K. Earl, J. D. Miller, and . Cohen, An integrative theory of prefrontal cortex function. Annual review of neuroscience, pp.167-202, 2001.

H. Damasio, T. Grabowski, R. Frank, M. Albert, . Galaburda et al., The return of Phineas Gage: clues about the brain from the skull of a famous patient, Science, vol.264, issue.5162, pp.1102-1105, 1994.
DOI : 10.1126/science.8178168

T. Shallice and P. Burgess, DEFICITS IN STRATEGY APPLICATION FOLLOWING FRONTAL LOBE DAMAGE IN MAN, Brain, vol.114, issue.2, pp.1-15, 1991.
DOI : 10.1093/brain/114.2.727

J. Rubinstein, E. Jeffrey, . Evans, E. David, and . Meyer, Task switching in patients with prefrontal cortex damage, annual meeting of the Cognitive Neuroscience Society, 1994.

M. Sara, . Szczepanski, T. Robert, and . Knight, Insights into Human Behavior from Lesions to the Prefrontal Cortex, Neuron, vol.83, issue.5, pp.1002-1018, 2014.

C. Azuar, P. Reyes, A. Slachevsky, E. Volle, S. Kinkingnehun et al., Testing the model of caudo-rostral organization of cognitive control in the human with frontal lesions, NeuroImage, vol.84, issue.C, pp.1053-1060, 2014.
DOI : 10.1016/j.neuroimage.2013.09.031

N. Louis and . Irwin, Comparative Neuroscience and Neurobiology, Encyclopedia of Neuroscience, pp.1-146, 1988.

G. Anne, . Collins, J. Michael, and . Frank, How much of reinforcement learning is working memory, not reinforcement learning? A behavioral, computational, and neurogenetic analysis, European Journal of Neuroscience, vol.35, issue.7, pp.1024-1035, 2012.

M. Petrides and D. Pandya, Comparative cytoarchitectonic analysis of the human and the macaque ventrolateral prefrontal cortex and corticocortical connection patterns in the monkey, European Journal of Neuroscience, vol.73, issue.2, pp.291-310, 2002.
DOI : 10.1016/0006-8993(78)90584-X

L. Paula, H. Croxson, T. E. Johansen-berg, . Behrens, D. Matthew et al., Quantitative investigation of connections of the prefrontal cortex in the human and macaque using probabilistic diffusion tractography, Journal of Neuroscience, issue.39, pp.258854-8866, 2005.

P. Steven and . Wise, Forward frontal fields: phylogeny and fundamental function, Trends in Neurosciences, issue.12, pp.31599-608, 2008.

M. Todd and . Preuss, Do rats have prefrontal cortex? The Rose-Woolsey-Akert program reconsidered, Journal of Cognitive Neuroscience, vol.7, issue.1, pp.1-24, 1995.

A. Thomas, . Stalnaker, K. Nisha, G. Cooch, and . Schoenbaum, What the orbitofrontal cortex does not do, Nature Neuroscience, vol.18, issue.5, pp.620-627, 2015.

E. Procyk, C. R. Wilson, M. Frederic, . Stoll, C. Ma¨?lysma¨?lys et al., Midcingulate Motor Map and Feedback Detection: Converging Data from Humans and Monkeys, Cerebral Cortex, pp.1-10, 1991.
DOI : 10.1093/cercor/bhu213

URL : https://academic.oup.com/cercor/article-pdf/26/2/467/17308456/bhu213.pdf

M. Petrides, F. Tomaiuolo, H. Edward, . Yeterian, N. Deepak et al., The prefrontal cortex: Comparative architectonic organization in the human and the macaque monkey brains, Cortex, vol.48, issue.1, pp.46-57, 2012.
DOI : 10.1016/j.cortex.2011.07.002

C. Amiez, R. Neveu, D. Warrot, M. Petrides, K. Knoblauch et al., The Location of Feedback-Related Activity in the Midcingulate Cortex Is Predicted by Local Morphology, Journal of Neuroscience, vol.33, issue.5, pp.2217-2228, 2013.
DOI : 10.1523/JNEUROSCI.2779-12.2013

A. Brent and . Vogt, Architecture, neurocytology and comparative organization of monkey and human cingulate cortices. Cingulate neurobiology and disease, pp.65-93, 2009.

F. Bonini, B. Burle, C. Liegeois-chauvel, J. Regis, P. Chauvel et al., Action Monitoring and Medial Frontal Cortex: Leading Role of Supplementary Motor Area, Science, vol.66, issue.4, pp.343888-891, 2014.
DOI : 10.1007/s00426-002-0105-6

URL : https://hal.archives-ouvertes.fr/hal-01384045

S. Michael and . Gazzaniga, The Cognitive Neurosciences, 2009.

S. Blakemore, The Developing Social Brain: Implications for Education, Neuron, vol.65, issue.6, pp.744-747, 2010.
DOI : 10.1016/j.neuron.2010.03.004

]. Blakemore, The social brain in adolescence, Nature Reviews Neuroscience, vol.123, issue.1, pp.267-277, 2008.
DOI : 10.1038/nrn2253

I. Dumontheil, R. Houlton, K. Christoff, and S. Blakemore, Development of relational reasoning during adolescence, Developmental Science, vol.25, issue.255, pp.15-24, 2010.
DOI : 10.1016/j.brainres.2009.05.096

R. Samuel, L. Chamberlain, A. Menzies, J. Hampshire, . Suckling et al., Orbitofrontal Dysfunction in Patients with Obsessive-Compulsive Disorder and Their Unaffected Relatives, Science, issue.5887, pp.321421-422, 2008.

R. Lewis, . Baxter, M. Jeffrey, . Schwartz, S. Kenneth et al., Caudate glucose metabolic rate changes with both drug and behavior therapy for obsessive-compulsive disorder, Archives of General Psychiatry, issue.9, pp.49681-689, 1992.

Z. Rita, . Goldstein, D. Nora, and . Volkow, Dysfunction of the prefrontal cortex in addiction: neuroimaging findings and clinical implications, Nature Reviews Neuroscience, vol.12, issue.652, pp.1-18, 2011.

D. Ruben, . Baler, D. Nora, and . Volkow, Drug addiction: the neurobiology of disrupted self-control. Trends in molecular medicine, pp.559-566, 2006.

S. Moritz, S. Todd, and . Woodward, Jumping to conclusions in delusional and non-delusional schizophrenic patients, British Journal of Clinical Psychology, vol.27, issue.Suppl. 20, pp.193-207, 2005.
DOI : 10.1017/S0033291796004540

URL : http://www3.telus.net/Todd_S_Woodward/pdfs/Jumping-to-conclusions.pdf

R. Jardri and S. Deneve, Circular inferences in schizophrenia, Brain, vol.136, issue.11, pp.3227-3241, 2013.
DOI : 10.1093/brain/awt257

URL : https://academic.oup.com/brain/article-pdf/136/11/3227/13795765/awt257.pdf

C. Paul, C. D. Fletcher, and . Frith, Perceiving is believing: a Bayesian approach to explaining the positive symptoms of schizophrenia, Nature Reviews Neuroscience, vol.10, issue.1, pp.48-58, 2008.

S. Todd, S. Woodward, M. Moritz, R. Menon, and . Klinge, Belief inflexibility in schizophrenia, Cognitive Neuropsychiatry, vol.13, issue.3, pp.267-277, 2008.

G. Barbalat, M. Rouault, N. Bazargani, S. Shergill, and S. Blakemore, The influence of prior expectations on facial expression discrimination in schizophrenia, Psychological Medicine, vol.10, issue.11, pp.2301-2311, 2012.
DOI : 10.1162/neco.1995.7.5.889

E. Valerian-chambon, G. Pacherie, P. Barbalat, N. Jacquet, C. Franck et al., Mentalizing under influence: abnormal dependence on prior expectations in patients with schizophrenia, Brain, vol.134, issue.12, pp.3728-3741, 2011.
DOI : 10.1093/brain/awr306

G. Barbalat, V. Chambon, N. Franck, E. Koechlin, and C. Farrer, Organization of Cognitive Control Within the Lateral Prefrontal Cortex in Schizophrenia, Archives of General Psychiatry, vol.66, issue.4, pp.377-386, 2009.
DOI : 10.1001/archgenpsychiatry.2009.10

URL : https://hal.archives-ouvertes.fr/hal-00383710

G. Barbalat, V. Chambon, P. J. Domenech, C. Ody, E. Koechlin et al., Impaired Hierarchical Control Within the Lateral Prefrontal Cortex in Schizophrenia, Biological Psychiatry, vol.70, issue.1, pp.73-80, 2011.
DOI : 10.1016/j.biopsych.2011.02.009

URL : https://hal.archives-ouvertes.fr/hal-00603225

E. Koechlin, C. Ody, and F. Kouneiher, The Architecture of Cognitive Control in the Human Prefrontal Cortex, Science, vol.302, issue.5648, pp.1181-1185, 2003.
DOI : 10.1126/science.1088545

G. Anne, . Collins, J. Michael, and . Frank, Cognitive control over learning: Creating, clustering, and generalizing task-set structure, Psychological Review, vol.120, issue.1, pp.190-229, 2013.

C. Christian, G. Ruff, E. Ugazio, and . Fehr, Changing Social Norm Compliance with Noninvasive Brain Stimulation, Science, vol.342, issue.6157, pp.482-484, 2013.

K. Sakai, E. Richard, and . Passingham, Prefrontal interactions reflect future task operations, Nature Neuroscience, vol.10, issue.1, pp.75-81, 2003.
DOI : 10.1006/nimg.1999.0484

K. Sakai, E. Richard, and . Passingham, Prefrontal Set Activity Predicts Rule-Specific Neural Processing during Subsequent Cognitive Performance, Journal of Neuroscience, vol.26, issue.4, pp.1211-1218, 2006.
DOI : 10.1523/JNEUROSCI.3887-05.2006

URL : http://www.jneurosci.org/content/jneuro/26/4/1211.full.pdf

E. Crone, K. Hoshi, and . Sakai, Neural Circuitry Underlying Rule Use in Humans and Nonhuman Primates, Journal of Neuroscience, vol.25, issue.45, pp.10347-10350, 2005.

A. Baddeley, Working memory, Current Biology, vol.20, issue.4, pp.136-140, 2010.
DOI : 10.1016/j.cub.2009.12.014

K. Oberauer, Access to information in working memory: Exploring the focus of attention., Journal of Experimental Psychology: Learning, Memory, and Cognition, vol.28, issue.3, p.411, 2002.
DOI : 10.1037/0278-7393.28.3.411

K. Oberauer and R. Kliegl, A formal model of capacity limits in working memory, Journal of Memory and Language, vol.55, issue.4, pp.601-626, 2006.
DOI : 10.1016/j.jml.2006.08.009

R. Lévy and P. Rakic, Association of Storage and Processing Functions in the Dorsolateral Prefrontal Cortex of the Nonhuman Primate, Journal of Neuroscience, vol.19, issue.12, pp.5149-5158, 1999.

M. Joaqu?n and . Fuster, The prefrontal cortex?an update: time is of the essence, Neuron, vol.30, issue.2, pp.319-333, 2001.

C. Padoa-schioppa, A. John, and . Assad, Neurons in the orbitofrontal cortex encode economic value, Nature, vol.106, issue.7090, pp.441223-226, 2006.
DOI : 10.1212/WNL.56.suppl_4.S6

M. Lebreton, S. Jorge, V. Michel, B. Thirion, and M. Pessiglione, An Automatic Valuation System in the Human Brain: Evidence from Functional Neuroimaging, Neuron, vol.64, issue.3, pp.431-439, 2009.
DOI : 10.1016/j.neuron.2009.09.040

URL : https://hal.archives-ouvertes.fr/hal-00504100

C. Prévost, M. Pessiglione, E. Météreau, M. Cléry-melin, and J. Dreher, Separate Valuation Subsystems for Delay and Effort Decision Costs, Journal of Neuroscience, vol.30, issue.42, pp.14080-14090, 2010.
DOI : 10.1523/JNEUROSCI.2752-10.2010

A. John, A. Clithero, and . Rangel, Informatic parcellation of the network involved in the computation of subjective value, Social Cognitive and Affective Neuroscience, p.106, 2013.

S. Vikram, A. Chib, S. Rangel, J. Shimojo, and . Doherty, Evidence for a Common Representation of Decision Values for Dissimilar Goods in Human Ventromedial Prefrontal Cortex, Journal of Neuroscience, vol.29, issue.39, pp.12315-12320, 2009.

H. Plassmann, P. John, A. Doherty, and . Rangel, Orbitofrontal Cortex Encodes Willingness to Pay in Everyday Economic Transactions, Journal of Neuroscience, vol.27, issue.37, pp.9984-9988, 2007.
DOI : 10.1523/JNEUROSCI.2131-07.2007

URL : http://www.jneurosci.org/content/jneuro/27/37/9984.full.pdf

P. John and . Doherty, The problem with value, Neuroscience and Biobehavioral Reviews, vol.43, pp.259-268, 2014.

K. Ryan, . Jessup, P. John, and . Doherty, Distinguishing informational from valuerelated encoding of rewarding and punishing outcomes in the human brain, European Journal of Neuroscience, vol.39, issue.11, pp.2014-2026, 2014.

N. Alan, P. Hampton, J. Bossaerts, and . Doherty, The Role of the Ventromedial Prefrontal Cortex in Abstract State-Based Inference during Decision Making in Humans, Journal of Neuroscience, vol.26, issue.32, pp.8360-8367, 2006.

L. Joshua, . Jones, R. Guillem, . Esber, A. Michael et al., Orbitofrontal Cortex Supports Behavior and Learning Using Inferred But Not Cached Values, Science, issue.6109, pp.338953-956, 2012.

M. Roy, D. Shohamy, D. Tor, and . Wager, Ventromedial prefrontal-subcortical systems and the generation of affective meaning, Trends in Cognitive Sciences, vol.16, issue.3, pp.147-156, 2012.
DOI : 10.1016/j.tics.2012.01.005

E. Mark, T. E. Walton, . Behrens, J. Mark, . Buckley et al., Separable Learning Systems in the Macaque Brain and the Role of Orbitofrontal Cortex in Contingent Learning, Neuron, issue.6, pp.65927-939, 2010.

E. Mark, T. E. Walton, . Behrens, P. Maryann, M. Noonan et al., Giving credit where credit is due: orbitofrontal cortex and valuation in an uncertain world, Annals of the New York Academy of Sciences, vol.1239, issue.1, pp.14-24, 2011.

H. Peter, . Rudebeck, A. Elisabeth, and . Murray, The Orbitofrontal Oracle: Cortical Mechanisms for the Prediction and Evaluation of Specific Behavioral Outcomes

C. Robert, . Wilson, K. Yuji, G. Takahashi, Y. Schoenbaum et al., Orbitofrontal Cortex as a Cognitive Map of Task Space, Neuron, vol.81, issue.2, pp.267-279, 2014.

A. Brent, L. Vogt, . Vogt, B. Nuri, G. Farber et al., Architecture and neurocytology of monkey cingulate gyrus, The Journal of Comparative Neurology, vol.485, issue.3, pp.218-239, 2005.

O. Devinsky, J. Martha, . Morrell, A. Brent, and . Vogt, Contributions of anterior cingulate cortex to behaviour, Brain, vol.118, issue.1, pp.279-306, 1995.
DOI : 10.1093/brain/118.1.279

J. William, B. Gehring, . Goss, G. Michael, . Coles et al., A neural system for error detection and compensation, Psychological Science, vol.4, issue.6, pp.385-390, 1993.

B. Clay, . Holroyd, G. Michael, and . Coles, The neural basis of human error processing: Reinforcement learning, dopamine, and the error-related negativity, Psychological Review, vol.109, issue.4, pp.679-709, 2002.

J. Michael, . Frank, S. Brion, T. Woroch, and . Curran, Error-Related Negativity Predicts Reinforcement Learning and Conflict Biases, Neuron, vol.47, issue.4, pp.495-501, 2005.

J. Martin, J. Herrmann, A. Römmler, A. Ehlis, A. J. Heidrich et al., Source localization (LORETA) of the error-relatednegativity (ERN/Ne) and positivity (Pe), Cognitive Brain Research, vol.20, issue.2, pp.294-299, 2004.

C. Roger, G. Christian, F. Bénar, T. Vidal, B. Hasbroucq et al., Rostral Cingulate Zone and correct response monitoring: ICA and source localization evidences for the unicity of correct- and error-negativities, NeuroImage, vol.51, issue.1, pp.391-403, 2010.
DOI : 10.1016/j.neuroimage.2010.02.005

URL : https://hal.archives-ouvertes.fr/hal-01293542

L. Spieser, T. Wery-van-den-wildenberg, R. Hasbroucq, B. Ridderinkhof, and . Burle, Controlling Your Impulses: Electrical Stimulation of the Human Supplementary Motor Complex Prevents Impulsive Errors, Journal of Neuroscience, vol.35, issue.7, pp.3010-3015, 2015.
DOI : 10.1523/JNEUROSCI.1642-14.2015

URL : https://hal.archives-ouvertes.fr/hal-01310100

M. Matthew, . Botvinick, S. Todd, . Braver, M. Deanna et al., Conflict monitoring and cognitive control, Psychological Review, vol.108, issue.3, p.624, 2001.

M. Matthew, . Botvinick, D. Jonathan, . Cohen, S. Cameron et al., Conflict monitoring and anterior cingulate cortex: an update, Trends in Cognitive Sciences, vol.8, issue.12, pp.539-546, 2004.

M. Deanna, . Barch, S. Todd, E. Braver, T. Akbudak et al., Anterior Cingulate Cortex and Response Conflict: Effects of Response Modality and Processing Domain, Cerebral cortex, vol.11, pp.837-848, 1991.

B. Burle, S. Allain, F. Vidal, and T. Hasbroucq, Sequential Compatibility Effects and Cognitive Control: Does Conflict Really Matter? Journal of Experimental Psychology: Human Perception and Performance, pp.831-837, 2005.

F. Kouneiher, S. Charron, and E. Koechlin, Motivation and cognitive control in the human prefrontal cortex, Nature Neuroscience, vol.19, issue.7, pp.939-945, 2009.
DOI : 10.1016/S1053-8119(03)00058-2

B. Simon, . Eickhoff, R. Angela, . Laird, T. Peter et al., Functional Segregation of the Human Dorsomedial Prefrontal Cortex, Cerebral cortex, pp.1-18, 1991.

P. Kang, J. Lee, S. Sul, and H. Kim, Dorsomedial prefrontal cortex activity predicts the accuracy in estimating others' preferences, Frontiers in Human Neuroscience, vol.7, pp.1-11, 2013.
DOI : 10.3389/fnhum.2013.00686

A. Waytz, J. Zaki, and J. P. Mitchell, Response of Dorsomedial Prefrontal Cortex Predicts Altruistic Behavior, Journal of Neuroscience, vol.32, issue.22, pp.7646-7650, 2012.
DOI : 10.1523/JNEUROSCI.6193-11.2012

Y. Benjamin, . Hayden, M. John, . Pearson, L. Michael et al., Neuronal basis of sequential foraging decisions in a patchy environment, pp.933-939, 2011.

N. Furl, B. Bruno, and . Averbeck, Parietal Cortex and Insula Relate to Evidence Seeking Relevant to Reward-Related Decisions, Journal of Neuroscience, vol.31, issue.48, pp.3117572-17582, 2011.
DOI : 10.1523/JNEUROSCI.4236-11.2011

URL : http://www.jneurosci.org/content/jneuro/31/48/17572.full.pdf

N. Kolling, T. E. Behrens, B. Rogier, M. Mars, and . Rushworth, Neural Mechanisms of Foraging, Science, vol.21, issue.4, pp.95-98, 2012.
DOI : 10.1016/j.neuroimage.2003.12.023

K. Matsumoto, Neuronal Correlates of Goal-Based Motor Selection in the Prefrontal Cortex, Science, vol.301, issue.5630, pp.229-232, 2003.
DOI : 10.1126/science.1084204

H. William, J. Alexander, W. Silvetti, T. Alexander, J. W. Verguts et al., Medial prefrontal cortex as an actionoutcome predictor From conflict management to reward-based decision making: Actors and critics in primate medial frontal cortex, Nature Neuroscience Neuroscience and Biobehavioral Reviews, vol.1486, issue.10, pp.1338-1344, 2011.

D. Erie, . Boorman, F. Matthew, T. E. Rushworth, and . Behrens, Ventromedial prefrontal and anterior cingulate cortex adopt choice and default reference frames during sequential multi-alternative choice, Journal of Neuroscience, vol.33, issue.6, pp.2242-2253, 2013.

W. Paul, I. Burgess, . Dumontheil, J. Sam, and . Gilbert, The gateway hypothesis of rostral prefrontal cortex (area 10) function, Trends in Cognitive Sciences, vol.11, issue.7, pp.290-298, 2007.

U. Frith, D. Christopher, and . Frith, Development and neurophysiology of mentalizing, Philosophical Transactions of the Royal Society B: Biological Sciences, vol.358, issue.1431, pp.459-473, 1431.
DOI : 10.1098/rstb.2002.1218

E. Koechlin and A. Hyafil, Anterior Prefrontal Function and the Limits of Human Decision-Making, Science, vol.298, issue.5598, pp.594-598, 2007.
DOI : 10.1126/science.298.5598.1569

E. Koechlin, The role of the anteriorprefrontal cortex in human cognition, Nature, pp.1-4, 1999.

D. Nathaniel, . Daw, P. John, P. Doherty, B. Dayan et al., Cortical substrates for exploratory decisions in humans, Nature, vol.441, issue.7095, pp.876-879, 2006.

D. Erie, T. E. Boorman, . Behrens, W. Mark, M. Woolrich et al., How Green Is the Grass on the Other Side? Frontopolar Cortex and the Evidence in Favor of Alternative Courses of Action, Neuron, vol.62, issue.5, pp.733-743, 2009.

M. Donoso, A. G. Collins, and E. Koechlin, Foundations of human reasoning in the prefrontal cortex, Science, vol.14, issue.6, pp.1481-1486, 2014.
DOI : 10.1162/089976602753712972

M. Stephen, . Fleming, S. Rimona, Z. Weil, R. J. Nagy et al., Relating introspective accuracy to individual differences in brain structure

M. Stephen, R. J. Fleming, and . Dolan, The neural basis of metacognitive ability, Philosophical Transactions of the Royal Society B: Biological Sciences, vol.367, pp.1338-1349, 1594.

C. Kent, . Berridge, E. Terry, and . Robinson, Parsing reward, Trends in Neurosciences, vol.26, issue.9, pp.507-513, 2003.

D. Kahneman and A. Tversky, Prospect Theory: An Analysis of Decision under Risk, Econometrica, vol.47, issue.2, pp.263-292, 1979.
DOI : 10.2307/1914185

A. Tversky, H. Richard, and . Thaler, Anomalies: Preference Reversals, Journal of Economic Perspectives, vol.4, issue.2, pp.201-211, 1990.
DOI : 10.1257/jep.4.2.201

URL : https://pubs.aeaweb.org/doi/pdfplus/10.1257/jep.4.2.201

A. Tversky and D. Kahneman, The framing of decisions and the psychology of choice, Science, vol.211, issue.4481, pp.453-458, 1981.
DOI : 10.1126/science.7455683

L. Festinger, A theory of cognitive dissonance, 1962.

K. Izuma and K. Matsumoto, Neural correlates of cognitive dissonance and choice-induced preference change, Proceedings of the National Academy of Sciences, pp.1-6, 2010.
DOI : 10.1038/46035

M. Salti, M. Karoui, L. Maillet, and . Naccache, Cognitive Dissonance Resolution Is Related to Episodic Memory, PLoS ONE, vol.17, issue.9, pp.108579-108588, 2014.
DOI : 10.1371/journal.pone.0108579.g003

URL : https://hal.archives-ouvertes.fr/hal-01369506

R. Duncan and L. , The Choice Axiom after Twenty Years, Journal of Mathematical Psychology, vol.15, pp.215-233, 1977.

E. Madeleine, J. Sharp, . Viswanathan, J. Linda, J. J. Lanyon et al., Sensitivity and Bias in Decision-Making under Risk: Evaluating the Perception of Reward, Its Probability and Value, PLoS ONE, vol.7, issue.4, p.33460, 2012.

M. Sabrina, . Tom, R. Craig, C. Fox, . Trepel et al., The neural basis of loss aversion in decision-making under risk, Science, issue.5811, pp.315-515, 2007.

S. Richard, . Sutton, G. Andrew, and . Barto, Reinforcement Learning: An Introduction, 1998.

A. Robert, . Rescorla, R. Allan, and . Wagner, A theory of Pavlovian conditioning: Variations in the effectiveness of reinforcement and nonreinforcement. Classical conditioning current research and theory, 1972.

D. Nathaniel, Y. Daw, P. Niv, and . Dayan, Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control, Nature Neuroscience, vol.8, issue.12, pp.1704-1711, 2005.

A. Dezfouli, W. Bernard, and . Balleine, Actions, Action Sequences and Habits: Evidence That Goal-Directed and Habitual Action Control Are Hierarchically Organized, PLoS Computational Biology, vol.106, issue.3, pp.1003364-1003378, 2013.
DOI : 10.1371/journal.pcbi.1003364.t002

URL : https://doi.org/10.1371/journal.pcbi.1003364

K. Doya, K. Samejima, K. Katagiri, and M. Kawato, Multiple Model-Based Reinforcement Learning, Neural Computation, vol.3, issue.6, pp.1347-1369, 2002.
DOI : 10.1016/S1364-6613(98)01221-2

K. Samejima and K. Doya, Multiple Representations of Belief States and Action Values in Corticobasal Ganglia Loops, Annals of the New York Academy of Sciences, vol.20, issue.1, pp.213-228, 2007.
DOI : 10.1038/nrn1884

J. Glascher, N. Daw, P. Dayan, and J. Doherty, States versus Rewards: Dissociable Neural Prediction Error Signals Underlying Model-Based and Model-Free Reinforcement Learning, Neuron, vol.66, issue.4, pp.585-595, 2010.
DOI : 10.1016/j.neuron.2010.04.016

D. Nathaniel, . Daw, J. Samuel, B. Gershman, P. Seymour et al., Model-Based Influences on Humans' Choices and Striatal Prediction Errors, Neuron, vol.69, issue.6, pp.1204-1215, 2011.

N. Suzanne and . Haber, The primate basal ganglia: parallel and integrative networks, Journal of Chemical Neuroanatomy, vol.26, issue.4, pp.317-330, 2003.

W. Schultz, P. Dayan, and P. Montague, A Neural Substrate of Prediction and Reward, Science, vol.263, issue.5149, pp.1593-1599, 1997.
DOI : 10.1126/science.7508638

W. Schultz, Predictive Reward Signal of Dopamine Neurons, Journal of Neurophysiology, vol.80, pp.1-28, 1998.

D. Christopher, . Fiorillo, N. Philippe, W. Tobler, and . Schultz, Discrete Coding of Reward Probability and Uncertainty by Dopamine Neurons, Science, vol.299, issue.5614, pp.1898-1902, 2003.

J. Michael, . Frank, C. Lauren, . Seeberger, C. Randall et al., By Carrot or by Stick: Cognitive Reinforcement Learning in Parkinsonism, Science, vol.306, issue.1, pp.1940-1943, 2004.

S. Palminteri, M. Lebreton, Y. Worbe, D. Grabli, A. Hartmann et al., Pharmacological modulation of subliminal learning in Parkinson's and Tourette's syndromes, Proceedings of the National Academy of Sciences, pp.19179-19184, 2009.
DOI : 10.1093/brain/awn045

G. Sescousse, J. Redouté, and J. Dreher, The Architecture of Reward Value Coding in the Human Orbitofrontal Cortex, Journal of Neuroscience, vol.30, issue.39, pp.13095-13104, 2010.
DOI : 10.1523/JNEUROSCI.3501-10.2010

P. John, P. Doherty, J. Dayan, R. Schultz, K. Deichmann et al., Dissociable Roles of Ventral and Dorsal Striatum in Instrumental Conditioning, Science, issue.5669, pp.304452-454, 2004.

P. John, A. Doherty, H. Hampton, and . Kim, Model-Based fMRI and Its Application to Reward Learning and Decision Making, Annals of the New York Academy of Sciences, vol.1104, issue.1, pp.35-53, 2007.

M. Pessiglione, B. Seymour, G. Flandin, R. J. Dolan, and C. D. Frith, Dopamine-dependent prediction errors underpin reward-seeking behaviour in humans, Nature, vol.15, issue.7106, pp.4421042-1045, 2006.
DOI : 10.1002/1531-8257(200009)15:5<869::AID-MDS1016>3.0.CO;2-I

URL : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2636869/pdf

L. Tremblay and W. Schultz, Relative reward preference in primate orbitofrontal cortex, Nature, vol.382, issue.6729, pp.704-708, 1999.
DOI : 10.1038/382629a0

O. Bartra, T. Joseph, J. W. Mcguire, and . Kable, The valuation system: A coordinate-based meta-analysis of BOLD fMRI experiments examining neural correlates of subjective value, NeuroImage, vol.76, pp.412-427, 2013.
DOI : 10.1016/j.neuroimage.2013.02.063

C. Helen, R. J. Barron, T. E. Dolan, and . Behrens, Online evaluation of novel choices by simultaneous representation of multiple memories, pp.1492-1498, 2013.

S. Bouret, J. Barry, and . Richmond, Ventromedial and Orbital Prefrontal Neurons Differentially Encode Internally and Externally Driven Motivational Values in Monkeys, Journal of Neuroscience, vol.30, issue.25, pp.8591-8601, 2010.
DOI : 10.1523/JNEUROSCI.0049-10.2010

URL : http://www.jneurosci.org/content/jneuro/30/25/8591.full.pdf

K. Matsumoto, W. Suzuki, and K. Tanaka, Neuronal Correlates of Goal-Based Motor Selection in the Prefrontal Cortex, Science, vol.301, issue.5630, pp.229-232, 2003.
DOI : 10.1126/science.1084204

C. Amiez, J. Joseph, and E. Procyk, Reward Encoding in the Monkey Anterior Cingulate Cortex, Cerebral Cortex, vol.16, issue.7, pp.1040-1055, 2006.
DOI : 10.1093/cercor/bhj046

URL : https://hal.archives-ouvertes.fr/inserm-00132137

H. Peter, . Rudebeck, J. Mark, . Buckley, E. Mark et al., A role for the macaque anterior cingulate gyrus in social valuation, Science, issue.5791, pp.3131310-1312, 2006.

E. Timothy, . Behrens, T. Laurence, . Hunt, W. Mark et al., Associative learning of social value, Nature, vol.456, issue.7219, pp.245-249, 2008.

X. Cai and C. Padoa-schioppa, Neuronal Encoding of Subjective Value in Dorsal and Ventral Anterior Cingulate Cortex, Journal of Neuroscience, vol.32, issue.11, pp.3791-3808, 2012.
DOI : 10.1523/JNEUROSCI.3864-11.2012

N. Camille, A. Tsuchida, K. Lesley, and . Fellows, Double Dissociation of Stimulus-Value and Action-Value Learning in Humans with Orbitofrontal or Anterior Cingulate Cortex Damage, Journal of Neuroscience, vol.31, issue.42, pp.3115048-15052, 2011.
DOI : 10.1523/JNEUROSCI.3164-11.2011

B. Sean, . Ostlund, W. Bernard, and . Balleine, Orbitofrontal cortex mediates outcome encoding in Pavlovian but not instrumental conditioning, The Journal of neuroscience : the official journal of the Society for Neuroscience, vol.27, issue.18, pp.4819-4825, 2007.

H. Peter, . Rudebeck, E. Timothy, . Behrens, W. Steven et al., Frontal Cortex Subregions Play Distinct Roles in Choices between Actions and Stimuli, Journal of Neuroscience, vol.28, issue.51, pp.13775-13785, 2008.

R. Peyron, B. Laurent, and L. Garcia-larrea, Functional imaging of brain responses to pain. A review and meta-analysis (2000), Neurophysiologie Clinique/Clinical Neurophysiology, vol.30, issue.5, pp.263-288, 2000.
DOI : 10.1016/S0987-7053(00)00227-6

S. Palminteri, D. Justo, C. Jauffret, B. Pavlicek, A. Dauta et al., Critical Roles for Anterior Insula and Dorsal Striatum in Punishment-Based Avoidance Learning, Neuron, vol.76, issue.5, pp.998-1009, 2012.
DOI : 10.1016/j.neuron.2012.10.017

B. Joshua, C. Tenenbaum, . Kemp, L. Thomas, N. Griffiths et al., How to Grow a Mind: Statistics, Structure, and Abstraction, Science, vol.331, pp.1279-1285, 2011.

M. Oaksford and N. Chater, Pr??cis of Bayesian Rationality: The Probabilistic Approach to Human Reasoning, Behavioral and Brain Sciences, vol.14, issue.01, pp.69-84, 2009.
DOI : 10.1017/S0140525X00070801

A. Pouget, M. Jeffrey, W. J. Beck, . Ma, E. Peter et al., Probabilistic brains: knowns and unknowns, Nature Neuroscience, vol.22, issue.9, pp.1170-1178, 2013.
DOI : 10.1016/j.neuron.2012.03.016

URL : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4487650/pdf

J. Angela, P. Yu, and . Dayan, Uncertainty, Neuromodulation, and Attention, Neuron, vol.46, issue.4, pp.681-692, 2005.

E. Payzan-lenestour and P. Bossaerts, Risk, Unexpected Uncertainty, and Estimation Uncertainty: Bayesian Learning in Unstable Settings, PLoS Computational Biology, vol.10, issue.9, p.1001048, 2011.
DOI : 10.1371/journal.pcbi.1001048.s005

D. Jonathan, A. J. Cohen, and . Yu, Sequential effects: superstition or rational behavior? Advances in neural information processing systems, pp.1873-1880, 2009.

L. Thomas, N. Griffiths, C. Chater, A. Kemp, J. B. Perfors et al., Probabilistic models of cognition: exploring representations and inductive biases, Trends in Cognitive Sciences, vol.14, issue.8, pp.357-364, 2010.

S. Goldwater, L. Thomas, M. Griffiths, and . Johnson, A Bayesian framework for word segmentation: Exploring the effects of context, Cognition, vol.112, issue.1, pp.21-54, 2009.
DOI : 10.1016/j.cognition.2009.03.008

C. Kemp and J. B. Tenenbaum, Structured statistical models of inductive reasoning., Psychological Review, vol.116, issue.1, pp.20-58, 2009.
DOI : 10.1037/a0014282

F. Xu and J. B. Tenenbaum, Word learning as Bayesian inference., Psychological Review, vol.114, issue.2, pp.245-272, 2007.
DOI : 10.1037/0033-295X.114.2.245

K. Friston, The anatomy of choice: active inference and agency, Frontiers in Human Neuroscience, vol.7, issue.598, pp.1-18, 2013.
DOI : 10.3389/fnhum.2013.00598

K. Friston, J. Kilner, and L. Harrison, A free energy principle for the brain, Journal of Physiology-Paris, vol.100, issue.1-3, pp.70-87, 2006.
DOI : 10.1016/j.jphysparis.2006.10.001

J. Karl, J. Friston, J. Daunizeau, S. J. Kilner, and . Kiebel, Action and behavior: a free-energy formulation, Biological Cybernetics, vol.102, issue.3, pp.227-260, 2010.

Y. Niv, R. Daniel, A. Geana, J. Samuel, Y. C. Gershman et al., Reinforcement Learning in Multidimensional Environments Relies on Attention Mechanisms, Journal of Neuroscience, vol.35, issue.21, pp.8145-8157, 2015.
DOI : 10.1523/JNEUROSCI.2978-14.2015

URL : http://www.jneurosci.org/content/jneuro/35/21/8145.full.pdf

A. Geana and Y. Niv, Causal Model Comparison Shows That Human Representation Learning Is Not Bayesian, Cold Spring Harbor Symposia on Quantitative Biology, vol.79, pp.24851-24860, 2015.
DOI : 10.1101/sqb.2014.79.024851

P. Miguel, . Eckstein, K. Craig, . Abbey, T. Binh et al., Perceptual learning through optimization of attentional weighting: Human versus optimal Bayesian learner, Journal of Vision, vol.4, issue.12, pp.3-3, 2004.

X. Jill, S. Reilly, . Jbabdi, E. Timothy, and . Behrens, How can a Bayesian approach inform neuroscience?, European Journal of Neuroscience, vol.35, issue.7, pp.1169-1179, 2012.

G. Coricelli, D. Hugo, M. Critchley, . Joffily, P. John et al., Regret and its avoidance: a neuroimaging study of choice behavior, Nature Neuroscience, vol.19, issue.9, pp.1255-1262, 2005.
DOI : 10.1016/S1053-8119(03)00073-9

G. Coricelli and A. Rustichini, Counterfactual thinking and emotions: regret and envy learning, Philosophical Transactions of the Royal Society B: Biological Sciences, vol.65, issue.5, pp.241-247, 1538.
DOI : 10.1006/obhd.1996.0013

URL : http://rstb.royalsocietypublishing.org/content/royptb/365/1538/241.full.pdf

S. Palminteri, M. Khamassi, M. Joffily, and G. Coricelli, Contextual modulation of value signals in reward and punishment learning, Nature Communications, vol.23, pp.1-14, 2015.
DOI : 10.1007/s10334-006-0067-6

URL : https://hal.archives-ouvertes.fr/halshs-01236045

A. Collins and E. Koechlin, Reasoning, Learning, and Creativity: Frontal Lobe Function and Human Decision-Making, PLoS Biology, vol.19, issue.3, pp.1001293-1001309, 2012.
DOI : 10.1371/journal.pbio.1001293.s008

URL : https://hal.archives-ouvertes.fr/inserm-00706739

G. Schraw, Promoting General Metacognitive Awareness, Instructional Science, vol.26, pp.113-125, 1998.
DOI : 10.1007/978-94-017-2243-8_1

E. Timothy, . Behrens, W. Mark, . Woolrich, E. Mark et al., Learning the value of information in an uncertain world, Nature Neuroscience, vol.10, issue.9, pp.1214-1221, 2007.

G. Bush, P. Luu, I. Michael, and . Posner, Cognitive and emotional influences in anterior cingulate cortex, Trends in Cognitive Sciences, vol.4, issue.6, pp.215-222, 2000.
DOI : 10.1016/S1364-6613(00)01483-2

A. Etkin, T. Egner, and R. Kalisch, Emotional processing in anterior cingulate and medial prefrontal cortex, Trends in Cognitive Sciences, vol.15, issue.2, pp.85-93, 2011.
DOI : 10.1016/j.tics.2010.11.004

URL : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3035157/pdf

J. Alexander, . Shackman, V. Tim, . Salomons, A. Heleen et al., The integration of negative affect, pain and cognitive control in the cingulate cortex, pp.154-167, 2011.

M. Keramati, A. Dezfouli, and P. Piray, Speed/Accuracy Trade-Off between the Habitual and the Goal-Directed Processes, PLoS Computational Biology, vol.35, issue.5, pp.1002055-1002076, 2011.
DOI : 10.1371/journal.pcbi.1002055.t002

URL : https://doi.org/10.1371/journal.pcbi.1002055

H. David and . Brainard, The Psychophysics Toolbox, Spatial Vision, vol.10, issue.4, pp.433-436, 1997.

M. Douglas and . Hawkins, The Problem of Overfitting, Journal of Chemical Information and Modeling, vol.44, issue.1, pp.1-12, 2004.

W. Gaissmaier, J. Lael, and . Schooler, The smart potential behind probability matching, Cognition, vol.109, issue.3, pp.416-422, 2008.
DOI : 10.1016/j.cognition.2008.09.007

D. Kahneman and A. Tversky, Choices, values, and frames., American Psychologist, vol.39, issue.4, pp.341-350, 1984.
DOI : 10.1037/0003-066X.39.4.341

A. Tversky and D. Kahneman, Judgment under Uncertainty: Heuristics and Biases, Science, vol.185, pp.1125-1131, 1974.
DOI : 10.21236/ad0767426

H. Zhang, T. Laurence, and . Maloney, Ubiquitous Log Odds: A Common Representation of Probability and Frequency Distortion in Perception, Action, and Cognition, Frontiers in Neuroscience, vol.6, issue.1, pp.1-14, 2012.
DOI : 10.3389/fnins.2012.00001

M. Khamassi, P. Enel, P. F. Dominey, and E. Procyk, Medial prefrontal cortex and the adaptive regulation of reinforcement learning parameters
DOI : 10.1016/B978-0-444-62604-2.00022-8

URL : https://hal.archives-ouvertes.fr/hal-01628829

R. Deichmann, A. Jay, C. A. Gottfried, R. Hutton, and . Turner, Optimized EPI for fMRI studies of the orbitofrontal cortex, NeuroImage, vol.19, issue.2, pp.430-441, 2003.
DOI : 10.1016/S1053-8119(03)00073-9

O. John, A. N. Doherty, H. Hampton, and . Kim, Model-Based fMRI and Its Application to Reward Learning and Decision Making, Annals of the New York Academy of Sciences, vol.1104, issue.1, pp.35-53, 2007.

J. Grinband, D. Tor, M. Wager, . Lindquist, P. Vincent et al., Detection of time-varying signals in event-related fMRI designs, NeuroImage, vol.43, issue.3, pp.509-520, 2008.
DOI : 10.1016/j.neuroimage.2008.07.065

F. Joseph, . Hair, C. William, . Black, J. Barry et al., Multivariate data analysis, 2006.

N. Kriegeskorte, K. Simmons, S. Patrick, C. I. Bellgowan, and . Baker, Circular analysis in systems neuroscience: the dangers of double dipping, Nature Neuroscience, vol.12, issue.5, pp.535-540, 2009.
DOI : 10.1016/j.neuroimage.2004.07.022

G. Sescousse, X. Caldú, J. Segura, and . Dreher, Processing of primary and secondary rewards: A quantitative meta-analysis and review of human functional neuroimaging studies, Neuroscience & Biobehavioral Reviews, vol.37, issue.4, pp.681-696, 2013.
DOI : 10.1016/j.neubiorev.2013.02.002

J. Trommershäuser, T. Laurence, . Maloney, S. Michael, and . Landy, Decision making, movement planning and statistical decision theory, Trends in Cognitive Sciences, vol.12, issue.8, pp.291-297, 2008.
DOI : 10.1016/j.tics.2008.04.010

S. Dehaene, Psychologie cognitive expérimentale, Vers une science de la vie mentale, pp.277-301, 2006.

M. Lebreton, M. Bertoux, C. Boutet, S. Lehericy, B. Dubois et al., A Critical Role for the Hippocampus in the Valuation of Imagined Outcomes, PLoS Biology, vol.55, issue.10, pp.1001684-1001697, 2013.
DOI : 10.1371/journal.pbio.1001684.s002

URL : https://hal.archives-ouvertes.fr/hal-01596344

W. Steven, . Kennerley, E. Mark, . Walton, E. Timothy et al., Optimal decision making and the anterior cingulate cortex, Nature Neuroscience, vol.9, issue.7, pp.940-947, 2006.

M. Jeffrey, W. J. Beck, X. Ma, . Pitkow, E. Peter et al., Not Noisy, Just Wrong: The Role of Suboptimal Inference in Behavioral Variability, Neuron, vol.74, issue.1, pp.30-39, 2012.

R. Hertwig, G. Barron, E. U. Weber, and I. Erev, Decisions from Experience and the Effect of Rare Events in Risky Choice, Psychological Science, vol.19, issue.8, pp.534-539, 2004.
DOI : 10.1037/0033-295X.111.2.430

R. Michalczuk, H. Bowden-jones, A. Verdejo-garcia, and L. Clark, Impulsivity and cognitive distortions in pathological gamblers attending the UK National Problem Gambling Clinic: a preliminary report, Psychological Medicine, vol.41, issue.12, pp.412625-2635, 2011.
DOI : 10.1017/S003329171100095X

C. Summerfield and K. Tsetsos, Do humans make good decisions?, Trends in Cognitive Sciences, vol.19, issue.1, pp.27-34, 2015.
DOI : 10.1016/j.tics.2014.11.005

URL : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4286584/pdf

F. Lieder, M. Hsu, L. Thomas, and . Griffiths, The high availability of extreme events serves resource-rational decision-making, Proceedings of the th Annual Conference of the Cognitive Science Society, pp.1-6, 2014.

C. Summerfield, E. Timothy, E. Behrens, and . Koechlin, Perceptual Classification in a Rapidly Changing Environment, Neuron, vol.71, issue.4, pp.725-736, 2011.
DOI : 10.1016/j.neuron.2011.06.022

URL : https://doi.org/10.1016/j.neuron.2011.06.022

B. Bradley, . Doll, A. Dylan, . Simon, and . Daw, The ubiquity of modelbased reinforcement learning, Current Opinion in Neurobiology, vol.22, issue.6, pp.1075-1081, 2012.

A. Dezfouli, W. Bernard, and . Balleine, Actions, Action Sequences and Habits: Evidence That Goal-Directed and Habitual Action Control Are Hierarchically Organized, PLoS Computational Biology, vol.106, issue.3, pp.1003364-1003378, 2013.
DOI : 10.1371/journal.pcbi.1003364.t002

URL : https://doi.org/10.1371/journal.pcbi.1003364

K. Teffer and K. Semendeferi, Human prefrontal cortex, Progress in brain research, pp.191-2012
DOI : 10.1016/B978-0-444-53860-4.00009-X

D. Kersten and A. Yuille, Bayesian models of object perception, Current Opinion in Neurobiology, vol.13, issue.2, pp.150-158, 2003.
DOI : 10.1016/S0959-4388(03)00042-4

K. Tsetsos, V. Wyart, P. Shorkey, and C. Summerfield, Neural mechanisms of economic commitment in the human medial prefrontal cortex. eLife, pp.1-17, 2014.

F. Esposito, A. Bertolino, T. Scarabino, V. Latorre, G. Blasi et al., Independent component model of the default-mode brain function: Assessing the impact of active thinking, Brain Research Bulletin, vol.70, issue.4-6, pp.263-269, 2006.
DOI : 10.1016/j.brainresbull.2006.06.012

P. Fransson, How default is the default mode of brain function? Neuropsychologia, pp.2836-2845, 2006.

J. Clithero and A. Rangel, Informatic parcellation of the network involved in the computation of subjective value, Social Cognitive and Affective Neuroscience, vol.9, issue.9, pp.1-53, 2013.
DOI : 10.1093/scan/nst106

M. Cornelia-klein-flugge, C. Helen, . Barron, H. Kay, R. J. Brodersen et al., Segregated Encoding of Reward-Identity and Stimulus-Reward Associations in Human Orbitofrontal Cortex, Journal of Neuroscience, vol.33, issue.7, pp.3202-3211, 2013.
DOI : 10.1523/JNEUROSCI.2532-12.2013

K. Ryan, . Jessup, P. John, and . Doherty, Distinguishing informational from valuerelated encoding of rewarding and punishing outcomes in the human brain, European Journal of Neuroscience, vol.39, issue.11, pp.2014-2026, 2014.

T. Kahnt, Q. Soyoung, J. Park, P. N. Haynes, and . Tobler, Disentangling neural representations of value and salience in the human brain, Proceedings of the National Academy of Sciences, pp.5000-5005, 2014.
DOI : 10.1038/nn.2303

. Benedetto-de-martino, M. Stephen, N. Fleming, R. J. Garrett, and . Dolan, Confidence in value-based choice, Nature Neuroscience, vol.16, issue.1, pp.105-110, 2012.
DOI : 10.1007/s12021-008-9042-x

M. Stephen, B. Fleming, Y. Maniscalco, N. Ko, T. Amendi et al., Action-specific disruption of perceptual confidence, Psychological Science, vol.26, issue.1, pp.89-98, 2015.

M. Lebreton, R. Abitbol, J. Daunizeau, and M. Pessiglione, Automatic integration of confidence in the brain valuation signal, Nature Neuroscience, vol.25, issue.8, pp.1159-1167, 2015.
DOI : 10.1016/j.neuroimage.2005.01.013

. Hayden, The firing rate in dACC neurons increased with time spent in a food patch, up to a certain threshold triggering patch leaving and exploration (reproduced from, p.15, 2011.

M. Double-dissociation, VMPFC regarding choice-independent (quadratic) brain activations. Left panel: 3D rendering of parametric brain activations correlating with relative chosen belief 2 (blue) and relative chosen affective value 2 (red) thresholded at p < 0, p.5