A bio-inspired attention model of anticipation in gaze-contingency experiments with infants

The empirical investigation of cognitive processes in infants is hampered by the limited development of their body and motor control skills. Novel gaze-contingent paradigms where infants control their environment with their eyes offer a solution to this problem since the control of gaze develops very early. Recently such a paradigm has been used to investigate the learning of anticipatory behaviour of 6/8-months-old infants in an experiment where gazing at a “button” causes the appearance of an interesting image. The experiment shows that infants learn in few trials to “press the button” with their gaze and, remarkably, to anticipate the image appearance in only few trials. The experiment raises two questions: (a) what are the processes supporting such fast learning? (b) why does the anticipatory behaviour emerge? This paper presents a bio-inspired model that puts forward possible answers to these questions. The model consists of: (a) a bottom-up attention component for scene saliency detection; (b) a top-down attention component that uses reinforcement learning (RL) to learn to control gaze based on current foveal stimuli; (c) a dynamic field map that decides where to look based on the bottom-up and top-down information. The results show that the model is indeed capable of learning to perform anticipatory saccades in few trials. The analysis of the system shows that this fast learning is due to the guidance exerted by the bottom-up component on the learning process of the top-down component.Moreover, it shows that the anticipatory behaviour can be explained in terms of the action of the top-down component exploiting the learned spatial relations of stimuli in an anticipatory fashion. Overall, the results indicate that the model has the essential features to theoretically frame, interpret, and design experiments using gaze contingency.

[1]  C. Von Hofsten An action perspective on motor development. , 2004, Trends in cognitive sciences.

[2]  M. Chun,et al.  Contextual cueing of visual attention , 2022 .

[3]  Katsunari Shibata,et al.  Learning of Active Perception Based on Reinforcement Learning , 1996 .

[4]  Christof Koch,et al.  A Model of Saliency-Based Visual Attention for Rapid Scene Analysis , 2009 .

[5]  Matthew de Brecht,et al.  A neural network implementation of a saliency map model , 2006, Neural Networks.

[6]  G. Bronson,et al.  Changes in infants' visual scanning across the 2- to 14-week age period. , 1990, Journal of experimental child psychology.

[7]  Subhransu Maji,et al.  Confidence Based updation of Motion Conspicuity in Dynamic Scenes , 2006, The 3rd Canadian Conference on Computer and Robot Vision (CRV'06).

[8]  Sridhar Mahadevan,et al.  A reinforcement learning model of selective visual attention , 2001, AGENTS '01.

[9]  A. Graybiel The Basal Ganglia and Chunking of Action Repertoires , 1998, Neurobiology of Learning and Memory.

[10]  Eliana M. Klier,et al.  The superior colliculus encodes gaze commands in retinal coordinates , 2001, Nature Neuroscience.

[11]  S. Kastner,et al.  Topographic maps in human frontal and parietal cortex , 2009, Trends in Cognitive Sciences.

[12]  G. Baldassarre,et al.  A neural-network reinforcement-learning model of domestic chicks that learn to localize the centre of closed arenas , 2007, Philosophical Transactions of the Royal Society B: Biological Sciences.

[13]  Andrew G. Barto,et al.  Reinforcement learning , 1998 .

[14]  A. Barto,et al.  Optimal Control Methods for Simulating the Perception of Causality in Young Infants , 2020, Proceedings of the Twenty First Annual Conference of the Cognitive Science Society.

[15]  C. Hofsten An action perspective on motor development , 2004, Trends in Cognitive Sciences.

[16]  S. Treue Visual attention: the where, what, how and why of saliency , 2003, Current Opinion in Neurobiology.

[17]  D. Parisi,et al.  TRoPICALS: a computational embodied neuroscience model of compatibility effects. , 2010, Psychological review.

[18]  C. Balkenius Attention, habituation and conditioning: toward a computational model , 2000 .

[19]  O. Hikosaka,et al.  Role of the basal ganglia in the control of purposive saccadic eye movements. , 2000, Physiological reviews.

[20]  Christian Balkenius,et al.  A Reinforcement-Learning Model of Top-Down Attention Based on a Potential-Action Map , 2008, The Challenge of Anticipation.

[21]  A. L. Yarbus,et al.  Eye Movements and Vision , 1967, Springer US.

[22]  Richard S. Sutton,et al.  Dimensions of Reinforcement Learning , 1998 .

[23]  D. Parisi,et al.  The Agent-Based Approach: A New Direction for Computational Models of Development , 2001 .

[24]  Giovanni Pezzulo,et al.  How can bottom-up information shape learning of top-down attention-control skills? , 2010, 2010 IEEE 9th International Conference on Development and Learning.

[25]  Jochen Triesch,et al.  Infants in Control: Rapid Anticipation of Action Outcomes in a Gaze-Contingent Paradigm , 2012, PloS one.

[26]  M. Haith,et al.  Expectation and anticipation of dynamic visual events by 3.5-month-old babies. , 1988, Child development.

[27]  Jochen Triesch,et al.  Emergence of Mirror Neurons in a Model of Gaze Following , 2007, Adapt. Behav..

[28]  Christof Koch,et al.  Modeling attention to salient proto-objects , 2006, Neural Networks.

[29]  Christian Balkenius,et al.  Integrating Epistemic Action (Active Vision) and Pragmatic Action (Reaching): A Neural Architecture for Camera-Arm Robots , 2008, SAB.

[30]  C. Koch,et al.  Computational modelling of visual attention , 2001, Nature Reviews Neuroscience.

[31]  Mark H. Johnson,et al.  Components of Visual Orienting in Early Infancy: Contingency Learning, Anticipatory Looking, and Disengaging , 1991, Journal of Cognitive Neuroscience.

[32]  Jürgen Schmidhuber,et al.  Learning to Generate Artificial Fovea Trajectories for Target Detection , 1991, Int. J. Neural Syst..

[33]  Gianluca Baldassarre,et al.  What are intrinsic motivations? A biological perspective , 2011, 2011 IEEE International Conference on Development and Learning (ICDL).

[34]  Jochen Triesch,et al.  Learning to Attend - From Bottom-Up to Top-Down , 2008, WAPCV.

[35]  S. Reder On-line monitoring of eye-position signals in contingent and noncontingent paradigms , 1973 .

[36]  G. Schöner,et al.  Dynamic Field Theory of Movement Preparation , 2022 .

[37]  Claes von Hofsten,et al.  Action in development. , 2007, Developmental science.

[38]  A. L. I︠A︡rbus Eye Movements and Vision , 1967 .

[39]  D. Hubel Eye, brain, and vision , 1988 .

[40]  Hanno Scharr,et al.  Principles of Filter Design , 1999 .

[41]  J. Lisman,et al.  The Hippocampal-VTA Loop: Controlling the Entry of Information into Long-Term Memory , 2005, Neuron.