Testing three coping strategies for time pressure in categorizations and similarity judgments.

This article compares three psychological mechanisms to make multi-attribute inferences under time pressure in the domains of categorization and similarity judgments. Specifically, we test if people under time pressure attend to fewer object features (attention focus), if they respond less precisely (lower choice sensitivity), or if they simplify a psychological similarity function (simplified similarity). The simpler psychological similarity considers the number of matching features but ignores the actual feature value differences. We conducted three experiments (two of them preregistered) in which we manipulated time pressure: one was a categorization task, which was designed based on optimal experimental design principles, and the other two involved a similarity judgment task. Computational cognitive modeling following an exemplar-similarity framework showed that the behavior of most participants under time pressure is in line with a lower choice sensitivity, this means less precise response selection, especially when people make similarity judgments. We find that the variability of participants' behavior increases with time pressure, to a point where participants are unlikely to make inferences anymore but instead start choosing readily available response options repeatedly. These findings are consistent with related research in other cognitive domains, such as risky choices, and add to growing evidence that time pressure and other forms of cognitive load do not necessarily alter core cognitive processes themselves but rather affect the precision of response selection.

The influence of sample size on preferences from experience.

People often learn from experience about the distribution of outcomes of risky options. Typically, people draw small samples, when they can actively sample information from risky gambles to make decisions. We examine how the size of the sample that people experience in decision from experience affects their preferences between risky options. In two studies (N = 40 each), we manipulated the size of samples that people could experience from risky gambles and measured subjective selling prices and the confidence in selling price judgements after sampling. The results show that, on average, sample size influenced neither the selling prices nor confidence. However, cognitive modelling of individual-level learning showed that around half of the participants could be classified as Bayesian learners, whereas the other half adhered to a frequentist learning strategy and that if learning was cognitively simpler more participants adhered to the latter. The observed selling prices of Bayesian learners changed with sample size as predicted by Bayesian principles, whereas sample size affected the judgements of frequentist learners much less. These results illustrate the variability in how people learn from sampled information and provide an explanation for why sample size often does not affect judgements.

A framework for building cognitive process models.

The term process model is widely used, but rarely agreed upon. This paper proposes a framework for characterizing and building cognitive process models. Process models model not only inputs and outputs but also model the ongoing information transformations at a given level of abstraction. We argue that the following dimensions characterize process models: They have a scope that includes different levels of abstraction. They specify a hypothesized mental information transformation. They make predictions not only for the behavior of interest but also for processes. The models' predictions for the processes can be derived from the input, without reverse inference from the output data. Moreover, the presumed information transformation steps are not contradicting current knowledge of human cognitive capacities. Lastly, process models require a conceptual scope specifying levels of abstraction for the information entering the mind, the proposed mental events, and the behavior of interest. This framework can be used for refining models before testing them or after testing them empirically, and it does not rely on specific modeling paradigms. It can be a guideline for developing cognitive process models. Moreover, the framework can advance currently unresolved debates about which models belong to the category of process models.

Naïve and Robust: Class-Conditional Independence in Human Classification Learning.

Humans excel in categorization. Yet from a computational standpoint, learning a novel probabilistic classification task involves severe computational challenges. The present paper investigates one way to address these challenges: assuming class-conditional independence of features. This feature independence assumption simplifies the inference problem, allows for informed inferences about novel feature combinations, and performs robustly across different statistical environments. We designed a new Bayesian classification learning model (the dependence-independence structure and category learning model, DISC-LM) that incorporates varying degrees of prior belief in class-conditional independence, learns whether or not independence holds, and adapts its behavior accordingly. Theoretical results from two simulation studies demonstrate that classification behavior can appear to start simple, yet adapt effectively to unexpected task structures. Two experiments-designed using optimal experimental design principles-were conducted with human learners. Classification decisions of the majority of participants were best accounted for by a version of the model with very high initial prior belief in class-conditional independence, before adapting to the true environmental structure. Class-conditional independence may be a strong and useful default assumption in category learning tasks.