Toward a Psychology of Individuals: The Ergodicity Information Index and a Bottom-up Approach for Finding Generalizations

In the last half of the twentieth century, psychology and neuroscience have experienced a renewed interest in intraindividual variation. To date, there are few quantitative methods to evaluate whether a population (between-person) structure is likely to hold for individual people, often referred to...

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Bibliographic Details
Published in:Multivariate behavioral research Vol. 60; no. 3; pp. 528 - 555
Main Authors: Golino, Hudson, Nesselroade, John, Christensen, Alexander P.
Format: Journal Article
Language:English
Published: United States Routledge 04.05.2025
Subjects:
Adult
algorithm complexity
Cluster Analysis
Computer Simulation
ergodicity
Humans
Information Theory
Monte Carlo Method
multilayer networks
Network psychometrics
Personality
multilayer networks
information theory
algorithm complexity
Network psychometrics
ergodicity
ISSN:0027-3171, 1532-7906, 1532-7906
Online Access:Get full text
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Summary:In the last half of the twentieth century, psychology and neuroscience have experienced a renewed interest in intraindividual variation. To date, there are few quantitative methods to evaluate whether a population (between-person) structure is likely to hold for individual people, often referred to as ergodicity. We introduce a new network information theoretic metric, the ergodicity information index (EII), that quantifies the amount of information lost by representing all individuals with a between-person structure. A Monte Carlo simulation demonstrated that EII can effectively delineate between ergodic and nonergodic systems. A bootstrap test is derived to statistically determine whether the empirical data is likely generated from an ergodic process. When a process is identified as nonergodic, then it's possible that a mixture of groups exist. To evaluate whether groups exist, we develop an information theoretic clustering method to detect groups. Finally, two empirical examples are presented using intensive longitudinal data from personality and neuroscience domains. Both datasets were found to be nonergodic, and meaningful groupings were identified in each dataset. Subsequent analysis showed that some of these groups are ergodic, meaning that the individuals can be represented with a single population structure without significant loss of information. Notably, in the neuroscience data, we could correctly identify two clusters of individuals (young vs. older adults) measured by a pattern separation task that were related to hippocampal connectivity to the default mode network.
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ISSN:0027-3171
1532-7906
1532-7906
DOI:10.1080/00273171.2025.2454901