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Behavioral and EEG data and results have been archived as an Open Science Framework repository (https://osf.io/hrmxn/).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).
-
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.
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.
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.
Metrics
-
- 1,801
- views
-
- 314
- downloads
-
- 24
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Further reading
-
- Neuroscience
During delayed ballistic reaches, motor areas consistently display movement-specific activity patterns prior to movement onset. It is unclear why these patterns arise: while they have been proposed to seed an initial neural state from which the movement unfolds, recent experiments have uncovered the presence and necessity of ongoing inputs during movement, which may lessen the need for careful initialization. Here, we modeled the motor cortex as an input-driven dynamical system, and we asked what the optimal way to control this system to perform fast delayed reaches is. We find that delay-period inputs consistently arise in an optimally controlled model of M1. By studying a variety of network architectures, we could dissect and predict the situations in which it is beneficial for a network to prepare. Finally, we show that optimal input-driven control of neural dynamics gives rise to multiple phases of preparation during reach sequences, providing a novel explanation for experimentally observed features of monkey M1 activity in double reaching.
-
- Neuroscience
Humans make irrational decisions in the presence of irrelevant distractor options. There is little consensus on whether decision making is facilitated or impaired by the presence of a highly rewarding distractor, or whether the distractor effect operates at the level of options’ component attributes rather than at the level of their overall value. To reconcile different claims, we argue that it is important to consider the diversity of people’s styles of decision making and whether choice attributes are combined in an additive or multiplicative way. Employing a multi-laboratory dataset investigating the same experimental paradigm, we demonstrated that people used a mix of both approaches and the extent to which approach was used varied across individuals. Critically, we identified that this variability was correlated with the distractor effect during decision making. Individuals who tended to use a multiplicative approach to compute value, showed a positive distractor effect. In contrast, a negative distractor effect (divisive normalisation) was prominent in individuals tending towards an additive approach. Findings suggest that the distractor effect is related to how value is constructed, which in turn may be influenced by task and subject specificities. This concurs with recent behavioural and neuroscience findings that multiple distractor effects co-exist.