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Related Experiment Video

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Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

Dynamic computational phenotyping of human cognition.

Roey Schurr¹, Daniel Reznik², Hanna Hillman³

¹Department of Psychology, Center for Brain Sciences, Harvard University, Cambridge, MA, USA. roey.schurr@mail.huji.ac.il.

Nature Human Behaviour

|February 9, 2024

Summary

This summary is machine-generated.

Understanding individual differences in cognition requires examining computational phenotypes. This study reveals that cognitive variability is influenced by practice and internal states, not just unreliability.

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Area of Science:

Cognitive Science
Computational Neuroscience
Psychometrics

Background:

Computational phenotyping offers interpretable parameters of individual cognitive variability.
Psychometric properties of computational phenotypes, crucial for interpretation, are understudied.
Temporal variability in computational phenotypes needs investigation to understand its sources.

Purpose of the Study:

To identify sources governing the temporal variability of computational phenotypes.
To examine the influence of practice and internal states on cognitive parameters.
To develop a dynamic framework for computational phenotyping.

Main Methods:

A 12-week longitudinal study with a battery of seven cognitive tasks (learning, memory, perception, decision-making).
Weekly participant reports on mood, habits, and daily activities to track internal states.
Development of a dynamic computational phenotyping framework to analyze behavioral data.

Main Results:

Many computational phenotype dimensions showed covariation with practice.
Affective factors (valence, arousal) also influenced phenotype dimensions.
Temporal variability in cognitive parameters is structured by practice and internal states.

Conclusions:

Cognitive variability within individuals is not solely unreliability but reflects underlying dynamic structure.
Computational phenotypes are influenced by both learning (practice) and internal states.
A dynamic understanding is essential for robust computational phenotyping.