Mimetic desire in autism spectrum disorder

International audienceMimetic desire (MD), the spontaneous propensity to pursue goals that others pursue, is a case of social influence that is believed to shape preferences. Autism spectrum disorder (ASD) is defined by both atypical interests and altered social interaction. We investigated whether MD is lower in adults with ASD compared to typically developed adults and whether MD correlates with social anhedonia and social judgment, two aspects of atypical social functioning in autism. Contrary to our hypotheses, MD was similarly present in both ASD and control groups. Anhedonia and social judgment differed between the ASD and control groups but did not correlate with MD. These results extend previous findings by suggesting that basic mechanisms of social influence are preserved in autism. The finding of intact MD in ASD stands against the intuitive idea that atypical interests stem from reduced social influence and indirectly favors the possibility that special interests might be selected for their intrinsic properties

Critical Roles for Anterior Insula and Dorsal Striatum in Punishment-Based Avoidance Learning

SummaryThe division of human learning systems into reward and punishment opponent modules is still a debated issue. While the implication of ventral prefrontostriatal circuits in reward-based learning is well established, the neural underpinnings of punishment-based learning remain unclear. To elucidate the causal implication of brain regions that were related to punishment learning in a previous functional neuroimaging study, we tested the effects of brain damage on behavioral performance, using the same task contrasting monetary gains and losses. Cortical and subcortical candidate regions, the anterior insula and dorsal striatum, were assessed in patients presenting brain tumor and Huntington disease, respectively. Both groups exhibited selective impairment of punishment-based learning. Computational modeling suggested complementary roles for these structures: the anterior insula might be involved in learning the negative value of loss-predicting cues, whereas the dorsal striatum might be involved in choosing between those cues so as to avoid the worst

Sour grapes and sweet victories: How actions shape preferences

Classical decision theory postulates that choices proceed from subjective values assigned to the probable outcomes of alternative actions. Some authors have argued that opposite causality should also be envisaged, with choices influencing subsequent values expressed in desirability ratings. The idea is that agents may increase their ratings of items that they have chosen in the first place, which has been typically explained by the need to reduce cognitive dissonance. However, evidence in favor of this reverse causality has been the topic of intense debates that have not reached consensus so far. Here, we take a novel approach using Bayesian techniques to compare models in which choices arise from stable (but noisy) underlying values (one-way causality) versus models in which values are in turn influenced by choices (two-way causality). Moreover, we examined whether in addition to choices, other components of previous actions, such as the effort invested and the eventual action outcome (success or failure), could also impact subsequent values. Finally, we assessed whether the putative changes in values were only expressed in explicit ratings, or whether they would also affect other value-related behaviors such as subsequent choices. Behavioral data were obtained from healthy participants in a rating-choice-rating-choice-rating paradigm, where the choice task involves deciding whether or not to exert a given physical effort to obtain a particular food item. Bayesian selection favored two-way causality models, where changes in value due to previous actions affected subsequent ratings, choices and action outcomes. Altogether, these findings may help explain how values and actions drift when several decisions are made successively, hence highlighting some shortcomings of classical decision theory

A neurocomputational account of how inflammation enhances sensitivity to punishments versus rewards

BACKGROUND: Inflammation rapidly impairs mood and cognition and, when severe, can appear indistinguishable from major depression. These sickness responses are characterized by an acute reorientation of motivational state; pleasurable activities are avoided, and sensitivity to negative stimuli is enhanced. However, it remains unclear how these rapid shifts in behavior are mediated within the brain. METHODS: Here, we combined computational modeling of choice behavior, experimentally induced inflammation, and functional brain imaging (functional magnetic resonance imaging) to describe these mechanisms. Using a double-blind, randomized crossover study design, 24 healthy volunteers completed a probabilistic instrumental learning task on two separate occasions, one 3 hours after typhoid vaccination and one 3 hours after saline (placebo) injection. Participants learned to select high probability reward (win £1) and avoid high probability punishment (lose £1) stimuli. An action-value learning algorithm was fit to the observed behavior, then used within functional magnetic resonance imaging analyses to identify neural coding of prediction error signals driving motivational learning. RESULTS: Inflammation acutely biased behavior, enhancing punishment compared with reward sensitivity, through distinct actions on neural representations of reward and punishment prediction errors within the ventral striatum and anterior insula. Consequently, choice options leading to potential rewards were less behaviorally attractive, and those leading to punishments were more aversive. CONCLUSIONS: Our findings demonstrate the neural mediation of a rapid, state-dependent reorientation of reward versus punishment sensitivity during inflammation. This mechanism may aid the adaptive reallocation of metabolic resources during acute sickness but might also account for

Glutamine-to-glutamate ratio in the nucleus accumbens predicts effort-based motivated performance in humans

Substantial evidence implicates the nucleus accumbens in motivated performance, but very little is known about the neurochemical underpinnings of individual differences in motivation. Here, we applied 1H magnetic resonance spectroscopy (1H-MRS) at ultra-high-field in the nucleus accumbens and inquired whether levels of glutamate (Glu), glutamine (Gln), GABA or their ratios predict interindividual differences in effort-based motivated task performance. Given the incentive value of social competition, we also examined differences in performance under self-motivated or competition settings. Our results indicate that higher accumbal Gln-to-Glu ratio predicts better overall performance and reduced effort perception. As performance is the outcome of multiple cognitive, motor and physiological processes, we applied computational modeling to estimate best-fitting individual parameters related to specific processes modeled with utility, effort and performance functions. This model-based analysis revealed that accumbal Gln-to-Glu ratio specifically relates to stamina; i.e., the capacity to maintain performance over long periods. It also indicated that competition boosts performance from task onset, particularly for low Gln-to-Glu individuals. In conclusion, our findings provide novel insights implicating accumbal Gln and Glu balance on the prediction of specific computational components of motivated performance. This approach and findings can help developing therapeutic strategies based on targeting metabolism to ameliorate deficits in effort engagement

How robust is the optimistic update bias for estimating self-risk and population base rates?

Humans hold unrealistically optimistic predictions of what their future holds. These predictions are generated and maintained as people update their beliefs more readily when receiving information that calls for adjustment in an optimistic direction relative to information that calls for adjustment in a pessimistic direction. Thus far this update bias has been shown when people make estimations regarding the self. Here, we examine whether asymmetric belief updating also exists when making estimations regarding population base rates. We reveal that while participants update beliefs regarding risk in the population in an asymmetric manner, such valence-dependent updating of base rates can be accounted for by priors. In contrast, we show that optimistic updating regarding the self is a robust phenomenon, which holds even under different empirical definitions of desirable information

Comptes Rendus Biologies

International audienceMotivation can be defined as a function that activates and directs the behavior toward goals. In neuropsychiatric conditions, motivation deficits such as apathy are frequent and interfere with treatment observance and clinical outcome. The current standard approach is to assess motivation deficits with subjective questionnaires that depend on patients’ insight and provide no information about underlying pathophysiological mechanisms. Here, I present a novel approach that consists in fitting computational models to the behavior observed in objective tests, so as to infer parameters that specify the patient’s motivational state. These computational parameters provide (1) an explanation at the cognitive level (e.g., reduced sensitivity to reward), (2) a dysfunction at the neural level (e.g., altered dopamine release) and (3) a prediction of treatment outcome (e.g., improvement with psychostimulants). Computational fingerprinting may therefore pave the way toward a more personalized medicine.La motivation peut être définie comme une fonction qui active et dirige le comportement vers des buts. Dans les pathologies neuropsychiatriques, les déficits de motivation tels que l’apathie sont fréquents et interfèrent avec l’observance des traitements et leur impact thérapeutique. L’approche standard actuelle consiste à évaluer les déficits de motivation à l’aide de questionnaires subjectifs qui dépendent de l’introspection du patient et ne fournissent aucune information sur les mécanismes physiopathologiques sous-jacents. Je présente ici une nouvelle approche, qui consiste à ajuster des modèles computationnels sur le comportement observé dans des tests objectifs, de manière à déduire des paramètres qui spécifient l’état motivationnel du patient. Ces paramètres computationnels fournissent (1) une explication au niveau cognitif (par exemple, une sensibilité réduite à la récompense), (2) un dysfonctionnement au niveau neuronal (par exemple, une libération de dopamine altérée) et (3) une prédiction concernant le résultat du traitement (par exemple, l’effet bénéfique des psychostimulants). L’empreinte computationnelle pourrait donc ouvrir la voie à une médecine davantage personnalisée

Bridging across functional models: The OFC as a value-making neural network.

International audienceMany functions have been attributed to the orbitofrontal cortex (OFC)-some classical roles, such as signaling the value of action outcomes, being challenged by more recent ones, such as signaling the position of a trial within a task space. In this paper, we propose a unifying neural network architecture, whose function is to generate a value from a set of attributes attached to a particular object. Our model reverses the logic of perceptual choice models, by considering values as outputs of (and not inputs to) the neural network. In doing so, the model explains why univariate value signals have been observed in both likeability rating and economic choice tasks, while the features associated with a particular task trial can be decoded using multivariate analysis. Moreover, simulations show that a globally positive correlation with subjective value at the population level can coexist with a variety of correlation coefficients at the single-unit level, bridging typical observations made in human neuroimaging and monkey electrophysiology studies of OFC activity. To better explain binary choice, we equipped the neural network with recurrent feedback connections that enable simultaneous coding of values associated with currently attended and previously considered objects. Simulations of this augmented model show that virtual lesions produce systematically intransitive preferences, as observed in patients with damage to the OFC. Thus, our neural network model is sufficiently general and flexible to account for a core set of observations and make specific predictions about both OFC activity during value judgment and behavioral consequence of OFC damage

Dopamine, ganglions de la base et selection de l'action (du singe MPTP au patient parkinsonien approche électrophysiologique et comportementale)

PARIS-BIUSJ-Thèses (751052125) / SudocPARIS-BIUSJ-Physique recherche (751052113) / SudocSudocFranceF