Interindividual variations in associative visual learning: Exploration, description, and partition of response characteristics.

Brandner, Catherine; Raynal, Elsa; Ruggeri, Paolo

Brandner, Catherine; Raynal, Elsa; Ruggeri, Paolo.

Afiliación

Brandner C; Brain Electrophysiology Attention Movement Laboratory, Institute of Psychology, University of Lausanne, Geopolis Quartier Mouline, CH-1015, Lausanne, Switzerland. Catherine.Brandner@unil.ch.
Raynal E; Brain Electrophysiology Attention Movement Laboratory, Institute of Psychology, University of Lausanne, Geopolis Quartier Mouline, CH-1015, Lausanne, Switzerland.
Ruggeri P; Brain Electrophysiology Attention Movement Laboratory, Institute of Psychology, University of Lausanne, Geopolis Quartier Mouline, CH-1015, Lausanne, Switzerland.

Behav Res Methods ; 2023 Aug 24.

Article en En | MEDLINE | ID: mdl-37620746

RESUMEN

Relying on existing literature to identify suitable techniques for characterizing individual differences presents practical and methodological challenges. These challenges include the frequent absence of detailed descriptions of raw data, which hinders the assessment of analysis appropriateness, as well as the exclusion of data points deemed outliers, or the reliance on comparing only extreme groups by categorizing continuous variables into upper and lower quartiles. Despite the availability of algorithmic modeling in standard statistical software, investigations into individual differences predominantly focus on factor analysis and parametric tests. To address these limitations, this application-oriented study proposes a comprehensive approach that leverages behavioral responses through the use of signal detection theory and clustering techniques. Unlike conventional methods, signal detection theory considers both sensitivity and bias, offering insights into the intricate interplay between perceptual ability and decision-making processes. On the other hand, clustering techniques enable the identification and classification of distinct patterns within the dataset, allowing for the detection of singular behaviors that form the foundation of individual differences. In a broader framework, these combined approaches prove particularly advantageous when analyzing large and heterogeneous datasets provided by data archive platforms. By applying these techniques more widely, our understanding of the cognitive and behavioral processes underlying learning can be expedited and enhanced.

Palabras clave

Associative learning; Interindividual differences; K-means; Principal component analysis; Signal detection theory

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Behav Res Methods Asunto de la revista: CIENCIAS DO COMPORTAMENTO Año: 2023 Tipo del documento: Article País de afiliación: Suiza Pais de publicación: Estados Unidos

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