Stereotypes are a foundational construct in psychological science, often defined as beliefs concerning characteristic group attributes. We present a cognitive-ecological theory of social perception that predicts and explains why such characteristic attributes are likely negative, that is, why most stereotypes are negative. The theory assumes that, cognitively, people characterize groups by attributes that distinguish them from other groups. Ecologically, positive social information is more frequent (i.e., positivity prevalence), and negative social information is more diverse (i.e., negativity diversity). If negative attributes are less frequent and more diverse relative to positive attributes, then they must be more likely to distinguish one group from another. Consequently, the attributes people see as characteristic of a group are likely to be negative. We illustrate the predicted stereotype negativity across two studies and two existing data sets. We then formalize the theory and illustrate it with simulations showing that rare attributes can be highly diagnostic of group membership while applying to only a minority of group members. Thus, the theory explains why most stereotype content is likely negative, despite the well-documented positivity prevalence, and why stereotypes may be diagnostic and comparatively accurate, despite being descriptively inaccurate. The theory explains stereotype negativity without requiring motivational derogation, essentialist group differences, or a general negativity bias, and it clarifies when positive stereotypes should occur. We also discuss the theory's relation to dimensional models of stereotype content and consequences for interventions aimed at reducing stereotype negativity. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Over the past three decades, psychology has documented the recurrence of specific themes in stories across cultures (e.g., love, alliances, monsters, and imaginary worlds) and linked them to identifiable motivational mechanisms (e.g., mate choice, cooperation, threat detection, and exploration). Yet this body of work has remained fragmented, organized around individual themes rather than integrated into a unified account of what makes narrative content psychologically engaging. Here, we propose the Motivational-Ingredient Framework. We identify a set of motivational ingredients: core narrative features, each defined by the specific motivational mechanism it activates. Drawing on evolutionary psychology, affective neuroscience, and the study of human motivation, we compile a comprehensive table of ingredients grounded in the current understanding of human motivational architecture. This framework offers a theory-driven, cross-culturally applicable foundation for the empirical study of narrative content, with implications for fields ranging from media psychology and computational humanities to education and public policy. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Visual working memory performance declines with the quantity of material stored and the duration of its maintenance, but it is disputed whether these constraints are independent or interact. Specifically, different studies have reached contradictory conclusions on whether greater memory load accelerates deterioration of the stored information during retention, a question with implications for storage mechanisms. Meanwhile, prominent models of visual working memory based on stable attractors predict that recall should become increasingly biased toward a fixed set of canonical features with longer retention, but empirical support is mixed. Uncertainty is further exacerbated by limited data, methodological variation, and inconsistent analytical approaches across studies. Moreover, previous studies have not always distinguished response variability from stimulus-specific biases nor considered the possibility of swap errors (intrusions). To address these issues, we conducted six new experiments and reanalyzed data from seven published experiments using consistent analytical methods to examine the effects of set size and delay on variability and bias. Our results show that recall variability increased with longer delays and higher set sizes, consistent with prior findings, and importantly, that these effects did not operate independently: The effect of delay was amplified at higher set sizes. We also observed consistent evidence for systematic biases across stimuli, but unlike variability, bias amplitude did not change with either set size or retention interval. These results support diffusion accounts of visual working memory maintenance, in which variability grows through the accumulation of random error, while challenging stable attractor accounts that predict systematic drift toward canonical values during maintenance. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Here we present a model of sequence learning and recall based on the idea that the function of memory is to maintain an up-to-date representation of the environment that can be used to guide future perception and action. The representation of the environment is considered to be a prior which is combined with information in short-term memory to construct a posterior representation. That posterior representation drives recall and, in turn, is used to update the priors. This prediction-update cycle is a form of Bayesian filter. We apply the model to data from Hebb's (1961) task in which participants learn sequences over repeated presentations in an immediate serial recall task. The model is shown to simulate a wide range of data on the Hebb effect including the ability to learn multiple lists at once, the effect of list spacing, the differential impact of variation in the beginning versus end of lists, learning from response errors, interference between similar lists, and the effects of repetition on forward and backward recall. The model's ability to account for these phenomena follows directly from its basic computational principles. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Some evidence suggests that faces are perceived holistically, but subjective experience and research on the neural organization of the visual system indicate that facial parts can also be analyzed individually. Thus, advances in theories of face perception are met with two major challenges: How can hierarchical face representations be used to integrate holistic and analytic encoding within the same framework? And how can the stages of face processing be integrated with higher level cognitive processes, such as memory and decision making? We propose a novel computational framework termed the "Modular Serial-Parallel Network" (MSPN), which synthesizes several perceptual and cognitive approaches including memory representations, signal detection theory, rule-based decision making, mental architectures (serial and parallel processing), random walks, and process interactivity. MSPN provides a computational modeling account of four stages in face perception-(a) representational, (b) decisional, (c) logical-rule implementation, and (d) modular stochastic accrual of information-and can account for both choice probabilities and response-time predictions. We used MSPN to explore and validate theories across multiple paradigms including the composite face task, the part-to-whole task, and a new study of the other-race effect. The application of MSPN suggests that face processing varies based on the task and that holistic processing does not necessarily imply complete pooling into a single face object, but MSPN can reveal the precise nature of processing that gives rise to holistic effects in different paradigms. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Person-organization (PO) fit turns on the idea of compatibility between people and organizations, but fit research has typically approached compatibility through a limited set of assumptions. Most PO fit research adopts a correspondence ontology, treating fit as a measurable alignment between individual traits and features of the organization. Drawing on constructivist psychology and interpretivist theory, this article distinguishes the established correspondence ontology of PO fit, expressed as aligned fit, from a constitutive ontology, expressed as constructed fit. I argue that PO fit theory rests on two distinct and often unacknowledged ontologies, each with its own epistemological foundations and methodological implications. Correspondence-based approaches explain fit as attribute alignment between person and organization, whereas constitutive approaches explain fit as the lived capacity to sustain a workable sense of self in organizational life through interpretation, identity work, and affective experience. A process model is developed for each ontology, illustrating how they generate distinct explanatory pathways linking organizational participation, experience, and outcomes. Recognizing these ontological differences allows researchers to specify more precisely what their studies seek to explain when they invoke the language of fit. The article advances a pluralist research agenda that preserves theoretical coherence while expanding the explanatory scope of fit research. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Generic language (e.g., "Birds have hollow bones") conveys generalizations about categories and is a fundamental and ubiquitous mechanism of learning about the world. How exactly learning from generics occurs, however, is unclear: Generics exhibit so much flexibility in how they are interpreted that a formal, quantitative theory of how generic language updates beliefs has not only never been developed or tested, but the very notion has often been explicitly dismissed. Here, we explore a hypothesis implicit in the work of Tessler and Goodman (2019) that generics update beliefs via an uncertain threshold, like a vague quantifier: A category-property generic ("Ks F") means that the property F is expected to be relatively widespread in the category K, where the threshold for being relatively widespread is a priori uncertain and resolved by consulting one's prior knowledge of the property. We compare this model to a family of alternatives with the same background knowledge but where generics have a fixed, context-invariant threshold meaning. We additionally formalize a novel quantitative model of an explicitly conceptual-based approach to generics, which does not involve any thresholds, where generics would act as a direct connection to conceptual structures. Across three experiments in which we both measure and manipulate prior beliefs, we find the uncertain threshold approach to generics to be the best explanation of participants' highly context-sensitive interpretations of novel generic statements. The sufficiency of this rational model of generic interpretation suggests that generics are an effective medium for faithful transmission of knowledge between interlocutors, arguing against proposals that generic language interpretation is biased in a fundamental way. This work adds to the growing enterprise of formal, quantitative studies of human understanding of generic language. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Inferring the true cause of noise-distinguishing between volatility (environmental change) and stochasticity (outcome randomness)-is essential for learning in noisy environments. While most studies rely on binary outcomes, previous models are designed for continuous outcome and use ad hoc approximations to handle binary data, introducing theoretical inconsistencies and interpretational issues. Here, we develop a normative framework for inferring the causes of noise from binary feedback that remains faithful to the discrete nature of the generative process and underlying statistical structure. First, we establish a generative model using a state space approach tailored for binary outcomes and derive the corresponding hidden Markov model inference procedure. Second, we introduce a computational model combining the hidden Markov model with particle filtering to simultaneously infer volatility and stochasticity from binary outcomes. Third, we validate predictions through a 2 × 2 probabilistic reversal-learning task with human participants, systematically manipulating both noise parameters. Results show that participants adjust their learning rates consistent with model predictions, increasing learning rates under volatile conditions and decreasing them under high stochasticity. Our theoretical and experimental results offer a principled approach for dissociating volatility and stochasticity from binary outcomes, providing insights into learning processes relevant to typical cognition and psychiatric conditions. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Continuous-outcome decision tasks, in which responses are made on continuous scales, are increasingly used to characterize the processes involved in speeded decision making. These tasks yield entire distributions of response errors rather than just error proportions. Two diffusion process models of continuous-outcome decisions have recently been proposed, the circular diffusion model, and the spatially continuous diffusion model, which generalize the two main classes of diffusion process models of two-choice decisions to continuous spaces. The circular diffusion model, which generalizes the two-boundary Wiener model, assumes evidence is accumulated by a two-dimensional Wiener process with a vector-valued drift rate on the interior of a disk, whose bounding circle represents the decision criterion. The spatially continuous diffusion model, which generalizes the class of multiple accumulator models, assumes evidence is accumulated by a Gaussian random process with a function-valued drift rate to a criterion. We compared the ability of the models to account for the distributions of errors and response times on three perceptual tasks that manipulated both the kind and the amount of evidence entering the decision process, which produced qualitatively different patterns of response times and errors. Despite some differences in their predicted joint distributions, both models generally provided good accounts of performance in all three tasks. Our findings reinforce and extend findings from two-choice studies showing that diffusive evidence processes and not model architecture are the main determinants of successful model performance, and show the theoretical unity of diffusion models across different domains. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Numerous studies in cognitive development have examined the relation between children's developing inhibitory control and their social cognition, including false belief, selective trust, and children's understanding of fairness norms. This research typically finds correlations between performance on inhibitory control measures and developmental milestones in social cognitive development, such as success on false belief measures, actively recognizing deceptive agents, and rejecting advantageous inequities. These studies, however, do not describe the role of inhibitory control as children are making these social cognitive inferences. We use reach trajectory tracking-a method commonly used with adults-to isolate the role of distinct inhibitory control mechanisms as children make such inferences. After describing this method, we present three case studies that describe how different inhibitory mechanisms are involved in understanding others' false beliefs, selective learning from human versus nonhuman stimuli, and making inferences about fair resource distributions. Taken together, these studies suggest that different inhibitory mechanisms relate to social evaluations about other people as opposed to evaluations of the information generated by others. Further, reach trajectory tracking offers a natural way of studying the role of inhibitory control in cognitive development, as children are making decisions. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Risk aversion for moderate-likelihood gains is perhaps the best-known stylized fact from decision research. Though studies documenting it have focused on decisions involving money, such behavior is presumed to prevail very generally, across diverse domains. We investigate the validity of this generalization by contrasting two types of decisions. In unimodal choices, outcomes are "apples-to-apples." Consider choosing between sure and uncertain monetary payoffs, or between sure receipt of a product and a chance at several units of it. In crossmodal choices, outcomes are "apples-to-oranges." Consider choosing between sure receipt of one product and uncertain receipt of a disparate product. We observe two patterns by which risk matters less crossmodally, contrary to straightforward generalizations. First, relative to unimodal preferences involving actuarially fair risky options, corresponding crossmodal preferences exhibit less risk aversion. Second, crossmodal preferences vary less across risk levels: As the likelihood and subjective value of a risky option's outcomes become increasingly unfavorable (favorable), people do not exhibit as much additional distaste (appetite) for it. These patterns of insensitivity engender an interaction: relative to unimodal settings, crossmodal settings yield less aversion to unfavorable and fair risk but more aversion to favorable risk. To explain this interaction, we present the translate-and-accommodate model, in which unimodal preferences follow standard accounts, but crossmodal preferences reflect processes of (a) deterministic translation and (b) risk accommodation. The translate-and-accommodate model also explains the uncertainty effect and related patterns of seemingly bizarre, dominated choices. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

The fleeing famine hypothesis (FFH) posits that a genetically and evolutionarily based predisposition for food restriction and hyperactivity is activated among susceptible humans and nonhumans upon experiencing a prolonged energy deficit. Such circumstances manifest in the counterintuitive responses to starvation that are characteristic of anorexia nervosa: continued dietary restriction, denial of declining physical state, and, in at least a subset of those with anorexia nervosa, increased motor activity. We consider the nine predictions derived by Guisinger (2003) in her original formulation of the FFH and evaluate the evidence regarding each, including, of course, evidence that has accumulated subsequent to the 2003 statement of the model. We further expand upon Guisinger's original hypothesis by addressing apparent contradictions, outlining important directions for future research, and discussing the clinical implications of the FFH. We conclude that the relevant literature is unsurprisingly imperfect but that, with a number of reconsiderations, the FFH has substantial explanatory scope. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

A puzzle for evolutionary theory is the existence of two seemingly offsetting behavioral "biases," overconfidence and loss aversion. Overconfidence is a call to action, while loss aversion curbs initiative. The most prominent evolutionary explanation of overconfidence, proposed by Trivers (1976), is that self-deceit arises to better deceive others. Missing from this account is why sincere messages are believed, especially given the widespread prevalence of self-deception. Moreover, if overconfidence is adaptive, why is it at least partially canceled by loss aversion? We propose a signaling theory according to which the role of self-deception is to better inform others. Since the decision error associated with high self-belief is less burdensome for the more able, and the benefit of being perceived as able increases with ability, hardwired overconfidence is a credible signal of true ability. Evidence supports this interpretation. A further implication of signaling is that loss aversion is part of the equilibrium. It partially ameliorates the decision costs of overconfidence, but as it is usually hidden, it does not eliminate its signaling role. "Biases" are thus symbiotic-the payoff to agents from an integrated set of biases is higher than would be the case in their absence. From this perspective, Kahneman's advice that individuals eliminate both overconfidence and loss aversion is poorly founded. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Synchrony can support psychotherapy by enhancing rapport, emotional attunement, trust, and attachment. However, synchrony is not inherently beneficial: In some contexts, excessive or maladaptive synchrony may reinforce conformity, dependence, or dysfunctional relational patterns. We argue that the clinical meaning of synchrony depends on the interaction between interpersonal context, the individual's trait-like synchrony signature, and state-like deviations from that baseline. From this perspective, both synchrony and disengagement-including asynchronous interaction-may be therapeutically meaningful when they promote autonomy, differentiation, and adaptive change. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Internalized racism, although widely studied, remains conceptually fragmented and theoretically underdeveloped. I advance the internalized racism process model (IRPM), a process framework that conceptualizes internalized racism as a recursive, motive-driven system of self-regulation through which structurally organized ethnoracial hierarchy becomes embedded in psychological functioning. The IRPM specifies how structural conditions, sociocultural socialization, and identity processes converge to organize internalized racism. The model identifies a functional-motivational architecture in which needs for self-integrity and belonging give rise to three regulatory functions-threat minimization, ideological acceptance, and identity translation-that structure diverse forms of internalized racism and their behavioral enactments. Through recursive feedback loops, these processes stabilize into enduring patterns of self-regulation. The IRPM integrates previously fragmented literatures by distinguishing forms from manifestations, linking motives to regulatory functions, and specifying cross-level dynamics between structural contexts and individual regulation. It explains variability in who internalizes (via vulnerability and resistance resources), when internalization is likely (under conditions of threat and belonging pressure), and why consequences differ (as a function of form and configuration). Finally, the model specifies how strategies that provide short-term regulatory relief can consolidate into entrenched patterns that undermine psychological functioning. Although designed to generalize across hierarchically organized systems, the IRPM centers on internalized racism among subordinated groups, where empirical foundations are most developed, while also providing a framework for examining internalized racial dominance among advantaged groups. The IRPM offers a unifying account that advances explanation, generates testable predictions, and establishes an agenda for future research on the psychological organization of inequality. (PsycInfo Database Record (c) 2026 APA, all rights reserved).