Dissociable forms of uncertainty-driven representational change across the human brain

Abstract

Environmental change can lead decision makers to shift rapidly among different behavioral regimes. These behavioral shifts can be accompanied by rapid changes in the firing pattern of neural networks. However, it is unknown what the populations of neurons that participate in such "network reset" phenomena are representing. Here, we investigated the following: (1) whether and where rapid changes in multivariate activity patterns are observable with fMRI during periods of rapid behavioral change and (2) what types of representations give rise to these phenomena. We did so by examining fluctuations in multivoxel patterns of BOLD activity from male and female human subjects making sequential inferences about the state of a partially observable and discontinuously changing variable. We found that, within the context of this sequential inference task, the multivariate patterns of activity in a number of cortical regions contain representations that change more rapidly during periods of uncertainty following a change in behavioral context. In motor cortex, this phenomenon was indicative of discontinuous change in behavioral outputs, whereas in visual regions, the same basic phenomenon was evoked by tracking of salient environmental changes. In most other cortical regions, including dorsolateral prefrontal and anterior cingulate cortex, the phenomenon was most consistent with directly encoding the degree of uncertainty. However, in a few other regions, including orbitofrontal cortex, the phenomenon was best explained by representations of a shifting context that evolve more rapidly during periods of rapid learning. These representations may provide a dynamic substrate for learning that facilitates rapid disengagement from learned responses during periods of change. Significance StatementBrain activity patterns tend to change more rapidly during periods of uncertainty and behavioral adjustment, yet the computational role of such rapid transitions is poorly understood. Here, we identify brain regions with fMRI BOLD activity patterns that change more rapidly during periods of behavioral adjustment and use computational modeling to attribute the phenomenon to specific causes. We demonstrate that the phenomenon emerges in different brain regions for different computational reasons, the most common being the representation of uncertainty itself, but that, in a selective subset of regions including orbitofrontal cortex, the phenomenon was best explained as a shifting latent state signal that may serve to control the degree to which recent temporal context affects ongoing expectations.

The stability flexibility tradeoff and the dark side of detail

“…Two recent studies that clearly dissociate salience from learning using a generative structure like our oddball condition have suggested that the latter may be the case (d' Acremont & Bossaerts, 2016;Nassar, Bruckner, & Frank, 2019a). One recent idea that attempts to rectify differences between the relationship between brain activity and learning rate observed across different statistical environments is that learning rate adjustments are implemented through changes in the active latent state (Nassar, Bruckner, & Frank, 2019a;Nassar, McGuire, Ritz, & Kable, 2019b;Wilson, Takahashi, Schoenbaum, & Niv, 2014). When changes to this latent state are carried forward in time (eg.…”

“…oddballs) then they could drive reductions in learning rate. Within this framework, the fronto-parietal control network could be thought of as providing a signal to load a new state representation, whereas regions including the orbitofrontal cortex seem to reflect the newly loaded state itself (Nassar, McGuire, Ritz, & Kable, 2019b). An important question raised by our work is where individual differences would fall in such a mechanistic process.…”

“…Successful decision making requires inferring important quantities such as the values and probabilities associated with potential decision outcomes through sequential observations over time. This inference process is difficult in changing environments, where optimal inference requires tracking the environmental statistics necessary to determine the most appropriate rate of learning (Behrens, Woolrich, Walton, & Rushworth, 2007;Browning, Behrens, Jocham, O'Reilly, & Bishop, 2015;McGuire, Nassar, Gold, & Kable, 2014;Nassar, McGuire, Ritz, & Kable, 2019b;Nassar et al, 2012;Prescott Adams & MacKay, 2007;Yu & Dayan, 2005). In general, learning should be slow during periods of environmental stability in order to average over as many relevant observations as possible, but fast during periods of environmental change that render prior observations irrelevant to the problem of predicting future ones (Behrens et al, 2007;Browning et al, 2015;Nassar et al, 2016;Nassar, Wilson, Heasly, & Gold, 2010;Vaghi et al, 2017;Wilson, Nassar, & Gold, 2013).…”

“…In general, learning should be slow during periods of environmental stability in order to average over as many relevant observations as possible, but fast during periods of environmental change that render prior observations irrelevant to the problem of predicting future ones (Behrens et al, 2007;Browning et al, 2015;Nassar et al, 2016;Nassar, Wilson, Heasly, & Gold, 2010;Vaghi et al, 2017;Wilson, Nassar, & Gold, 2013). Human behavior, fMRI BOLD responses, and measures of physiological arousal display qualitative hallmarks of this sort of learning rate adjustment, suggesting that the brain implements meta-control over its own rate of learning in order to optimize behaviorally relevant inferences (Behrens et al, 2007;Browning et al, 2015;McGuire et al, 2014;Nassar et al, 2012;Nassar, McGuire, Ritz, & Kable, 2019b;Prescott Adams & MacKay, 2007;Wilson et al, 2010;Yu & Dayan, 2005).…”

Neural representation of abstract task structure during generalization

“…Second, these hypothesis RDMs were related to the empirical RDM through multiple linear regression analysis, where a coefficient was estimated relating each hypothesis RDM to empirical RDMs allowing us to parcel out variance in representational distances due to multiple factors in the same model 33 . The lower-triangle of all hypothesis RDMs were extracted as vectors, mean centered, and included as predictors, along with an intercept term, in a multiple linear regression analysis to calculate beta coefficients relating each hypothesis RDM to the empirical RDM.…”