Perspectives on Psychological Science最新文献

Noise in behavior is often considered a nuisance: Although the mind aims for the best possible action, it is let down by unreliability in the sensory and response systems. Researchers often represent noise as additive, Gaussian, and independent. Yet a careful look at behavioral noise reveals a rich structure that defies easy explanation. First, in both perceptual and preferential judgments sensory and response noise may potentially play only minor roles, with most noise arising in the cognitive computations. Second, the functional form of the noise is both non-Gaussian and nonindependent, with the distribution of noise being better characterized as heavy-tailed and as having substantial long-range autocorrelations. It is possible that this structure results from brains that are, for some reason, bedeviled by a fundamental design flaw, albeit one with intriguingly distinctive characteristics. Alternatively, noise might not be a bug but a feature. Specifically, we propose that the brain approximates probabilistic inference with a local sampling algorithm, one using randomness to drive its exploration of alternative hypotheses. Reframing cognition in this way explains the rich structure of noise and leads to the surprising conclusion that noise is not a symptom of cognitive malfunction but plays a central role in underpinning human intelligence.

People often categorize the same object variably over time. Such intraindividual behavioral variability is difficult to identify because it can be confused with a bias and can originate in different categorization steps. The current work discusses possible sources of behavioral variability in categorization, focusing on perceptual and cognitive processes, and reports a simulation with a similarity-based categorization model to disentangle these sources. The simulation showed that noise during perceptual or cognitive processes led to considerable misestimations of a response determinism parameter. Category responses could not identify the source of the behavioral variability because different forms of noise led to similar response patterns. However, continuous model predictions could identify the noise: Noisy feature perception led to variable predictions for central stimuli on the category boundary, noisy feature attention increased the prediction variability for stimuli differing from each category on another feature, and noisy similarity computation increased the variability for stimuli with moderate predictions. Measuring category beliefs in a continuous way (e.g., through category probability judgments) may therefore help to disentangle perceptual and process-related sources of behavioral variability. Ultimately, this can inform interventions aimed at improving human categorizations (e.g., diagnosis training) by indicating which steps of the categorization mechanism to target.

Chaotic responses to COVID-19, political polarization, and pervasive misinformation raise the question of whether some or many individuals exercise irrational moral judgment. We provide the first mathematically correct test for transitivity of moral preferences. Transitivity is the most prominent rationality criterion of the behavioral, biological, and economic sciences. However, transitivity is conceptually, mathematically, and statistically difficult to evaluate empirically. We tested three parsimonious, order-constrained, probabilistic characterizations: First, the weak utility model treats an individual's choices as noisy reflections of a single, deterministic, underlying transitive preference; second, a variant severely limits the allowable response noise; and third, by the general random utility hypothesis, individuals' choices reveal uncertain, but transitive, moral preferences. Among 28 individuals, everyone's data were consistent with the weak utility model and general random utility model, thus supporting both operationalizations. Tightening the bounds on error rates in noisy responses yielded a poorly performing model, thus rejecting the model according to which choices are highly consistent with a single transitive preference. Bayesian model selection favored probabilistic transitive preferences and hence the equivalent random utility hypothesis. This suggests that there is some order underlying the apparent chaos: Rather than presume widespread disregard for moral principles, policymakers may build on navigating and reconciling extreme heterogeneity compounded with individual uncertainty.

In psychological research, noise is often considered a nuisance that obscures rather than contributes information. This simplification overlooks that noise can be informative and that by exploring the nature of the noise one can often draw additional conclusions concerning the underlying psychological processes. It is arguably only in recent years that the mainstream of researchers has taken this idea to heart and demonstrated that it can lead to breakthroughs in the understanding of human behavior. The aim of this special section is to showcase some of the ways in which systematic exploration of noise can be achieved and how it can enrich psychological research. In this introductory article, we introduce the idea of treating noise as endogenous as opposed to exogenous to the theoretical and statistical models of psychological phenomena. We then contribute a historical review of the role of noise in psychological research, including discussions of previous endogenous treatments of noise in the literature. As an illustration, we describe our own research on the precise/not precise model and show how noise distributions can be used to delineate analytic and intuitive modes of reasoning. Finally, we briefly introduce the other contributions to this special section.

In the present age of unprecedented access to information, it is important to understand how and why people avoid information. Multiple definitions of "information avoidance" exist, and key aspects of these definitions deserve attention, such as distinguishing information avoidance from (lack of) information seeking, considering the intentionality and temporal nature of information avoidance, and considering the personal relevance of the information. In this review, we provide a cross-disciplinary historical account of theories and empirical research on information avoidance and seeking, drawing from research in multiple fields. We provide a framework of antecedents of information avoidance, categorized into beliefs about the information (e.g., risk perceptions), beliefs about oneself (e.g., coping resources), and social and situational factors (e.g., social norms), noting that constructs across categories overlap and are intertwined. We suggest that research is needed on both positive and negative consequences of information avoidance and on interventions to reduce information avoidance (when appropriate). Research is also needed to better understand temporal dynamics of information avoidance and how it manifests in everyday life. Finally, comprehensive theoretical models are needed that differentiate avoidance from seeking. Research on information avoidance is quickly expanding, and the topic will only grow in importance.

Many new technologies, such as smartphones, computers, or public-access systems (like ticket-vending machines), are a challenge for older adults. One feature that these technologies have in common is that they involve underlying, partially observable, structures (state spaces) that determine the actions that are necessary to reach a certain goal (e.g., to move from one menu to another, to change a function, or to activate a new service). In this work we provide a theoretical, neurocomputational account to explain these behavioral difficulties in older adults. Based on recent findings from age-comparative computational- and cognitive-neuroscience studies, we propose that age-related impairments in complex goal-directed behavior result from an underlying deficit in the representation of state spaces of cognitive tasks. Furthermore, we suggest that these age-related deficits in adaptive decision-making are due to impoverished neural representations in the orbitofrontal cortex and hippocampus.

Research on aggression usually aims at gaining a better understanding of its more negative aspects, such as the role and effects of aversive social interactions, hostile cognitions, or negative affect. However, there are conditions under which an act of aggression can elicit a positive affective response, even among the most nonviolent of individuals. One might experience the "sweetness of revenge" on reacting aggressively to a betrayal or social rejection. A soldier may feel elated after "shooting to kill" in the name of the flag. There are many factors that contribute to the appeal of aggression, but despite growing interest in researching these phenomena, there is still no unitary framework that organizes existing theories and empirical findings and can be applied to a model to generate testable hypotheses. This article presents a narrative review of the literature on positive-affect-related forms of aggression and explores the role of aggression in eliciting positive affect across diverse social situations and relational contexts. An integrative model that unifies existing theories and findings is proposed, with the objective to inspire and inform future research.

Psychologists address social-justice problems in their research and applied work, and their scholarly efforts have been influenced by assumptions, constructs, and hypotheses from the political left. Recently, some psychologists have called for increased intellectual and political diversity in psychology, particularly as such diversity may lead to improved problem-solving. As an attempt to increase intellectual diversity in psychology, we review here the scholarship of Thomas Sowell. His work represents a rich source of hypotheses for psychologists' future research. We focus on his views on the importance of freedom; the extent to which reforms can reduce freedom; the importance of free markets to human flourishing; the role of free markets in producing costs for discrimination; the way spontaneously ordered systems can contain knowledge that can be overlooked in reforms; and the importance of culture and cultural capital. We will also discuss Sowell's more thoroughgoing economic analyses of problems and solutions and his analyses of contingencies operating on politicians and reformers, as well as his views on conflicts in fundamental visions about human nature and the pivotal role of improvements in minority education.

In this article, I argue for a number of important changes to the conceptual foundations of construct validity theory. I begin by suggesting that construct validity theorists should shift their attention from the validation of constructs to the process of evaluating scientific theories. This shift in focus is facilitated by distinguishing construct validation (understood as theory evaluation) from test validation, thereby freeing it from its long-standing focus on psychological measurement. In repositioning construct validity theory in this way, researchers should jettison the outmoded but superficially popular notion that theories are nomological networks in favor of a more plausible pragmatic view of their natures, such as the idea that theories are explanatorily coherent networks. Consistent with this shift in understanding the nature of theories, my recommendation is that construct validation should embrace an explanationist perspective on the theory evaluation process to complement its focus on hypothetico-deductive theory testing. On this view, abductive research methods have an important role to play. The revisionist perspective on construct validity proposed here is discussed in light of relevant developments in scientific methodology and is applied to an influential account of the validation process that has shaped research practice.

Understanding language requires readers and listeners to cull meaning from fast-unfolding messages that often contain conflicting cues pointing to incompatible ways of interpreting the input (e.g., "The cat was chased by the mouse"). This article reviews mounting evidence from multiple methods demonstrating that cognitive control plays an essential role in resolving conflict during language comprehension. How does cognitive control accomplish this task? Psycholinguistic proposals have conspicuously failed to address this question. We introduce an account in which cognitive control aids language processing when cues conflict by sending top-down biasing signals that strengthen the interpretation supported by the most reliable evidence available. We also provide a computationally plausible model that solves the critical problem of how cognitive control "knows" which way to direct its biasing signal by allowing linguistic knowledge itself to issue crucial guidance. Such a mental architecture can explain a range of experimental findings, including how moment-to-moment shifts in cognitive-control state-its level of activity within a person-directly impact how quickly and successfully language comprehension is achieved.