Abstract
Experimental research on decision making has been mainly focused on binary perceptual tasks. The generally accepted models describing the decision process in these tasks are the integrator models. These models suggest that perceptual evidence is accumulated over time until a decision is made. Therefore, the final decision is based solely on recent perceptual information. In behaviorally more relevant tasks such as foraging, it is however probable, that the current choice also depends on previous experience. To understand the implications of considering previous experience in an integrator model we investigate it using a cognitive architecture (DAC9) with a robot performing foraging tasks. Compared to an instantaneous decision making model we show that an integrator model improves performance and robustness to task complexity. Further we show that it compresses the information stored in memory. This result suggests a change in the way actions are retrieved from memory leading to self-generated actions.
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References
Gold, J.I., Shadlen, M.N.: The neural basis of decision making. Annu. Rev. Neurosci. 30, 535–574 (2007)
Smith, P.L., Ratcliff, R.: Psychology and neurobiology of simple decisions. Trends Neurosci. 27, 161–168 (2004)
Logan, G.D., Cowan, W.B.: On the ability to inhibit thought and action: A theory of an act of control. Psychol. Rev. 91, 295–327 (1984)
Ratcliff, R., Rouder, J.N.: Modelling response times for two-choice decisions. Psychol. Sci. 9, 347–356 (1998)
Verschure, P.F.M.J., Voegtlin, T., Douglas, R.J.: Environmentally mediated synergy between perception and behavior robots. Nature 425, 620–624 (2003)
Shadlen, M.N., Newsome, W.T.: Neural basis of a perceptual decision in the parietal cortex (area lip) of the rhesus monkey. J. Neurophysiol. 86, 1916–1936 (2001)
Verschure, P.F.M.J., Althaus, P.: A real-world rational agent: unifying old and new ai. Cognitive Science 27, 561–590 (2003)
Bayes, T.: An essay towards solving a problem in the doctrine of chances. Transactions of the Royal Society 53, 370–418 (1763)
Verschure, P.F.M.J.: Distributed adaptive control: A theory of the mind, brain, body nexus. In: BICA (in press, 2012)
Duff, A., Fibla, M.S., Verschure, P.F.M.J.: A biologically based model for the integration of sensory-motor contingencies in rules and plans: A prefrontal cortex based extension of the distributed adaptive control architecture. Brain Res. Bull. (2010)
Mathews, Z., i Badia, S.B., Verschure, P.F.: Pasar: An integrated model of prediction, anticipation, sensation, attention and response for artificial sensorimotor systems. Information Sciences 186(1), 1–19 (2012)
Marcos, E., Duff, A., Sánchez-Fibla, M., Verschure, P.F.M.J.: The neuronal substrate underlying order and interval representations in sequential tasks: a biologically based robot study. In: IEEE World Congress on Computational Intelligence (2010)
Pavlov, I.P.: Conditioned reflexes: an investigation of the physiological activity of the cerebral cortex. Oxford University Press (1927)
Duff, A., Verschure, P.F.M.J.: Unifying perceptual and behavioral learning with a correlative subspace learning rule. Neurocomputing 73(10-12), 1818–1830 (2010)
Thorndike, E.: Animal intelligence. Macmillan, New York (1911)
de Almeida, L., Idiart, M., Lisman, J.E.: A Second Function of Gamma Frequency Oscillations: An E%-Max Winner-Take-All Mechanism Selects Which Cells Fire. J. Neurosci. 29(23), 7497–7503 (2009)
Wyss, R.: Sensory and motor coding in the organization of behavior. PhD thesis, ETHZ (2003)
Wyss, R., König, P., Verschure, P.: A model of the ventral visual system based on temporal stability and local memory. PLoS Biol. 4 (2006)
Hanes, D.P., Schall, J.D.: Neural control of voluntary movement initiation. Science 274, 427–430 (1996)
Mathews, Z., Lechón, M., Calvo, J.M.B., Dhir, A., Duff, A., Badia, S.B.: Verschure, P.F.M.J.: Insect-like mapless navigation based on head direction cells and contextual learning using chemo-visual sensors. In: Proceedings of the 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009, pp. 2243–2250. IEEE Press (2009)
Jung, M.W., McNaughton, B.L.: Spatial selectivity of unit activity in the hippocampal granular layer. Hippocampus 3, 165–182 (1993)
Asaad, W.F., Rainer, G., Miller, E.K.: Neural activity in the primate prefrontal cortex during associative learning. Neuron 21, 1399–1407 (1998)
Ratcliff, R., Hasegawa, Y.T., Hasegawa, R.P., Smith, P.L., Segraves, M.A.: Dual diffusion model for single-cell recording data from the superior colliculus in a brightness-discrimination task. J. Neurophysiol. 97, 1756–1774 (2007)
Roitman, J.D., Shadlen, M.N.: Response of neurons in the lateral intraparietal area during combined visual discrimination reaction time task. J. Neurosci. 22, 9475–9489 (2002)
Gold, J.I., Shadlen, M.N.: The influence of behavioral context on the representation of a perceptual decision in developing oculomotor commands. J. Neurosci. 23, 632–651 (2003)
Kim, J.N., Shadlen, M.N.: Neural correlates of a decision in the dorsolateral prefrontal cortex of the macaque. Nat. Neurosci. 2, 176–185 (1999)
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Marcos, E., Duff, A., Sánchez-Fibla, M., Verschure, P.F.M.J. (2012). Generalization of Integrator Models to Foraging: A Robot Study Using the DAC9 Model. In: Prescott, T.J., Lepora, N.F., Mura, A., Verschure, P.F.M.J. (eds) Biomimetic and Biohybrid Systems. Living Machines 2012. Lecture Notes in Computer Science(), vol 7375. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31525-1_14
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DOI: https://doi.org/10.1007/978-3-642-31525-1_14
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