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

Amir Abolfazli,Myra Spiliopoulou,André Brechmann,Susann Wolff

doi:10.1038/s41598-020-61703-x

Amir Abolfazli, Myra Spiliopoulou + Show 2 more

Open Access

https://doi.org/10.1038/s41598-020-61703-x

Copy DOI

Abstract

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.

Highlights

Human learning is one of the main topics in psychology and cognitive neuroscience
We give a short overview of the task design in the auditory category learning experiment[16]
The auditory category learning experiment used tones differing in five dichotomous features, of which two determined the category to be learned: sound duration and direction of pitch change

Summary

Introduction

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. 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. When learners establish a category from a given set of objects without any explicit instruction, they need to test relevant features in a series of decisions, informed by feedback, and often extract explicit rules that define the target category Such experiments are known as rule-based category learning experiments in which the category structures can be learned through some explicit reasoning process[3].

Objectives

Methods

Results

Conclusion