No abstract

The Implicit Association Test [IAT], like many behavioral measures, seeks to quantify meaningful individual differences in cognitive processes that are difficult to assess with approaches like self-reports. However, much like other behavioral measures, the IAT appears to show low test-retest reliability and typical scoring methods fail to quantify all of the decision-making processes that generate the overt task performance. Here, we develop a new modeling approach for the IAT, the CAVEAT model, that leverages both response times and accuracy on the task to make inferences about representational similarity between the stimuli and categories, as in computational linguistic models of representation. The model disentangles processes related to cognitive control, stimulus encoding, associations between concepts and categories, and processes unrelated to the choice itself. This approach to analyzing IAT data illustrates that the unreliability in the IAT is almost entirely attributable to the methods used to analyze data from the task: the model parameters show test-retest reliability around .8-.9, on par with that of many of the most reliable self-report measures. Furthermore, we demonstrate how model parameters are better and more unbiased compared to the IAT D-score in predicting outcomes related to intergroup contact and motivation. Put together, the model provides much greater reliability, discriminant and predictive validity, and the ability to make inferences about processes like associations and response caution that are not otherwise possible. We conclude by reviewing new, model-based insights about the IAT related to awareness, strategic caution, faking, and the role of associations in decision-making.

Subjective value has long been measured using binary choice experiments, yet responses like willingness-to-pay prices can be an effective and efficient way to assess individual differences risk preferences and value. Tony Marley's work illustrated that dynamic, stochastic models permit meaningful inferences about cognition from process-level data on paradigms beyond binary choice, yet many of these models remain difficult to use because their likelihoods must be approximated from simulation. In this paper, we develop and test an approach that uses deep neural networks to address this problem, performing instantaneous parameter estimation from a set of willingness-to-pay data. We show that a trained network is able to accurately recover true risk preferences related to utility, response caution, anchoring biases, and non-decision processes. This model was applied to estimate model parameters for a large, demographically representative sample of U.S. participants who completed a short, 20-question pricing task. It successfully characterizes the risk preferences of a diverse sample, showing that age and level of income are related to thresholds and anchoring biases, respectively. The results illustrate the utility of machine-learning approaches for wider adoption and integration of cognitive and economic models, providing efficient methods for quantifying meaningful differences in risk preferences from simple experiments.

Une enquête réalisée auprès de 300 femmes atteintes d’un cancer du sein a permis d’examiner leur intérêt pour la réalité virtuelle (RV), les modalités d’immersion attendues ainsi que leurs attentes vis-à-vis de ce dispositif. Les résultats indiquent que la majorité des femmes (93 %) souhaiterait avoir recours à la RV durant leurs traitements, sachant que leurs préférences d’immersion portent sur un environnement naturel accompagné de musique et/ou de relaxation guidée. La RV est envisagée comme un outil pertinent pour s’évader, mieux accepter les soins et réguler leurs émotions. Pour faciliter l’immersion virtuelle, cette étude souligne combien il est important de connaître leurs aspirations personnelles pour leur offrir un soutien technologique individualisé.

Preference reversals in risky choice -- where people favor low-risk prospects in binary choice but assign higher prices to high-risk prospects -- have led to models of response processes that differentiate pricing from choice. Theories of intertemporal choice do not distinguish between response processes, assuming instead that eliciting choices or prices will lead to the same inferences about people’s preferences for delayed outcomes. Here, we show that this assumption is incorrect. Participants in a price-choice experiment showed systematic preferences for smaller-sooner (SS) over larger-later (LL) options in binary choice, but reversed this apparent preference by pricing the exact same LL options higher than the SS options. This reversal in pricing results in less impulsive behavior, suggesting that pricing frames may reduce choice impulsivity. To explain these diverging price and choice findings in a common framework, we propose a variant of a pricing model from risky choice that accommodates these effects.