Statistical context dictates the relationship between feedback-related EEG signals and learning
- Brown University, United States
- Freie Universität Berlin, Germany
Abstract
Learning should be adjusted according to the surprise associated with observed outcomes but calibrated according to statistical context. For example, when occasional changepoints are expected, surprising outcomes should be weighted heavily to speed learning. In contrast, when uninformative outliers are expected to occur occasionally, surprising outcomes should be less influential. Here we dissociate surprising outcomes from the degree to which they demand learning using a predictive inference task and computational modeling. We show that the P300, a stimulus-locked electrophysiological response previously associated with adjustments in learning behavior, does so conditionally on the source of surprise. Larger P300 signals predicted greater learning in a changing context, but less learning in a context where surprise was indicative of a one-off outlier (oddball). Our results suggest that the P300 provides a surprise signal that is interpreted by downstream learning processes differentially according to statistical context in order to appropriately calibrate learning across complex environments.
Data availability
All analysis code has been made available on GitHub (https://github.com/learning-memory-and-decision-lab/NassarBrucknerFrank_eLife_2019.git). All behavioral and EEG data has been made available on Dryad (doi:10.5061/dryad.570pf8n).
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Data from: Statistical context dictates the relationship between feedback-related EEG signals and learningDryad Digital Repository, doi:10.5061/dryad.570pf8n.
Article and author information
Author details
Funding
National Institute of Mental Health (F32MH102009)
- Matthew R Nassar
National Institute on Aging (K99AG054732)
- Matthew R Nassar
National Institute of Mental Health (R01 MH080066-01)
- Michael J Frank
National Science Foundation (1460604)
- Michael J Frank
German Academic Exchange Service London (Promos travel grant)
- Rasmus Bruckner
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Human subjects: Informed consent was obtained from each participant in the study and all procedures were performed in accordance with the Declaration of Helsinki. All procedures were approved by the Brown University Institutional Review Board (Brown University Federal Wide Assurance #00004460).
Copyright
© 2019, Nassar 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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Statistical context dictates the relationship between feedback-related EEG signals and learning
eLife 8:e46975.
https://doi.org/10.7554/eLife.46975
Further reading
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- Computational and Systems Biology
- Neuroscience
Accumulating evidence to make decisions is a core cognitive function. Previous studies have tended to estimate accumulation using either neural or behavioral data alone. Here, we develop a unified framework for modeling stimulus-driven behavior and multi-neuron activity simultaneously. We applied our method to choices and neural recordings from three rat brain regions—the posterior parietal cortex (PPC), the frontal orienting fields (FOF), and the anterior-dorsal striatum (ADS)—while subjects performed a pulse-based accumulation task. Each region was best described by a distinct accumulation model, which all differed from the model that best described the animal’s choices. FOF activity was consistent with an accumulator where early evidence was favored while the ADS reflected near perfect accumulation. Neural responses within an accumulation framework unveiled a distinct association between each brain region and choice. Choices were better predicted from all regions using a comprehensive, accumulation-based framework and different brain regions were found to differentially reflect choice-related accumulation signals: FOF and ADS both reflected choice but ADS showed more instances of decision vacillation. Previous studies relating neural data to behaviorally inferred accumulation dynamics have implicitly assumed that individual brain regions reflect the whole-animal level accumulator. Our results suggest that different brain regions represent accumulated evidence in dramatically different ways and that accumulation at the whole-animal level may be constructed from a variety of neural-level accumulators.
