Interaction of top-down and bottom-up processing in the fast visual analysis of natural scenes.

The influence of task requirements on the fast visual processing of natural scenes was studied in 14 human subjects performing in alternation an "animal" categorization task and a single-photograph recognition task. Target photographs were randomly mixed with non-target images and flashed for only 20 ms. Subjects had to respond to targets within 1 s. Processing time for image-recognition was 30-40 ms shorter than for the categorization task, both for the fastest behavioral responses and for the latency at which event related potentials evoked by target and non-target stimuli started to diverge. The faster processing in image-recognition is shown to be due to the use of low-level cues, but source analysis produced evidence that, regardless of the task, the dipoles accounting for the differential activity had the same localization and orientation in the occipito-temporal cortex. We suggest that both tasks involve the same visual pathway and the same decisional brain area but because of the total predictability of the target in the image recognition task, the first wave of bottom-up feed-forward information is speeded up by top-down influences that might originate in the prefrontal cortex and preset lower levels of the visual pathway to the known target features.

[1]  G. E. Alexander,et al.  Neuron Activity Related to Short-Term Memory , 1971, Science.

[2]  R. Knight,et al.  Prefrontal modulation of visual processing in humans , 2000, Nature Neuroscience.

[3]  M. Valdés-Sosa,et al.  Switching Attention without Shifting the Spotlight: Object-Based Attentional Modulation of Brain Potentials , 1998, Journal of Cognitive Neuroscience.

[4]  Y Sugita.  Electrophysiological correlates of visual search asymmetry in humans , 1995, Neuroreport.

[5]  N. Kanwisher,et al.  The Fusiform Face Area: A Module in Human Extrastriate Cortex Specialized for Face Perception , 1997, The Journal of Neuroscience.

[6]  David J. Freedman.  Categorical representation of visual stimuli in the primate prefrontal and inferior temporal cortices , 2002 .

[7]  Michel Vidal-Naquet,et al.  Visual features of intermediate complexity and their use in classification , 2002, Nature Neuroscience.

[8]  S. Luck,et al.  Spatio‐temporal dynamics of attention to color: Evidence from human electrophysiology , 1998, Human brain mapping.

[9]  S. Thorpe,et al.  Rapid categorization of foveal and extrafoveal natural images: Associated ERPs and effects of lateralization , 2005, Brain and Cognition.

[10]  K. Nakayama,et al.  Priming of pop-out: II. The role of position , 1996, Perception & psychophysics.

[11]  P. Goldman-Rakic.  Cellular basis of working memory , 1995, Neuron.

[12]  S. Thorpe,et al.  The Time Course of Visual Processing: From Early Perception to Decision-Making , 2001, Journal of Cognitive Neuroscience.

[13]  S. Thorpe,et al.  A Limit to the Speed of Processing in Ultra-Rapid Visual Categorization of Novel Natural Scenes , 2001, Journal of Cognitive Neuroscience.

[14]  J L Barbur,et al.  Visual processing levels revealed by response latencies to changes in different visual attributes , 1998, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[15]  L. Cooper,et al.  The influence of task requirements on priming in object decision and matching , 2001, Memory & cognition.

[16]  Michèle Fabre-Thorpe,et al.  Brain Areas Involved in Rapid Categorization of Natural Images: An Event-Related fMRI Study , 2000, NeuroImage.

[17]  I. Gauthier,et al.  Expertise for cars and birds recruits brain areas involved in face recognition , 2000, Nature Neuroscience.

[18]  S. Thorpe,et al.  Seeking Categories in the Brain , 2001, Science.

[19]  J. Haxby,et al.  Attribute-based neural substrates in temporal cortex for perceiving and knowing about objects , 1999, Nature Neuroscience.

[20]  Hans-Jochen Heinze,et al.  Localizing visual discrimination processes in time and space. , 2002, Journal of neurophysiology.

[21]  D Friedman,et al.  Cognitive event-related potential components during continuous recognition memory for pictures. , 1990, Psychophysiology.

[22]  Y. Miyashita,et al.  Categorizing the world: expert neurons look into key features , 2002, Nature Neuroscience.

[23]  A. Dale,et al.  Functional-Anatomic Correlates of Object Priming in Humans Revealed by Rapid Presentation Event-Related fMRI , 1998, Neuron.

[24]  David J. Freedman,et al.  Categorical representation of visual stimuli in the primate prefrontal cortex. , 2001, Science.

[25]  M. Kutas,et al.  Neurophysiological evidence for visual perceptual categorization of words and faces within 150 ms. , 1998, Psychophysiology.

[26]  A. Oliva,et al.  Coarse Blobs or Fine Edges? Evidence That Information Diagnosticity Changes the Perception of Complex Visual Stimuli , 1997, Cognitive Psychology.

[27]  S. Hillyard,et al.  Event-related brain potentials in the study of visual selective attention. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[28]  Robert M. McPeek,et al.  Saccades require focal attention and are facilitated by a short-term memory system , 1999, Vision Research.

[29]  K. Nakayama,et al.  Priming of pop-out: I. Role of features , 1994, Memory & cognition.

[30]  Michael C. Doyle,et al.  Modulation of event-related potentials by the repetition of drawings of novel objects. , 1995, Brain research. Cognitive brain research.

[31]  K. Nakayama,et al.  Priming of popout: II. Role of position , 1996 .

[32]  F. Karayanidis,et al.  Evidence of visual processing negativity with attention to orientation and color in central space. , 1997, Electroencephalography and clinical neurophysiology.

[33]  Philippe G Schyns,et al.  Diagnostic recognition: task constraints, object information, and their interactions , 1998, Cognition.

[34]  G Richard,et al.  Ultra-rapid categorisation of natural scenes does not rely on colour cues: a study in monkeys and humans , 2000, Vision Research.

[35]  E. Miller,et al.  An integrative theory of prefrontal cortex function. , 2001, Annual review of neuroscience.

[36]  Ken Nakayama,et al.  Serial and parallel processing of visual feature conjunctions , 1986, Nature.

[37]  T. Sejnowski,et al.  Dynamic Brain Sources of Visual Evoked Responses , 2002, Science.

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

[39]  E. Miller,et al.  Memory fields of neurons in the primate prefrontal cortex. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[40]  Ronald A. Rensink,et al.  Influence of scene-based properties on visual search. , 1990, Science.

[41]  Heinrich H. Bülthoff,et al.  Object recognition in man, monkey, and machine , 1999 .

[42]  Ava J. Senkfor,et al.  Memory for words and novel visual patterns: repetition, recognition, and encoding effects in the event-related brain potential. , 1996, Psychophysiology.