Replay in human visual cortex is linked to the formation of successor representations and independent of consciousness
bioRxiv
This is an updated preprint. Please find a summary of major changes below and in the bioRxiv revision summary. More information about the first version can be found here.
Abstract
Humans automatically infer higher-order relationships between events in the environment from their statistical co-occurrence, often without conscious awareness. Neural replay of task representations is a candidate mechanism by which the brain learns such relational information or samples from a learned model in the service of adaptive behavior. Here, we tested whether cortical reactivation is related to learning higher-order sequential relationships without consciousness. Human participants viewed sequences of images that followed probabilistic transitions determined by ring-like graph structures. Behavioral modeling revealed that participants acquired multi-step transition knowledge through gradual updating of an internal successor representation (SR) model, although half of participants did not indicate conscious knowledge about the sequential task structure. To investigate neural replay, we analyzed the temporal dynamics of multivariate functional magnetic resonance imaging (fMRI) patterns during brief 10 seconds pauses from the ongoing statistical learning task. We found evidence for backward sequential replay of multi-step sequences in visual cortical areas. These findings indicate that implicit learning of higher-order relationships establishes an internal SR-based map of the task, and is accompanied by cortical on-task replay.
✨ New (updated) preprint! ✨
We find that “Replay in human visual cortex is linked to the formation of successor representations and independent of consciousness”! 🧠
Fabulous team work with @lenikip, @koch_means_cook & @nico_schuck! 🌟
🧵 👇 [1/5]
https://www.biorxiv.org/content/10.1101/2022.02.02.478787v2
Humans infer multi-step relations between events. Replay could be a mechanism to learn relational information. Participants viewed probabilistic image sequences. We tested the link between predictive learning and temporal dynamics of fMRI patterns during on-task pauses. [2/5]
- We find backward replay of multi-step sequences derived from a successor representation model in visual cortical areas during brief (10 secs) on-task pauses (unlike previous reports of replay during sleep / longer rest), after accounting for stimulus-driven activity. [3/5]
- Using successor representation (SR) models, we show that participants learn multi-step transition knowledge in the incidental sequence learning task and provide evidence linking on-task replay to predictive SR learning, as theoretically stipulated. [4/5]
- We investigated the influence of conscious task knowledge on behavioral SR learning and SR replay and, interestingly, found both to be independent of consciousness.
Find out more: https://www.biorxiv.org/content/10.1101/2022.02.02.478787v2 Happy for any comments! Thanks for reading! 🙏 [5/5]
Changelog
The manuscript has been substantially revised. Major updates include:
- We have added participant-specific fitting of successor representation (SR) models, which confirmed our previous finding that participants learn multi-step transition knowledge in an incidental sequence learning task.
- We extended our modeling approach to capture the temporal dynamics of multivariate classifier time courses, allowing us to account for stimulus-driven activation patterns in on-task intervals and distinguish them from putative replay-driven (re)activation patterns.
- We found evidence for backward sequential replay of multi-step (SR) sequences in visual cortical areas after accounting for stimulus-driven activity.
- We investigated the influence of conscious task knowledge on SR learning and SR relay and found both to be independent of consciousness.
- Finally, we found a correlation between the depth of the SR predictive horizon (gamma parameter) and the strength of SR replay during on-task intervals.
Illustration
