Biology subjects

Coricelli, G.

Publications and source records attributed to Coricelli, G..

5 recordsLinked to original sources

bioRxiv2025

Neural Oscillations Coordinate Continuous Error Correction During Force Control

Effective motor control depends on the brains ability to monitor performance and make continuous corrections. While many studies focus on discrete errors, everyday actions often require ongoing feedback-based adjustments. Here, we used an isometric force control task with EEG to investigate the neural dynamics supporting real-time error correction. Participants maintained a constant grip force with or without continuous visual feedback. With feedback, behavior showed [~]6 Hz rhythmic fluctuations, consistent with active correction. These fluctuations were mirrored in EEG activity across theta, beta, and alpha bands--oscillations linked to performance monitoring, updating, and attentional control. Without feedback, performance decayed linearly, and the corresponding neural signatures were reduced. These findings suggest that continuous sensory feedback engages a dynamic feedback loop involving distinct neural processes that support adaptive behavior. Our results highlight the importance of oscillatory activity in tracking and correcting moment-to-moment fluctuations in force, offering insight into the neural basis of feedback-loop force control.

neuroscience↗

bioRxiv2020

An empirical test of the role of value certainty in decision making

Most contemporary models of value-based decisions are built on value estimates that are typically self-reported by the decision maker. Such models have been successful in accounting for choice accuracy and response time, and more recently choice confidence. The fundamental driver of such models is choice difficulty, which is almost always defined as the absolute value difference between the subjective value ratings of the options in a choice set. Yet a decision maker is not necessarily able to provide a value estimate with the same degree of certainty for each option that he encounters. We propose that choice difficulty is determined not only by absolute value distance of choice options, but also by their value certainty. In this study, we first demonstrate the reliability of the concept of an option-specific value certainty using three different experimental measures. We then demonstrate the influence that value certainty has on choice, including accuracy (consistency), choice confidence, response time, and choice-induced preference change (i.e., the degree to which value estimates change from pre- to post-choice evaluation). We conclude with a suggestion of how popular contemporary models of choice (e.g., race model, drift-diffusion model) could be improved by including option-specific value certainty as one of their inputs.

neuroscience↗

bioRxiv2020

The effect of counterfactual information on outcome value coding in medial prefrontal and cingulate cortex: from an absolute to a relative neural code.

Adaptive coding of stimuli in visual cortex is well documented in perception, where it supports efficient encoding over a broad range of possible percepts. Recently, a similar neural mechanism has been reported also in value-based decision, where it allows optimal encoding of vast ranges of values in PFC: neuronal response to value depends on the choice context (relative coding), rather than being invariant across contexts (absolute coding). Additionally, value learning is sensitive to the amount of feedback information: providing complete feedback (both obtained and forgone outcomes) instead of partial feedback (only obtained outcome) improves learning. However, it is unclear whether relative coding occurs in all PFC regions and how it is affected by feedback information. We systematically investigated univariate and multivariate feedback encoding in various PFC regions and compared three modes of neural coding: absolute, partially-adaptive and fully-adaptive. Twenty-eight human participants (both sexes) performed a learning task while undergoing fMRI scanning. On each trial, they chose between two symbols associated with a certain outcome. Then, the decision outcome was revealed. Notably, in half of the trials participants received partial feedback, while in the other half they got complete feedback. We used univariate and multivariate analysis to explore value encoding in different feedback conditions. We found that both obtained and forgone outcomes were encoded in mPFC, but with opposite sign in ventral and dorsal subdivisions. Moreover, we showed that increasing feedback information induced a switch from absolute to relative coding. Our results suggest that complete feedback information promotes context-dependent outcome encoding. SIGNIFICANCE STATEMENTThis study offers a systematic investigation of the effect of the amount of feedback information (partial vs. complete) on univariate and multivariate outcome value encoding, within multiple regions in mPFC/ACC critical for value-based decisions and behavioural adaptation. Moreover, we provide the first comparison of three possible models of neural coding (i.e., absolute, partially-adaptive, and fully-adaptive coding) of value signal in mPFC by using commensurable measures of model accuracy. Taken together, our results help build a more comprehensive picture of how the human brain encodes and processes outcome value. In particular, our results suggest that simultaneous presentation of obtained and foregone outcomes promotes relative value representation.

neuroscience↗

bioRxiv2018

The computation of strategic learning in repeated social competitive interactions: Learning sophistication, reward attractor points and strategic asymmetry

Social interactions rely on our ability to learn and adjust our behavior to the behavior of others. Strategic games provide a useful framework to study the cognitive processes involved in the formation of beliefs about the others intentions and behavior, what we may call strategic theory of mind. Through the years, the growing field of behavioral economics provided evidence of a systematic departure of humans behavior from the optimal game theoretical prescriptions. One hypothesis posits that humans ability to accurately process the others behavior is somehow bounded. The question of what constraints the formation of sufficiently high order beliefs remained unanswered. We hypothesize that maximizing final earnings in a competitive repeated game setting, requires moving away from reward-based learning to engage in sophisticated belief-based learning. Overcoming the attraction of the immediate rewards by displaying a computationally costly type of learning might not be a strategy shared among all individuals. In this work, we manipulated the reward structure of the interaction so that the action displayed by the two types of learning becomes (respectively not) discriminable, giving a relative strategic (resp. dis) advantage to the participant given the role endorsed during the interaction. We employed a computational modeling approach to characterize the individual level of belief learning sophistication in three types of interactions (agent-agent, human-human and human-agent). The analysis of the participants choice behavior revealed that the strategic learning level drives the formation of more accurate beliefs and eventually leads to convergence towards game optimality (equilibrium). More specifically we show that the game structure interacts with the level of engagement in strategically sophisticated learning to explain the outcome of the interaction. This study provides the first evidence of a key implication of strategic learning heterogeneity in equilibrium departure and provides insight to explain the emergence of a leader-follower dynamics of choice.\n\nAUTHOR SUMMARYDynamic interaction between individuals appears to be a cornerstone for understanding how humans grasp other minds. During a strategic interaction, in which the outcome of ones action depends directly on what the other individual decides, it appears crucial to anticipate the others actions in order to adjust our own behavior. In theory, choosing optimally in a strategic setting requires that both players hold correct beliefs over their opponents behavior and best-respond to it. However, in practice humans systematically deviate from the game-theoretical (equilibrium), suggesting that our ability to form accurate beliefs is cognitively and/or contextually constrained. Previous studies using computational modelling suggested that during a repeated game interaction humans vary in the sophistication of their learning process leading to the formation of beliefs over their opponents behavior of different orders of complexity (level of recursive thinking such as \"I think that you think that ...\"). In this work we show that the individual engagement in sophisticated (belief-based) learning drives the convergence towards equilibrium and ultimately performance. Moreover, we show that this effect is influenced by both the game environment and the cognitive capacity of the participants, shaping the very dynamic of the social interaction.\n\nDATA AVAILABILITYThe authors confirm that upon publication the raw behavioral data and Matlab code for reconstruction of all figures, computational models and statistical analyses will be made available for download at the following URL: https://zenodo.org/

neuroscience↗

bioRxiv2018

Reference-point centering and range-adaptation enhance human reinforcement learning at the cost of irrational preferences

In economics and in perceptual decision-making contextual effects are well documented, where decision weights are adjusted as a function of the distribution of stimuli. Yet, in reinforcement learning literature whether and how contextual information pertaining to decision states is integrated in learning algorithms has received comparably little attention. Here, in an attempt to fill this gap, we investigated reinforcement learning behavior and its computational substrates in a task where we orthogonally manipulated both outcome valence and magnitude, resulting in systematic variations in state-values. Over two experiments, model comparison indicated that subjects behavior is best accounted for by an algorithm which includes both reference point-dependence and range-adaptation - two crucial features of state-dependent valuation. In addition, we found state-dependent outcome valuation to progressively emerge over time, to be favored by increasing outcome information and to be correlated with explicit understanding of the task structure. Finally, our data clearly show that, while being locally adaptive (for instance in negative valence and small magnitude contexts), state-dependent valuation comes at the cost of seemingly irrational choices, when options are extrapolated out from their original contexts.

animal behavior and cognition↗