Hasil untuk "Psychology"

M. Slater

This commentary briefly reviews the history of virtual reality and its use for psychology research, and clarifies the concepts of immersion and the illusion of presence.

A. Tversky

H. Joffe, L. Yardley

A. Madill, A. Jordan, Caroline Shirley

G. Gillund, R. Shiffrin

T. Yarkoni

Abstract Most theories and hypotheses in psychology are verbal in nature, yet their evaluation overwhelmingly relies on inferential statistical procedures. The validity of the move from qualitative to quantitative analysis depends on the verbal and statistical expressions of a hypothesis being closely aligned – that is, that the two must refer to roughly the same set of hypothetical observations. Here, I argue that many applications of statistical inference in psychology fail to meet this basic condition. Focusing on the most widely used class of model in psychology – the linear mixed model – I explore the consequences of failing to statistically operationalize verbal hypotheses in a way that respects researchers' actual generalization intentions. I demonstrate that although the “random effect” formalism is used pervasively in psychology to model intersubject variability, few researchers accord the same treatment to other variables they clearly intend to generalize over (e.g., stimuli, tasks, or research sites). The under-specification of random effects imposes far stronger constraints on the generalizability of results than most researchers appreciate. Ignoring these constraints can dramatically inflate false-positive rates, and often leads researchers to draw sweeping verbal generalizations that lack a meaningful connection to the statistical quantities they are putatively based on. I argue that failure to take the alignment between verbal and statistical expressions seriously lies at the heart of many of psychology's ongoing problems (e.g., the replication crisis), and conclude with a discussion of several potential avenues for improvement.

J. Deese

Charles E. Osgood

W. D. Ellis

Jonathan Tang

Listening to music can be a rewarding experience for many. Research has shown that multiple factors influence musical reward including personality, age, and musical expertise. However, the role of culture in shaping musical reward remains underexplored. Most cross-cultural studies in music psychology have compared individuals from different countries. This study adopted a novel approach by examining self-construal, an individual-level explanation for cultural differences, in relation to musical rewards associated with favourite music across cultures. A cross-sectional online questionnaire was administered to 435 participants. Results from the multilevel regression analyses, using the two-dimensional model of self-construal, revealed that only within-region variation of interdependent and independent self-construals, not between-region variation of interdependence and independence, were positively associated with musical reward. Specifically, both self-construals were associated with emotion evocation and social reward, while independent self-construal was associated with musical seeking, mood regulation, and sensory-motor subtypes. When applying the eight-dimensional model of self-construal, distinct self-construal profiles emerged in relation to different musical reward subtypes, with the interdependent pole of connectedness to others positively associated with most subtypes except for emotion evocation reward. These findings provide preliminary evidence that self-construal influences the types of rewards experienced across cultures. In particular, one's sense of self, whether construed as interdependent or independent, shapes the types of rewards experienced with favourite music. This study underscores the importance of incorporating specific cultural factors in cross-cultural research on musical reward. By examining self-construal, this work contributes to a more nuanced understanding of cultural diversity in music psychology.

D. Krech, R. Crutchfield

Dmitry Bennett, Fernand Gobet

A key issue in cognitive science concerns the fundamental psychological processes that underlie the formation and retrieval of multiple types of concepts in short-term and long-term memory (STM and LTM, respectively). We propose that chunking mechanisms play an essential role and show how the CogAct computational model grounds concept learning in fundamental cognitive processes and structures (such as chunking, attention, STM and LTM). First are the in-principle demonstrations, with CogAct automatically adapting to learn a range of categories from simple logical functions, to artificial categories, to natural raw (as opposed to natural pre-processed) concepts in the dissimilar domains of literature, chess and music. This kind of adaptive learning is difficult for most other psychological models, e.g., with cognitive models stopping at modelling artificial categories and (non-GPT) models based on deep learning requiring task-specific changes to the architecture. Secondly, we offer novel ways of designing human benchmarks for concept learning experiments and simulations accounting for subjectivity, ways to control for individual human experiences, all while keeping to real-life complex categories. We ground CogAct in simulations of subjective conceptual spaces of individual human participants, capturing humans subjective judgements in music, with the models learning from raw music score data without bootstrapping to pre-built knowledge structures. The CogAct simulations are compared to those obtained by a deep-learning model. These findings integrate concept learning and adaptation to complexity into the broader theories of cognitive psychology. Our approach may also be used in psychological applications that move away from modelling the average participant and towards capturing subjective concept space.

Mika Hämäläinen

We study the primacy effect in three commercial LLMs: ChatGPT, Gemini and Claude. We do this by repurposing the famous experiment Asch (1946) conducted using human subjects. The experiment is simple, given two candidates with equal descriptions which one is preferred if one description has positive adjectives first before negative ones and another description has negative adjectives followed by positive ones. We test this in two experiments. In one experiment, LLMs are given both candidates simultaneously in the same prompt, and in another experiment, LLMs are given both candidates separately. We test all the models with 200 candidate pairs. We found that, in the first experiment, ChatGPT preferred the candidate with positive adjectives listed first, while Gemini preferred both equally often. Claude refused to make a choice. In the second experiment, ChatGPT and Claude were most likely to rank both candidates equally. In the case where they did not give an equal rating, both showed a clear preference to a candidate that had negative adjectives listed first. Gemini was most likely to prefer a candidate with negative adjectives listed first.

Cosimo Iaia, Bhavin Choksi, Emily Wiebers et al.

The nouns of our language refer to either concrete entities (like a table) or abstract concepts (like justice or love), and cognitive psychology has established that concreteness influences how words are processed. Accordingly, understanding how concreteness is represented in our mind and brain is a central question in psychology, neuroscience, and computational linguistics. While the advent of powerful language models has allowed for quantitative inquiries into the nature of semantic representations, it remains largely underexplored how they represent concreteness. Here, we used behavioral judgments to estimate semantic distances implicitly used by humans, for a set of carefully selected abstract and concrete nouns. Using Representational Similarity Analysis, we find that the implicit representational space of participants and the semantic representations of language models are significantly aligned. We also find that both representational spaces are implicitly aligned to an explicit representation of concreteness, which was obtained from our participants using an additional concreteness rating task. Importantly, using ablation experiments, we demonstrate that the human-to-model alignment is substantially driven by concreteness, but not by other important word characteristics established in psycholinguistics. These results indicate that humans and language models converge on the concreteness dimension, but not on other dimensions.

Aditya Shrivastava, Komal Gupta, Shraddha Arora

The international refugee crisis deepens, exposing millions of dis placed children to extreme psychological trauma. This research suggests a com pact, AI-based framework for processing unstructured refugee health data and distilling knowledge on child mental health. We compare two Retrieval-Aug mented Generation (RAG) pipelines, Zephyr-7B-beta and DeepSeek R1-7B, to determine how well they process challenging humanitarian datasets while avoid ing hallucination hazards. By combining cutting-edge AI methods with migration research and child psychology, this study presents a scalable strategy to assist policymakers, mental health practitioners, and humanitarian agencies to better assist displaced children and recognize their mental wellbeing. In total, both the models worked properly but significantly Deepseek R1 is superior to Zephyr with an accuracy of answer relevance 0.91

Paul X. McCarthy, Xian Gong, Marieth Coetzer et al.

This study explores the relationship between personality diversity and national economic performance, introducing the Global Personality Diversity Index ($Ψ$-GPDI) as a novel metric. Leveraging a dataset of 760,242 individuals across 135 countries, we quantify within-country diversity based on the Big Five personality traits. Our findings reveal that personality diversity accounts for 19.9% of the variance in GDP per capita and provides an additional 2.8% explanatory power beyond institutional quality and immigration, underscoring its unique contribution to economic vitality. Through multi-factor analysis, we demonstrate how personality diversity complements existing economic frameworks, offering actionable insights for policymakers seeking to enhance innovation, productivity, and resilience. This research positions psychological diversity as a critical yet under explored factor in driving economic growth, bridging the fields of psychology and economics.

Masanori Takano, Mao Nishiguchi, Fujio Toriumi

Online sexual predators target children by building trust, creating dependency, and arranging meetings for sexual purposes. This poses a significant challenge for online communication platforms that strive to monitor and remove such content and terminate predators' accounts. However, these platforms can only take such actions if sexual predators explicitly violate the terms of service, not during the initial stages of relationship-building. This study designed and evaluated a strategy to prevent sexual predation and victimization by delivering warnings and raising awareness among high-risk individuals based on the routine activity theory in criminal psychology. We identified high-risk users as those with a high probability of committing or being subjected to violations, using a machine learning model that analyzed social networks and monitoring data from the platform. We conducted a randomized controlled trial on a Japanese avatar-based communication application, Pigg Party. High-risk players in the intervention group received warnings and awareness-building messages, while those in the control group did not receive the messages, regardless of their risk level. The trial involved 12,842 high-risk players in the intervention group and 12,844 in the control group for 138 days. The intervention successfully reduced violations and being violated among women for 12 weeks, although the impact on men was limited. These findings contribute to efforts to combat online sexual abuse and advance understanding of criminal psychology.

P. Fraisse

Natàlia Fort, Anaïs Orobitg

Erin Henshaw, Marie Cooper, Teresa Wood et al.

Abstract Background Breastfeeding self-efficacy has been identified as an important influence on breastfeeding outcomes. Among new parent couples, partners are uniquely positioned to be sources of support for developing breastfeeding self-efficacy, yet few breastfeeding programs have attempted to involve partners directly. The purpose of this study was to test the impact of a novel program, Happy, Healthy, Loved, on breastfeeding self-efficacy and maternal mood through emphasizing partner support and actively addressing postpartum-specific stress management in a tailored text message delivery program. Methods A randomized trial was conducted in which primiparous mother-partner dyads intending to exclusively breastfeed were recruited at midwestern hospitals 2–3 days after delivery. The clinical trial was pre-registered at clinicaltrials.gov (#NCT04578925, registration date 7/24/2020). Couples were randomized to receive intervention or an attentional control. Couples randomized to the intervention group then completed a brief interactive educational tablet program together (Happy, Healthy, Loved), followed by 6 weeks of tailored text messages providing reminders, coping strategies, and motivational milestones to improve breastfeeding self-efficacy. Participants in the control group received usual care followed by 6 weeks of attentional control text messages about infant development. Surveys were delivered at baseline, 6 weeks, and 6 months postpartum to both mother and partner to assess breastfeeding self-efficacy, mood, and social support (n = 62 couples). Results Outcomes of ANCOVA with baseline self-efficacy as a covariate showed a significant effect of intervention on 6 months breastfeeding self-efficacy when compared to control group. No other significant differences were found at 6 weeks or 6 months postpartum in breastfeeding self-efficacy, depressive or anxious symptoms. Conclusions Results of the present investigation suggest that a text-based dyad intervention improved breastfeeding self-efficacy at 6 months, but not 6 weeks, postpartum, indicating that text-based mother-partner interventions are a promising direction to continue exploring in postpartum health research. Trial registration Clinicaltrials.gov #NCT04578925.