The Neural Bases of Action-Outcome Learning in Humans

What Role Does Striatal Dopamine Play in Goal-directed Action?

Evidence suggests that dopamine activity provides a US-related prediction error for Pavlovian conditioning and the reinforcement signal supporting the acquisition of habits. However, its role in goal-directed action is less clear. There are currently few studies that have assessed dopamine release as animals acquire and perform self-paced instrumental actions. Here we briefly review the literature documenting the psychological, behavioral and neural bases of goal-directed actions in rats and mice, before turning to describe recent studies investigating the role of dopamine in instrumental learning and performance. Plasticity in dorsomedial striatum, a central node in the network supporting goal-directed action, clearly requires dopamine release, the timing of which, relative to cortical and thalamic inputs, determines the degree and form of that plasticity. Beyond this, bilateral release appears to reflect reward prediction errors as animals experience the consequences of an action. Such signals feedforward to update the value of the specific action associated with that outcome during subsequent performance, with dopamine release at the time of action reflecting the updated predicted action value. More recently, evidence has also emerged for a hemispherically lateralised signal associated with the action; dopamine release is greater in the hemisphere contralateral to the spatial target of the action. This effect emerges over the course of acquisition and appears to reflect the strength of the action-outcome association. Thus, during goal-directed action, dopamine release signals the action, the outcome and their association to shape the learning and performance processes necessary to support this form of behavioral control.

The successor representation subserves hierarchical abstraction for goal-directed behavior

Humans have the ability to craft abstract, temporally extended and hierarchically organized plans. For instance, when considering how to make spaghetti for dinner, we typically concern ourselves with useful "subgoals" in the task, such as cutting onions, boiling pasta, and cooking a sauce, rather than particulars such as how many cuts to make to the onion, or exactly which muscles to contract. A core question is how such decomposition of a more abstract task into logical subtasks happens in the first place. Previous research has shown that humans are sensitive to a form of higher-order statistical learning named "community structure". Community structure is a common feature of abstract tasks characterized by a logical ordering of subtasks. This structure can be captured by a model where humans learn predictions of upcoming events multiple steps into the future, discounting predictions of events further away in time. One such model is the "successor representation", which has been argued to be useful for hierarchical abstraction. As of yet, no study has convincingly shown that this hierarchical abstraction can be put to use for goal-directed behavior. Here, we investigate whether participants utilize learned community structure to craft hierarchically informed action plans for goal-directed behavior. Participants were asked to search for paintings in a virtual museum, where the paintings were grouped together in "wings" representing community structure in the museum. We find that participants' choices accord with the hierarchical structure of the museum and that their response times are best predicted by a successor representation. The degree to which the response times reflect the community structure of the museum correlates with several measures of performance, including the ability to craft temporally abstract action plans. These results suggest that successor representation learning subserves hierarchical abstractions relevant for goal-directed behavior.

Emotion, motivation, decision-making, the orbitofrontal cortex, anterior cingulate cortex, and the amygdala

The orbitofrontal cortex and amygdala are involved in emotion and in motivation, but the relationship between these functions performed by these brain structures is not clear. To address this, a unified theory of emotion and motivation is described in which motivational states are states in which instrumental goal-directed actions are performed to obtain rewards or avoid punishers, and emotional states are states that are elicited when the reward or punisher is or is not received. This greatly simplifies our understanding of emotion and motivation, for the same set of genes and associated brain systems can define the primary or unlearned rewards and punishers such as sweet taste or pain. Recent evidence on the connectivity of human brain systems involved in emotion and motivation indicates that the orbitofrontal cortex is involved in reward value and experienced emotion with outputs to cortical regions including those involved in language, and is a key brain region involved in depression and the associated changes in motivation. The amygdala has weak effective connectivity back to the cortex in humans, and is implicated in brainstem-mediated responses to stimuli such as freezing and autonomic activity, rather than in declarative emotion. The anterior cingulate cortex is involved in learning actions to obtain rewards, and with the orbitofrontal cortex and ventromedial prefrontal cortex in providing the goals for navigation and in reward-related effects on memory consolidation mediated partly via the cholinergic system.

Transcranial direct-current stimulation enhances Pavlovian tendencies during intermittent loss of control

Introduction

Pavlovian bias is an innate motivational tendency to approach rewards and remain passive in the face of punishment. The relative reliance on Pavlovian valuation has been found to increase when the perceived control over environmental reinforcers is compromised, leading to behavior resembling learned helplessness (LH).

Methods

Sixty healthy young adults underwent a Go-NoGo reinforcement learning task and received anodal high-definition transcranial direct current stimulation (HD-tDCS) over the medial prefrontal/dorsal anterior cingulate cortex in our randomized, double-blind, sham- controlled study. Furthermore, we evaluated changes in cue-locked mid-frontal theta power derived from simultaneous electroencephalography (EEG). We hypothesized that active stimulation would reduce Pavlovian bias during manipulation of outcome controllability, and the effect would be accompanied by stronger mid-frontal theta activity, representing arbitration between choice strategies in favor of instrumental relative to Pavlovian valuation.

Results

We found a progressive decrease in Pavlovian bias during and after loss of control over feedback. Active HD-tDCS counteracted this effect while not affecting the mid-frontal theta signal.

Discussion

The results were at odds with our hypotheses but also with previous findings reporting LH-like patterns during and after loss of control without brain stimulation. The discrepancy may be related to different protocols for the controllability manipulation. We argue that the subjective evaluation of task controllability is crucial in mediating the balance between Pavlovian and instrumental valuation during reinforcement learning and that the medial prefrontal/dorsal anterior cingulate cortex is a key region in this respect. These findings have implications for understanding the behavioral and neural underpinnings of LH in humans.

Learning when effort matters: neural dynamics underlying updating and adaptation to changes in performance efficacy

To determine how much cognitive control to invest in a task, people need to consider whether exerting control matters for obtaining rewards. In particular, they need to account for the efficacy of their performance-the degree to which rewards are determined by performance or by independent factors. Yet it remains unclear how people learn about their performance efficacy in an environment. Here we combined computational modeling with measures of task performance and EEG, to provide a mechanistic account of how people (i) learn and update efficacy expectations in a changing environment and (ii) proactively adjust control allocation based on current efficacy expectations. Across 2 studies, subjects performed an incentivized cognitive control task while their performance efficacy (the likelihood that rewards are performance-contingent or random) varied over time. We show that people update their efficacy beliefs based on prediction errors-leveraging similar neural and computational substrates as those that underpin reward learning-and adjust how much control they allocate according to these beliefs. Using computational modeling, we show that these control adjustments reflect changes in information processing, rather than the speed-accuracy tradeoff. These findings demonstrate the neurocomputational mechanism through which people learn how worthwhile their cognitive control is.

Elevated prefrontal dopamine interferes with the stress-buffering properties of behavioral control in female rats

Stress-linked disorders are more prevalent in women than in men and differ in their clinical presentation. Thus, investigating sex differences in factors that promote susceptibility or resilience to stress outcomes, and the circuit elements that mediate their effects, is important. In male rats, instrumental control over stressors engages a corticostriatal system involving the prelimbic cortex (PL) and dorsomedial striatum (DMS) that prevent many of the sequelae of stress exposure. Interestingly, control does not buffer against stress outcomes in females, and here, we provide evidence that the instrumental controlling response in females is supported instead by the dorsolateral striatum (DLS). Additionally, we used in vivo microdialysis, fluorescent in situ hybridization, and receptor subtype pharmacology to examine the contribution of prefrontal dopamine (DA) to the differential impact of behavioral control. Although both sexes preferentially expressed D1 receptor mRNA in PL GABAergic neurons, there were robust sex differences in the dynamic properties of prefrontal DA during controllable stress. Behavioral control potently attenuated stress-induced DA efflux in males, but not females, who showed a sustained DA increase throughout the entire stress session. Importantly, PL D1 receptor blockade (SCH 23390) shifted the proportion of striatal activity from the DLS to the DMS in females and produced the protective effects of behavioral control. These findings suggest a sex-selective mechanism in which elevated DA in the PL biases instrumental responding towards prefrontal-independent striatal circuitry, thereby eliminating the protective impact of coping with stress.

Topographic organization of the human caudate functional connectivity and age-related changes with resting-state fMRI

The striatum is postulated to play a central role in gating cortical processing during goal-oriented behavior. While many human neuroimaging studies have treated the striatum as an undivided whole or several homogeneous compartments, some recent studies showed that its circuitry is topographically organized and has more complex relations with the cortical networks than previously assumed. Here, we took a gradient functional connectivity mapping approach that utilizes the entire anatomical space of the caudate nucleus to examine the organization of its functional relationship with the rest of the brain and how its topographic mapping changes with age. We defined the topography of the caudate functional connectivity using three publicly available resting-state fMRI datasets. We replicated and extended previous findings. First, we found two stable gradients of caudate connectivity patterns along its medial-lateral (M-L) and anterior-posterior (A-P) axes, supporting findings from previous tract-tracing studies of non-human primates that there are at least two main organizational principles within the caudate nucleus. Second, unlike previous emphasis of the A-P topology, we showed that the differential connectivity patterns along the M-L gradient of caudate are more clearly organized with the large-scale neural networks; such that brain networks associated with internal vs. external orienting behavior are respectively more closely linked to the medial vs. lateral extent of the caudate. Third, the caudate's M-L organization showed greater age-related reduction in integrity, which was further associated with age-related changes in behavioral measures of executive functions. In sum, our analysis confirmed a sometimes overlooked M-L functional connectivity gradient within the caudate nucleus, with its lateral longitudinal zone more closely linked to the frontoparietal cortical circuits and age-related changes in cognitive control. These findings provide a more precise mapping of the human caudate functional connectivity, both in terms of the gradient organization with cortical networks and age-related changes in such organization.