Personality and behavioral outcomes associated with risk-taking are accurately inferred from faces

Growing evidence suggests that people are able to accurately infer some personality traits and behavioral outcomes from facial photographs. However, little research has examined whether people are able to accurately infer personality traits or behavioral outcomes associated with risk-taking. In this study, we examined whether people were able to accurately infer, on average, others’ personality traits associated with risk-taking from facial photographs. We further examined whether such first impressions were associated with relevant and important behavioral outcomes—specifically, future discounting and gambling and problem gambling tendencies. Results suggest that people are able to accurately infer, on average, some personality traits and behavioral outcomes associated with risk-taking from faces.     

Speaker reliability guides children's inductive inferences about novel properties

Prior work shows that children can make inductive inferences about objects based on their labels rather than their appearance (Gelman, 2003). A separate line of research shows that children's trust in a speaker's label is selective. Children accept labels from a reliable speaker over an unreliable speaker (e.g., Koenig & Harris, 2005). In the current paper, we tested whether 3- and 5-year-old children attend to speaker reliability when they make inductive inferences about a non-obvious property of a novel artifact based on its label. Children were more likely to use a reliable speaker's label than an unreliable speaker's label when making inductive inferences. Thus, children not only prefer to learn from reliable speakers, they are also more likely to use information from reliable speakers as the basis for future inferences. The findings are discussed in light of the debate between a similarity-driven and a label-driven approach to inductive inferences.     

When promoting a charity can hurt charitable giving: A metacognitive analysis

Charities need to come to mind to enter a potential donor's consideration set. However, feeling familiar with a charity and its cause can facilitate or impair giving. In most cases, perceived good memory for details of the cause fosters the impression of personal importance, which increases giving (Studies 1 and 3). But when the charity aims to increase awareness of a cause, good memory for the cause suggests that awareness is already high, which impairs giving (Studies 2 and 3). Hence, promotions for awareness-raising charities can actually have negative consequences, confirming the predictions of a metacognitive analysis.     

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.     

Some but not all dispreferred turn markers help to interpret scalar terms in polite contexts

In polite contexts, people find it difficult to perceive whether they can derive scalar inferences from what others say (e.g., does “some people hated your idea” mean that not everyone hated it?). Because this uncertainty can lead to costly misunderstandings, it is important to identify the cues people can rely on to solve their interpretative problem. In this article, we consider two such cues: Making a long Pause before the statement, and prefacing the statement with Well. Data from eight experiments show that Pauses are more effective than Wells as cues to scalar inferences in polite contexts—because they appear to give a specific signal to switch expectations in the direction of bad news, whereas Well appears to give a generic signal to make extra processing effort. We consider the applied value of these findings for human–human and human–machine interaction, as well as their implications for the study of reasoning and discourse.     

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.