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Filename: controllers/Detail.php
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File: /var/www/html/index.php
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Function: _error_handler
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Filename: models/Detail_model.php
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Function: strpos
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Function: insertAPISummary
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Filename: helpers/my_audit_helper.php
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Function: formatAIDetailSummary
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Filename: controllers/Detail.php
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Practice in real-world settings exhibits many idiosyncracies of scheduling and duration that can only be roughly approximated by laboratory research. Here we investigate 39,157 individuals' performance on two cognitive games on the Lumosity platform over a span of 5 years. The large-scale nature of the data allows us to observe highly varied lengths of uncontrolled interruptions to practice and offers a unique view of learning in naturalistic settings. We enlist a suite of models that grow in the complexity of the mechanisms they postulate and conclude that long-term naturalistic learning is best described with a combination of long-term skill and task-set preparedness. We focus additionally on the nature and speed of relearning after breaks in practice and conclude that those components must operate interactively to produce the rapid relearning that is evident even at exceptionally long delays (over 2 years). Naturalistic learning over long time spans provides a strong test for the robustness of theoretical accounts of learning, and should be more broadly used in the learning sciences.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9532425 | PMC |
http://dx.doi.org/10.1038/s41539-022-00142-x | DOI Listing |
Psychon Bull Rev
December 2024
Laboratoire Cognition Langage & Développement (LCLD), Centre de Recherche Cognition et Neurosciences (CRCN), Université Libre de Bruxelles (ULB), Av. F. Roosevelt, 50 /CP 191, 1050, Brussels, Belgium.
Lexical competition between newly acquired and already established representations of written words is considered a marker of word integration into the mental lexicon. To date, studies about the emergence of lexical competition involved mostly artificial training procedures based on overexposure and explicit instructions for memorization. Yet, in real life, novel word encounters occur mostly without explicit learning intent, through reading texts with words appearing rarely.
View Article and Find Full Text PDFObjective: Machine learning has a great potential for prospectively forecasting individual patient response to mental health care (MHC), thereby enabling treatment personalization. However, previous efforts have been limited to populations living in predominantly higher income, developed countries. This study aimed to extend the reach of precision MHC systems by developing and testing a feasible and readily implementable algorithm for identifying patients at risk of nonresponse to routinely delivered psychotherapy in Chile, a developing country in Latin America.
View Article and Find Full Text PDFJ Early Interv
June 2024
Michigan State University, Department of Psychology, East Lansing, MI.
Limited research has examined the active ingredients and mechanisms of change of naturalistic developmental behavioral interventions (NDBIs). The present study used an exploratory sequential mixed-methods design to develop a comprehensive Theory of Change of Project ImPACT, an empirically supported NDBI. We used qualitative data from interviews with intervention experts (n=10), community providers (n=22), and caregivers (n=12) to develop a comprehensive causal model of the intervention process.
View Article and Find Full Text PDFPsychol Res
December 2024
Department of Experimental Psychology, University of Oxford, Oxford, UK.
What are emotions? Despite being a century-old question, emotion scientists have yet to agree on what emotions exactly are. Emotions are diversely conceptualised as innate responses (evolutionary view), mental constructs (constructivist view), cognitive evaluations (appraisal view), or self-organising states (dynamical systems view). This enduring fragmentation likely stems from the limitations of traditional research methods, which often adopt narrow methodological approaches.
View Article and Find Full Text PDFBehav Res Methods
December 2024
CAP Team, Centre de Recherche en Neurosciences de Lyon - INSERM U1028 - CNRS UMR 5292 - UCBL - UJM, 95 Boulevard Pinel, 69675, Bron, France.
Artificial intelligence techniques offer promising avenues for exploring human body features from videos, yet no freely accessible tool has reliably provided holistic and fine-grained behavioral analyses to date. To address this, we developed a machine learning tool based on a two-level approach: a first lower-level processing using computer vision for extracting fine-grained and comprehensive behavioral features such as skeleton or facial points, gaze, and action units; a second level of machine learning classification coupled with explainability providing modularity, to determine which behavioral features are triggered by specific environments. To validate our tool, we filmed 16 participants across six conditions, varying according to the presence of a person ("Pers"), a sound ("Snd"), or silence ("Rest"), and according to emotional levels using self-referential ("Self") and control ("Ctrl") stimuli.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!