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Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task

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Predictive Movements and Human Reinforcement Learning of Sequential Action.

Roy de Kleijn¹, George Kachergis², Bernhard Hommel¹

¹Cognitive Psychology Unit, Leiden University.

Cognitive Science

|March 3, 2018

Summary

This study introduces a mouse-based serial reaction time (SRT) task to reveal predictive motor learning. Findings show predictive movements and a centering strategy, with reinforcement learning models explaining high performance.

Keywords:

Implicit motor learning Movement trajectory Reinforcement learning Sequence learning Sequential action Serial reaction time task

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Area of Science:

Cognitive Psychology
Motor Learning
Neuroscience

Background:

Human daily activities involve sequential actions, but the learning mechanisms are not fully understood.
Traditional serial reaction time (SRT) tasks using keypresses do not capture the full motion time-course, including predictive movements.

Purpose of the Study:

To develop and validate a mouse movement trajectory SRT task to investigate predictive motor learning.
To explore how humans learn sequential actions under cued and reinforcement learning conditions.

Main Methods:

Developed a mouse movement trajectory SRT task, analyzing cursor movement data.
Conducted a second experiment using reinforcement learning (RL) without explicit cues, incorporating rewards and penalties.
Compared performance of high- and low-learners using computational models.

Main Results:

Replicated keypress SRT findings and observed increased predictive movement in the mouse task.
Identified a 'centering strategy' under uncertainty and successful sequence acquisition via reinforcement learning.
Model-free RL models fit high performers, while low performers showed a negative recency bias.

Conclusions:

Mouse trajectory analysis reveals predictive processes in motor learning, distinct from reactive responses.
Reinforcement learning is effective for acquiring complex action sequences, with individual differences in learning strategies.
Computational models can differentiate learning mechanisms in motor sequence acquisition.