Orbitofrontal cortex reflects changes in response-outcome contingencies during probabilistic reversal learning.

In a continuously changing environment, in which behavioral outcomes are rarely certain, animals must be able to learn to integrate feedback from their choices over time and adapt to changing reward contingencies to maintain flexible behavior. The orbitofrontal region of prefrontal cortex (OFC) has been widely implicated as playing a role in the ability to flexibly control behavior. We used a probabilistic reversal learning task to measure rats' behavioral flexibility and its neural basis in the activity of single neurons in OFC. In this task, one lever, designated as 'correct', was rewarded at a high probability (80%) and a second, spatially distinct lever, designated as 'incorrect', was rewarded at a low probability (20%). Once rats reached a learning criterion for reliably selecting the correct lever, reward contingencies of the two levers were switched, and daily sessions were conducted until rats reliably selected the new correct lever. All rats performed the initial Acquisition and subsequent Reversal successfully, with more sessions needed to learn the Reversal. OFC neurons were recorded during five behavioral sessions spanning Acquisition and Reversal learning. The dominant pattern of neural responding in OFC, identified by principal component analysis of the population of neurons recorded, was modulated by reward outcome across behavioral sessions. Generally, activity was higher following rewarded choices than unrewarded. However, there was a correlation between reduced responses to reward following incorrect choices and the establishment of the preference for the correct lever. These results show how signaling by individual OFC neurons may participate in the flexible adaptation of behavior under changing reward contingencies.

A physiologically-inspired model of numerical classification based on graded stimulus coding.

In most natural decision contexts, the process of selecting among competing actions takes place in the presence of informative, but potentially ambiguous, stimuli. Decisions about magnitudes - quantities like time, length, and brightness that are linearly ordered - constitute an important subclass of such decisions. It has long been known that perceptual judgments about such quantities obey Weber's Law, wherein the just-noticeable difference in a magnitude is proportional to the magnitude itself. Current physiologically inspired models of numerical classification assume discriminations are made via a labeled line code of neurons selectively tuned for numerosity, a pattern observed in the firing rates of neurons in the ventral intraparietal area (VIP) of the macaque. By contrast, neurons in the contiguous lateral intraparietal area (LIP) signal numerosity in a graded fashion, suggesting the possibility that numerical classification could be achieved in the absence of neurons tuned for number. Here, we consider the performance of a decision model based on this analog coding scheme in a paradigmatic discrimination task - numerosity bisection. We demonstrate that a basic two-neuron classifier model, derived from experimentally measured monotonic responses of LIP neurons, is sufficient to reproduce the numerosity bisection behavior of monkeys, and that the threshold of the classifier can be set by reward maximization via a simple learning rule. In addition, our model predicts deviations from Weber Law scaling of choice behavior at high numerosity. Together, these results suggest both a generic neuronal framework for magnitude-based decisions and a role for reward contingency in the classification of such stimuli.