Machine learning identifies the dynamics and influencing factors in an auditory category learning experiment

Human learning is one of the main topics in psychology and cognitive neuroscience. The analysis of experimental data, e.g. from category learning experiments, is a major challenge due to confounding factors related to perceptual processing, feedback value, response selection, as well as inter-indivi...

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Bibliographic Details
Published in:Scientific reports 2020-04, Vol.10 (1), p.6548-6548, Article 6548
Main Authors: Abolfazli, Amir, Brechmann, André, Wolff, Susann, Spiliopoulou, Myra
Format: Article
Language:English
Subjects:
631/114/1305
631/477/2811
Adolescent
Adult
Algorithms
Auditory discrimination learning
Auditory Perception
Cognitive ability
Computational neuroscience
Female
Hearing
Humanities and Social Sciences
Humans
Learning
Learning algorithms
Machine Learning
Male
multidisciplinary
Nervous system
Normal Distribution
Science
Science (multidisciplinary)
Time Factors
Trends
Young Adult
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Summary:Human learning is one of the main topics in psychology and cognitive neuroscience. The analysis of experimental data, e.g. from category learning experiments, is a major challenge due to confounding factors related to perceptual processing, feedback value, response selection, as well as inter-individual differences in learning progress due to differing strategies or skills. We use machine learning to investigate (Q1) how participants of an auditory category-learning experiment evolve towards learning, (Q2) how participant performance saturates and (Q3) how early we can differentiate whether a participant has learned the categories or not. We found that a Gaussian Mixture Model describes well the evolution of participant performance and serves as basis for identifying influencing factors of task configuration (Q1). We found early saturation trends (Q2) and that CatBoost, an advanced classification algorithm, can separate between participants who learned the categories and those who did not, well before the end of the learning session, without much degradation of separation quality (Q3). Our results show that machine learning can model participant dynamics, identify influencing factors of task design and performance trends. This will help to improve computational models of auditory category learning and define suitable time points for interventions into learning, e.g. by tutorial systems.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-020-61703-x