Functional Modeling of Learning and Memory Dynamics in Cognitive Disorders

TL;DR

This paper introduces a functional data analysis approach to decompose learning and memory performance curves in animal models, enabling separate assessment of learning speed and overall success in cognitive disorders.

Contribution

It presents a novel method to analyze success probability curves, separating amplitude and phase to distinguish between performance level and learning speed in cognitive disorder studies.

Findings

Able to separate learning speed from overall performance

Identified whether deficits are due to speed or success rate

Provides a joint analysis of speed and performance variations

Abstract

Deficits in working memory, which includes both the ability to learn and to retain information short-term, are a hallmark of many cognitive disorders. Our study analyzes data from a neuroscience experiment on animal subjects, where performance on a working memory task was recorded as repeated binary success or failure data. We estimate continuous probability of success curves from this binary data in the context of functional data analysis, which is largely used in biological processes that are intrinsically continuous. We then register these curves to decompose each function into its amplitude, representing overall performance, and its phase, representing the speed of learning or response. Because we are able to separate speed from performance, we can address the crucial question of whether a cognitive disorder impacts not only how well subjects can learn and remember, but also how fast. This allows us to analyze the components jointly to uncover how speed and performance co-vary, and to compare them separately to pinpoint whether group differences stem from a deficit in peak performance or a change in speed.