Heterogeneous value coding in orbitofrontal populations

Abstract

Value signals in the brain are important for learning, decision-making, and orienting behavior toward relevant goals. Although they can play different roles in behavior and cognition, value representations are often considered to be uniform and static signals. Nonetheless, contextual and mixed representations of value have been widely reported. Here, we review the evidence for heterogeneity in value coding and dynamics in the orbitofrontal cortex. We argue that this diversity plays a key role in the representation of value itself and allows neurons to integrate value with other behaviorally relevant information. We also discuss modeling approaches that can dissociate potential functions of heterogeneous value codes and provide further insight into its importance in behavior and cognition. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Figure 1

Nonlinear value coding in OFC neurons. Z-scored firing rates of six example neurons recorded from monkeys performing a reward expectation task. Green and black lines show neurons’ responses to cues that predict different types of rewards. Amounts of each reward were titrated so that outcomes assigned the same ordinal value (1 to 4) were chosen with similar probabilities. To be labeled as nonlinear, (1) the coefficient for value in a linear regression must not be significant, (2) the interaction between value and type in an ANOVA is not significant (to avoid cue confound) (3) and the value coefficient is significant in the same ANOVA. Reproduced from Enel et al. (Enel et al., 2020).

Figure 2

Non-monotonic codes improve value decoding from artificial neuron populations. A. Left panels show examples of artificial units designed to monotonically or non-monotonically encode value, as well as nonselective units with the same noise statistics. Each plot shows the mean +/− SEM response across trials of the same ordinal value (1 to 4). Right panels show histograms of regression coefficients for value from 1000 generated units, corresponding to each unit type to the left. B. Value decoding increases asymptotically with the percent of non-monotonic value coding neurons in the population. Each point is decoding accuracy (with 10-fold cross validation), averaged across 500 different synthetic populations. Chance decoding of the four simulated values is 25%. Error bars = SEM.

Figure 3

Similarities between spatial and value information maintained across delays. In two studies, cross-temporal analyses compared representations of remembered information across time to determine the extent of generalization. Each pixel is a score resulting from the comparison of representations at two time points, such that the diagonal compares a time point with itself, and higher scores (warmer colors) indicate greater similarity. A. Data from Spaak et al. (Spaak et al., 2017), showing dlPFC neurons recorded from monkeys performing a spatial working memory task. Colors represent discriminability scores, which are pairwise correlations of condition differences between time points. Panels show the same scores. White contours on the left indicate significant cross-temporal correlations, and those on the right outline significant reductions in generalizations (i.e. more dynamic signals), suggesting the presence of both stable and dynamic representations in the same dlPFC population. Black = non-significant correlations. B. Data from Enel et al. (Enel et al., 2020), showing OFC neurons recorded from monkeys performing a value-based choice task. Colors represent decoding accuracies, where the decoding algorithm was trained and tested on all pairwise combinations of time points. The first panel shows decoding from the original population of firing rates that suggests the presence of both stable and dynamic representations in the population. The second shows decoding from the population found to have the most stable encoding subspace, and the last shows decoding from the population with the most dynamic encoding. White lines indicate task events. Gray = non-significant decoding.