Depression is vastly heterogeneous in its symptoms, neuroimaging data, and treatment responses. As such, describing how it develops at the network level has been notoriously difficult. In an attempt to overcome this issue, a theoretical "negative prediction mechanism" is proposed. Here, eight key brain regions are connected in a transient, state-dependent, core network of pathological communication that could facilitate the development of depressive cognition. In the context of predictive processing, it is suggested that this mechanism is activated as a response to negative/adverse stimuli in the external and/or internal environment that exceed a vulnerable individual's capacity for cognitive appraisal. Specifically, repeated activation across this network is proposed to update individual's brain so that it increasingly predicts and reinforces negative experiences over time-pushing an individual at-risk for or suffering from depression deeper into mental illness. Within this, the negative prediction mechanism is poised to explain various aspects of prognostic outcome, describing how depression might ebb and flow over multiple timescales in a dynamically changing, complex environment. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Experiencing stressful or traumatic events can lead to a range of responses, from mild disruptions to severe and persistent mental health issues. Understanding the various trajectories of response to adversity is crucial for developing effective interventions and support systems. Researchers have identified four commonly observed response trajectories to adversity, from which the resilient is the most common one. Resilience refers to the maintenance of healthy psychological functioning despite facing adversity. However, it remains an open question how to understand and anticipate resilience, due to its dynamic and multifactorial nature. This article presents a novel formalized framework to conceptualize resilience from a complex systems perspective. We use the network theory of psychopathology, which states that mental disorders are self-sustaining endpoints of direct symptom-symptom interactions organized in a network system. The internal structure of the network determines the most likely trajectory of symptom development. We introduce the resilience quadrant, which organizes the state of symptom networks on two domains: (1) healthy versus dysfunctional and (2) stable versus unstable. The quadrant captures the four commonly observed response trajectories to adversity along those dimensions: resilient trajectories in the face of adversity, as well as persistent symptoms despite treatment interventions. Subsequently, an empirical illustration, by means of a proof-of-principle, shows how simulated observations from four different network architectures lead to the four commonly observed responses to adversity. As such, we present a novel outlook on resilience by combining existing statistical symptom network models with simulation techniques. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Attention control has been proposed as an ability construct that explains individual differences in fluid intelligence. Evaluating this hypothesis is complicated by a lack of clarity in the definition of attention control. Here, I propose a definition of attention control, based on experimental research and computational models of what guides attention, and how cognitive processes are controlled. Attention is the selection of mental representations for prioritized processing, and the ability to control attention is the ability to prioritize those representations that are relevant for the person's current goal, thereby enabling them to think and act in accordance with their intentions. This definition can be used to identify appropriate and less appropriate ways to measure individual differences in attention control. An analysis of various approaches to measurement reveals that the current practice of measuring attention control leaves room for improvement. Aligning our psychometric measurements with a clear, theoretically grounded concept of attention control can lead to more valid measures of that construct. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The optimal way to make decisions in many circumstances is to track the difference in evidence collected in favor of the options. The drift diffusion model (DDM) implements this approach and provides an excellent account of decisions and response times. However, existing DDM-based models of confidence exhibit certain deficits, and many theories of confidence have used alternative, nonoptimal models of decisions. Motivated by the historical success of the DDM, we ask whether simple extensions to this framework might allow it to better account for confidence. Motivated by the idea that the brain will not duplicate representations of evidence, in all model variants decisions and confidence are based on the same evidence accumulation process. We compare the models to benchmark results, and successfully apply four qualitative tests concerning the relationships between confidence, evidence, and time, in a new preregistered study. Using computationally cheap expressions to model confidence on a trial-by-trial basis, we find that a subset of model variants also provide a very good to excellent account of precise quantitative effects observed in confidence data. Specifically, our results favor the hypothesis that confidence reflects the strength of accumulated evidence penalized by the time taken to reach the decision (Bayesian readout), with the penalty applied not perfectly calibrated to the specific task context. These results suggest there is no need to abandon the DDM or single accumulator models to successfully account for confidence reports. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Dominant theoretical accounts of interpersonal synchrony, the temporal coordination of biobehavioral processes between several individuals, have employed a linear approach, generally considering synchrony as a positive state, and utilizing aggregate scores. However, synchrony is known to take on a dynamical form with continuous shifts in its timeline. Acting as one continuously, is not always the optimal state, due to an intrinsic tension between individualistic and synergistic forms of action that exist in many social situations. We propose an alternative theory of flexible multimodal synchrony which highlights context as a key component that defines "pulls" toward synchrony and "pulls" toward segregation inherent to the social situation. Traitlike individual differences and relationship variables then sensitize individuals to these contextual "pulls." In this manner, context, individual differences, and relationship variables provide the backdrop to the emergence of flexible and dynamical synchrony patterns, which we consider adaptive, in several modalities-behavioral, physiological, and neural. We point to three consequences of synchrony patterns: social-, task, and self-oriented. We discuss multimodal associations that arise in different contexts considering the theory and delineate hypotheses that emanate from the theory. We then provide two empirical proofs-of-concept: First, we show how individual differences modulate the effect of context on synchrony's outcomes in a novel dyadic motor game. Second, we reanalyze previously reported data, to show how a "flexibility" approach to synchrony data analysis improves predictive ability when testing for synchrony's effects on social cohesion. We provide ways to standardize the characterization of context and guidelines for future synchrony research. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

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 : 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).

To navigate changes within a highly dynamic and complex environment, it is crucial to compare current visual representations of a scene to previously formed representations stored in memory. This process of mental comparison requires integrating information from multiple sources to inform decisions about changes within the environment. In the present article, we combine a novel systems factorial technology change detection task (Blunden et al., 2022) with a set size manipulation. Participants were required to detect 0, 1, or 2 changes of low and high detectability between a memory and probe array of 1-4 spatially separated luminance discs. Analyses using systems factorial technology indicated that the processing architecture was consistent across set sizes but that capacity was always limited and decreased as the number of distractors increased. We developed a novel model of change detection based on the statistical principles of basic sampling theory (Palmer, 1990; Sewell et al., 2014). The sample size model, instantiated parametrically, predicts the architecture and capacity results a priori and quantitatively accounted for several key results observed in the data: (a) increasing set size acted to decrease sensitivity (') in proportion to the square root of the number of items in the display; (b) the effect of redundancy benefited performance by a factor of the square root of the number of changes; and (c) the effect of change detectability was separable and independent of the sample size costs and redundancy benefits. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A focal stimulus (object, end state, outcome, event, experience, characteristic, possibility, etc.) may represent a presence, an occurrence, or something, or it may represent an absence, a nonoccurrence, or nothing. This presence-absence distinction has received extensive and explicit attention in cognitive psychology (it is the central figure), but it has received minimal and primarily implicit attention in motivation science (it is the ground, not the figure). Herein, we explicitly place the presence-absence distinction in the role of figure in a motivational account of behavior, and we do so in the context of the foundational approach-avoidance motivation distinction. We review pertinent literature in cognitive psychology and motivation science, and we provide a model integrating the approach-avoidance and the presence-absence distinctions, along with numerous examples, illustrations, and observations. We believe that attending to the presence-absence distinction in motivation science holds great promise for theory, research, and application, and we encourage researchers to attend to this distinction moving forward. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Just as animals forage for food, humans forage for social connections. People often face a decision between exploring new relationships versus deepening existing ones. This trade-off, known in optimal foraging theory as the , is featured prominently in other disciplines such as animal foraging, learning, and organizational behavior. Many of the framework's principles can be applied to humans' choices about their social resources, which we call . Using known principles in the domain of social exploration/exploitation can help social psychologists better understand how and why people choose their relationships, which ultimately affect their health and well-being. In this article, we discuss the costs and benefits of social exploration and social exploitation. We then synthesize known person- and situation-level predictors of social decision making, reframing them in the language of the explore-exploit trade-off. We propose that people explore more when they find it more rewarding and less costly, and when the environment has many opportunities to do so. We conclude by discussing hypotheses generated by applying optimal foraging theory to social decision making. (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).

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).

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).

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).

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).

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).

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).

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 (₁ = 973, ₂ = 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).

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).

Although the focus of research for decades, there is a surprising lack of consensus on what is (and what is not) self-control. We review some of the most prominent theoretical models of self-control, including those that highlight conflicts between smaller-sooner versus larger-later rewards, "hot" emotions versus "cool" cognitions, and efficient automatic versus resource-intensive controlled processes. After discussing some of their shortcomings, we propose an alternative approach based on tenets of construal level theory (Trope et al., 2021) that integrates these disparate models while also providing novel insights. Specifically, we model self-control as a problem of regulatory scope-the range of considerations one accounts for in any decision or behavior. Self-control conflicts occur when the pursuit of specific local opportunities threatens the ability to address motivational priorities that span a broader array of time, places, individuals, and possibilities. Whereas a more contractive consideration of relevant concerns may prompt indulgence in temptation, a more expansive consideration of concerns should not only help people identify the self-control conflict but also successfully resolve it. We review empirical evidence that supports this new framework and discuss implications and new directions. This regulatory framework not only clarifies what is and what is not self-control but also provides new insights that can be leveraged to enhance self-control in all its various forms. (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).

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).

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).

Generative models of decision now permeate all subfields of psychology, cognitive, and clinical neuroscience. To successfully investigate decision mechanisms from behavior, it is necessary to assume the presence of delays prior and after the decision process itself. However, directly observing this "non-decision time (NDT)" from behavior long appeared beyond reach, the field mainly relying on models to estimate it. Here, we propose a biological definition of decision that includes perceptual discrimination and action selection, and in turn, explicitly equates NDT with the minimum sensorimotor delay, or "deadtime." We show how this delay is directly observable in behavioral data, without modeling assumptions, using the visual interference approach. We apply this approach to 11 novel and archival data sets from humans and monkeys gathered from multiple labs. We validate the method by showing that visual properties (brightness, color, size) consistently affect empirically measured visuomotor deadtime (VMDT), as predicted by neurophysiology. We then show that endogenous factors (strategic slowing, attention) do not affect VMDT. Therefore, VMDT consistently satisfies widespread selective influence assumptions, in contrast to NDT parameters from model fits. Last, contrasting empirically observed VMDT with NDT estimates from the EZ, drift diffusion, and linear ballistic accumulator models, we conclude that NDT parameters from these models are unlikely to consistently reflect visuomotor delays, neither at a group level nor for individual differences, in contrast to a widely held assumption. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Within psychology, the underachievement of students from working-class backgrounds has often been explained as a product of individual characteristics such as a lack of intelligence or motivation. Here, we propose an integrated model illustrating how contribute to social class disparities in education over and beyond individual characteristics. According to this new social class disparities in education are due to several mismatches between the experiences that students from working-class backgrounds bring with them to the classroom and those valued in academic contexts-specifically, mismatches between (a) academic contexts' culture of independence and the working-class orientation to interdependence, (b) academic contexts' culture of competition and the working-class orientation toward cooperation, (c) the knowledge valued in academic contexts and the knowledge developed through working-class socialization, and (d) the social identities valued in academic contexts and the negatively stereotyped social identities of students from working-class backgrounds. Because of these mismatches, students from working-class backgrounds are likely to experience discomfort and difficulty in the classroom. We further propose that, when attempting to make sense of these students and teachers rely on inherent characteristics (e.g., ability, motivation) more often than warranted; conversely, they overlook extrinsic, contextual factors. In turn, this explanatory bias toward inherent features leads (a) students from working-class backgrounds to experience self-threat and (b) their teachers to treat them unfairly. These magnify social class disparities in education. This integrated model has the potential to reshape research and discourse on social class and education. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Cognitive scientists have become increasingly interested in understanding how natural minds represent and reason about possible ways the world could be. However, there is currently little agreement on how to understand this remarkable capacity for modal thought. We argue that the capacity for modal thought is built from a set of relatively simple component parts, centrally involving an ability to consider possible extensions of a part of the actual world. Natural minds can productively combine this ability with a range of other capacities, eventually allowing for the observed suite of increasingly more sophisticated ways of modal reasoning. We demonstrate how our (de)compositional account is supported by both the trajectory of children's developing capacity for reasoning about possible ways the world could be and by what we know about how such modal thought is expressed within and across natural languages. Our approach makes new predictions about which kinds of capacities are required by which kinds of experimental tasks and, as a result, contributes to settling currently open theoretical questions about the development of modal thought and the acquisition of modal vocabulary in children. Our work also provides a more systematic way of understanding possible variation in modal thought and talk, and, more generally, paves the way toward a unified theory that will ultimately allow researchers across disciplines to relate their findings to each other within a framework of shared assumptions. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The explanation of psychological phenomena is a central aim of psychological science. However, the nature of explanation and the processes by which we evaluate whether a theory explains a phenomenon are often unclear. Consequently, it is often unknown whether a given psychological theory indeed explains a phenomenon. We address this shortcoming by proposing a productive account of explanation: a theory explains a phenomenon to some degree if and only if a formal model of the theory produces the statistical pattern representing the phenomenon. Using this account, we outline a workable methodology of explanation: (a) explicating a verbal theory into a formal model, (b) representing phenomena as statistical patterns in data, and (c) assessing whether the formal model produces these statistical patterns. In addition, we provide three major criteria for evaluating the goodness of an explanation (precision, robustness, and empirical relevance), and examine some cases of explanatory breakdowns. Finally, we situate our framework within existing theories of explanation from philosophy of science and discuss how our approach contributes to constructing and developing better psychological theories. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Some neural representations gradually change across multiple timescales. Here we argue that modeling this "drift" could help explain the spacing effect (the long-term benefit of distributed learning), whereby differences between stored and current temporal context activity patterns produce greater error-driven learning. We trained a neurobiologically realistic model of the entorhinal cortex and hippocampus to learn paired associates alongside temporal context vectors that drifted between learning episodes and/or before final retention intervals. In line with spacing effects, greater drift led to better model recall after longer retention intervals. Dissecting model mechanisms revealed that greater drift increased error-driven learning, strengthened weights in slower drifting temporal context neurons (temporal abstraction), and improved direct cue-target associations (decontextualization). Intriguingly, these results suggest that decontextualization-generally ascribed only to the neocortex-can occur within the hippocampus itself. Altogether, our findings provide a mechanistic formalization for established learning concepts such as spacing effects and errors during learning. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Partisan animosity has been growing in the United States and around the world over the past few decades, fueling efforts by researchers and practitioners to help heal the divide. Many studies have been conducted to test interventions that aim to promote open-mindedness; however, these studies have been conducted in disparate literatures that do not always use the same terminology. In this review, we integrate research on open-mindedness in order to facilitate cross-talk and collaboration between disciplines. We review various concepts related to open-mindedness and then offer a conceptual model to help guide the further development of interventions and research to understand open-mindedness. We propose that open-mindedness is multifaceted and dynamic, such that interventions should focus on targeting multiple psychological pathways in order to maximize and sustain their effects. Specifically, we propose that interventions that target cognitive and/or motivational pathways can induce open-mindedness initially. Then, training in emotion regulation and/or social skills can help to sustain and build on open-mindedness once individuals enter into a situation where their beliefs are challenged. We conclude with a discussion of potential future directions for research on open-mindedness interventions. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Understanding the mechanisms enabling the learning and flexible use of knowledge in context-appropriate ways has been a major focus of research in the study of both semantic cognition and cognitive control. We present a unified model of semantics and control that addresses these questions from both perspectives. The model provides a coherent view of how semantic knowledge, and the ability to flexibly access and deploy that knowledge to meet current task demands, arises from end-to-end learning of the statistics of the environment. We show that the model addresses unresolved issues from both literatures, including how control operates over features that covary with one another and how control representations themselves are structured and emerge through learning, through a series of behavioral experiments and simulations. We conclude by discussing the implications of our approach to other fundamental questions in cognitive science, machine learning, and artificial intelligence. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Memory theories distinguish between item and associative information, which are engaged by different tasks: item recognition uses item information to decide whether an event occurred in a particular context; associative recognition uses associative information to decide whether two events occurred together. Associative recognition is slower and less accurate than item recognition, suggesting that item and associative information may be represented in different forms and retrieved using different processes. Instead, I show how a dynamic model (Cox & Criss, 2020; Cox & Shiffrin, 2017) accounts for accuracy and response time distributions in both item and associative recognition with the same set of representations and processes. Item and associative information are both represented as vectors of features. Item and associative recognition both depend on comparing traces in memory with probes of memory in which item and associative features gradually accumulate. Associative features are slower to accumulate, but largely because they emerge from conjunctions of already-accumulated item features. I apply the model to data from 453 participants, each of whom performed an item and performed associative recognition following identical study conditions (Cox et al., 2018). Comparisons among restricted versions of the model show that its account of associative feature formation, coupled with limits on the rate at which features accumulate from multiple items, explains how and why the dynamics of associative recognition differ from those of item recognition even while both tasks rely on the same underlying representations. (PsycInfo Database Record (c) 2024 APA, all rights reserved).