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Interferences Between Decisional Variables: Behavioural and Computational Studies Emmanuelle Bioud Interferences between decisional variables: Behavioural and computational studies Emmanuelle Bioud To cite this version: Emmanuelle Bioud. Interferences between decisional variables: Behavioural and computational stud- ies. Cognitive science. Sorbonne Université, 2019. English. tel-02460056 HAL Id: tel-02460056 https://tel.archives-ouvertes.fr/tel-02460056 Submitted on 29 Jan 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Sorbonne Université École Doctorale Cerveau Cognition Comportement Institut du Cerveau et de la Moelle épinière - Équipe Motivation Brain & Behavior Interferences between decisional variables Behavioural and computational studies Par Emmanuelle Bioud Thèse de doctorat de Sciences Cognitives Dirigée par Mathias Pessiglione Présentée et soutenue publiquement le 27 septembre 2019 Devant un jury composé de : Miriam Klein-Flügge Oxford University Rapportrice Philippe Tobler University of Zurich Rapporteur Vincent de Gardelle Université Panthéon Sorbonne Examinateur Nathalie George Sorbonne Université Examinatrice Emmanuel Guigon Sorbonne Université Examinateur Mathias Pessiglione Sorbonne Université Directeur de thèse i To Francis T. White, eponymous inventor of a most salutary noise. ii Acknowledgements First, I would like to thank the researchers who kindly agreed to be part of my jury and to do me the honour of evaluating my work: Vincent de Gardelle, Nathalie George, Emmanuel Guigon, with a special thank you to Miriam Klein-Flügge and Philippe Tobler, who accepted to review this dissertation. I gratefully acknowledge the funding received towards my PhD from the Ministère de l’Enseignement Supérieur et de la Recherche et de l’Innovation, and from the Fondation pour la Recherche Médicale, without which this work could not have been completed. My thanks also go to the ICM support teams, and in particular the PRISME platform, managed by the very helpful Karim N’Diaye and Gilles Rautureau, as well as the CENIR team, who provided great assistance with fMRI data acquisition and analysis. I am particularly grateful to my thesis supervisor, Mathias Pessiglione, for giving me the opportunity to learn (a lot) from him and from his team, for his patient guidance, his optimism, and for trusting me more than I trusted myself. I am also grateful for the lovely company with whom I shared the joys and frustrations of academic research, and much more. I feel very lucky to have been surrounded by such kind, smart and entertaining labmates. To the 2.0 generation: Alizée, for her enlightening theory of human character, and our common gastronomic passions; Bastien, for all his valuable teachings. Josiane and I shall never forget them; Vassilissa, for her kind-heartedness and dedication to the important stuff; Chiara, Lionel, Marie and Raphaëlle, for making me feel most welcome and for helping me take my first steps in the team. To the 2.1 generation: Caroline, for her energy and enthusiasm; Nico B., for his inspiring scientific (and extra-scientific) curiosity. To the 3.0 generation: Chen, for being such a sweet, attentive mate, with whom I could share my feelings and thoughts; Delphine, for her reliability as a partner in crime, and for making the lab a happier place. To the 3.1 generation: Doug, for his admirable consistency and the raw broccoli; Jules, for the cookie bits and the nerd wisdom bits; Nico C., for being silly in a good way, and also for his substantial help with fMRI; Toni, for quite literally being the guy. To the 3.2: Elodie, for her soothing presence; Julia, for her contagious smile; Pauline B., for her precious honesty; Quentin, for his heart-warming generosity; R., for anonymously creating an hilarious MBBingo. To the newcomers: Claire, Cynthia, Jacob, Pauline P., and Will, for the fun and the talks we had in the little time we were together. To the new PIs, Fabien and Raphaël, for their kindness, and for always being willing to help. iii My special thanks go to the “less new” PIs, Jean and Sébastien, for open-handedly sharing their treasurable scientific expertise. I would also like to thank Florent Meyniel for his advice and helpful feedbacks. Thank you to my very lucid mates, Lindsay, Alex, Ben, and Delphine again, for giving me the opportunity to participate in a brilliant project, and for your beautiful smiles and perpetual good vibes. Merci à mes amis et à mes (littéralement innombrables) colocataires pour tous ces excellents moments à Paris ou en région, pendant lesquels il m’est même arrivé de penser à autre chose que ma thèse. Une spéciale dédicace à Mélanie, pour ces vingt précieuses années d’amitié qui ne nous rajeunissent pas, à Julio, pour sa détermination sans bornes et les conversations toujours passionnantes, et à Thomas, pour les bons plans, les tisanes, les parties de Catane dans un esprit toujours bon enfant, et les aventures passées et à venir. Enfin, j’adresse un immense merci à ma famille, à mes frères Jérémy et Maxime, et tout particulièrement à mes parents, Pascal et Valérie. Et j’adresse un merci tout aussi immense à Florent. Ce travail n’aurait pu être accompli sans eux. Grâce à leur amour et à leur soutien sans faille, même les jours de gros temps, cette traversée ne s’est jamais faite en solitaire. iv Summary Should I try to grow an orange tree on my balcony? According to modern decision theory, when individuals consider taking on a course of action that involves expending effort and other costs in the pursuit of valuable yet uncertain outcomes, their decision typically follows on a specific, subjective assessment of the overall value of that course of action, relative to the value of alternative courses of action. It is assumed that the overall value of a given course of action is its expected utility, i.e., the sum of the subjective values of all its possible outcomes weighted by their respective probability, discounted by the costs that this course of action entails. Over the last forty years, many violations of the expected utility maximization principle have been pointed out, and almost as many adjusted decision models have been proposed, in an effort to better account for actual human behaviour. Despite these consecutive adjustments, one implicit assumption has remained at the core of decision theory until this day: that of a mutual independence between the decisional variables that are combined together in action valuation, in particular 1) the subjective value of possible outcomes, 2) the subjective probability of these outcomes, 3) the subjective cost of action. Yet, over the past decades, substantial evidence from behavioural psychology and neuroeconomics experiments has accumulated, showing that value, probability and cost judgments can influence, or interfere with, each other. In this PhD work, we investigated such manifestations, with the aim of elucidating the computational and neural mechanisms underlying these interferences. For this, we conducted three studies on healthy human volunteers. In the first study, we had participants anticipate the energetic cost of composite running routes whose later completion could entail more or less valuable monetary outcomes, either framed as obtained gains or avoided losses. We found that a given route was anticipated as more costly when it was paired with a larger monetary stake compared to a smaller one (either in the gain or loss frame). Besides, our behavioural data were most in line with a cognitive scenario according to which individuals’ prospective effort judgment is contaminated by the output of a cost-benefit computation. In the second study, we moved on to the investigation of interferences between value and probability, with a focus on a particular case of probability judgment: confidence, that is, the subjective probability of being correct or successful at a given task. Our participants were trained to perform a motor precision task; next, they underwent testing trials in which they were first presented with information about the upcoming motor challenge: its difficulty (adjusted via the size of the target to be hit), the magnitude of their monetary gain in case of success, and the magnitude of monetary loss in case of failure; then, they were prompted to estimate their prospective chances of success (i.e. to report their confidence); finally, they v tried to hit the target but did not receive any feedback about their performance. Our main finding was that individuals tend to be more confident when faced with a larger prospective gain, but less confident when faced with a larger prospective loss. This pattern of distortions is evocative of an affect-as-information type of misattribution. In our third study, we drew inspiration from earlier neuroimaging findings to predict that value-confidence misattributions may occur bidirectionally, and between unrelated items. To test our predictions, we had participants answer more or less difficult general-knowledge quiz questions while hearing more or less pleasant musical extracts, after which we asked them to make a confidence rating about their answer to the quiz question,
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