Alan Jern

A probabilistic account of exemplar and category generation

People are capable of imagining and generating new category exemplars and categories. This ability has not been addressed by previous models of categorization, most of which focus on classifying category exemplars rather than generating them. We develop a formal account of exemplar and category generation which proposes that category knowledge is represented by probability distributions over exemplars and categories, and that new exemplars and categories are generated by sampling from these distributions. This sampling account of generation is evaluated in two pairs of behavioral experiments. In the first pair of experiments, participants were asked to generate novel exemplars of a category. In the second pair of experiments, participants were asked to generate a novel category after observing exemplars from several related categories. The results suggest that generation is influenced by both structural and distributional properties of the observed categories, and we argue that our data are better explained by the sampling account than by several alternative approaches.

A taxonomy of inductive problems

Inductive inferences about objects, features, categories, and relations have been studied for many years, but there are few attempts to chart the range of inductive problems that humans are able to solve. We present a taxonomy of inductive problems that helps to clarify the relationships between familiar inductive problems such as generalization, categorization, and identification, and that introduces new inductive problems for psychological investigation. Our taxonomy is founded on the idea that semantic knowledge is organized into systems of objects, features, categories, and relations, and we attempt to characterize all of the inductive problems that can arise when these systems are partially observed. Recent studies have begun to address some of the new problems in our taxonomy, and future work should aim to develop unified theories of inductive reasoning that explain how people solve all of the problems in the taxonomy.

Belief Polarization Is Not Always Irrational

Belief polarization occurs when 2 people with opposing prior beliefs both strengthen their beliefs after observing the same data. Many authors have cited belief polarization as evidence of irrational behavior. We show, however, that some instances of polarization are consistent with a normative account of belief revision. Our analysis uses Bayesian networks to characterize different kinds of relationships between hypotheses and data, and distinguishes between cases in which normative reasoners with opposing beliefs should both strengthen their beliefs, cases in which both should weaken their beliefs, and cases in which one should strengthen and the other should weaken his or her belief. We apply our analysis to several previous studies of belief polarization and present a new experiment that suggests that people tend to update their beliefs in the directions predicted by our normative account.

A preliminary study of the educational benefits of conducting replications in the classroom

Some have argued that having students conduct rigorous replications of published studies would provide benefits to both psychological science and the students themselves. However, while it seems clear that replications are beneficial to psychological science, there is little empirical evidence that having students conduct replications provides benefits to students. In this study, I conducted a preliminary test (N = 37) of one purported benefit to students of conducting a classroom replication: the development of scientific critical thinking skills. Students completed a 1-term research methods course centered on a class replication project. I assessed students’ critical thinking development after completing the course. The results were largely inconclusive, showing no significant change in performance between a pretest (M = 11.00, SD = 3.44) and a posttest (M = 10.30, SD = 2.62), t(36) = −1.21, p = .23. This study highlights the need for additional research on the question of whether having students conduct replications provides educational benefits beyond those offered by other pedagogical methods.

Corrigendum to “People learn other people’s preferences through inverse decision-making” [Cognition 168 (2017) 46–64]

Corrigendum to “People learn other people’s preferences through inverse decision-making” [Cognition 168 (2017) 46–64] Alan Jerna,, Christopher G. Lucas, Charles Kemp a Department of Humanities and Social Sciences, Rose-Hulman Institute of Technology, United States b School of Informatics, The University of Edinburgh, United Kingdom c Department of Psychology, Carnegie Mellon University, United States