From Consensus to Individual Differences: Typicality Links Brain and Behavior Across Naturalistic Contexts
- 1Weizmman institute of science
Abstract
Naturalistic behaviors largely lack objective measures of performance, making it difficult to quantify individual differences and establish links between brain and behavior. Here, we propose typicality, the degree to which an individual's response aligns with the group average, as a framework for identifying and relating stable individual differences across behavioral and neural domains. We propose that, when observers share similar objectives and constraints, convergence toward a consensus response may reflect convergence toward an effective or optimal solution, allowing typicality to approximate optimal processing even when objective ground truth is lacking. To evaluate this framework, we combined naturalistic movie viewing during fMRI with a behavioral battery across multiple tasks spanning social and non-social cognition. Behavioral and neural typicality proved highly stable within individuals while remaining sensitive to the specific computations engaged by different stimuli. Crucially, behavioral typicality was related to neural typicality across multiple domains, with different behavioral measures mapping onto neural systems relevant to the corresponding computations. Neural typicality also predicted objectively measured performance in motion prediction and face recognition tasks, extending the framework beyond consensus-based measures alone. Together, these findings establish typicality as a stable, computation-sensitive measure that links individual differences in behavior to the neural systems supporting them. More broadly, they suggest that the group consensus provides more than a reference for quantifying individual differences: under appropriate conditions, proximity to this shared response may provide an empirical approximation of optimal processing. Typically, therefore, offers a framework for linking brain and behavior in complex naturalistic contexts.
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BibTeX
@misc{zambergelad2026from,
title = {{From Consensus to Individual Differences: Typicality Links Brain and Behavior Across Naturalistic Contexts}},
author = {M. Zamberg-Elad and I. Har-Shalom and A. Jarbi and M. Wilf and M. Ramot},
howpublished = {bioRxiv},
year = {2026},
month = aug,
doi = {10.64898/2026.08.14.744685},
url = {https://www.biorxiv.org/content/10.64898/2026.08.14.744685v3},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - From Consensus to Individual Differences: Typicality Links Brain and Behavior Across Naturalistic Contexts
AU - M. Zamberg-Elad
AU - I. Har-Shalom
AU - A. Jarbi
AU - M. Wilf
AU - M. Ramot
PY - 2026
DA - 2026/08/14
PB - bioRxiv
DO - 10.64898/2026.08.14.744685
UR - https://www.biorxiv.org/content/10.64898/2026.08.14.744685v3
AB - Naturalistic behaviors largely lack objective measures of performance, making it difficult to quantify individual differences and establish links between brain and behavior. Here, we propose typicality, the degree to which an individual's response aligns with the group average, as a framework for identifying and relating stable individual differences across behavioral and neural domains. We propose that, when observers share similar objectives and constraints, convergence toward a consensus response may reflect convergence toward an effective or optimal solution, allowing typicality to approximate optimal processing even when objective ground truth is lacking. To evaluate this framework, we combined naturalistic movie viewing during fMRI with a behavioral battery across multiple tasks spanning social and non-social cognition. Behavioral and neural typicality proved highly stable within individuals while remaining sensitive to the specific computations engaged by different stimuli. Crucially, behavioral typicality was related to neural typicality across multiple domains, with different behavioral measures mapping onto neural systems relevant to the corresponding computations. Neural typicality also predicted objectively measured performance in motion prediction and face recognition tasks, extending the framework beyond consensus-based measures alone. Together, these findings establish typicality as a stable, computation-sensitive measure that links individual differences in behavior to the neural systems supporting them. More broadly, they suggest that the group consensus provides more than a reference for quantifying individual differences: under appropriate conditions, proximity to this shared response may provide an empirical approximation of optimal processing. Typically, therefore, offers a framework for linking brain and behavior in complex naturalistic contexts.
ER -