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Workshop at Cosyne 2011:
Integrating perception, action and learning through natural goal directed behavior

Although there is a long tradition of separating perception and decision making, theoretical advances in control and machine learning show that perception, motor control and learning can only be separated under very restricted circumstances. The conditions for separation exclude key aspects of agency, like learning and modifying the agent's cognitive architecture. By embodying cognition and perception in an agent, a new set of explanatory principles emerges that has the potential to resolve long-standing debates in perception, action, and learning considered as separate disciplines. Theoretical tools such as POMDPs, Bayesian RL, and stochastic optimal control have the potential to give natural accounts for many aspects of brain and behavior, including attention, sensory coding, active perception, neural organization, goal selection, specificity in learning.

The aim of the workshop is to provide an opportunity to integrate related work performed in different fields that all relate to the same core theoretical ideas. Speakers will include theoreticians who have been developing this framework and experimentalists who have used these ideas to interpret behavioral and neural data. The workshop has the potential to produce interactions between the researchers as the common threads become clearer, and innovations can be shared. We will continue the tradition of ending the workshop with an invitation to perceive and act upon a selection of wines.


Ehud Ahissar (Weizmann):
Closed loop perception via sense-specific action-sensation loops

- Simony E, Bagdasarian K, Herfst L, Brecht M, Ahissar E, Golomb D. Temporal and spatial characteristics of vibrissa responses to motor commands. J Neurosci. 2010 Jun 30;30(26):8935-52.
- Simony E, Saraf-Sinik I, Golomb D, Ahissar E. Sensation-targeted motor control: Every spike counts? J Neurophysiol. 2008 May 28.
- Kleinfeld D, Ahissar E, Diamond ME. Active sensation: insights from the rodent vibrissa sensorimotor system. Curr Opin Neurobiol. 2006 Aug; 16(4):435-44. Epub 2006 Jul 11.

Michael Platt (Duke U.):
A neuroethological perspective on uncertainty reduction, learning and decision making

- Huettel, S.A., Stowe, C.J., Gordon, E.M., Warner, B.T. and Platt, M.L.. 2006. Neural signatures of economic preferences for risk and ambiguity. Neuron 49(5): 765-75.
- Deaner, R.O., Khera, A.V. and Platt, M.L. 2005. Monkeys pay per view: Adaptive valuation of social images by rhesus macaques. Current Biology 15: 543-8.
- Platt, M.L. and P.W. Glimcher (1999) Neural correlates of decision variables in parietal cortex. Nature 400:233-238.

Naftali Tishby (Hebrew U.):
Information flow in the perception-action-cycle and the representation of surprise in the brain

- Jonathan Rubin, Ohad Shamir and Naftali Tishby. Trading Information and Value in MDP’s- Preprint, Submitted.
- Naftali Tishby and Daniel Ploani, Information Theory of Decisions and Actions. In: Vassilis, Hussain, Taylor (editors). "Perception-reason-action cycle: Models, algorithms and systems" (Springer, 2010)
- Slonim N., Friedman N., Tishby N. Multivariate Information Bottleneck. Neural Compution, (2006) Aug;18(8):1739-89.

Constantin Rothkopf (FIAS):
Natural visuomotor behavior: from receptive fields to value functions

- C. A. Rothkopf, D. H. Ballard: 'Credit assignment in multiple goal embodied visuomotor behavior', Frontiers in Psychology, Special Topic: 'Embodied and grounded cognition', 2010
- C. A. Rothkopf, T. H. Weisswange, J. Triesch: 'Learning independent causes in natural images explains the spacevariant oblique effect', IEEE 8th International Conference on Development and Learning, June 5-7, 2009
- C. A. Rothkopf, D. H. Ballard, M.M. Hayhoe: 'Task and context determine where you look'. Journal of Vision, 7(14):16, 1-20, 2007

Mate Lengyel (U. Cambridge):
The powers and perils of Bayesian inference in the brain: perception, learning, and decision making

- Fiser J, Berkes B, Orbán G, Lengyel M. Statistically optimal perception and learning: from behavior to neural representations. Trends in Cognitive Sciences 14:119-130, 2010.
- Lengyel M, Dayan P. Hippocampal contributions to control: the third way. Advances in Neural Information Processing Systems 20, 889-896, 2008.
- Orbán G, Fiser J, Aslin RN, Lengyel M. Bayesian learning of visual chunks by human observers. Proceedings of the National Academy of Sciences USA 105:2745-2750, 2008.

Jozsef Fiser (Brandeis U.):
Possible neural implementations of probabilistic inference and learning in the brain: for ex-sample

- Pietro Berkes, Gergő Orbán, Máté Lengyel, József Fiser. Spontaneous cortical activity reveals hallmarks of an optimal internal model of the environment. Science 2011. 331:83-87
- Small Modulations of Ongoing Cortical Dynamics by Sensory Input During Natural Vision", József Fiser, Chiayu Chiu and Michael Weliky, Nature. 2004 Sep 30; 431:573-578
- Joseph E. Atkins, József Fiser and Robert A. Jacobs. Experience-dependent Visual Cue Intergration Based on Consistencies Between Visual & Haptic Percepts. Vision Research, 2001; 41, 449-461.

Paul Schrater (U. Minnesota):
Perceptual learning through the lens of control

- Acuna, D., and Schrater, P.  (2010) Structure Learning in Human Sequential Decision-making. PLOS Computational Biology PLoS Computional Biol 6(12): e1001003.doi: 10.1371/journal.pcbi.1001003
- Green, C.S., Benson, C., Kersten, D., and Schrater, P. (2010) Alterations in choice behavior by manipulations of world-model.  Proceedings of the National Academy of Sciences, 107(37) 16401-16406.
- V.N. Christopoulos, Paul R. Schrater (2009). Grasping objects with environmentally induced position uncertainty. PLoS Comput Biol 5(10): e1000538.

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