Biology subjects

Stefano Palminteri

Publications and source records attributed to Stefano Palminteri.

4 recordsLinked to original sources

bioRxiv2016

Computational cognitive neuroscience: what is a good model of brain processes?

Cognitive neuroscience, especially in the fields of learning and decision-making, is witnessing the blossoming of computational model-based analyses. Several methodological and review papers have indicated how and why candidate models should be compared by trading off their ability to predict the data as a function of their complexity. However, the importance of simulating candidate models has been so far largely overlooked, which entails several drawbacks and leads to invalid conclusions. Here we argue that the analysis of model simulations is often necessary to support the specific claims about behavioral function that most of model-based studies make. We defend this argument both informally by providing a large-scale (N>300) review of recent studies, and formally by showing how model simulations are necessary to interpret model comparison results. Finally, we propose guidelines for future work, which combine model comparison and simulation.

Neuroscience↗

bioRxiv2016

Specific effect of dopamine partial agonist on counterfactual learning: evidence from Gilles de la Tourette syndrome

The dopamine partial agonist aripiprazole is increasingly used to treat pathologies for which other antipsychotics are indicated because it displays fewer side effects, such as sedation and depression-like symptoms, than other dopamine receptor antagonists. Previously, we showed that aripiprazole may protect motivational function by preserving reinforcement-related signals used to sustain reward-maximization behaviour in a simple action-outcome learning task. However, the effect of aripiprazole on more cognitive facets of human reinforcement learning, such as learning from the hypothetical outcomes of alternative courses of action (i.e., counterfactual learning), is unknown.\n\nTo test the influence of aripiprazole on counterfactual learning, we administered a reinforcement-learning task that involves both direct learning from obtained outcomes and indirect learning from forgone outcomes to two groups of Gilles de la Tourette (GTS) patients, one consisting of patients who were completely unmedicated and the other consisting of patients who were receiving aripiprazole monotherapy, and to healthy subjects. We replicated a previous finding showing that aripiprazole does not affect direct learning from obtained outcomes in GTS. We also found that whereas learning performance improved in the presence of counterfactual feedback in both healthy controls and unmedicated GTS patients, this was not the case in aripiprazole-medicated GTS patients.\n\nOur results suggest that whereas aripiprazole preserves direct learning of action-outcome associations, it may impair more complex inferential processes, such as counterfactual learning, from forgone outcomes.

Neuroscience↗

bioRxiv2016

Optimistic reinforcement learning: computational and neural bases

While forming and updating beliefs about future life outcomes, people tend to consider good news and to disregard bad news. This tendency is supposed to support the optimism bias. Whether this learning bias is specific to \"high-level\" abstract belief update or a particular expression of a more general \"low-level\" reinforcement learning process is unknown. Here we report evidence in favor of the second hypothesis. In a simple instrumental learning task, participants incorporated better-than-expected outcomes at a higher rate compared to worse-than-expected ones. In addition, functional imaging indicated that inter-individual difference in the expression of optimistic update corresponds to enhanced prediction error signaling in the reward circuitry. Our results constitute a new step in the understanding of the genesis of optimism bias at the neurocomputational level.

Neuroscience↗

bioRxiv2016

Revisiting the assessment of inter-individual differences in fMRI activations-behavior relationships.

Characterizing inter-individual differences induced by clinical and social factors constitutes one of the most promising applications of neuroimaging. Paving the way for such applications, neuroimaging studies often report between-group differences in \"activations\" or correlations between such \"activations\" and individual traits. Here we raise cautionary warnings about some of those inter-individual analytic strategies. These warnings become critical when measures of \"activations\" are unstandardized coefficients of regressions between BOLD signal and individual behavior.\n\nFirst, using simple algebraic derivations, we show how inter-individual differences results can spuriously arise from neglecting the statistical relationships which link the ranges of individual BOLD activation and of recorded behavior. We also demonstrate how apparently contradictory results and interpretations may simply arise from the interaction of this scaling issue and the pre-processing of the behavioral variables. Second, using computational simulations, we illustrate how this issue percolates the booming field of model-based fMRI. Finally, we outline a set of recommendations, which might prove useful for researcher and reviewers confronted with questions involving inter-individual differences in neuroimaging.\n\nAuthor SummaryCharacterizing inter-individual differences induced by clinical and societal factors constitutes one of the most promising applications of neuroimaging. Paving the way for such applications, an increasing fraction of neuroimaging studies reports between-group differences in \"activations\" or correlations between \"activations\" and individual traits. In this manuscript, we focus on the typical analytical strategies employed in studies investigating how differences in behavior between individuals or groups of individuals are translated to differences of activations in specific brain regions. We notably question whether they are suitable to support inferences and claims about the neural underpinnings of differential cognition. Our core results demonstrate that typical inter-individual results can spuriously arise by overlooking plausible statistical relationships that link the ranges of individual BOLD activation with the ranges of produced behavior. We argue that these results challenge current classical interpretations of inter-individual results. Highlighting the methodological and theoretical gaps regarding the analysis and interpretation of inter-individual differences is fundamental to fulfilling the promises of neuroimaging.

Neuroscience↗