Decision making in a social group has two distinguishing features. First, humans and other animals routinely alter their behavior in response to changes in their physical and social environment. As a ...result, the outcomes of decisions that depend on the behavior of multiple decision makers are difficult to predict and require highly adaptive decision-making strategies. Second, decision makers may have preferences regarding consequences to other individuals and therefore choose their actions to improve or reduce the well-being of others. Many neurobiological studies have exploited game theory to probe the neural basis of decision making and suggested that these features of social decision making might be reflected in the functions of brain areas involved in reward evaluation and reinforcement learning. Molecular genetic studies have also begun to identify genetic mechanisms for personal traits related to reinforcement learning and complex social decision making, further illuminating the biological basis of social behavior.
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Impulsivity refers to a set of heterogeneous behaviors that are tuned suboptimally along certain temporal dimensions. Impulsive intertemporal choice refers to the tendency to forego a large but ...delayed reward and to seek an inferior but more immediate reward, whereas impulsive motor responses also result when the subjects fail to suppress inappropriate automatic behaviors. In addition, impulsive actions can be produced when too much emphasis is placed on speed rather than accuracy in a wide range of behaviors, including perceptual decision making. Despite this heterogeneous nature, the prefrontal cortex and its connected areas, such as the basal ganglia, play an important role in gating impulsive actions in a variety of behavioral tasks. Here, we describe key features of computations necessary for optimal decision making and how their failures can lead to impulsive behaviors. We also review the recent findings from neuroimaging and single-neuron recording studies on the neural mechanisms related to impulsive behaviors. Converging approaches in economics, psychology, and neuroscience provide a unique vista for better understanding the nature of behavioral impairments associated with impulsivity.
Reinforcement learning is an adaptive process in which an animal utilizes its previous experience to improve the outcomes of future choices. Computational theories of reinforcement learning play a ...central role in the newly emerging areas of neuroeconomics and decision neuroscience. In this framework, actions are chosen according to their value functions, which describe how much future reward is expected from each action. Value functions can be adjusted not only through reward and penalty, but also by the animal's knowledge of its current environment. Studies have revealed that a large proportion of the brain is involved in representing and updating value functions and using them to choose an action. However, how the nature of a behavioral task affects the neural mechanisms of reinforcement learning remains incompletely understood. Future studies should uncover the principles by which different computational elements of reinforcement learning are dynamically coordinated across the entire brain.
Knowledge about hypothetical outcomes from unchosen actions is beneficial only when such outcomes can be correctly attributed to specific actions. Here we show that during a simulated ...rock-paper-scissors game, rhesus monkeys can adjust their choice behaviors according to both actual and hypothetical outcomes from their chosen and unchosen actions, respectively. In addition, neurons in both dorsolateral prefrontal cortex and orbitofrontal cortex encoded the signals related to actual and hypothetical outcomes immediately after they were revealed to the animal. Moreover, compared to the neurons in the orbitofrontal cortex, those in the dorsolateral prefrontal cortex were more likely to change their activity according to the hypothetical outcomes from specific actions. Conjunctive and parallel coding of multiple actions and their outcomes in the prefrontal cortex might enhance the efficiency of reinforcement learning and also contribute to their context-dependent memory.
► Monkeys can learn from hypothetical outcomes of unchosen actions ► Neurons in the prefrontal cortex encode hypothetical outcomes from specific actions ► Orbitofrontal cortex encodes hypothetical outcomes from multiple actions similarly ► Activity related to actual and hypothetical outcomes shows a similar time course
The process of decision making in humans and other animals is adaptive and can be tuned through experience so as to optimize the outcomes of their choices in a dynamic environment. Previous studies ...have demonstrated that the anterior cingulate cortex plays an important role in updating the animal's behavioral strategies when the action outcome contingencies change. Moreover, neurons in the anterior cingulate cortex often encode the signals related to expected or actual reward. We investigated whether reward-related activity in the anterior cingulate cortex is affected by the animal's previous reward history. This was tested in rhesus monkeys trained to make binary choices in a computer-simulated competitive zero-sum game. The animal's choice behavior was relatively close to the optimal strategy but also revealed small systematic biases that are consistent with the use of a reinforcement learning algorithm. In addition, the activity of neurons in the dorsal anterior cingulate cortex that was related to the reward received by the animal in a given trial often was modulated by the rewards in the previous trials. Some of these neurons encoded the rate of rewards in previous trials, whereas others displayed activity modulations more closely related to the reward prediction errors. In contrast, signals related to the animal's choices were represented only weakly in this cortical area. These results suggest that neurons in the dorsal anterior cingulate cortex might be involved in the subjective evaluation of choice outcomes based on the animal's reward history.
Specialization and hierarchy are organizing principles for primate cortex, yet there is little direct evidence for how cortical areas are specialized in the temporal domain. We measured timescales of ...intrinsic fluctuations in spiking activity across areas and found a hierarchical ordering, with sensory and prefrontal areas exhibiting shorter and longer timescales, respectively. On the basis of our findings, we suggest that intrinsic timescales reflect areal specialization for task-relevant computations over multiple temporal ranges.
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Neurons in the dorsolateral prefrontal cortex (DLPFC) encode a diverse array of sensory and mnemonic signals, but little is known about how this information is dynamically routed during decision ...making. We analyzed the neuronal activity in the DLPFC of monkeys performing a probabilistic reversal task where information about the probability and magnitude of reward was provided by the target color and numerical cues, respectively. The location of the target of a given color was randomized across trials and therefore was not relevant for subsequent choices. DLPFC neurons encoded signals related to both task-relevant and irrelevant features, but only task-relevant mnemonic signals were encoded congruently with choice signals. Furthermore, only the task-relevant signals related to previous events were more robustly encoded following rewarded outcomes. Thus, multiple types of neural signals are flexibly routed in the DLPFC so as to favor actions that maximize reward.
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Learning from reward feedback is essential for survival but can become extremely challenging with myriad choice options. Here, we propose that learning reward values of individual features can ...provide a heuristic for estimating reward values of choice options in dynamic, multi-dimensional environments. We hypothesize that this feature-based learning occurs not just because it can reduce dimensionality, but more importantly because it can increase adaptability without compromising precision of learning. We experimentally test this hypothesis and find that in dynamic environments, human subjects adopt feature-based learning even when this approach does not reduce dimensionality. Even in static, low-dimensional environments, subjects initially adopt feature-based learning and gradually switch to learning reward values of individual options, depending on how accurately objects' values can be predicted by combining feature values. Our computational models reproduce these results and highlight the importance of neurons coding feature values for parallel learning of values for features and objects.
In choosing between different rewards expected after unequal delays, humans and animals often prefer the smaller but more immediate reward, indicating that the subjective value or utility of reward ...is depreciated according to its delay. Here, we show that neurons in the primate caudate nucleus and ventral striatum modulate their activity according to temporally discounted values of rewards with a similar time course. However, neurons in the caudate nucleus encoded the difference in the temporally discounted values of the two alternative targets more reliably than neurons in the ventral striatum. In contrast, neurons in the ventral striatum largely encoded the sum of the temporally discounted values, and therefore, the overall goodness of available options. These results suggest a more pivotal role for the dorsal striatum in action selection during intertemporal choice.
► Temporally discounted values are encoded in both dorsal and ventral striatum ► Time course of temporally discounted values is similar in dorsal and ventral striatum ► Temporally discounted values for actions appear largely in the dorsal striatum ► Activity related to chosen values develops gradually in the striatum
Despite widespread neural activity related to reward values, signals related to upcoming choice have not been clearly identified in the rodent brain. Here we examined neuronal activity in the lateral ...(AGl) and medial (AGm) agranular cortex, corresponding to the primary and secondary motor cortex, respectively, in rats performing a dynamic foraging task. Choice signals, before behavioral manifestation of the rat's choice, arose in the AGm earlier than in any other areas of the rat brain previously studied under free-choice conditions. The AGm also conveyed neural signals for decision value and chosen value. By contrast, upcoming choice signals arose later, and value signals were weaker, in the AGl. We also found that AGm lesions made the rats' choices less dependent on dynamically updated values. These results suggest that rodent secondary motor cortex might be uniquely involved in both representing and reading out value signals for flexible action selection.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
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