Psychological review最新文献

Repeated forecasts of changing values are common in many everyday tasks, from predicting the weather to financial markets. A particularly simple and informative instance of such fluctuating values are random walks: Sequences in which each point is a random movement from only its preceding value, unaffected by any previous points. Moreover, random walks often yield basic rational forecasting solutions in which predictions of new values should repeat the most recent value, and hence replicate the properties of the original series. In previous experiments, however, we have found that human forecasters do not adhere to this standard, showing systematic deviations from the properties of a random walk such as excessive volatility and extreme movements between subsequent predictions. We suggest that such deviations reflect general statistical signatures of cognition displayed across multiple tasks, offering a window into underlying mechanisms. Using these deviations as new criteria, we here explore several cognitive models of forecasting drawn from various approaches developed in the existing literature, including Bayesian, error-based learning, autoregressive, and sampling mechanisms. These models are contrasted with human data from two experiments to determine which best accounts for the particular statistical features displayed by participants. We find support for sampling models in both aggregate and individual fits, suggesting that these variations are attributable to the use of inherently stochastic prediction systems. We thus argue that variability in predictions is strongly influenced by computational noise within the decision making process, with less influence from "late" noise at the output stage. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

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).

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).

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).

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).

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).

We present a theory of belief dynamics that explains the interplay between internal beliefs in people's minds and beliefs of others in their external social environments. The networks of belief theory goes beyond existing theories of belief dynamics in three ways. First, it provides an explicit connection between belief networks in individual minds and belief dynamics on social networks. The connection, absent from most previous theories, is established through people's social beliefs or perceived beliefs of others. Second, the theory recognizes that the correspondence between social beliefs and others' actual beliefs can be imperfect, because social beliefs are affected by personal beliefs as well as by the actual beliefs of others. Past theories of belief dynamics on social networks do not distinguish between perceived and actual beliefs of others. Third, the theory explains diverse belief dynamics phenomena parsimoniously through the differences in attention and the resulting felt dissonances in personal, social, and external parts of belief networks. We implement our theoretical assumptions in a computational model within a statistical physics framework and derive model predictions. We find support for our theoretical assumptions and model predictions in two large survey studies (N₁ = 973, N₂ = 669). We then derive insights about diverse phenomena related to belief dynamics, including group consensus and polarization, group radicalization, minority influence, and different empirically observed belief distributions. We discuss how the theory goes beyond different existing models of belief dynamics and outline promising directions for future research. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Dynamic models of choice typically describe the decision-making process in terms of the degree or balance of support for available response options. However, these alternative-specific representations of support are liable to fail when the available options change during the course of a decision. We suggest that people may use alternative-general representations, where stimulus feature information-rather than option-specific support-is accumulated over time and mapped onto support for available options as they appear. We tested alternative-specific and alternative-general models of choice in two perceptual experiments where the available options could change during a trial. In the first study, we showed that changing the choice options partway through a trial resulted in no substantial difference in performance relative to a condition where the final options were always onscreen. This was supported by a quantitative model comparison that strongly favored an alternative-general (geometric) model over two alternative-specific models (diffusion and racing accumulator models). In the second study, the stimulus primed specific unavailable responses to test whether irrelevant support for unavailable options was integrated into the decision process. This study elicited a pattern of accuracy that could not have occurred unless participants accumulated support for options that were not yet available. Together, these experiments and modeling results indicate that the majority of participants rely on alternative-general representations of evidence during dynamic decisions among options that can change over time. Future work on decision behavior and its neural antecedents should explore the predictions of these alternative-general theories of choice. (PsycInfo Database Record (c) 2024 APA, all rights reserved).