M. Guitart-masip, E. Duzel, R. Dolan, and P. Dayan, Action versus valence in decision making, Trends Cogn. Sci, vol.18, pp.194-202, 2014.

B. Knutson, K. Katovich, and G. Suri, Inferring affect from fMRI data, Trends Cogn. Sci, vol.18, pp.422-428, 2014.

E. Yechiam and G. Hochman, Losses as modulators of attention: review and analysis of the unique effects of losses over gains, Psychol. Bull, vol.139, pp.497-518, 2013.

R. S. Sutton and A. G. Barto, Reinforcement learning: an introduction, IEEE Trans. Neural Netw, vol.9, pp.1054-1054, 1998.

R. A. Rescorla and A. R. Wagner, A theory of Pavlovian conditioning: variations in the effectiveness of reinforcement and nonreinforcement, Class. Cond. II Curr. Res. Theory, vol.2, pp.64-99, 1972.

W. Schultz, P. Dayan, and P. R. Montague, A neural substrate of prediction and reward, Science, vol.275, pp.1593-1599, 1997.

J. O'doherty, Dissociable roles of ventral and dorsal striatum in instrumental conditioning, Science, vol.304, pp.452-454, 2004.

M. J. Frank, L. C. Seeberger, and R. C. O'reilly, By carrot or by stick: cognitive reinforcement learning in parkinsonism, Science, vol.306, pp.1940-1943, 2004.

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.442, pp.1042-1045, 2006.

S. Palminteri, Pharmacological modulation of subliminal learning in Parkinson's and Tourette's syndromes, Proc. Natl Acad. Sci. USA, vol.106, pp.19179-19184, 2009.

J. M. Mcnamara, P. C. Trimmer, and A. I. Houston, The ecological rationality of state-dependent valuation, Psychol. Rev, vol.119, pp.114-119, 2012.

L. Pompilio and A. Kacelnik, Context-dependent utility overrides absolute memory as a determinant of choice, Proc. Natl Acad. Sci. USA, vol.107, pp.508-512, 2010.

M. Bar, Visual objects in context, Nat. Rev. Neurosci, vol.5, pp.617-629, 2004.

O. Schwartz, A. Hsu, and P. Dayan, Space and time in visual context, Nat. Rev. Neurosci, vol.8, pp.522-535, 2007.

D. Kahneman and A. Tversky, Choices, values, and frames, Am. Psychol, vol.39, pp.341-350, 1984.

N. Doi, , vol.9, 2018.

K. Louie and B. De-martino, Chapter 24-The neurobiology of contextdependent valuation and choice, Neuroeconomics, pp.455-476, 2014.

D. Kahneman and A. Tversky, Prospect theory: an analysis of decision under risk, Econ. J. Econ. Soc, vol.47, pp.263-291, 1979.

T. A. Klein, M. Ullsperger, and G. Jocham, Learning relative values in the striatum induces violations of normative decision making, Nat. Commun, vol.8, p.16033, 2017.

S. Palminteri, M. Khamassi, M. Joffily, and G. Coricelli, Contextual modulation of value signals in reward and punishment learning, Nat. Commun, vol.6, p.8096, 2015.
URL : https://hal.archives-ouvertes.fr/halshs-01236045

F. Rigoli, K. J. Friston, and R. J. Dolan, Neural processes mediating contextual influences on human choice behaviour, Nat. Commun, vol.7, p.12416, 2016.

G. T. Fechner, Elemente der psychophysik, 1860.

M. Carandini and D. J. Heeger, Normalization as a canonical neural computation, Nat. Rev. Neurosci, vol.13, pp.51-62, 2011.

R. Webb, W. Glimcher, P. Louie, and K. , Rationalizing context-dependent preferences: divisive normalization and neurobiological constraints on choice. SSRN Electron, 2014.

H. Yamada, K. Louie, A. Tymula, and P. W. Glimcher, Free choice shapes normalized value signals in medial orbitofrontal cortex, Nat. Commun, vol.9, p.162, 2018.

C. Padoa-schioppa, Range-adapting representation of economic value in the orbitofrontal cortex, J. Neurosci, vol.29, pp.14004-14014, 2009.

A. Rustichini, K. E. Conen, X. Cai, and C. Padoa-schioppa, Optimal coding and neuronal adaptation in economic decisions, Nat. Commun, vol.8, p.1208, 2017.

S. Palminteri, E. J. Kilford, G. Coricelli, and S. Blakemore, The computational development of reinforcement learning during adolescence, PLoS Comput. Biol, vol.12, p.1004953, 2016.

C. J. Burke, M. Baddeley, P. N. Tobler, and W. Schultz, Partial adaptation of obtained and observed value signals preserves information about gains and losses, J. Neurosci, vol.36, pp.10016-10025, 2016.
DOI : 10.1523/jneurosci.0487-16.2016
URL : http://www.jneurosci.org/content/36/39/10016.full.pdf

J. Neumann and O. Morgenstern, Theory of Games and Economic Behavior, 1953.

S. Palminteri, V. Wyart, and E. Koechlin, The importance of falsification in computational cognitive modeling, Trends Cogn. Sci, vol.21, pp.425-433, 2017.

J. Li and N. D. Daw, Signals in human striatum are appropriate for policy update rather than value prediction, J. Neurosci, vol.31, pp.5504-5511, 2011.
DOI : 10.1523/jneurosci.6316-10.2011
URL : http://www.jneurosci.org/content/31/14/5504.full.pdf

A. Rangel and J. A. Clithero, Value normalization in decision making: theory and evidence, Curr. Opin. Neurobiol, vol.22, pp.970-981, 2012.
DOI : 10.1016/j.conb.2012.07.011
URL : https://authors.library.caltech.edu/35377/2/nihms448944.pdf

C. R. Fox and R. A. Poldrack, Appendix-prospect theory and the brain, Neuroeconomics, pp.533-567, 2014.
DOI : 10.1016/b978-0-12-416008-8.00042-5

A. Pedroni, The risk elicitation puzzle, Nat. Hum. Behav, vol.1, pp.803-809, 2017.
DOI : 10.1038/s41562-017-0219-x

A. Kacelnik, Tools for thought or thoughts for tools?, Proc. Natl Acad. Sci. USA, vol.106, pp.10071-10072, 2009.
DOI : 10.1073/pnas.0904735106
URL : http://www.pnas.org/content/106/25/10071.full.pdf

G. E. Wimmer and D. Shohamy, Preference by association: how memory mechanisms in the hippocampus bias decisions, Science, vol.338, pp.270-273, 2012.

L. Holper, Adaptive value normalization in the prefrontal cortex is reduced by memory load. eNeuro ENEURO, pp.365-382, 2017.

A. R. Otto, C. M. Raio, A. Chiang, E. A. Phelps, and N. D. Daw, Workingmemory capacity protects model-based learning from stress, Proc. Natl Acad. Sci. USA, vol.110, pp.20941-20946, 2013.
DOI : 10.1073/pnas.1312011110
URL : http://www.pnas.org/content/110/52/20941.full.pdf

H. Ogmen, B. G. Breitmeyer, and R. Melvin, The what and where in visual masking, Vision Res, vol.43, pp.1337-1350, 2003.

M. Pessiglione, How the brain translates money into force: a neuroimaging study of subliminal motivation, Science, vol.316, pp.904-906, 2007.

N. D. Daw, Y. Niv, and P. Dayan, Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control, Nat. Neurosci, vol.8, pp.1704-1711, 2005.
DOI : 10.1038/nn1560

J. Gläscher, N. Daw, P. Dayan, and J. P. O'doherty, States versus Rewards: Dissociable neural prediction error signals underlying model-based and model-free reinforcement learning, Neuron, vol.66, pp.585-595, 2010.

F. Lesaint, O. Sigaud, S. B. Flagel, T. E. Robinson, and M. Khamassi, Modelling individual differences in the form of pavlovian conditioned approach responses: a dual learning systems approach with factored representations, PLOS Comput. Biol, vol.10, p.1003466, 2014.

J. S. Evans, Dual-processing accounts of reasoning, judgment, and social cognition, Annu. Rev. Psychol, vol.59, pp.255-278, 2008.
DOI : 10.1146/annurev.psych.59.103006.093629

D. Kahneman, A perspective on judgment and choice: mapping bounded rationality, Am. Psychol, vol.58, pp.697-720, 2003.
DOI : 10.1037/0003-066x.58.9.697

K. Louie, T. Lofaro, R. Webb, and P. W. Glimcher, Dynamic divisive normalization predicts time-varying value coding in decision-related circuits, J. Neurosci, vol.34, pp.16046-16057, 2014.
DOI : 10.1523/jneurosci.2851-14.2014
URL : http://www.jneurosci.org/content/34/48/16046.full.pdf

K. Louie, M. W. Khaw, and P. W. Glimcher, Normalization is a general neural mechanism for context-dependent decision making, Proc. Natl Acad. Sci. USA, vol.110, pp.6139-6144, 2013.
DOI : 10.1073/pnas.1217854110
URL : http://www.pnas.org/content/110/15/6139.full.pdf

F. Herzberg, The Motivation to Work, 1959.

D. Ariely, J. Huber, and K. Wertenbroch, When do losses loom larger than gains?, J. Mark. Res, vol.42, pp.134-138, 2005.
DOI : 10.1509/jmkr.42.2.134.62283

A. Camilleri and B. Newell, Within-subject Preference Reversals in Description-and Experience-based Choice, pp.449-454, 2009.

R. Hertwig and I. Erev, The description-experience gap in risky choice, Trends Cogn. Sci, vol.13, pp.517-523, 2009.

E. A. Ludvig and M. L. Spetch, Of black swans and tossed coins: is the description-experience gap in risky choice limited to rare events?, PLOS ONE, vol.6, p.20262, 2011.

C. J. Watkins and P. Dayan, Mach. Learn, vol.8, pp.279-292, 1992.

I. Vlaev, N. Chater, N. Stewart, and G. D. Brown, Does the brain calculate value?, Trends Cogn. Sci, vol.15, pp.546-554, 2011.
DOI : 10.1016/j.tics.2011.09.008
URL : http://wrap.warwick.ac.uk/40844/2/WRAP_stewart3_coversheet_ASTRO.pdf

J. M. Gold, Negative symptoms and the failure to represent the expected reward value of actions: behavioral and computational modeling evidence, Arch. Gen. Psychiatry, vol.69, pp.129-138, 2012.

P. Dayan and L. F. Abbott, Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems, 2001.