Intelligence and negation biases on the Conditional Inference Task: A dual-processes analysis

We examined a large set of conditional inference data compiled from several previous studies and asked three questions: How is normative performance related to intelligence? Does negative conclusion bias stem from Type 1 or Type 2 processing? Does implicit negation bias stem from Type 1 or Type 2 processing? Our analysis demonstrated that rejecting denial of the antecedent and affirmation of the consequent inferences was positively correlated with intelligence, while endorsing modus tollens inferences was not; that the occurrence of negative conclusion bias was related to the extent of Type 2 processing; and that the occurrence of implicit negation bias was not related to the extent of Type 2 processing. We conclude that negative conclusion bias is, at least in part, a product of Type 2 processing, while implicit negation bias is not.     

Learning about the internal structure of categories through classification and feature inference

Previous research on category learning has found that classification tasks produce representations that are skewed toward diagnostic feature dimensions, whereas feature inference tasks lead to richer representations of within-category structure. Yet, prior studies often measure category knowledge through tasks that involve identifying only the typical features of a category. This neglects an important aspect of a category's internal structure: how typical and atypical features are distributed within a category. The present experiments tested the hypothesis that inference learning results in richer knowledge of internal category structure than classification learning. We introduced several new measures to probe learners' representations of within-category structure. Experiment 1 found that participants in the inference condition learned and used a wider range of feature dimensions than classification learners. Classification learners, however, were more sensitive to the presence of atypical features within categories. Experiment 2 provided converging evidence that classification learners were more likely to incorporate atypical features into their representations. Inference learners were less likely to encode atypical category features, even in a “partial inference” condition that focused learners' attention on the feature dimensions relevant to classification. Overall, these results are contrary to the hypothesis that inference learning produces superior knowledge of within-category structure. Although inference learning promoted representations that included a broad range of category-typical features, classification learning promoted greater sensitivity to the distribution of typical and atypical features within categories.     

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.     

Belief-based and analytic processing in transitive inference: Further evidence for the importance of premise integration

Transitive inference problems were presented in a modified conclusion evaluation procedure. Conclusions were believable or unbelievable and valid, invalid or indeterminate. The 67 undergraduate students read the premises, evaluated the conclusions (accept, reject, cannot tell), then provided confidence ratings. Fluid intelligence was also assessed. Acceptance of valid conclusions, rejection of invalid conclusions and cannot tell responses to indeterminate conclusions of non-integrable problems indicated use of analytic processing. Believability effects indicated the use of heuristic processing. Fluid intelligence and premise integration ability (non-integrable problems) predicted greater use of analytic processing on valid and invalid problems. Premise integration ability was associated with reduced belief bias on invalid problems. Premise integration ability appears to influence the extent of heuristic versus analytic processing. Confidence was sensitive to the presence of belief–logic conflict. Conflict detection scores reflecting this sensitivity were not associated with analytic processing suggesting that conflict detection occurs automatically and reflects an intuitive logic.     

A computational-level explanation of the speed of goal inference

The ability to understand the goals that drive another person’s actions is an important social and cognitive skill. This is no trivial task, because any given action may in principle be explained by different possible goals (e.g., one may wave ones arm to hail a cab or to swat a mosquito). To select which goal best explains an observed action is a form of abduction. To explain how people perform such abductive inferences, Baker, Tenenbaum, and Saxe (2007) proposed a computational-level theory that formalizes goal inference as Bayesian inverse planning (BIP). It is known that general Bayesian inference–be it exact or approximate–is computationally intractable (NP-hard). As the time required for computationally intractable computations grows excessively fast when scaled from toy domains to the real world, it seems that such models cannot explain how humans can perform Bayesian inferences quickly in real world situations. In this paper we investigate how the BIP model can nevertheless explain how people are able to make goal inferences quickly. The approach that we propose builds on taking situational constraints explicitly into account in the computational-level model. We present a methodology for identifying situational constraints that render the model tractable. We discuss the implications of our findings and reflect on how the methodology can be applied to alternative models of goal inference and Bayesian models in general.     

Causal Inference from Descriptions of Experimental and Non-Experimental Research: Public Understanding of Correlation-Versus-Causation

The human tendency to conflate correlation with causation has been lamented by various scientists (Kida, 2006; Stanovich, 2009), and vivid examples of it can be found in both the media and peer-reviewed literature. However, there is little systematic data on the extent to which individuals conflate correlation with causation. In three experiments, we presented people with one of four research vignettes generated from the combination of two independent variables: whether the vignette described an experimental or non-experimental design, and whether it revealed a positive or negative association. Upon reading their vignette, participants selected inferences that could be drawn from the findings. Participants drew causal inferences from non-experimental vignettes as often as they did from experimental vignettes, and more frequently for causal statements and directions of association that fit with intuitive notions than for those that did not. We discuss our findings in relation to other biases in human thinking.     

Preverbal infants identify emotional reactions that are incongruent with goal outcomes

Identifying the goal of another agent’s action allows an observer to make inferences not only about the outcomes the agent will pursue in the future and the means to be deployed in a given context, but also about the emotional consequences of goal-related outcomes. While numerous studies have characterized the former abilities in infancy, expectations about emotions have gone relatively unexplored. Using a violation of expectation paradigm, we present infants with an agent who attains or fails to attain a demonstrated goal, and reacts with positive or negative affect. Across several studies, we find that infants’ attention to a given emotional display differs depending on whether that reaction is congruent with the preceding goal outcome. Specifically, infants look longer at a negative emotional display when it follows a completed goal compared to when it follows a failed goal. The present results suggest that infants’ goal representations support expectations not only about future actions but also about emotional reactions, and that infants in the first year of life can relate different emotional reactions to conditions that elicit them.     

Rational Hypocrisy: A Bayesian Analysis Based on Informal Argumentation and Slippery Slopes

Moral hypocrisy is typically viewed as an ethical accusation: Someone is applying different moral standards to essentially identical cases, dishonestly claiming that one action is acceptable while otherwise equivalent actions are not. We suggest that in some instances the apparent logical inconsistency stems from different evaluations of a weak argument, rather than dishonesty per se. Extending Corner, Hahn, and Oaksford's (2006) analysis of slippery slope arguments, we develop a Bayesian framework in which accusations of hypocrisy depend on inferences of shared category membership between proposed actions and previous standards, based on prior probabilities that inform the strength of competing hypotheses. Across three experiments, we demonstrate that inferences of hypocrisy increase as perceptions of the likelihood of shared category membership between precedent cases and current cases increase, that these inferences follow established principles of category induction, and that the presence of self‐serving motives increases inferences of hypocrisy independent of changes in the actions themselves. Taken together, these results demonstrate that Bayesian analyses of weak arguments may have implications for assessing moral reasoning.     

消费决策过程如何彰显社会地位?基于最优化决策策略的视角

彰显社会地位是消费者行为的一项基本动机。以往的消费心理学研究大量聚焦产品特征如何彰显社会地位(即令观察者产生高社会地位的推断), 却很少探讨消费决策过程这一购买决策中不可忽视的重要因素对社会地位推断有何影响。本研究试图构建一个基于最优化决策过程信息的社会地位推断模型, 系统探讨: (1)最优化决策过程能否及如何彰显消费者的社会地位; (2)彰显社会地位动机是否为消费者进行最优化决策的原因之一; (3)最优化决策过程如何通过社会地位推断影响观察者后续的消费行为。本研究将为社会地位推断研究提供新的视角, 扩展最优化决策研究的外延, 并从全新的角度揭示最优化决策的内涵。