The Role of Implicit Context Information in Guiding Visual-Spatial Attention

Flexibility and adaptability are desirable features of cognitive technical systems. However, in comparison to humans, the development of these features for technical systems is still at the beginning. One approach to improve their realization is to study human cognitive processes and to develop appropriate algorithms, which can be transferred and implemented into technical systems. One example for a typical task common to humans and robots is to find a specific task-relevant object (or target) among other similar but task-irrelevant objects (or distractors). Although this task is quite demanding, humans are doing well in finding task-relevant objects even in unknown environments by applying specific search strategies. For example, when an object is located in a familiar rather than in a new context, humans use the context information to localize the object without recognizing the context as familiar. This phenomenon is known as contextual cueing : it is supposed that implicitly learned context information of the environment, i.e. the spatial layout of objects and their relations, guides visual-spatial attention to the target location and thus helps to localize the task-relevant object. However, in most of the previous psychological studies, artificial objects were used to investigate this effect and thus the ecologic validity is at least dubious. Therefore, the study reported here uses natural objects (LEGO® bricks). In contrast to artificial objects, natural objects are not only different in their visual features but also in their action relevance. Visual search is found to be faster and more accurate when the target is presented within a familiar context and when the knowledge about the context is implicit. This result is encouraging for further adaptation of stimulus material as well as for transferring psychological knowledge to technical applications.

[1]  Paolo Traverso,et al.  Automated Planning: Theory & Practice , 2004 .

[2]  Jeffrey S. Johnson,et al.  Implicit memory influences the allocation of attention in visual cortex , 2007, Psychonomic bulletin & review.

[3]  Thomas Dean,et al.  Automated planning , 1996, CSUR.

[4]  M. Chun,et al.  Memory deficits for implicit contextual information in amnesic subjects with hippocampal damage , 1999, Nature Neuroscience.

[5]  M. Shaw,et al.  Optimal allocation of cognitive resources to spatial locations. , 1977, Journal of experimental psychology. Human perception and performance.

[6]  W. Prinz.  Perception and Action Planning , 1997 .

[7]  A. Schubö,et al.  Cognitive processes facilitated by contextual cueing: evidence from event-related brain potentials. , 2009, Psychophysiology.

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

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

[10]  Kevin J. Hawley,et al.  Attention capture by novel stimuli. , 1990, Journal of experimental psychology. General.

[11]  Michael A. Stadler,et al.  Handbook of implicit learning , 1998 .

[12]  T Allison,et al.  Contextual guidance of attention: human intracranial event-related potential evidence for feedback modulation in anatomically early temporally late stages of visual processing. , 2001, Brain : a journal of neurology.

[13]  M. Nissen,et al.  Attentional requirements of learning: Evidence from performance measures , 1987, Cognitive Psychology.

[14]  A. Reber.  Implicit learning and tacit knowledge , 1993 .

[15]  M. Bravo,et al.  The role of attention in different visual-search tasks , 1992, Perception & psychophysics.

[16]  Scott T. Grafton,et al.  Premotor Cortex Activation during Observation and Naming of Familiar Tools , 1997, NeuroImage.

[17]  M. Chun,et al.  Top-Down Attentional Guidance Based on Implicit Learning of Visual Covariation , 1999 .

[18]  James R. Brockmole,et al.  Using real-world scenes as contextual cues for search , 2006 .

[19]  S. Yantis,et al.  Abrupt visual onsets and selective attention: Evidence from visual search. , 1984 .

[20]  William L. Gross,et al.  Hippocampal differentiation without recognition: an fMRI analysis of the contextual cueing task. , 2007, Learning & memory.

[21]  A. Greenwald,et al.  A choice reaction time test of ideomotor theory. , 1970, Journal of experimental psychology.

[22]  Denis Fize,et al.  Speed of processing in the human visual system , 1996, Nature.

[23]  Bernhard Hommel,et al.  Codes and their vicissitudes , 2001 .

[24]  M. Chun,et al.  Contextual Cueing: Implicit Learning and Memory of Visual Context Guides Spatial Attention , 1998, Cognitive Psychology.

[25]  P Cavanagh,et al.  Familiarity and pop-out in visual search , 1994, Perception & psychophysics.

[26]  M. Chun,et al.  Implicit, long-term spatial contextual memory. , 2003, Journal of experimental psychology. Learning, memory, and cognition.

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

[28]  Marilyn L. Shaw,et al.  A capacity allocation model for reaction time. , 1978 .

[29]  Melina A. Kunar,et al.  Contextual cuing by global features , 2006, Perception & psychophysics.

[30]  E Tulving,et al.  Priming and human memory systems. , 1990, Science.

[31]  James R. Brockmole,et al.  Contextual cueing in naturalistic scenes: Global and local contexts. , 2006, Journal of experimental psychology. Learning, memory, and cognition.

[32]  G. Aschersleben,et al.  The Theory of Event Coding (TEC): a framework for perception and action planning. , 2001, The Behavioral and brain sciences.

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

[34]  A Treisman,et al.  Feature analysis in early vision: evidence from search asymmetries. , 1988, Psychological review.