Where do hypotheses come from?

Why are human inferences sometimes remarkably close to the Bayesian ideal and other times systematically biased? In particular, why do humans make near-rational inferences in some natural domains where the candidate hypotheses are explicitly available, whereas tasks in similar domains requiring the self-generation of hypotheses produce systematic deviations from rational inference. We propose that these deviations arise from algorithmic processes approximating Bayes’ rule. Specifically in our account, hypotheses are generated stochastically from a sampling process, such that the sampled hypotheses form a Monte Carlo approximation of the posterior. While this approximation will converge to the true posterior in the limit of infinite samples, we take a small number of samples as we expect that the number of samples humans take is limited. We show that this model recreates several well-documented experimental findings such as anchoring and adjustment, subadditivity, superadditivity, the crowd within as well as the self-generation effect, the weak evidence, and the dud alternative effects. We confirm the model’s prediction that superadditivity and subadditivity can be induced within the same paradigm by manipulating the unpacking and typicality of hypotheses. We also partially confirm our model’s prediction about the effect of time pressure and cognitive load on these effects.     

学经济学降低人际信任?经济类专业学习对大学生人际信任的影响

本研究试图证明,经济类专业学习会降低大学生的人际信任.研究者测量了某财经类重点高校经济类和非经济类专业的大一和大三共290名本科生的人际信任水平,发现专业类型和年级存在显著交互作用,大一的经济类学生和非经济类学生在人际信任水平上无显著差异,而大三的经济类学生在人际信任水平上却显著低于非经济类专业.这说明经济类专业学习时间的增加可能是学生人际信任水平下降的原因,可能的机制是,学习经济学会使个体逐渐接受和认同“理性人假设”所包含的人性观点,认为他人的行为都是基于自私和功利的目的.     

Inferring causal networks from observations and interventions

Information about the structure of a causal system can come in the form of observational data—random samples of the system's autonomous behavior—or interventional data—samples conditioned on the particular values of one or more variables that have been experimentally manipulated. Here we study people's ability to infer causal structure from both observation and intervention, and to choose informative interventions on the basis of observational data. In three causal inference tasks, participants were to some degree capable of distinguishing between competing causal hypotheses on the basis of purely observational data. Performance improved substantially when participants were allowed to observe the effects of interventions that they performed on the systems. We develop computational models of how people infer causal structure from data and how they plan intervention experiments, based on the representational framework of causal graphical models and the inferential principles of optimal Bayesian decision‐making and maximizing expected information gain. These analyses suggest that people can make rational causal inferences, subject to psychologically reasonable representational assumptions and computationally reasonable processing constraints.     

Openness, fluid intelligence, and crystallized intelligence: Toward an integrative model

Many studies are concerned with the bivariate relationships between Openness, fluid intelligence (Gf), and crystallized intelligence (Gc). Results suggest an influence of Gf and Openness on Gc. However, the overlap between Gf and Openness is rarely controlled for. Moreover, interaction effects or longitudinal influences are also often neglected. The present two studies aimed to elucidate exactly these interactions and longitudinal influences. Besides a main effect of Gf on Gc, Study 1 (N = 180) revealed an interaction effect between Openness and Gf. Study 2 utilized longitudinal data (N = 172) and identified an effect of Openness on the development of Gf. Gf and Openness predicted Gc 6 years later. A model integrating the results and providing a theoretical framework and outlook is proposed.     

The pseudodiagnosticity trap: Should participants consider alternative hypotheses?

The pseudodiagnosticity task has been used as an example of the tendency on the part of participants to incorrectly assess Bayesian constraints in assessing data, and as a failure to consider alternative hypotheses in a probabilistic inference task. In the task, participants are given one value, the anchor value, corresponding to P(D1|H) and may choose one other value, either P(D1|¬!H), P(D2|H), or P(D2|not;!H). Most participants select P(D2|H), or P(D2|¬!H) which have been considered inappropriate (and called pseudodiagnostic) because only P(D1|¬!H) allows use of Bayes' theorem. We present a new analysis based on probability intervals and show that selection of either P(D2|H), or P(D2|¬!H) is in fact pseudodiagnostic, whereas choice of P(D1|¬!H) is diagnostic. Our analysis shows that choice of the pseudodiagnostic values actually increases uncertainty regarding the posterior probability of H, supporting the original interpretation of the experimental findings on the pseudodiagnosticity task. The argument illuminates the general proposition that evolutionarily adaptive heuristics for Bayesian inference can be misled in some task situations.     

A peculiar prevalence of p values just below .05

In null hypothesis significance testing (NHST), p values are judged relative to an arbitrary threshold for significance (.05). The present work examined whether that standard influences the distribution of p values reported in the psychology literature. We examined a large subset of papers from three highly regarded journals. Distributions of p were found to be similar across the different journals. Moreover, p values were much more common immediately below .05 than would be expected based on the number of p values occurring in other ranges. This prevalence of p values just below the arbitrary criterion for significance was observed in all three journals. We discuss potential sources of this pattern, including publication bias and researcher degrees of freedom.     

Eye Movements Reveal the Dynamic Simulation of Speed in Language

This study investigates how speed of motion is processed in language. In three eye‐tracking experiments, participants were presented with visual scenes and spoken sentences describing fast or slow events (e.g., The lion ambled/dashed to the balloon). Results showed that looking time to relevant objects in the visual scene was affected by the speed of verb of the sentence, speaking rate, and configuration of a supporting visual scene. The results provide novel evidence for the mental simulation of speed in language and show that internal dynamic simulations can be played out via eye movements toward a static visual scene.     

Implication and reasoning in mental state attribution: Comments on Jane Heal's theory of co-cognition

Simulation theory explains third-person mental state attribution in terms of an attributor's ability to imaginatively mimic other people's mental processes. Jane Heal's version of simulation theory, which she calls a theory of “co-cognition,” maintains that one can know and can predict others’ beliefs primarily by thinking about what their antecedent beliefs imply. I argue that Heal's account of belief attribution elides crucial differences between reasoning and merely discovering relations among propositions.     

文学小说阅读影响社会认知的心理机制

摘要：文学小说阅读可以提高个体社会认知能力,但其中具体心理机制有待探索。本文通过梳理以往研究,从文学小说的社会性和文学性两个视角入手,结合模拟理论,精加工似然模型和叙事传输理论讨论了文学小说阅读影响社会认知的心理机制,并在此基础上初步构建了文学小说影响社会认知的动机-情感-认知加工策略框架。文学小说的社会性会增强读者对故事的情感卷入（叙事传输理论）,并引发读者对故事内容进行心理模拟（模拟理论）;文学小说的文学性同样能够增强读者对故事的情感卷入（叙事传输理论）,同时促使读者在动机层面愿意耗费更多认知资源去理解故事内容（精加工似然模型）,进而促进心理模拟。未来需要更多实证研究来检验这一框架的解释力,完善其边界条件,寻找脑机制层面的证据。     

Modeling contagion in policy systems

Scholars of the policy process offer compelling explanations for patterns in the aggregate-level attention of policymakers. Yet, we have little systematic understanding of the day-to-day behavior of these individuals. Why does a given policymaker, on a given day, decide to focus on one pressing issue while ignoring many others? I approach this question from a cognitive systems perspective and argue that policymakers are highly interdependent actors who are subject to cognitive limits and have incentives to closely monitor the political environment. These tendencies contribute to the emergence of widespread herd behavior in their individual attention to policy issues, a phenomenon I conceptualize as ‘issue contagion.’ I then utilize the methods of computational social science to build an agent-based simulation model of policymakers’ issue attention over time. I also outline three empirical expectations regarding the density of communication ties between actors, the presence of segmented groups (e.g. political parties and coalitions), and the rate at which actors take cues from one another. Through a series of sensitivity tests, I document the internal validity of the model and show that incremental changes in network density, segmentation, and cue-taking all generate clear and visible trends in the frequency of issue contagion events.