Perspectives on Psychological Science最新文献

How should romantic-relationship quality be approached psychometrically? This is a complicated theoretical and methodological challenge that we begin to address through three studies. In Study 1a, we identified 25 distinct romantic-relationship categories among 754 items from 26 romantic-relationship-quality instruments with a weak Jaccard index (0.38), indicating that the scales' item content was extremely heterogeneous. Study 1b then demonstrated limited structure validity evidence in 43 scale-development-validation articles of 23 of these 26 instruments. Finally, Study 2 surveyed 587 French-speaking participants in a romantic relationship on romantic-relationship quality. Applying a network-based model, we identified four dimensions, three of which are central to relationship quality. The inferences were mostly limited to French-speaking, monogamous, heterosexual women. To resolve challenges detected in the literature, we recommend a multicountry qualitative approach, more diverse sampling, better definitions of romantic-relationship quality, and a dynamic-systems approach to measuring romantic-relationship quality.

Understanding how genetics influences human psychology is something that the evolutionary sciences emphasize. However, the functions of complex genetic influences on behavior have been overlooked in favor of perspectives that posit unitary influences of genes on behavior. One such example is the belief that human growth, development, and behavior are influenced uniformly by their genes even though previous research has highlighted the genetic conflict endemic in these domains. Although much psychological research has robustly documented areas in which we see the footprints of genetic conflict in human behavior, these areas are referred to by different names that prevent researchers from making connections under a unifying framework. In this article, I outline what genetic conflict is and how genetic conflict can provide a unifying framework for psychological investigations of social relationships. I also discuss avenues for future research on genetic conflict in humans and the importance of considering cultural, ecological, and other developmental factors when researching the genetic influences on human behavior.

The psychological study of systemic racism can benefit from the converging insights of "Black Marxism" and development economics, which illustrate how modern systemic racism is rooted in the political and economic institutions established during the historical period of European colonization. This article explores how these insights can be used to study systemic racism and challenge scientific racism in psychology by rethinking traditional research paradigms to incorporate the histories of race, class, and capitalism. Antiracism strategies that make use of these histories are also discussed, which include disrupting the psychological processes that sustain racist systems.

The ongoing debate between basic emotion theories (BETs) and the theory of constructed emotion (TCE) hampers progress in the field of emotion research. Providing a new perspective, here we aim to bring the theories closer together by dissecting them according to Tinbergen's four questions to clarify a focus on their evolutionary basis. On the basis of our review of the literature, we conclude that whereas BETs focus on the evolution question of Tinbergen, the TCE is more concerned with the causation of emotion. On the survival value of emotions both theories largely agree: to provide the best reaction in specific situations. Evidence is converging on the evolutionary history of emotions but is still limited for both theories-research within both frameworks focuses heavily on the causation. We conclude that BETs and the TCE explain two different phenomena: emotion and feeling. Therefore, they seem irreconcilable but possibly supplementary for explaining and investigating the evolution of emotion-especially considering their similar answer to the question of survival value. Last, this article further highlights the importance of carefully describing what aspect of emotion is being discussed or studied. Only then can evidence be interpreted to converge toward explaining emotion.

Some studies show that living conditions, such as economy, gender equality, and education, are associated with the magnitude of psychological sex differences. We systematically and quantitatively reviewed 54 articles and conducted new analyses on 27 meta-analyses and large-scale studies to investigate the association between living conditions and psychological sex differences. We found that sex differences in personality, verbal abilities, episodic memory, and negative emotions are more pronounced in countries with higher living conditions. In contrast, sex differences in sexual behavior, partner preferences, and math are smaller in countries with higher living conditions. We also observed that economic indicators of living conditions, such as gross domestic product, are most sensitive in predicting the magnitude of sex differences. Taken together, results indicate that more sex differences are larger, rather than smaller, in countries with higher living conditions. It should therefore be expected that the magnitude of most psychological sex differences will remain unchanged or become more pronounced with improvements in living conditions, such as economy, gender equality, and education.

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.