Psychological review最新文献

Developing an accurate model of another agent's knowledge is central to communication and cooperation between agents. In this article, we propose a hierarchical framework of knowledge assessment that explains how people construct mental models of their own knowledge and the knowledge of others. Our framework posits that people integrate information about their own and others' knowledge via Bayesian inference. To evaluate this claim, we conduct an experiment in which participants repeatedly assess their own performance (a metacognitive task) and the performance of another person (a type of theory of mind task) on the same image classification tasks. We contrast the hierarchical framework with simpler alternatives that assume different degrees of differentiation between mental models of self and others. Our model accurately captures participants' assessment of their own performance and the performance of others in the task: Initially, people rely on their own self-assessment process to reason about the other person's performance, leading to similar self- and other-performance predictions. As more information about the other person's ability becomes available, the mental model for the other person becomes increasingly distinct from the mental model of self. Simulation studies also confirm that our framework explains a wide range of findings about human knowledge assessment of themselves and others. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The additive utility theory of discounting is extended to probability and commodity discounting. Because the utility of a good and the disutility of its delay combine additively, increases in the utility of a good offset the disutility of its delay: Increasing the former slows the apparent discount even with the latter, time-disutility, remaining invariant, giving the magnitude effect. Conjoint measurement showed the subjective value of money to be a logarithmic function of its amount, and subjective probability-the probability weighting function-to be Prelec's (1998). This general theory of discounting (GTD) explains why large amounts are probability discounted more quickly, giving the negative magnitude effect. Whatever enhances the value of a delayed asset, such as its ability to satisfy diverse desires, offsets its delay and reduces discounting. Money's liquidity permits optimization of the portfolio of desired goods, providing added value that accounts for its shallow temporal discount gradient. GTD predicts diversification effects for delay but none for probability discounting. Operations such as episodic future thinking that increase the larder of potential expenditures-the portfolio of desirable goods-increase the value of the asset, flattening the discount gradient. States that decrease the larder, such as stress, depression, and overweening focus on a single substance like a drug, constrict the portfolio, decreasing its utility and thereby steepening the gradient. GTD provides a unified account of delay, probability, and cross-commodity discounting. It explains the effects of motivational states, dispositions, and cognitive manipulations on discount gradients. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Overprecision is the excessive certainty in the accuracy of one's judgment. This article proposes a new theory to explain it. The theory holds that overprecision in judgment results from neglect of all the ways in which one could be wrong. When there are many ways to be wrong, it can be difficult to consider them all. Overprecision is the result of being wrong and not knowing it. This explanation can account for why question formats have such a dramatic influence on the degree of overprecision people report. It also explains the ubiquity of overprecision not only among people but also among artificially intelligent agents. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Memory should make more available things that are more likely to be needed. Across multiple environmental domains, it has been shown that such a system would match qualitatively the memory effects involving repetition, delay, and spacing (Schooler & Anderson, 2017). To obtain data of sufficient size to study how detailed patterns of past appearance predict probability of being needed again, we examined the patterns with which words appear in large two data sets: tweets from popular sources and comments on popular subreddits. The two data sets show remarkably similar statistics, which are also consistent with earlier, smaller studies of environmental statistics. None of a candidate set of mathematical models of memory do well at predicting the observed patterns in these environments. A new model of human memory based on the environmental model proposed by Anderson and Milson (1989) did better at predicting the environmental data and a wide range of behavioral studies that measure memory availability by probability of recall and speed of retrieval. A critical variable in this model was range, the span of time over which an item occurs, which was discovered in mining the environmental data. These results suggest that theories of memory can be guided by mining of the statistical structure of the environment. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Mindsets of ability (i.e., "fixed" and "growth" mindsets) play a pivotal role in students' academic trajectories. However, relatively little is known about the mechanisms underlying mindset development. Identifying these mechanisms is vital for understanding, and potentially influencing, how mindsets emerge and change over time. In this article, we formulate a comprehensive theoretical model that purports to account for the emergence and development of ability mindsets: the process model of mindsets (PMM). The PMM is rooted in complex dynamic systems and enactive perspectives, which allow for conceptualizing psychological phenomena as dynamic and socially situated. The PMM accounts for how mindset-related behaviors, action tendencies, beliefs, and social interactions can become codependent and robust over time. We discuss how the model helps to further our understanding of the efficacy of mindset interventions and the heterogeneity thereof. The PMM has a broad explanatory scope, is generative, and paves the way for future process studies of mindsets and mindset interventions. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Most words have multiple meanings, but there are foundationally distinct accounts for this. Categorical theories posit that humans maintain discrete entries for distinct word meanings, as in a dictionary. Continuous ones eschew discrete sense representations, arguing that word meanings are best characterized as trajectories through a continuous state space. Both kinds of approach face empirical challenges. In response, we introduce two novel "hybrid" theories, which reconcile discrete sense representations with a continuous view of word meaning. We then report on two behavioral experiments, pairing them with an analytical approach relying on neural language models to test these competing accounts. The experimental results are best explained by one of the novel hybrid accounts, which posits both distinct sense representations and a continuous meaning space. This hybrid account accommodates both the dynamic, context-dependent nature of word meaning, as well as the behavioral evidence for category-like structure in human lexical knowledge. We further develop and quantify the predictive power of several computational implementations of this hybrid account. These results raise questions for future research on lexical ambiguity, such as why and when discrete sense representations might emerge in the first place. They also connect to more general questions about the role of discrete versus gradient representations in cognitive processes and suggest that at least in this case, the best explanation is one that integrates both factors: Word meaning is both categorical and continuous. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Self-control describes the processes by which individuals control their habits, desires, and impulses in the service of long-term goals. Research has identified important components of self-control and proposed theoretical frameworks integrating these components (e.g., Inzlicht et al., 2021; Kotabe & Hofmann, 2015). In our perspective, these frameworks, however, do not yet fully incorporate important metacognitive aspects of self-control. We therefore introduce a framework explicating the role of metacognition for self-control. This framework extends existing frameworks, primarily from the domains of self-regulated learning and problem-solving (e.g., Schraw & Moshman, 1995; Zimmerman, 2000), and integrates past and contemporary research and theorizing on self-control that involves aspects of metacognition. It considers two groups of metacognitive components, namely, (a) individual metacognitive characteristics, that is a person's declarative, procedural, and conditional metacognitive knowledge about self-control, as well as their self-awareness (or metacognitive awareness), and (b) metacognitive regulatory processes that unfold before a self-control conflict (forethought and prevention), when a self-control conflict is identified, during a self-control conflict (regulation and monitoring), and after a self-control conflict (reflection and evaluation). The proposed framework integrates existing research and will be useful for highlighting new directions for research on the role of metacognition for self-control success and failure. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Extensive research in the behavioral sciences has addressed people's ability to learn stationary probabilities, which stay constant over time, but only recently have there been attempts to model the cognitive processes whereby people learn-and track-nonstationary probabilities. In this context, the old debate on whether learning occurs by the gradual formation of associations or by occasional shifts between hypotheses representing beliefs about distal states of the world has resurfaced. Gallistel et al. (2014) pitched the two theories against each other in a nonstationary probability learning task. They concluded that various qualitative patterns in their data were incompatible with trial-by-trial associative learning and could only be explained by a hypothesis-testing model. Here, we contest that claim and demonstrate that it was premature. First, we argue that their experimental paradigm consisted of two distinct tasks: probability tracking (an estimation task) and change detection (a decision-making task). Next, we present a model that uses the (associative) delta learning rule for the probability tracking task and bounded evidence accumulation for the change detection task. We find that this combination of two highly established theories accounts well for all qualitative phenomena and outperforms the alternative model proposed by Gallistel et al. (2014) in a quantitative model comparison. In the spirit of cumulative science, we conclude that current experimental data on human learning of nonstationary probabilities can be explained as a combination of associative learning and bounded evidence accumulation and does not require a new model. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Induction-the ability to generalize from existing knowledge-is the cornerstone of intelligence. Cognitive models of human induction are largely limited to toy problems and cannot make quantitative predictions for the thousands of different induction arguments that have been studied by researchers, or to the countless induction arguments that could be encountered in everyday life. Leading large language models (LLMs) go beyond toy problems but fail to mimic observed patterns of human induction. In this article, we combine rich knowledge representations obtained from LLMs with theories of human inductive reasoning developed by cognitive psychologists. We show that this integrative approach can capture several benchmark empirical findings on human induction and generate human-like responses to natural language arguments with thousands of common categories and properties. These findings shed light on the cognitive mechanisms at play in human induction and show how existing theories in psychology and cognitive science can be integrated with new methods in artificial intelligence, to successfully model high-level human cognition. (PsycInfo Database Record (c) 2023 APA, all rights reserved).