Mathias Pessiglione

Whether the brain encodes the subjective value of pleasant and unpleasant situations via a single integrated circuit or through distinct pathways remains unresolved. How such signals guide choice across contexts also remains unclear. To address these questions, we recorded intracerebral activity from participants who rated pleasant and unpleasant scenarios and then made binary choices across domains and frames. Ventromedial prefrontal cortex (vmPFC) broadband gamma activity (BGA, 50–150 Hz) tracked the value of pleasant items during rating and choice, correlating positively with the most pleasant item irrespective of choice frame. Similarly, anterior insula (aIns) BGA tracked the value of unpleasant items during rating and choice, correlating negatively with the most unpleasant item irrespective of choice frame. Relative value signals in the vmPFC and aIns diverged most strongly when item domain and choice frame conflicted. These findings reveal complementary neural systems for pleasant and unpleasant valuation and their role in value-based choices.

The decision-making field has long attempted to understand the origins of human choice variability. A potential source of variability lies in spontaneous fluctuations of ongoing neural activity, which may influence behavior across several cognitive domains. Here, we developed a closed-loop intracranial brain-computer interface that detects transient fluctuations of broadband gamma activity (BGA, 70-150 Hz) to trigger stimulus presentations contingent on high or low neural states. Using this approach, we examined how spontaneous activity in the anterior insula influences accept/reject decisions involving multi-attribute offers combining pleasant and unpleasant components. Offers preceded by high BGA in the anterior insula were associated with a transient post-offer suppression in anterior insula activity, which biased decisions toward accepting an unpleasant item in exchange for a pleasant one in hypothetical multi-attribute choices. These findings demonstrate that sub-second endogenous neural fluctuations directly modulate choice behavior, challenging current neuro-computational models by emphasizing the critical role of intrinsic brain states in shaping decision variability.

Goal-directed behavior often requires sustained effort across a sequence of interdependent decisions, yet the determinants of persistence in such contexts remain poorly understood. Here, we investigated how individuals regulate persistence in a novel sequential effort-based task in which they controlled an avatar through successive checkpoints to reach a final goal and could make repeated attempts following failure. At each attempt, participants could choose either to persist in the same task or to disengage toward an easier but less rewarding alternative. We found that decisions to persist or disengage were jointly shaped by multiple interacting factors. Disengagement increased with task difficulty and lower skill level. It also increased with repeated attempts and varied with time-on-task, indexing fatigue, and with accumulated errors, indexing lack of progress. Conversely, proximity to the goal promoted persistence and shaped decision dynamics with greater hesitation when disengaging near the goal. Notably, clearing the first checkpoint produced a sharp increase in persistence, suggesting that early success plays a pivotal role. Furthermore, persistence reflected both retrospective and prospective evaluations of effort, with prior investment promoting commitment and anticipated effort reducing it. Finally, disengagement was preceded by short-term performance decline but not by a detectable increase in decision conflict. Together, these findings provide a comprehensive account of persistence in sequential effortful tasks, showing that decisions to persist or disengage are jointly shaped by multiple factors related to fatigue, (lack of) progress, goal proximity, and early success.

The decision-making field has long attempted to understand the origins of human choice variability. A potential source of variability lies in the ongoing fluctuations of brain oscillations, which might influence behavior across several cognitive domains. Here, we developed a closed-loop intracranial brain-computer interface that detects transient fluctuations of broadband gamma activity (BGA, 70–150 Hz) to trigger stimulus presentations contingent on high or low neural states. Using this approach, we examined how spontaneous activity in the anterior insula influences accept/reject decisions involving multi-attribute offers combining pleasant and unpleasant components. Offers preceded by high pre-offer BGA in the anterior insula were associated with a transient post-offer suppression in aIns activity, which biased decisions toward accepting an unpleasant item in exchange for a pleasant one in hypothetical multi-attribute choices. These findings demonstrate that sub-second endogenous neural fluctuations directly modulate choice behavior, challenging current neuro-computational models by emphasizing the critical role of intrinsic brain states in decision variability.

Motivation drives individuals to overcome costs to achieve desired outcomes, such as rewards or avoidance of punishment, with significant variability across individuals. The dorsomedial prefrontal cortex/dorsal anterior cingulate cortex (dmPFC/dACC) and anterior insula are key brain regions implicated in effort-based decision-making. Here, we utilized proton magnetic resonance spectroscopy (¹H-MRS) at 7 Tesla on 69 healthy participants in these brain regions to uncover the neurometabolic factors that influence these differences. We designed and applied an effort-based decision-making task requiring mental and physical effort to probe motivated behavior, complemented by computational modeling to extract key behavioral parameters. Gradient boosting machine learning was applied to explore the predictive role of specific metabolites in motivated behavior. Our results reveal that a model established on dmPFC/dACC metabolites explains decisions to exert high mental effort and sensitivity to mental effort. In particular, glutamate, aspartate, and lactate in dmPFC/dACC, three metabolites linked through the tricarboxylic acid cycle and glycolysis, were identified as key discriminative metabolites in the dmPFC/dACC, predictive of mental effort choices, underpinning energy supply and cognitive processes. Anterior insula metabolites did not significantly relate to effort-related decisions. Notably, glutamine and lactate levels between the periphery (plasma) and the dmPFC/dACC were correlated, suggesting a metabolic link between peripheral and central biomarkers of effort. Our findings provide novel insights into the neurometabolic underpinnings of motivated behavior and propose novel biomarkers for mental effort-based decision-making. Importantly, our study highlights the relevance of multivariable approaches in elucidating complex cognitive functions.

Making good decisions is essential for survival and success, yet humans and animals often exhibit perplexing irrational decision-making whose biological origin remains poorly understood. Recent empirical and computational work suggests that altered computations in perceptual, motor and memory systems in the brain may arise from informational, metabolic or robustness constraints on their internal connectivity structure. However, whether and how such neurobiological constraints may have molded the architecture of decision systems (such as the orbitofrontal cortex) and eventually distorted decision-relevant computations, remains largely unknown. We first train cohorts of artificial neural nets to perform ten variants of rational decision-relevant computations. Those variants that operate under a specific option-encoding format exhibit most of the electrophysiological coding properties observed in orbitofrontal neurons of monkeys making decisions under risk. We then distort these neural nets’ internal wiring to reproduce monkeys’ irrational choices. This induces deterministic spillover interferences in decision-relevant computations that generalize across individuals, at both the behavioral and neural level. Importantly, although irrational nets do not seem to bring informational or metabolic benefits, they display enhanced tolerance to damage and noise when compared to their rational counterparts. This suggests that some forms of irrational behavior may be the incidental outcome of distal evolutionary pressure on the robustness of orbitofrontal circuits.

When making decisions, humans are susceptible to all sorts of biases, relative to rational norms. An important factor is incidental changes in affective states, such as variations in mood between happiness and sadness. We previously developed a computational model, in which mood affects choice by forming a predisposition to face costs and seek more rewards. Here, we generalized this theory to account for how specific inductions of happiness and sadness affect different types of economic decisions involving a tradeoff between costs (risk, delay, effort) and benefits (financial rewards). Across exploratory and confirmatory studies (N = 94), we observed a consistent bias exerted by transitory mood states, whether they were assessed through self-reports (rated happiness minus sadness) or inferred from physiological measures (valence of facial expression times intensity of autonomous arousal). This choice bias was best explained by our computational model, with a mood-scaled bonus added to the value of the more rewarded but more costly option, irrespective of the cost type (risk, delay or effort). Additionally, gaze tracking during decision making confirmed that the choice bias was driven by an early preference for the mood-congruent option. Together, these results demonstrate the feasibility of predicting irrational choices from objective measures of affective states.