heterogeneity in the decision to cheat. Some decisions on whether to cheat or be honest exhibit relatively little variation and appear to be oriented towards cheating (or honesty) with apparently little consideration of the alternative, whereas other decisions seem to be characterized by a higher degree of consideration for both decision alternatives. Our second experiment (n = 299) demonstrates that a gaze dependent intervention in the choices process is able to affect the behavioral outcomes.     

Mental Health Clinicians' Beliefs About the Biological, Psychological, and Environmental Bases of Mental Disorders

The current experiments examine mental health clinicians' beliefs about biological, psychological, and environmental bases of the DSM-IV-TR mental disorders and the consequences of those causal beliefs for judging treatment effectiveness. Study 1 found a large negative correlation between clinicians' beliefs about biological bases and environmental/psychological bases, suggesting that clinicians conceptualize mental disorders along a single continuum spanning from highly biological disorders (e.g., autistic disorder) to highly nonbiological disorders (e.g., adjustment disorders). Study 2 replicated this finding by having clinicians list what they thought were the specific causes of nine familiar mental disorders and rate their bio–psycho–environmental bases. Study 3 further found that clinicians believe medication to be more effective for biologically based mental disorders and psychotherapy to be more effective for psychosocially based mental disorders. These results demonstrate that even expert mental health clinicians make strong distinctions between psychological and biological phenomena.     

团队多样性与组织支持对团队创造力的影响

采用实验法在专业异质性与群体断层两个水平上操纵团队多样性, 通过两个实验考察团队多样性与组织支持的交互作用对团队创造力的影响。研究结果表明：(1)在独创性维度上, 专业异质团队在工具支持条件下的独创性显著高于专业同质团队, 情感和物质支持条件下二者差异不显著。当团队出现群体断层时, 情感支持与工具支持具有同样的促进作用, 即在这两种组织支持下, 强断层团队的独创性显著高于弱断层团队; 物质支持条件下二者差异不显著。(2)在适宜性维度上, 组织支持的主效应显著, 两个实验一致地发现物质支持条件下的适宜性显著高于情感支持和工具支持。本研究从团队多样性与组织支持交互作用的角度考察不同复杂程度、不同组织目标的多样性团队所需要的组织支持条件, 对促进团队创造力具有一定的理论与实践价值。     

Seeing the forest and not the trees: When impact uncertainty heightens causal complexity

This study attempts to isolate the effects of experiencing uncertainty on people's cognitive processes. I argue that people can believe that their actions affect the outcome (i.e. outcome control), but still face uncertainty regarding the extent to which actions will make a difference (i.e. impact uncertainty). To this end, I introduce a novel experimental paradigm which isolates the effects of impact uncertainty from outcome control. The findings revealed that after experiencing impact uncertainty, participants demonstrated greater causal complexity (i.e. more likely to make situational attributions and judge outcomes as having a “ripple effect”), but did not make fewer effort attributions for the outcomes. These findings demonstrate how the experience of impact uncertainty can affect cognitive processing, without compromising outcome control. Implications of these findings for developing more nuanced theories on control and uncertainty are discussed.