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In many choice environments, it seems that risks and rewards or probabilities and payoffs are tightly coupled such that high payoffs only occur with low probabilities. This structure may, in the face of uncertainty, afford an adaptive mind an opportunity to exploit it---for instance, by using the size of potential rewards to infer otherwise unknown probabilities of obtaining them. However, a mind can only adapt to and exploit an environmental structure if it is frequent and recurrent. We show that the ecology of competition makes the association of high rewards with low probabilities ubiquitous. It is a consequence of an ecological principle known as the ideal free distribution. This principle posits that the number of competitors in a resource patch is proportional to the gross total amount of resources in it. This principle implies a predictable inverse relationship between probabilities and payoffs, the risk\--reward structure. Harnessing this principle, we develop an ecological theory that identifies important boundary conditions for the structure. For instance, heterogeneity of resources in a given patch, computational limits among competitors, and scarcity of resources, systematically distort the risk-reward structure. Finally, we show that peoples mental representations of the risk--reward structure often conform to the distortions that are associated with these different ecological conditions. Grounding peoples inferences in the theory of the competitive risk-reward ecology makes it possible to predict when an adaptive decision maker should use the risk-reward structure. In sum, our framework demonstrates that the predictability of specific behaviors of a boundedly rational mind will only be possible if theories of the mind and the environment are coupled.

Evidence accumulation models have been used to describe the cognitive processes underlying performance in tasks involving 2-choice decisions about unidimensional stimuli, such as motion or orientation. Given the multidimensionality of natural stimuli, however, we might expect qualitatively different patterns of evidence accumulation in more applied perceptual tasks. One domain that relies heavily on human decisions about complex natural stimuli is fingerprint discrimination. We know little about the ability of evidence accumulation models to account for the dynamic decision process of a fingerprint examiner resolving if 2 different prints belong to the same finger or different fingers. Here, we apply a dynamic decision-making model-the linear ballistic accumulator (LBA)-to fingerprint discrimination decisions to gain insight into the cognitive processes underlying these complex perceptual judgments. Across 3 experiments, we show that the LBA provides an accurate description of the fingerprint discrimination decision process with manipulations in visual noise, speed-accuracy emphasis, and training. Our results demonstrate that the LBA is a promising model for furthering our understanding of applied decision-making with naturally varying visual stimuli. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

The present study investigated how task-irrelevant attributes of a stimulus affected responses in a multiattribute version of the Implicit Association Test (IAT). In Experiment 1, participants categorized images of Black and White male and female individuals on the basis of either race or gender. Both the race and gender of the individuals affected task performance regardless of which attribute was currently relevant to performing the task, yielding the IAT effects on both attributes. However, the influences of a task-irrelevant attribute depended on whether the task-relevant attribute was categorized compatibly or incompatibly with the underlying implicit biases. These results suggest that individuals are still categorized implicitly based on task-irrelevant social attributes and that the explicit categorization required in the standard IAT has a considerable impact on implicit social biases. Experiment 2 considered a third, nonsocial attribute (the color of the picture frame) and reproduced task-irrelevant IAT effects and their dependence on explicit categorization. However, Experiments 3 and 4 suggested that the task-irrelevant IAT effects based on social attributes are determined by whether the task-relevant attribute is a social or nonsocial attribute. The results raise fundamental questions about the basic assumptions underpinning the interpretations of the results from the IAT. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Evaluative conditioning (EC) is an assimilative change in liking of neutral conditioned stimuli (CS) following pairings with positive or negative stimuli (unconditioned stimulus, US). Dual-process theories postulate that, in addition to a controlled propositional process underlying conscious learning of CS-US contingencies, a second automatic and associative learning process exists that underlies EC effects. A central argument for dual-process theories is the dissociation between US expectancy and CS evaluation in extinction learning: When CSs are presented alone subsequent to CS-US pairings, participants cease to expect USs but continue to exhibit EC effects. This dissociation is typically interpreted as demonstration that EC is resistant to extinction, and consequently, that EC is driven by a distinct learning process. We tested an alternative single-process account, namely that expectancy-liking dissociations are caused by different judgment strategies afforded by the dependent measures rather than due to separable learning processes. According to this view, CS evaluations are by default integrative judgments---summaries of large portions of the learning history---whereas US expectancy reflects momentary judgments focusing on recent events. In a counterconditioning and two extinction experiments, we eliminate the expectancy-liking dissociations reported in the literature by inducing nondefault momentary evaluative judgments, and demonstrate a reversed dissociation when we additionally induced integrative expectancy judgments. Our findings corroborated a-priori predictions based on MINERVA 2, a formal single-process memory model. Hence, dissociations between US expectancy and conditioned CS liking reflect judgment properties and can be accounted for parsimoniously by a single learning process.

It is well known that people attempting to perform hypothesis testing show a positive test bias, preferring to request evidence that is consistent (rather than inconsistent) with their current hypothesis. Rather than viewing this as an irrational bias, information theoretic accounts of hypothesis testing have argued that selecting tests likely to produce positive evidence is adaptive when most hypotheses are small (i.e., true of few entities in the world) and respond positively to very few queries. These accounts make the prediction that as hypotheses get larger, the relative utility of positive evidence will decrease; when hypotheses are large enough, negative evidence will become more useful than positive evidence. We test if people are sensitive to this change in utility with an experiment inspired by the game “Battleship,” in which people attempt to discover the correct arrangement of ships by asking for positive or negative evidence. As predicted, as hypotheses become larger people request less positive evidence, and when hypotheses are large requests for negative evidence are more likely than requests for positive evidence. Implications for the nature of the positive test bias are discussed.