M. Platt and . Glimcher, Neural correlates of decision variables in parietal cortex, Nature, vol.400, p.233, 1999.

J. Schall, Neural basis of deciding, choosing and acting, Nat. Rev. Neurosci, vol.2, p.33, 2001.

J. Gold and M. Shadlen, The neural basis of decision making, Annu. Rev. Neurosci, vol.30, p.535, 2007.

P. P-r-roelfsema, H. Khayat, and . Spekreijse, Subtask sequencing in the primary visual cortex, P. Natl. Acad. Sci. USA, vol.100, p.5467, 2003.

R. Romo and . Salinas, Flutter discrimination: Neural codes, perception, memory and decision making, Nat. Rev. Neurosci, vol.4, p.203, 2003.

M. S-i-moro, P. Tolboom, P. Khayat, and . Roelfsema, Neuronal activity in the visual cortex reveals the temporal order of cognitive operations, J. Neurosci, vol.30, p.16293, 2010.

A. Newell, Unified theories of cognition, 1990.

C. J-r-anderson and . Lebiere, The atomic components of thought, Lawrence Erlbaum, Mahwah, 1998.

. S-ullman, Visual routines, vol.18, p.97, 1984.

A. Newell, Productions systems: Models of control structures, In: Visual Information Processing, vol.463, 1973.

. S-dehaene and . Sigman, From a single decision to a multi-step algorithm, Curr. Opin. Neurobio, vol.22, p.937, 2012.

J. Gottlieb and . Balan, Attention as a decision in information space, Trends Cogn. Sci, vol.14, p.240, 2010.

M. J-d-roitman and . Shadlen, Response of neurons in the lateral intraparietal area during a combined visual discrimination reaction time task, J. Neurosci, vol.22, p.9475, 2002.

W. M-n-shadlen and . Newsome, Motion perception: Seeing and deciding, P. Natl. Acad. Sci. USA, vol.93, p.628, 1996.

Y. Huang, T. Friesen, . Hanks, . Shadlen, ;. Rao et al., How prior probability influences decision making: A unifying probabilistic model, Advances in Neural Information Processing Systems, vol.25, 2012.

G. L-p-sugrue, W. Corrado, and . Newsome, Matching behavior and the representation of value in the parietal cortex, Science, vol.304, p.1782, 2004.

J. Wallis, K. Anderson, and E. Miller, Single neurons in prefrontal cortex encode abstract rules, Nature, vol.411, p.953, 2001.

. J-von-neumann, The computer and the brain, 1958.

A. Zylberberg, P. Dehaene, M. R-roelfsema, and . Sigman, The human Turing machine: A neural framework for mental programs, Trends Cogn. Sci, vol.15, p.293, 2011.

G. Maimon and . Assad, A cognitive signal for the proactive timing of action in macaque lip, Nat. Neurosci, vol.9, p.948, 2006.

R. M-n-shadlen, T. Kiani, A. Hanks, and . Churchland, Neurobiology of decision making an intentional framework, Better Than Conscious, vol.71, 2008.

A. Zylberberg, G. Dehaene, M. B-mindlin, and . Sigman, Neurophysiological bases of exponential sensory decay and top-down memory retrieval: A model, Front. Comput. Neurosci, vol.3, p.4, 2009.

G. Mongillo, M. Barak, and . Tsodyks, Synaptic theory of working memory, Science, vol.319, p.1543, 2008.

R. Reilly, Biologically based computational models of high-level cognition, Science, vol.314, p.91, 2006.

L. Shastri and . Ajjanagadde, From simple associations to systematic reasoning: A connectionist representation of rules, variables and dynamic bindings using temporal synchrony, Behav. Brain Sci, vol.16, p.417, 1993.

R. Hahnloser, M. Douglas, . Mahowald, and . Hepp, Feedback interactions between neuronal pointers and maps for attentional processing, Nat. Neurosci, vol.2, p.746, 1999.

W. X-j, Introduction to computational neuroscience, Technical report Volen Center for Complex Systems, 2006.

C. Carr and . Konishi, A circuit for detection of interaural time differences in the brain stem of the barn owl, J. Neurosci, vol.10, p.3227, 1990.

J. Slaney and . Thiébaux, Blocks world revisited, Artif. Intell, vol.125, p.119, 2001.

. P-r-roelfsema, H. V-a-lamme, and . Spekreijse, The implementation of visual routines, Vision Res, vol.40, p.1385, 2000.

. P-r-roelfsema, Elemental operations in vision, Trends Cogn. Sci, vol.9, p.226, 2005.

J. S-dehaene and . Changeux, Development of elementary numerical abilities: A neuronal model, J. Cognitive Neurosci, vol.5, p.390, 1993.

M. Piazza, P. Izard, L. Pinel, S. Bihan, and . Dehaene, Tuning curves for approximate numerosity in the human intraparietal sulcus, Neuron, vol.44, p.547, 2004.
URL : https://hal.archives-ouvertes.fr/hal-00349682

A. Nieder and . Dehaene, Representation of number in the brain, Annu. Rev. Neurosci, vol.32, p.185, 2009.

C. Lebiere, The dynamics of cognition: An ACT-R model of cognitive arithmetic, Doctoral dissertation, 1998.

C. D-y-ts'o, T. Gilbert, and . Wiesel, Relationships between horizontal interactions and functional architecture in cat striate cortex as revealed by cross-correlation analysis, J. Neurosci, vol.6, p.1160, 1986.

C. B-a-mcguire, P. Gilbert, T. K-rivlin, and . Wiesel, Targets of horizontal connections in macaque primary visual cortex, J. Comp. Neurol, vol.305, p.370, 1991.

C. Gilbert, Y. Daniel, and T. Wiesel, Lateral interactions in visual cortex, In: From pigments to perception, vol.239, 1991.

M. Sigman, C. Cecchi, M. Gilbert, and . Magnasco, Natural scenes and gestalt rules, vol.98, p.1935, 2001.

C. Gilbert, M. Sigman, and R. Crist, The neural basis of perceptual learning, Neuron, vol.31, p.681, 2001.

C. Gilbert and M. Sigman, Brain states: Topdown influences in sensory processing, Neuron, vol.54, p.677, 2007.

M. Kapadia, M. Ito, C. Gilbert, and G. Westheimer, Improvement in visual sensitivity by changes in local context: Parallel studies in human observers and in v1 of alert monkeys, Neuron, vol.15, p.843, 1995.

P. V-a-lamme and . Roelfsema, The distinct modes of vision offered by feedforward and recurrent processing, Trends Neurosci, vol.23, p.571, 2000.

S. Thorpe, C. Fize, and . Marlot, Speed of processing in the human visual system, Nature, vol.381, p.520, 1996.

D. D-j-felleman and . Van-essen, Distributed hierarchical processing in the primate cerebral cortex, Cereb. Cortex, vol.1, p.1, 1991.

G. Sperling, The information available in brief visual presentations, Psychol. Monogr. Gen. A, vol.74, p.1, 1960.

M. Graziano and . Sigman, The dynamics of sensory buffers: Geometric spatial and experience-dependent shaping of iconic memory, J. Vision, vol.8, p.1, 2008.

F. Hamker and ;. Heinke, The role of feedback connections in task-driven visual search, In: Connectionist models in cognitive neuroscience, Pag, vol.252, 1999.

F. Hamker, A dynamic model of how feature cues guide spatial attention, Vision Res, vol.44, p.501, 2004.

D. Heinke and G. Humphreys, Attention, spatial representation, and visual neglect: Simulating emergent attention and spatial memory in the selective attention for identification model (saim), Psychol. Rev, vol.110, p.29, 2003.

T. Shallice, Specific impairments of planning, Phil. Trans. R. Soc. Lond. B, vol.298, p.199, 1982.

D. Ballard, M. Hayhoe, P. Pook, and R. Rao, Deictic codes for the embodiment of cognition, Behav. Brain Sci, vol.20, p.723, 1997.

A. Zylberberg, P. Paz, S. R-roelfsema, M. Dehaene, and . Sigman, , 2013.

R. Sutton and A. Barto, Reinforcement learning: An introduction, 1998.

. P-r-roelfsema, T. Van-ooyen, and . Watanabe, Perceptual learning rules based on reinforcers and attention, Trends Cogn. Sci, vol.14, p.64, 2010.

S. J-o-rombouts, P. Bohte, ;. R-roelfsema, F. Bartlett, C. Pereira et al., Neurally plausible reinforcement learning of working memory tasks, Advances in Neural Information Processing Systems, vol.25, 2012.

J. S-dehaene and . Changeux, A hierarchical neuronal network for planning behavior, P. Natl. Acad. Sci. USA, vol.94, p.13293, 1997.