Cognitive flexibility is critical for intelligent behavior. However, its execution is effortful and often suboptimal. Recent work indicates that flexible behavior can be improved by the prospect of reward, which suggests that rewards optimize flexible control processes. Here we investigated how different reward prospects influence neural encoding of task rule information to optimize cognitive flexibility. We applied representational similarity analysis to human electroencephalograms, recorded while female and male participants performed a rule-guided decision-making task. During the task, the prospect of reward varied from trial to trial. Participants made faster, more accurate judgements on high-reward trials. Critically, high reward boosted neural coding of the active task rule, and the extent of this increase was associated with improvements in task performance. Additionally, the effect of high reward on task rule coding was most pronounced on switch trials, where rules were updated relative to the previous trial. These results suggest that reward prospect can promote cognitive performance by strengthening neural coding of task rule information, helping to improve cognitive flexibility during complex behavior.SIGNIFICANCE STATEMENT The importance of motivation is evident in the ubiquity with which reward prospect guides adaptive behavior and the striking number of neurological conditions associated with motivational impairments. In this study, we investigated how dynamic changes in motivation, as manipulated through reward, shape neural coding for task rules during a flexible decision-making task. The results of this work suggest that motivation to obtain reward modulates the encoding of task rules needed for flexible behavior. The extent to which reward increased task rule coding also tracked improvements in behavioral performance under high-reward conditions. These findings help to inform how motivation shapes neural processing in the healthy human brain.

Experience-related brain activity patterns reactivate during sleep, wakeful rest, and brief pauses from active behavior. In parallel, machine learning research has found that experience replay can lead to substantial performance improvements in artificial agents. Together, these lines of research suggest replay has a variety of computational benefits for decision-making and learning. Here, we provide an overview of putative computational functions of replay as suggested by machine learning and neuroscientific research. We show that replay can lead to faster learning, less forgetting, reorganization or augmentation of experiences, and support planning and generalization. In addition, we highlight the benefits of reactivating abstracted internal representations rather than veridical memories, and discuss how replay could provide a mechanism to build internal representations that improve learning and decision-making.

It has recently been recognized that orbitofrontal cortex has 2 subdivisions that are anatomically and functionally distinct. Most rodent research has focused on the lateral subdivision, leaving the medial subdivision (mOFC) relatively unexplored. We recently showed that inhibiting mOFC neurons eliminated the differential impact of reward probability cues on discrimination accuracy in a sustained attention task. In the present study, we tested whether increasing mOFC neuronal activity in rats would accelerate acquisition of reward contingencies. mOFC neuronal activity was increased using the DREADD (Designer Receptors Exclusively Activated by Designer Drugs) method, in which clozapine-N-oxide administration leads to neuronal modulation by acting on synthetic receptors not normally expressed in the rat brain. We predicted that rats with neuronal activation in mOFC would require fewer sessions than controls for acquisition of a task in which visual cues signal the probability of reward for correct discrimination performance. Contrary to this prediction, mOFC neuronal activation impaired task acquisition, suggesting mOFC may play a role in learning relationships between environmental cues and reward probability or for using that information in adaptive decision-making. In addition, disrupted mOFC activity may contribute to psychiatric conditions in which learning associations between environmental cues and reward probability is impaired. (PsycINFO Database Record

Many complex real-world decisions, such as deciding which house to buy or whether to switch jobs, involve trying to maximise reward across a sequence of choices. Optimal Foraging Theory is well suited to study these kinds of choices because it provides formal models for reward-maximisation in sequential situations. In this article, we review recent insights from foraging neuroscience, behavioural ecology and computational modelling. We find that a commonly used approach in foraging neuroscience, in which choice items are encountered at random, does not reflect the way animals direct their foraging efforts in real-world settings, nor does it reflect efficient reward-maximising behaviour. Based on this, we propose that task designs allowing subjects to encounter choice items strategically will further improve the ecological validity of foraging approaches used in neuroscience, as well as give rise to new behavioural and neural predictions that deepen our understanding of sequential, value-based choice.