The ability to generate new concepts and ideas is among the most fascinating aspects of human cognition, but we do not have a strong understanding of the cognitive processes and representations underlying concept generation. In this paper, we study the generation of new categories using the computational and behavioral toolkit of traditional artificial category learning. Previous work in this domain has focused on how the statistical structure of known categories generalizes to generated categories, overlooking whether (and if so, how) contrast between the known and generated categories is a factor. We report three experiments demonstrating that contrast between what is known and what is created is of fundamental importance for categorization. We propose two novel approaches to modeling category contrast: one focused on exemplar dissimilarity and another on the representativeness heuristic. Our experiments and computational analyses demonstrate that both models capture different aspects of contrast’s role in categorization.

People’s desire to seek or avoid information is not only influenced by the possible outcomes of an event, but the probability of those particular outcomes occurring. There are competing explanations however as to how and why people’s desire for non-instrumental information is affected by factors including expected value, probability of outcome, and a unique formulation of outcome uncertainty. Over two experiments, we find that people’s preference for non-instrumental information is positively correlated with probability when the outcome is positive (i.e., winning money) and negatively correlated when the outcome is negative (i.e., losing money). Furthermore, at the aggregate level, we find the probability of an outcome to be a better predictor of information preference than the expected value of the event or its outcome uncertainty.

Recent developments in modern probabilistic programming have offered users many practical tools of Bayesian data analysis. However, the adoption of such techniques by the general psychology community is still fairly limited. This tutorial aims to provide non-technicians with an accessible guide to PyMC3, a robust probabilistic programming language that allows for straightforward Bayesian data analysis. We focus on a series of increasingly complex Gaussian mixture models – building up from fitting basic univariate models to more complex multivariate models fit to real-world data. We also explore how PyMC3 can be configured to obtain significant increases in computational speed by taking advantage of a machine’s GPU, in addition to the conditions under which such acceleration can be expected. All example analyses are detailed with step-by-step instructions and corresponding Python code.

Proposed psychological mechanisms generating non-instrumental information seeking in hu- mans can be broadly categorised into two competing accounts: the maximisation of antici- pating rewards versus an aversion to uncertainty. We compare three separate formalisations of these theories on their ability to track the dependency of information seeking behaviour on increasing levels of cue-outcome delay as well as their sensitivity to outcome valence. Across three experiments using a variety of different stimuli, we observe a flat to monotoni- cally increasing pattern of delay dependency and minimal evidence of sensitivity to outcome valence––patterns which are better predicted, qualitatively and quantitatively, by an uncer- tainty aversion information model.