A frontopolar-temporal circuit determines the impact of social information in macaque decision making

Memories or decisions? Bridging accounts of frontopolar function

Frontopolar cortex (FPC), for a long time elusive to functional description, is now associated with a wide range of cognitive processes. Prominent accounts of FPC function emerged from studies of memory (e.g., episodic and prospective memory; EM and PM, respectively) and of executive function (e.g., planning, multi-tasking, relational reasoning, cognitive branching, etc). In recent years, FPC function has begun to be described within the context of value-based decision making in terms of monitoring the value of alternatives and optimizing cognitive resources to balance the explore/exploit dilemma in the face of volatile environments. In this perspective, we propose that the broad counterfactual inference and behavioural flexibility account can help re-interpret findings from EM and PM studies and offer an explanatory bridge between the memory and executive function accounts. More specifically, we propose that counterfactual value monitoring in FPC modulates the reallocation of cognitive resources between present and past information and contributes to efficient episodic and prospective retrieval by concurrently assessing the value of competing memories in relation to the decision at hand and proactively evaluating future potential scenarios to anticipate optimal engagement of intentions.

Dissociating Frontal Lobe Lesion Induced Deficits in Rule Value Learning Using Reinforcement Learning Models and a WCST Analog

Distinct frontal regions make dissociable contributions to rule-guided decision-making, including the ability to learn and exploit associations between abstract rules and reward value, maintain those rules in memory, and evaluate choice outcomes. Value-based learning can be quantified using reinforcement learning (RL) models predicting optimal trial-wise choices and estimating learning rates, which can then be related to the intact functioning of specific brain areas by combining a modeling approach with lesion-behavioral data. We applied a three-parameter feedback-dependent RL model to behavioral data obtained from macaques with circumscribed lesions to the principal sulcus (PS), anterior cingulate cortex (ACC), orbitofrontal cortex (OFC), superior dorsolateral prefrontal cortex (sdlPFC), and frontopolar cortex (FPC) performing a Wisconsin card sorting task (WCST) analog. Our modeling-based approach identified distinct lesion effects on component cognitive mechanisms contributing to WCST performance. OFC lesions decreased the rate of rule value updating following both positive and negative feedback. In contrast, we found no deficit in rule value updating following PS lesions, which instead made monkeys less likely to repeat correct choices when rule values were well established, suggesting a crucial role of the PS in the working memory maintenance of rule representations. Finally, ACC lesions produced a specific deficit in learning from negative feedback, as well as impaired the ability to repeat choices following highly surprising reward, supporting a proposed role for ACC in flexibly switching between a trial-and-error mode and a working memory mode in response to increased error likelihood.

Dorsomedial and ventromedial prefrontal cortex lesions differentially impact social influence and temporal discounting

The medial prefrontal cortex (mPFC) has long been associated with economic and social decision-making in neuroimaging studies. Several debates question whether different ventral mPFC (vmPFC) and dorsal mPFC (dmPFC) regions have specific functions or whether there is a gradient supporting social and nonsocial cognition. Here, we tested an unusually large sample of rare participants with focal damage to the mPFC (N = 33), individuals with lesions elsewhere (N = 17), and healthy controls (N = 71) (total N = 121). Participants completed a temporal discounting task to estimate their baseline discounting preferences before learning the preferences of two other people, one who was more temporally impulsive and one more patient. We used Bayesian computational models to estimate baseline discounting and susceptibility to social influence after learning others' economic preferences. mPFC damage increased susceptibility to impulsive social influence compared to healthy controls and increased overall susceptibility to social influence compared to those with lesions elsewhere. Importantly, voxel-based lesion-symptom mapping (VLSM) of computational parameters showed that this heightened susceptibility to social influence was attributed specifically to damage to the dmPFC (area 9; permutation-based threshold-free cluster enhancement (TFCE) p < 0.025). In contrast, lesions in the vmPFC (areas 13 and 25) and ventral striatum were associated with a preference for seeking more immediate rewards (permutation-based TFCE p < 0.05). We show that the dmPFC is causally implicated in susceptibility to social influence, with distinct ventral portions of mPFC involved in temporal discounting. These findings provide causal evidence for sub-regions of the mPFC underpinning fundamental social and cognitive processes.

Transcranial ultrasound stimulation effect in the redundant and synergistic networks consistent across macaques

Low-intensity transcranial ultrasound stimulation (TUS) is a noninvasive technique that safely alters neural activity, reaching deep brain areas with good spatial accuracy. We investigated the effects of TUS in macaques using a recent metric, the synergy minus redundancy rank gradient, which quantifies different kinds of neural information processing. We analyzed this high-order quantity on the fMRI data after TUS in two targets: the supplementary motor area (SMA-TUS) and the frontal polar cortex (FPC-TUS). The TUS produced specific changes at the limbic network at FPC-TUS and the motor network at SMA-TUS and altered the sensorimotor, temporal, and frontal networks in both targets, mostly consistent across macaques. Moreover, there was a reduction in the structural and functional coupling after both stimulations. Finally, the TUS changed the intrinsic high-order network topology, decreasing the modular organization of the redundancy at SMA-TUS and increasing the synergistic integration at FPC-TUS.

Ventral frontostriatal circuitry mediates the computation of reinforcement from symbolic gains and losses

Reinforcement learning (RL), particularly in primates, is often driven by symbolic outcomes. However, it is usually studied with primary reinforcers. To examine the neural mechanisms underlying learning from symbolic outcomes, we trained monkeys on a task in which they learned to choose options that led to gains of tokens and avoid choosing options that led to losses of tokens. We then recorded simultaneously from the orbitofrontal cortex (OFC), ventral striatum (VS), amygdala (AMY), and mediodorsal thalamus (MDt). We found that the OFC played a dominant role in coding token outcomes and token prediction errors. The other areas contributed complementary functions, with the VS coding appetitive outcomes and the AMY coding the salience of outcomes. The MDt coded actions and relayed information about tokens between the OFC and VS. Thus, the OFC leads the processing of symbolic RL in the ventral frontostriatal circuitry.

Computational processes of simultaneous learning of stochasticity and volatility in humans

Making adaptive decisions requires predicting outcomes, and this in turn requires adapting to uncertain environments. This study explores computational challenges in distinguishing two types of noise influencing predictions: volatility and stochasticity. Volatility refers to diffusion noise in latent causes, requiring a higher learning rate, while stochasticity introduces moment-to-moment observation noise and reduces learning rate. Dissociating these effects is challenging as both increase the variance of observations. Previous research examined these factors mostly separately, but it remains unclear whether and how humans dissociate them when they are played off against one another. In two large-scale experiments, through a behavioral prediction task and computational modeling, we report evidence of humans dissociating volatility and stochasticity solely based on their observations. We observed contrasting effects of volatility and stochasticity on learning rates, consistent with statistical principles. These results are consistent with a computational model that estimates volatility and stochasticity by balancing their dueling effects.

Causal involvement of dorsomedial prefrontal cortex in learning the predictability of observable actions

Social learning is well established across species. While recent neuroimaging studies show that dorsomedial prefrontal cortex (DMPFC/preSMA) activation correlates with observational learning signals, the precise computations that are implemented by DMPFC/preSMA have remained unclear. To identify whether DMPFC/preSMA supports learning from observed outcomes or observed actions, or possibly encodes even a higher order factor (such as the reliability of the demonstrator), we downregulate DMPFC/preSMA excitability with continuous theta burst stimulation (cTBS) and assess different forms of observational learning. Relative to a vertex-cTBS control condition, DMPFC/preSMA downregulation decreases performance during action-based learning but has no effect on outcome-based learning. Computational modeling reveals that DMPFC/preSMA cTBS disrupts learning the predictability, a proxy of reliability, of the demonstrator and modulates the rate of learning from observed actions. Thus, our results suggest that the DMPFC is causally involved in observational action learning, mainly by adjusting the speed of learning about the predictability of the demonstrator.

Lesions to the mediodorsal thalamus, but not orbitofrontal cortex, enhance volatility beliefs linked to paranoia

Beliefs-attitudes toward some state of the environment-guide action selection and should be robust to variability but sensitive to meaningful change. Beliefs about volatility (expectation of change) are associated with paranoia in humans, but the brain regions responsible for volatility beliefs remain unknown. The orbitofrontal cortex (OFC) is central to adaptive behavior, whereas the magnocellular mediodorsal thalamus (MDmc) is essential for arbitrating between perceptions and action policies. We assessed belief updating in a three-choice probabilistic reversal learning task following excitotoxic lesions of the MDmc (n = 3) or OFC (n = 3) and compared performance with that of unoperated monkeys (n = 14). Computational analyses indicated a double dissociation: MDmc, but not OFC, lesions were associated with erratic switching behavior and heightened volatility belief (as in paranoia in humans), whereas OFC, but not MDmc, lesions were associated with increased lose-stay behavior and reward learning rates. Given the consilience across species and models, these results have implications for understanding paranoia.