Psychological review最新文献

Several measurement models have been proposed for data from the continuous-reproduction paradigm for studying visual working memory (WM): The original mixture model (Zhang & Luck, 2008) and its extension (Bays et al., 2009); the interference measurement model (IMM; Oberauer et al., 2017), and the target confusability competition (TCC) model (Schurgin et al., 2020). This article describes a space of possible measurement models in which all these models can be placed. The space is defined by three dimensions: (a) The choice of an activation function (von-Mises or Laplace), (b) the choice of a response-selection function (variants of Luce's choice rule or of signal-detection theory), (c) and whether or not memory precision is assumed to be a constant over manipulations affecting memory. A factorial combination of these three variables generates all possible models in the model space. Fitting all models to eight data sets revealed a new model as empirically most adequate, which combines a von-Mises activation function with a signal-detection response-selection rule. The precision parameter can be treated as a constant across many experimental manipulations, though it probably varies between individuals. All modeling code and the raw data modeled are available on the OSF: https://osf.io/zwprv/ (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Understanding the cognitive processes underlying choice requires theories that can disentangle the representation of stimuli from the processes that map these representations onto observed responses. We develop a dynamic theory of how stimuli are mapped onto discrete (choice) and onto continuous response scales. It proposes that the mapping from a stimulus to an internal representation and then to an evidence accumulation process is accomplished using multiple reference points or "anchors." Evidence is accumulated until a threshold amount for a particular response is obtained, with the relative balance of support for each anchor at that time determining the response. We tested this multiple anchored accumulation theory (MAAT) using the results of two experiments requiring discrete or continuous responses to line length and color stimuli. We manipulated the number of options for discrete responses, the number of different stimuli, and the similarity among them, and compared the outcomes to continuous response conditions. We show that MAAT accounts for several key phenomena: more accurate, faster, and more skewed distributions of responses near the ends of a response scale; lower accuracy and slower responses as the number of discrete choice options increases; and longer response times and lower accuracy when alternative responses are more similar to the target response. Our empirical and modeling results suggest that discrete and continuous response tasks can share a common evidence representation, and that the decision process is sensitive to the perceived similarity among the response options. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Logan (2021) presented an impressive unification of serial order tasks including whole report, typing, and serial recall in the form of the context retrieval and updating (CRU) model. Despite the wide breadth of the model's coverage, its reliance on encoding and retrieving context representations that consist of the previous items may prevent it from being able to address a number of critical benchmark findings in the serial order literature that have shaped and constrained existing theories. In this commentary, we highlight three major challenges that motivated the development of a rival class of models of serial order, namely positional models. These challenges include the mixed-list phonological similarity effect, the protrusion effect, and interposition errors in temporal grouping. Simulations indicated that CRU can address the mixed-list phonological similarity effect if phonological confusions can occur during its output stage, suggesting that the serial position curves from this paradigm do not rule out models that rely on interitem associations, as has been previously been suggested. The other two challenges are more consequential for the model's representations, and simulations indicated the model was not able to provide a complete account of them. We highlight and discuss how revisions to CRU's representations or retrieval mechanisms can address these phenomena and emphasize that a fruitful direction forward would be to either incorporate positional representations or approximate them with its existing representations. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Police investigators worldwide use lineups to test an eyewitness's memory of a perpetrator. A typical lineup consists of one suspect (who is innocent or guilty) plus five or more fillers who resemble the suspect and who are known to be innocent. Although eyewitness identification decisions were once biased by police pressure and poorly constructed lineups, decades of social science research led to the development of reformed lineup procedures that provide a more objective test memory. Under these improved testing conditions, cognitive models of memory can be used to better understand and ideally enhance eyewitness identification performance. In this regard, one question that has bedeviled the field for decades is how similar the lineup fillers should be to the suspect to optimize performance. Here, we model the effects of manipulating filler similarity to better understand why such manipulations have the intriguing effects they do. Our findings suggest that witnesses rely on a decision variable consisting of the degree to which the memory signal for a particular face in the lineup stands out relative to the crowd of memory signals generated by the set of faces in the lineup. The use of that decision variable helps to explain why discriminability is maximized by choosing fillers that match the suspect on basic facial features typically described by the eyewitness (e.g., age, race, gender) but who otherwise are maximally dissimilar to the suspect. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Human evolution has been marked by a striking increase in total brain volume relative to body size. While a prominent and characteristic feature of this volumetric shift has been the disproportionate expansion of association cortex across our evolutionary lineage, descent with modification is apparent throughout all neural systems in both human and nonhuman primates. However, despite evidence for the ubiquitous and complex influence of evolutionary forces on brain biology, within the psychological sciences the vast majority of the literature on human brain evolution is entirely corticocentric. This selective focus has contributed to a flawed theoretical framework in which the evolution of association cortex is viewed as an isolated process, removed from the rest of the brain. Here, we review our current understanding of how evolutionary pressures have acted across anatomically and functionally coupled networks, highlighting the diverse set of rules and principles that govern human brain development. In doing so we challenge the systemic mischaracterization of human cognition and behavior as a competition that pits phylogenetically recent cortical territories against evolutionarily ancient subcortical and cerebellar systems. Rather, we propose a comprehensive view of human brain evolution with critical importance for the use of animal models, theory development, and network-focused approaches in the study of behavior across health and disease. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

In this article, we argue that a predictive processing framework (PP) may provide elements for a proximate model of play in children and adults. We propose that play is a behavior in which the agent, in contexts of freedom from the demands of certain competing cognitive systems, deliberately seeks out or creates surprising situations that gravitate toward sweet-spots of relative complexity with the goal of resolving surprise. We further propose that play is experientially associated with a feel-good quality because the agent is reducing significant levels of prediction error (i.e., surprise) faster than expected. We argue that this framework can unify a range of well-established findings in play and developmental research that highlights the role of play in learning, and that casts children as Bayesian learners. The theory integrates the role of positive valence in play (i.e., explaining why play is fun); and what it is to be in a playful mood. Central to the account is the idea that playful agents may create and establish an environment tailored to the generation and further resolution of surprise and uncertainty. Play emerges here as a variety of niche construction where the organism modulates its physical and social environment in order to maximize the productive potential of surprise. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

In this article, existing research investigating how school performance relates to cognitive, self-awareness, language, and personality processes is reviewed. We outline the architecture of the mind, involving a general factor, g, that underlies distinct mental processes (i.e., executive, reasoning, language, cognizance, and personality processes). From preschool to adolescence, g shifts from executive to reasoning and cognizance processes; personality also changes, consolidating in adolescence. There are three major trends in the existing literature: (a) All processes are highly predictive of school achievement if measured alone, each accounting for ∼20% of its variance; (b) when measured together, cognitive processes (executive functions and representational awareness in preschool and fluid intelligence after late primary school) dominate as predictors (over ∼50%), drastically absorbing self-concepts and personality dispositions that drop to ∼3%-5%; and (c) predictive power changes according to the processes forming g at successive levels: attention control and representational awareness in preschool (∼85%); fluid intelligence, language, and working memory in primary school (∼53%); fluid intelligence, language, self-evaluation, and school-specific self-concepts in secondary school (∼70%). Stability and plasticity of personality emerge as predictors in secondary school. A theory of educational priorities is proposed, arguing that (a) executive and awareness processes; (b) information management; and (c) reasoning, self-evaluation, and flexibility in knowledge building must dominate in preschool, primary, and secondary school, respectively. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Referring to probabilistic concepts (such as randomness, sampling, and probability distributions among others) is commonplace in contemporary explanations of how people learn and make decisions in the face of environmental unknowns. Here, we critically evaluate this practice and argue that such concepts should only play a relatively minor part in psychological explanations. To make this point, we provide a theoretical analysis of what people need to do in order to deal with unknown aspects of a typical decision-making task (a repeated-choice gamble). This analysis reveals that the use of probabilistic concepts in psychological explanations may and often does conceal essential, nonprobabilistic steps that people need to take to attempt to solve the problems that environmental unknowns present. To give these steps a central role, we recast how people solve these problems as a type of hypothesis generation and evaluation, of which using probabilistic concepts to deal with unknowns is one of many possibilities. We also demonstrate some immediate practical consequences of our proposed approach in two experiments. This perspective implies a shift in focus toward nonprobabilistic aspects of psychological explanations. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

The notion of equality (identity) is simple and ubiquitous, making it a key case study for broader questions about the representations supporting abstract relational reasoning. Previous work suggested that neural networks were not suitable models of human relational reasoning because they could not represent mathematically identity, the most basic form of equality. We revisit this question. In our experiments, we assess out-of-sample generalization of equality using both arbitrary representations and representations that have been pretrained on separate tasks to imbue them with structure. We find neural networks are able to learn (a) basic equality (mathematical identity), (b) sequential equality problems (learning ABA-patterned sequences) with only positive training instances, and (c) a complex, hierarchical equality problem with only basic equality training instances ("zero-shot" generalization). In the two latter cases, our models perform tasks proposed in previous work to demarcate human-unique symbolic abilities. These results suggest that essential aspects of symbolic reasoning can emerge from data-driven, nonsymbolic learning processes. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Machines have achieved a broad and growing set of linguistic competencies, thanks to recent progress in Natural Language Processing (NLP). Psychologists have shown increasing interest in such models, comparing their output to psychological judgments such as similarity, association, priming, and comprehension, raising the question of whether the models could serve as psychological theories. In this article, we compare how humans and machines represent the meaning of words. We argue that contemporary NLP systems are fairly successful models of human word similarity, but they fall short in many other respects. Current models are too strongly linked to the text-based patterns in large corpora, and too weakly linked to the desires, goals, and beliefs that people express through words. Word meanings must also be grounded in perception and action and be capable of flexible combinations in ways that current systems are not. We discuss promising approaches to grounding NLP systems and argue that they will be more successful, with a more human-like, conceptual basis for word meaning. (PsycInfo Database Record (c) 2023 APA, all rights reserved).