We do not all make choices, reason, interpret our experience, or respond to our environment in the same way. A recent surge of scientific interest has thrust these individual differences into the spotlight: researchers in cognitive psychology and neuroscience are now devoting increasing attention to cognitive variation. The philosophical dimensions of this research, however, have yet to be systematically explored. Here I make an initial foray by considering how cognitive variation is characterized. I present a central dilemma facing descriptions of (...) individual differences, discuss several distinctions used to categorize variation, and show that these distinctions require further elaboration. Finally, by canvassing several philosophical topics for which the characterization of cognitive variation may have significant implications, I argue that philosophers should take note of how and why our minds differ. (shrink)

Topics in Cognitive Science, Volume 14, Issue 1, Page 189-208, January 2022.

The Iowa Gambling Task (IGT) is widely used to study decision‐making within healthy and psychiatric populations. However, the complexity of the IGT makes it difficult to attribute variation in performance to specific cognitive processes. Several cognitive models have been proposed for the IGT in an effort to address this problem, but currently no single model shows optimal performance for both short‐ and long‐term prediction accuracy and parameter recovery. Here, we propose the Outcome‐Representation Learning (ORL) model, a novel model that provides (...) the best compromise between competing models. We test the performance of the ORL model on 393 subjects' data collected across multiple research sites, and we show that the ORL reveals distinct patterns of decision‐making in substance‐using populations. Our work highlights the importance of using multiple model comparison metrics to make valid inference with cognitive models and sheds light on learning mechanisms that play a role in underweighting of rare events. (shrink)

The spacing effect is among the most widely replicated empirical phenomena in the learning sciences, and its relevance to education and training is readily apparent. Yet successful applications of spacing effect research to education and training is rare. Computational modeling can provide the crucial link between a century of accumulated experimental data on the spacing effect and the emerging interest in using that research to enable adaptive instruction. In this paper, we review relevant literature and identify 10 criteria for rigorously (...) evaluating computational models of the spacing effect. Five relate to evaluating the theoretic adequacy of a model, and five relate to evaluating its application potential. We use these criteria to evaluate a novel computational model of the spacing effect called the Predictive Performance Equation. Predictive Performance Equation combines elements of earlier models of learning and memory including the General Performance Equation, Adaptive Control of Thought—Rational, and the New Theory of Disuse, giving rise to a novel computational account of the spacing effect that performs favorably across the complete sets of theoretic and applied criteria. We implemented two other previously published computational models of the spacing effect and compare them to PPE using the theoretic and applied criteria as guides. (shrink)

In most decision-making situations, there is a plethora of information potentially available to people. Deciding what information to gather and what to ignore is no small feat. How do decision makers determine in what sequence to collect information and when to stop? In two experiments, we administered a version of the German cities task developed by Gigerenzer and Goldstein (1996), in which participants had to decide which of two cities had the larger population. Decision makers were not provided with the (...) names of the cities, but they were able to collect different kinds of cues for both response alternatives (e.g., “Does this city have a university?”) before making a decision. Our experiments differed in whether participants were free to determine the number of cues they examined. We demonstrate that a novel model, using hierarchical latent mixtures and Bayesian inference (Lee & Newell, 2011) provides a more complete description of the data from both experiments than simple conventional strategies, such as the take–the–best or the Weighted Additive heuristics. (shrink)

This article demonstrates the potential of using hierarchical Bayesian methods to relate models and data in the cognitive sciences. This is done using a worked example that considers an existing model of category representation, the Varying Abstraction Model (VAM), which attempts to infer the representations people use from their behavior in category learning tasks. The VAM allows for a wide variety of category representations to be inferred, but this article shows how a hierarchical Bayesian analysis can provide a unifying explanation (...) of the representational possibilities using 2 parameters. One parameter controls the emphasis on abstraction in category representations, and the other controls the emphasis on similarity. Using 30 previously published data sets, this work shows how inferences about these parameters, and about the category representations they generate, can be used to evaluate data in terms of the ongoing exemplar versus prototype and similarity versus rules debates in the literature. Using this concrete example, this article emphasizes the advantages of hierarchical Bayesian models in converting model selection problems to parameter estimation problems, and providing one way of specifying theoretically based priors for competing models. (shrink)

We apply a cognitive modeling approach to the problem of measuring expertise on rank ordering problems. In these problems, people must order a set of items in terms of a given criterion (e.g., ordering American holidays through the calendar year). Using a cognitive model of behavior on this problem that allows for individual differences in knowledge, we are able to infer people's expertise directly from the rankings they provide. We show that our model-based measure of expertise outperforms self-report measures, taken (...) both before and after completing the ordering of items, in terms of correlation with the actual accuracy of the answers. These results apply to six general knowledge tasks, like ordering American holidays, and two prediction tasks, involving sporting and television competitions. Based on these results, we discuss the potential and limitations of using cognitive models in assessing expertise. (shrink)

This commentary gives a personal perspective on modeling and modeling developments in cognitive science, starting in the 1950s, but focusing on the author’s personal views of modeling since training in the late 1960s, and particularly focusing on advances since the official founding of the Cognitive Science Society. The range and variety of modeling approaches in use today are remarkable, and for many, bewildering. Yet to come to anything approaching adequate insights into the infinitely complex fields of mind, brain, and intelligent (...) systems, an extremely wide array of modeling approaches is vital and necessary. (shrink)

Distributional models of lexical semantics are capable of acquiring sophisticated representations of word meanings. The main theoretical insight provided by these models is that they demonstrate the systematic connection between the knowledge that people acquire and the experience that they have with the natural language environment. However, linguistic experience is inherently variable and differs radically across people due to demographic and cultural variables. Recently, distributional models have been used to examine how word meanings vary across languages and it was found (...) that there is considerable variability in the meanings of words across languages for most semantic categories. The goal of this article is to examine how variable word meanings are across individual language users within a single language. This was accomplished by assembling 500 individual user corpora attained from the online forum Reddit. Each user corpus ranged between 3.8 and 32.3 million words each, and a count‐based distributional framework was used to extract word meanings for each user. These representations were then used to estimate the semantic alignment of word meanings across individual language users. It was found that there are significant levels of relativity in word meanings across individuals, and these differences are partially explained by other psycholinguistic factors, such as concreteness, semantic diversity, and social aspects of language usage. These results point to word meanings being fundamentally relative and contextually fluid, with this relativeness being related to the individualized nature of linguistic experience. (shrink)

Common methods for analysing response time (RT) tasks, frequently used across different disciplines of psychology, suffer from a number of limitations such as the failure to directly measure the underlying latent processes of interest and the inability to take into account the uncertainty associated with each individual's point estimate of performance. Here, we discuss a Bayesian hierarchical diffusion model and apply it to RT data. This model allows researchers to decompose performance into meaningful psychological processes and to account optimally for (...) individual differences and commonalities, even with relatively sparse data. We highlight the advantages of the Bayesian hierarchical diffusion model decomposition by applying it to performance on Approach–Avoidance Tasks, widely used in the emotion and psychopathology literature. Model fits for two experimental data-sets demonstrate that the model performs well. The Bayesian hierarchical diffusion model overcomes important limitations of current analysis procedures and provides deeper insight in latent psychological processes of interest. (shrink)

The breadth-first search adopted by Bayesian researchers to map out the conceptual space and identify what the framework can do is beneficial for science and reflective of its collaborative and incremental nature. Theoretical pluralism among researchers facilitates refinement of models within various levels of analysis, which ultimately enables effective cross-talk between different levels of analysis.

Cognitive Science, Volume 46, Issue 7, July 2022.