Neural correlates of memory in a naturalistic spatiotemporal context

Dougherty, Megan; Chang, William S.K.; Rudoler, Joseph H; Katerman, B.S.; Halpern, David; Bruska, James P; Diamond, Nicholas; Kahana, Michael J.

doi:10.1101/2022.11.30.518606

Cited by 2 publications

(1 citation statement)

References 72 publications

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“…Participants played the role of a bicycle courier, actively navigating through a virtual town to deliver objects to target locations (stores) and later attempting to recall all of the delivered objects. Dougherty et al (in press) explore behavior in this task in detail and further discusses spatio-temporal memory dynamics.…”

Section: Introductionmentioning

confidence: 99%

Decoding EEG for optimizing naturalistic memory

Rudoler,

Bruska,

Chang

et al. 2023

Preprint

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Spectral features of human electroencephalographic (EEG) recordings predict variability in both memory encoding and retrieval. Here we develop and test a non-invasive closed-loop (NICL) system for optimizing human learning in real time by capitalizing on these fluctuating neural features. Participants play a virtual navigation and memory game; recording multisession data across days allowed us to build participant-specific classification models of recall success. In subsequent closed-loop sessions, our platform manipulated the timing of memory encoding, selectively presenting items during periods of predicted good or poor memory function based on EEG features decoded in real time. Whereas this manipulation of stimulus timing did not reliably modulate recall performance, we hypothesized that our ability to predict memory performance from EEG would dictate the success of the procedure. We confirm this by showing that the induced recall differences between presentation conditions correlated with a participant’s out-of-sample classification performance. These findings present a proof-of-concept for using non-invasive closed-loop technology to optimize human learning and memory and provide evidence that generalizing prediction to new recording sessions is a key hurdle to the success of these systems.

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Section: Introductionmentioning

confidence: 99%

Decoding EEG for optimizing naturalistic memory

Rudoler,

Bruska,

Chang

et al. 2023

Preprint

Self Cite

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Causal and Chronological Relationships Predict Memory Organization for Nonlinear Narratives

Antony,

Lozano,

Dhoat

et al. 2024

Journal of Cognitive Neuroscience

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While recounting an experience, one could employ multiple strategies to transition from one part to the next. For instance, if the event was learned out of linear order, one could recall events according to the time they were learned (temporal), similar events (semantic), events occurring nearby in time (chronological), or events produced by the current event (causal). To disentangle the importance of these factors, we had participants watch the nonlinear narrative, “Memento,” under different task instructions and presentation orders. For each scene of the film, we also separately computed semantic and causal networks. From these derivations, we contrasted the evidence for temporal, semantic, chronological, or causal strategies during recall. Critically, there was stronger evidence for the causal and chronological strategies than semantic or temporal strategies. Moreover, the causal and chronological strategies outperformed the temporal one even after asking participants to recall the film in the presented order, underscoring the fundamental nature of causal structure in scaffolding understanding and organizing recall. Nevertheless, time still marginally predicted recall transitions, suggesting it still operates as a weak signal in the presence of more salient forms of structure. In addition, semantic and causal network properties predicted scene memorability, including showing a stronger role for causes of an event than its outgoing effects. In summary, these effects highlight the importance of accounting for complex, causal networks in knowledge building and memory.

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Neural correlates of memory in a naturalistic spatiotemporal context

Cited by 2 publications

References 72 publications

Decoding EEG for optimizing naturalistic memory

Decoding EEG for optimizing naturalistic memory

Causal and Chronological Relationships Predict Memory Organization for Nonlinear Narratives

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