Psychological review最新文献

In this article, we formally present the Integrated Self-Categorization model of Autism (ISCA). This model brings together the cognitive-perceptual and social-communication features of autism under a single explanatory framework. Specifically, ISCA proposes that the social-communication features that are related to theory of mind dysfunction emerge from the cognitive-perceptual features related to enhanced perceptual functioning and weak central coherence, and proposes that they are linked by dysfunction in the self-categorization process. We present the assumptions on which the model is based, and from these, we derive a set of precise, testable hypotheses, including a set of novel hypotheses that do not emerge from any existing models of autism. We then provide evidence that supports the model, derived from a number of direct tests of the hypotheses that it generates. We conclude by discussing the implications of the model for understanding autism and for intervention to improve the lives of autistic people, as well as future directions. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Phonological memory, or the ability to remember a novel word string well enough to repeat it, has long been characterized as a time-limited store. An alternative embodiment model sees it as the product of the dynamic sensorimotor (perceptual and production) processes that inform responses to speech. Keren-Portnoy et al. (2010) demonstrated that this capacity, often tested through nonword repetition and found to predict lexical advance, is itself predicted by the first advances in babbling. Pursuing the idea that phonological memory develops through vocal production, we trace its development-drawing on illustrative data from children learning six languages-from the earliest adult-like vocalizations through to the first words and the consolidation of early words into an initial lexical network and more stable representational capacity. We suggest that it is the interaction of perceptual and production experience that mediates the mapping of new forms onto lexical representations. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

A revised view of the amygdala, its relationship with the prefrontal cortex (PFC), and its role in intelligent human decision-making is proposed. Based on recent findings, we present a framework in which the amygdala plays a central role in the value computations that determine which goals are worth pursuing, while the PFC plays a central role in generating and evaluating possible action plans to realize these goals. We suggest that the amygdala and PFC continuously work together during decision-making and goal pursuit as individuals compute and recompute the value and likelihood of different goals while interacting with a dynamic world. Once seen as chiefly involved in simple stimulus-outcome associative learning, the amygdala is shown to play a sophisticated role in human decision-making by contributing to the moment-by-moment integration of multiple costs and benefits to determine optimal choices. We discuss implications of the framework for brain development, emotion regulation, intelligence, and psychopathology. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Mental fatigue is usually accompanied by drops in task performance and reduced willingness for further exertion. A value-based theoretical account may help to explain such negative effects. In this view, mental fatigue influences perceived costs and rewards of exerting effort. However, no formal mathematical framework has yet been proposed to model and quantitatively estimate the effects of mental fatigue on subjective evaluations of effort expenditure, under possibly imperfect self-perceptions of internal fatigue states. We proposed a mathematical framework to model human cognitive effort allocations, assuming mental fatigue states are partially observable with semi-Markov dynamics. We modeled effort allocation decisions as a means to the goal of maximizing cumulative subjective values over a given time horizon. We developed an estimation method to identify subjective values and the hidden dynamics of mental fatigue, which can in future work be applied to self-reports, psychophysiological indices, and behavioral outcomes associated with fatigue. The modeling and estimation method was tested using a simulated n-back task under a free-choice paradigm, with model parameters fine-tuned from past studies. The proposed approach was able to recapitulate task performance and task engagement patterns observed under mental fatigue. This work advances a reward/cost trade-off account for explaining the exertion of mental effort and suggests new avenues for both theoretically and empirically relevant understandings of how cognitive operations are affected by mental fatigue. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

It has been recently suggested that research on human multitasking is best organized according to three research perspectives, which differ in their focus on cognitive structure, flexibility, and plasticity. Even though it is argued that the perspectives should be seen as complementary, there has not been a formal approach describing or explaining the intersections between the three perspectives. With this theoretical note, we would like to show that the explicit consideration of individual differences is one possible way to elaborate in more detail on how and why the perspectives complement each other. We will define structure, flexibility, and plasticity; describe what constitutes individual differences; will outline selected empirical examples; and raise possible future research questions helping to develop the research field. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Higher-order conditioning results from a simple training procedure: Pairing two relatively neutral conditioned stimuli, A and X, allows properties separately conditioned to X (e.g., through pairing it with an unconditioned stimulus, US) to be evident during A. The phenomenon extends the range of ways in which Pavlovian conditioned responding can be expressed and increases its translational relevance. Given this relevance and the wealth of available behavioral analysis, it is a surprisingly underdeveloped territory for formal theoretical analysis. Here, we first provide a critical review of two (informal) classes of account for higher-order conditioning that reflect either: (a) processes that are analogous to Pavlovian conditioning, but involving associatively activated representations (e.g., A→US); or (b) the formation of an associative chain (e.g., A→X, and X→US). Our review first identifies fundamental theoretical and empirical challenges to both classes of account. We then develop a new computational model of higher-order conditioning that meets the challenges identified by showing: how reciprocal associations between A, X, and the US are formed and affect performance; and how the similarity of stimuli, their traces, and associatively retrieved representations modulate this process. The model generates a wealth of novel predictions, providing a platform for further empirical and theoretical analysis. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

The human ability to flexibly reason using analogies with domain-general content depends on mechanisms for identifying relations between concepts, and for mapping concepts and their relations across analogs. Building on a recent model of how semantic relations can be learned from nonrelational word embeddings, we present a new computational model of mapping between two analogs. The model adopts a Bayesian framework for probabilistic graph matching, operating on semantic relation networks constructed from distributed representations of individual concepts and of relations between concepts. Through comparisons of model predictions with human performance in a novel mapping task requiring integration of multiple relations, as well as in several classic studies, we demonstrate that the model accounts for a broad range of phenomena involving analogical mapping by both adults and children. We also show the potential for extending the model to deal with analog retrieval. Our approach demonstrates that human-like analogical mapping can emerge from comparison mechanisms applied to rich semantic representations of individual concepts and relations. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Temporal preparation is the cognitive function that takes place when anticipating future events. This is commonly considered to involve a process that maximizes preparation at time points that yield a high hazard. However, despite their prominence in the literature, hazard-based theories fail to explain the full range of empirical preparation phenomena. Here, we present the formalized multiple trace theory of temporal preparation (fMTP), an integrative model which develops the alternative perspective that temporal preparation results from associative learning. fMTP builds on established computational principles from the domains of interval timing, motor planning, and associative memory. In fMTP, temporal preparation results from associative learning between a representation of time on the one hand and inhibitory and activating motor units on the other hand. Simulations demonstrate that fMTP can explain phenomena across a range of time scales, from sequential effects operating on a time scale of seconds to long-term memory effects occurring over weeks. We contrast fMTP with models that rely on the hazard function and show that fMTP's learning mechanisms are essential to capture the full range of empirical effects. In a critical experiment using a Gaussian distribution of foreperiods, we show the data to be consistent with fMTP's predictions and to deviate from the hazard function. Additionally, we demonstrate how changing fMTP's parameters can account for participant-to-participant variations in preparation. In sum, with fMTP we put forward a unifying computational framework that explains a family of phenomena in temporal preparation that cannot be jointly explained by conventional theoretical frameworks. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Conflict tasks have become one of the most dominant paradigms within cognitive psychology, with their key finding being the conflict effect: That participants are slower and less accurate when task-irrelevant information conflicts with task-relevant information (i.e., incompatible trials), compared to when these sources of information are consistent (i.e., compatible trials). However, the conflict effect can consist of two separate effects: Facilitation effects, which is the amount of benefit provided by consistent task-irrelevant information, and interference effects, which is the amount of impairment caused by conflicting task-irrelevant information. While previous studies have attempted to disentangle these effects using neutral trials, which contrast compatible and incompatible trials to trials that are designed to have neutral task-irrelevant information, these analyses rely on the assumptions of Donder's subtractive method, which are difficult to verify and may be violated in some circumstances. Here, we develop a model-based approach for disentangling facilitation and interference effects, which extends the existing diffusion model for conflict tasks (DMC) framework to allow for different levels of automatic activation in compatible and incompatible trials. Comprehensive parameter recovery assessments display the robust measurement properties of our model-based approach, which we apply to nine previous data sets from the flanker (6) and Simon (3) tasks. Our findings suggest asymmetric facilitation and interference effects, where interference effects appear to be present for most participants across most studies, whereas facilitation effects appear to be small or nonexistent. We believe that our novel model-based approach provides an important step forward for understanding how information processing operates in conflict tasks, allowing researchers to assess the convergence or divergence between experimental-based (i.e., neutral trials) and model-based approaches when investigating facilitation and interference effects. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Numerous researchers have put forward heuristics as models of human decision-making. However, where such heuristics come from is still a topic of ongoing debate. In this work, we propose a novel computational model that advances our understanding of heuristic decision-making by explaining how different heuristics are discovered and how they are selected. This model-called bounded meta-learned inference (BMI)-is based on the idea that people make environment-specific inferences about which strategies to use while being efficient in terms of how they use computational resources. We show that our approach discovers two previously suggested types of heuristics-one reason decision-making and equal weighting-in specific environments. Furthermore, the model provides clear and precise predictions about when each heuristic should be applied: Knowing the correct ranking of attributes leads to one reason decision-making, knowing the directions of the attributes leads to equal weighting, and not knowing about either leads to strategies that use weighted combinations of multiple attributes. In three empirical paired comparison studies with continuous features, we verify predictions of our theory and show that it captures several characteristics of human decision-making not explained by alternative theories. (PsycInfo Database Record (c) 2022 APA, all rights reserved).