Psychological review最新文献

Forming impressions of others is a fundamental aspect of social life. These impressions necessitate the integration of many and varied sources of information about other people, including social group memberships, apparent personality traits, inferences from observed behaviors, and so forth. However, methodological limitations have hampered progress in understanding this integration process. In particular, extant approaches have been unable to measure the independent contributions of multiple features to a given impression. In this article, after describing these limitations and their constraints on theory testing and development, we present a multinomial processing tree model as a computational solution to the problem. Specifically, the model distinguishes the contributions of multiple cues to social judgment. We describe an empirical demonstration of how applying the model can resolve long-standing debates among person perception researchers. Finally, we survey a variety of questions to which this approach can be profitably applied. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Compared to individuals who are rated as less creative, higher creative individuals tend to produce ideas more quickly and with more novelty-what we call faster-and-further phenomenology. This has traditionally been explained either as supporting an associative theory-based on differences in the structure of cognitive representations-or as supporting an executive theory-based on the principle that higher creative individuals utilize cognitive control to navigate their cognitive representations differently. Though extensive research demonstrates evidence of differences in semantic structure, structural explanations are limited in their ability to formally explain faster-and-further phenomenology. At the same time, executive abilities also correlate with creativity, but formal process models explaining how they contribute to faster-and-further phenomenology are lacking. Here, we introduce entropy modulation theory which integrates structure and process-based creativity accounts. Relying on a broad set of evidence, entropy modulation theory assumes that the difference between lower and higher creative individuals lies in the executive modulation of entropy during cognitive search (e.g., memory retrieval). With retrieval targets racing to reach an activation threshold, activation magnitude and variance both independently enhance the entropy of target retrieval and increase retrieval speed, reproducing the faster-and-further phenomenology. Thus, apparent differences in semantic structure can be produced via an entropy modulating retrieval process, which tunes cognitive entropy to mediate cognitive flexibility and the exploration-exploitation trade-off. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Emotion understanding goes beyond recognizing emotional displays-it also involves reasoning about how people's emotions are affected by their subjective evaluations of what they experienced. Inspired by work in adults on cognitive appraisal theories of emotion, we propose a framework that can guide systematic investigations of how an adult-like, sophisticated understanding of emotion develops from infancy to adulthood. We integrate basic concepts of appraisal theories with developmental theories of emotion understanding and suggest that over development, young children construct an intuitive, theory-like understanding of other people's emotions that is structurally similar to appraisal theories. That is, children are increasingly able to evaluate other people's situations from those people's perspectives along various appraisal dimensions and use such third-person appraisals to understand those people's emotional responses to events. This "third-person-appraisal" framework can not only incorporate existing empirical findings but can also identify gaps in the literature, providing a guiding framework for systematically investigating the development of emotion understanding. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The efficient representation of visual information is essential for learning and decision making due to the complexity and uncertainty of the world, as well as inherent constraints on the capacity of cognitive systems. We hypothesize that biological agents learn to efficiently represent visual information in a manner that balances performance across multiple potentially competing objectives. In this article, we examine two such objectives: storing information in a manner that supports accurate recollection (maximizing veridicality) and in a manner that facilitates utility-based decision making (maximizing behavioral utility). That these two objectives may be in conflict is not immediately obvious. Our hypothesis suggests that neither behavior nor representation formation can be fully understood by studying either in isolation, with information processing constraints exerting an overarching influence. Alongside this hypothesis we develop a computational model of representation formation and behavior motivated by recent methods in machine learning and neuroscience. The resulting model explains both the beneficial aspects of human visual learning, such as fast acquisition and high generalization, as well as the biases that result from information constraints. To test this model, we developed two experimental paradigms, in decision making and learning, to evaluate how well the model's predictions match human behavior. A key feature of the proposed model is that it predicts the occurrence of commonly found biases in human decision making, resulting from the desire to form efficient representations of visual information that are useful for behavioral goals in learning and decision making and optimized under an information processing constraint. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Improving emotion regulation abilities, a process that requires learning, can enhance psychological well-being and mental health. Empirical evidence suggests that emotion regulation can be learned-during development and the lifespan, and most explicitly in psychotherapeutic interventions and experimental training paradigms. There is little work however that directly addresses such learning mechanisms. The present article proposes that learning in specific components of emotion regulation-emotion goals, emotional awareness, and strategy selection-may drive skill learning and long-term changes in regulatory behavior. Associative learning (classical and instrumental conditioning) and social learning (including observational, instructed, or interpersonal emotion regulation processes) are proposed to function as underlying mechanisms, while reinforcement-learning models may be useful for quantifying how these learning systems operate. A framework for how people learn emotion regulation will guide basic science investigations and impact clinical interventions. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Violations of transitive preference can be accounted for by both the noncompensatory lexicographic semiorder heuristic and the compensatory additive difference model. However, the two have not been directly compared. Here, we fully develop a simplified additive difference (SAD) model, which includes a graphical analysis of precisely which parameter values are consistent with adherence to, or violation of, transitive preference, as specified by weak stochastic transitivity (WST) and triangle inequalities (TI). The model is compatible with compensatory, within-dimension evaluation. We also develop a stochastic difference threshold model that also predicts intransitive preferences and encompasses a stochastic lexicographic semiorder model. We apply frequentist methods to compare the goodness of fit of both of these models to Tversky's (1969) data and four replications and Bayes factor methods to determine the strength of evidence for each model. We find that the two methods of analysis converge and that, for two thirds of the participants for whom predictions can be made, one of these models predicting violations of WST has a good and the best fit and has strong Bayesian support relative to an encompassing model. Furthermore, for about 20% of all participants, the SAD model (consistent with violations of WST or TI) is significantly better-fitting and has stronger Bayesian support than the stochastic difference threshold model. Finally, Bayes factor analysis finds strong evidence against transitive models for most participants for whom the SAD model consistent with violation of WST or TI is strongly supported. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Food and eating are fundamental for survival but also have significant impacts on health, psychology, sociology, and economics. Understanding what motivates people to eat can provide insights into "adaptive" eating behavior, which is especially important due to the increasing prevalence of health-related conditions such as obesity. There has been considerable interest in developing theoretical models and associated constructs that explain individual differences in eating behavior. However, many of these models contain overlapping theories and shared theoretical mechanisms of action. Currently, there is no recognized standard framework that integrates psychological, physiological, and neurobiological theory to help explain human eating behavior. The aim of the current article was to review key psychological theories in relation to energy balance, homeostasis, energy intake, and motivation to eat and begin to develop a comprehensive framework of relevant factors that drive eating behavior. The key findings from this review suggest that eating behavior is conceptualized by elements of dual process models, which include conscious processing (reflective factors) and automatic responses to desires, environmental cues, habits, and associative learning. These processes are mediated by neurobiology and physiological signaling (homeostatic feedback) of energy balance, which is more tolerant of positive than negative energy balances. From a synthesis of available evidence, it is suggested that eating behavior constructs (traits) can be explained by three latent constructs: reflective, reactive, and homeostatic eating. By understanding the interplay between reflective, reactive, and homeostatic processes, interventions can be developed that tailor treatments to target key aspects of eating behavior. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

One of the most important challenges in decision theory has been how to reconcile the normative expectations from Bayesian theory with the apparent fallacies that are common in probabilistic reasoning. Recently, Bayesian models have been driven by the insight that apparent fallacies are due to sampling errors or biases in estimating (Bayesian) probabilities. An alternative way to explain apparent fallacies is by invoking different probability rules, specifically the probability rules from quantum theory. Arguably, quantum cognitive models offer a more unified explanation for a large body of findings, problematic from a baseline classical perspective. This work addresses two major corresponding theoretical challenges: first, a framework is needed which incorporates both Bayesian and quantum influences, recognizing the fact that there is evidence for both in human behavior. Second, there is empirical evidence which goes beyond any current Bayesian and quantum model. We develop a model for probabilistic reasoning, seamlessly integrating both Bayesian and quantum models of reasoning and augmented by a sequential sampling process, which maps subjective probabilistic estimates to observable responses. Our model, called the Quantum Sequential Sampler, is compared to the currently leading Bayesian model, the Bayesian Sampler (J. Zhu et al., 2020) using a new experiment, producing one of the largest data sets in probabilistic reasoning to this day. The Quantum Sequential Sampler embodies several new components, which we argue offer a more theoretically accurate approach to probabilistic reasoning. Moreover, our empirical tests revealed a new, surprising systematic overestimation of probabilities. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

In this article, we compare two theories of habituation: the standard operating processes (SOP) and the multiple time scales (MTS) models. Both theories propose that habituation is due to a reduction in the difference between actual and remembered stimulation. Although the two approaches explain short-term habituation using a similar nonassociative mechanism based on a time-decaying memory of recent stimulus presentations, their understanding of retention of habituation or long-term habituation differs. SOP suggests that retention of habituation happens through associative retrieval from a long-term memory store, while MTS relies on the differential decay rate of a series of memory units. This essential difference implies that spontaneous recovery, which refers to the return of the response to levels above those reached during habituation, is predominantly a consequence of a mixture of decay and loss of association for SOP and exclusively of decay for MTS. We analyze these mechanisms conceptually and mathematically and demonstrate their functioning with computer simulations of conceptual and published experiments. We evaluate both theories regarding parsimony and explanatory power and propose potential experiments to evaluate their predictions. We provide MATLAB-Simulink and Python codes for the simulations. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

I propose a flexible threshold theory of change perception in self and social judgment. Traditionally, change perception is viewed as a basic cognitive process entailing the act of discriminating informational differences. This article takes a more dynamic view of change perception, highlighting people's motivations in interpreting those differences. Specifically, I propose people's change perceptions depend not only on the salience and quality of the evidence for change but they also depend on the adaptation implications of the change, as people are sensitive to whether their prompted response would be worth it. Variables that exacerbate perceived adaptation implications should thus lead people to contract their change perception thresholds (people should become less open to concluding things have changed and so less likely to act), while variables that alleviate perceived adaptation implications should thus lead people to expand their change perception thresholds (people should become more open to concluding things have changed and so more likely to act), all else equal in the evidence. Moreover, these effects should emerge for perceiving declines and improvements alike so long as change bears on adaptation implications. I review support for these proposals and use the theory to generate novel predictions, contributions, and applications. The theory can explain anew why people respond (or fail to respond) to changing climates and economies, worsening personal health, growing social progress, and many other self and social phenomena. Change perception is more than an act of discriminating differences-it also entails people's threshold judgments of whether and how these differences matter. (PsycInfo Database Record (c) 2024 APA, all rights reserved).