Binding is a local problem for natural objects and scenes

Current theories hold that attention is necessary for binding the features of a visual object into a coherent representation, implying that interference should be observed when two objects must be recognized simultaneously: this is the well-known binding problem. Recent studies have suggested, however, that discriminating isolated natural scenes, objects or faces might be possible in the near absence of attention. It is still unclear what mechanisms underlie this remarkable ability. Here, we investigate whether the binding problem affects natural objects in the same way as other stimuli: is interference observed when two natural objects or scenes must be simultaneously processed? We show that in the presence of competing objects, performance in the near absence of attention depends on the relative distance between stimuli: discrimination is good for stimuli far enough apart, and poor for close enough stimuli. In contrast, seemingly simpler but unfamiliar synthetic objects could not be bound in the near absence of attention, independent of the distance between them. Thus, natural objects are special in that they suffer from the binding problem, but only locally. We surmise that this particular type of local binding for natural objects and scenes could be "hardwired" by dedicated neuronal populations.

[1]  Pieter R. Roelfsema,et al.  Object-based attention in the primary visual cortex of the macaque monkey , 1998, Nature.

[2]  S. Thorpe,et al.  Speed of processing in the human visual system , 1996, Nature.

[3]  S. A. Hillyard,et al.  Sustained division of the attentional spotlight , 2003, Nature.

[4]  R. Eckhorn,et al.  Coherent oscillations: A mechanism of feature linking in the visual cortex? , 1988, Biological Cybernetics.

[5]  Guillaume A. Rousselet,et al.  Processing of one, two or four natural scenes in humans: the limits of parallelism , 2004, Vision Research.

[6]  Alan Cowey,et al.  Does parietal cortex contribute to feature binding? , 1999, Neuropsychologia.

[7]  A. Treisman,et al.  A feature-integration theory of attention , 1980, Cognitive Psychology.

[8]  U. Neisser VISUAL SEARCH. , 1964, Scientific American.

[9]  A. Treisman,et al.  Illusory conjunctions in the perception of objects , 1982, Cognitive Psychology.

[10]  R. Desimone,et al.  Neural mechanisms of selective visual attention. , 1995, Annual review of neuroscience.

[11]  D. Sagi,et al.  Vision outside the focus of attention , 1990, Perception & psychophysics.

[12]  Reinhard Eckhorn,et al.  Different types of signal coupling in the visual cortex related to neural mechanisms of associative Processing and perception , 2004, IEEE Transactions on Neural Networks.

[13]  C. Malsburg Binding in models of perception and brain function , 1995, Current Opinion in Neurobiology.

[14]  B. Julesz,et al.  Withdrawing attention at little or no cost: Detection and discrimination tasks , 1998, Perception & psychophysics.

[15]  R. Desimone,et al.  The Role of Neural Mechanisms of Attention in Solving the Binding Problem , 1999, Neuron.

[16]  Jitendra Malik,et al.  When is scene identification just texture recognition? , 2004, Vision Research.

[17]  Guillaume A. Rousselet,et al.  Parallel processing in high-level categorization of natural images , 2002, Nature Neuroscience.

[18]  T. Poggio,et al.  Hierarchical models of object recognition in cortex , 1999, Nature Neuroscience.

[19]  Steven A. Hillyard,et al.  Independent hemispheric attentional systems mediate visual search in split-brain patients , 1989, Nature.

[20]  V. Lamme,et al.  The distinct modes of vision offered by feedforward and recurrent processing , 2000, Trends in Neurosciences.

[21]  John C Gore,et al.  The role of the parietal cortex in visual feature binding , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[22]  Kenji Kawano,et al.  Global and fine information coded by single neurons in the temporal visual cortex , 1999, Nature.

[23]  P. Perona,et al.  Rapid natural scene categorization in the near absence of attention , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[24]  Kunihiko Fukushima,et al.  Neocognitron: A new algorithm for pattern recognition tolerant of deformations and shifts in position , 1982, Pattern Recognit..

[25]  Michael C. Mozer,et al.  Computational modeling of spatial attention , 1996 .

[26]  Antonio Torralba,et al.  Statistics of natural image categories , 2003, Network.

[27]  J. Findlay,et al.  Face Detection in Peripheral Vision: Do Faces Pop Out? , 1997, Perception.

[28]  D. Purcell,et al.  It Takes a Confounded Face to Pop Out of a Crowd , 1996, Perception.

[29]  W. Singer,et al.  Reduced Synchronization in the Visual Cortex of Cats with Strabismic Amblyopia , 1994, The European journal of neuroscience.

[30]  S. Thorpe,et al.  How parallel is visual processing in the ventral pathway? , 2004, Trends in Cognitive Sciences.

[31]  A. Treisman,et al.  Parietal contributions to visual feature binding: evidence from a patient with bilateral lesions , 1995, Science.

[32]  C. Koch,et al.  Visual Search and Dual Tasks Reveal Two Distinct Attentional Resources , 2004, Journal of Cognitive Neuroscience.

[33]  W Singer,et al.  Visual feature integration and the temporal correlation hypothesis. , 1995, Annual review of neuroscience.

[34]  H. Nothdurft Faces and Facial Expressions do not Pop Out , 1993, Perception.

[35]  Vision Research , 1961, Nature.

[36]  Leila Reddy,et al.  Face-gender discrimination is possible in the near-absence of attention. , 2004, Journal of vision.

[37]  P. Perona,et al.  Why does natural scene categorization require little attention? Exploring attentional requirements for natural and synthetic stimuli , 2005 .