Social-affective features drive human representations of observed actions
Abstract
Humans observe actions performed by others in many different visual and social settings. What features do we extract and attend when we view such complex scenes, and how are they processed in the brain? To answer these questions, we curated two large-scale sets of naturalistic videos of everyday actions and estimated their perceived similarity in two behavioral experiments. We normed and quantified a large range of visual, action-related and social-affective features across the stimulus sets. Using a cross-validated variance partitioning analysis, we found that social-affective features predicted similarity judgments better than, and independently of, visual and action features in both behavioral experiments. Next, we conducted an electroencephalography (EEG) experiment, which revealed a sustained correlation between neural responses to videos and their behavioral similarity. Visual, action, and social-affective features predicted neural patterns at early, intermediate and late stages respectively during this behaviorally relevant time window. Together, these findings show that social-affective features are important for perceiving naturalistic actions, and are extracted at the final stage of a temporal gradient in the brain.
Data availability
Behavioral and EEG data and results have been archived as an Open Science Framework repository (https://osf.io/hrmxn/). Analysis code is available on GitHub (https://github.com/dianadima/mot_action).
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Social-affective features drive human representations of observed actionsOpen Science Framework, https://osf.io/hrmxn/.
Article and author information
Author details
Funding
National Science Foundation (CCF-1231216)
- Leyla Isik
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Chris I Baker, National Institute of Mental Health, National Institutes of Health, United States
Ethics
Human subjects: All procedures for data collection were approved by the Johns Hopkins University Institutional Review Board, with protocol numbers HIRB00009730 for the behavioral experiments and HIRB00009835 for the EEG experiment. Informed consent was obtained from all participants.
Version history
- Preprint posted: October 26, 2021 (view preprint)
- Received: October 26, 2021
- Accepted: May 24, 2022
- Accepted Manuscript published: May 24, 2022 (version 1)
- Version of Record published: June 1, 2022 (version 2)
Copyright
© 2022, Dima et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Further reading
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- Neuroscience
Probing memory of a complex visual image within a few hundred milliseconds after its disappearance reveals significantly greater fidelity of recall than if the probe is delayed by as little as a second. Classically interpreted, the former taps into a detailed but rapidly decaying visual sensory or ‘iconic’ memory (IM), while the latter relies on capacity-limited but comparatively stable visual working memory (VWM). While iconic decay and VWM capacity have been extensively studied independently, currently no single framework quantitatively accounts for the dynamics of memory fidelity over these time scales. Here, we extend a stationary neural population model of VWM with a temporal dimension, incorporating rapid sensory-driven accumulation of activity encoding each visual feature in memory, and a slower accumulation of internal error that causes memorized features to randomly drift over time. Instead of facilitating read-out from an independent sensory store, an early cue benefits recall by lifting the effective limit on VWM signal strength imposed when multiple items compete for representation, allowing memory for the cued item to be supplemented with information from the decaying sensory trace. Empirical measurements of human recall dynamics validate these predictions while excluding alternative model architectures. A key conclusion is that differences in capacity classically thought to distinguish IM and VWM are in fact contingent upon a single resource-limited WM store.
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- Neuroscience
Our ability to recall details from a remembered image depends on a single mechanism that is engaged from the very moment the image disappears from view.