Good translatability of behavioral measures of affect (emotion) between human and nonhuman animals is core to comparative studies. The judgment bias (JB) task, which measures “optimistic” and “pessimistic” decision-making under ambiguity as indicators of positive and negative affective valence, has been used in both human and nonhuman animals. However, one key disparity between human and nonhuman studies is that the former typically use secondary reinforcers (e.g., money) whereas the latter typically use primary reinforcers (e.g., food). To address this deficiency and shed further light on JB as a measure of affect, we developed a novel version of a JB task for humans using primary reinforcers. Data on decision-making and reported affective state during the JB task were analyzed using computational modeling. Overall, participants grasped the task well, and as anticipated, their reported affective valence correlated with trial-by-trial variation in offered volume of juice. In addition, previous findings from monetary versions of the task were replicated: More positive prediction errors were associated with more positive affective valence, a higher lapse rate was associated with lower affective arousal, and affective arousal decreased as a function of number of trials completed. There was no evidence that more positive valence was associated with greater “optimism,” but instead, there was evidence that affective valence influenced the participants' decision stochasticity, whereas affective arousal tended to influence their propensity for errors. This novel version of the JB task provides a useful tool for investigation of the links between primary reward and punisher experience, affect, and decision-making, especially from a comparative perspective.

Central to the concept of the “cognitive map” is that it confers behavioral flexibility, allowing animals to take efficient detours, exploit shortcuts, and avoid alluring, but unhelpful, paths. The neural underpinnings of such naturalistic and flexible behavior remain unclear. In two neuroimaging experiments, we tested human participants on their ability to navigate to a set of goal locations in a virtual desert island riven by lava, which occasionally spread to block selected paths (necessitating detours) or receded to open new paths (affording real shortcuts or false shortcuts to be avoided). Detours activated a network of frontal regions compared with shortcuts. Activity in the right dorsolateral PFC specifically increased when participants encountered tempting false shortcuts that led along suboptimal paths that needed to be differentiated from real shortcuts. We also report modulation in event-related fields and theta power in these situations, providing insight to the temporal evolution of response to encountering detours and shortcuts. These results help inform current models as to how the brain supports navigation and planning in dynamic environments.

Substantial evidence indicates that subjective value is adapted to the statistics of reward expected within a given temporal context. However, how these contextual expectations are learned is poorly understood. To examine such learning, we exploited a recent observation that participants performing a gambling task adjust their preferences as a function of context. We show that, in the absence of contextual cues providing reward information, an average reward expectation was learned from recent past experience. Learning dependent on contextual cues emerged when two contexts alternated at a fast rate, whereas both cue-independent and cue-dependent forms of learning were apparent when two contexts alternated at a slower rate. Motivated by these behavioral findings, we reanalyzed a previous fMRI data set to probe the neural substrates of learning contextual reward expectations. We observed a form of reward prediction error related to average reward such that, at option presentation, activity in ventral tegmental area/substantia nigra and ventral striatum correlated positively and negatively, respectively, with the actual and predicted value of options. Moreover, an inverse correlation between activity in ventral tegmental area/substantia nigra (but not striatum) and predicted option value was greater in participants showing enhanced choice adaptation to context. The findings help understanding the mechanisms underlying learning of contextual reward expectation.

Dopamine plays a key role in motivation. Phasic dopamine response reflects a reinforcement prediction error (RPE), whereas tonic dopamine activity is postulated to represent an average reward that mediates motivational vigor. However, it has been hard to find evidence concerning the neural encoding of average reward that is uncorrupted by influences of RPEs. We circumvented this difficulty in a novel visual search task where we measured participants' button pressing vigor in a context where information (underlying an RPE) about future average reward was provided well before the average reward itself. Despite no instrumental consequence, participants' pressing force increased for greater current average reward, consistent with a form of Pavlovian effect on motivational vigor. We recorded participants' brain activity during task performance with fMRI. Greater average reward was associated with enhanced activity in dopaminergic midbrain to a degree that correlated with the relationship between average reward and pressing vigor. Interestingly, an opposite pattern was observed in subgenual cingulate cortex, a region implicated in negative mood and motivational inhibition. These findings highlight a crucial role for dopaminergic midbrain in representing aspects of average reward and motivational vigor.

Two fundamental questions underlie the expression of behavior, namely what to do and how vigorously to do it. The former is the topic of an overwhelming wealth of theoretical and empirical work particularly in the fields of reinforcement learning and decision-making, with various forms of affective prediction error playing key roles. Although vigor concerns motivation, and so is the subject of many empirical studies in diverse fields, it has suffered a dearth of computational models. Recently, Niv et al. [Niv, Y., Daw, N. D., Joel, D., & Dayan, P. Tonic dopamine: Opportunity costs and the control of response vigor. Psychopharmacology (Berlin), 191, 507–520, 2007] suggested that vigor should be controlled by the opportunity cost of time, which is itself determined by the average rate of reward. This coupling of reward rate and vigor can be shown to be optimal under the theory of average return reinforcement learning for a particular class of tasks but may also be a more general, perhaps hard-wired, characteristic of the architecture of control. We, therefore, tested the hypothesis that healthy human participants would adjust their RTs on the basis of the average rate of reward. We measured RTs in an odd-ball discrimination task for rewards whose magnitudes varied slowly but systematically. Linear regression on the subjects' individual RTs using the time varying average rate of reward as the regressor of interest, and including nuisance regressors such as the immediate reward in a round and in the preceding round, showed that a significant fraction of the variance in subjects' RTs could indeed be explained by the rate of experienced reward. This validates one of the key proposals associated with the model, illuminating an apparently mandatory form of coupling that may involve tonic levels of dopamine.