A computational cognitive neuroscience approach was used to examine processes of visual attention in the human andmonkey brain. The aim of the work was to produce a biologically plausible neurodynamical model of both spatial andobject-based attention that accounted for observations in monkey visual areas V4, inferior temporal cortex (IT) and thelateral intraparietal area (LIP), and was able to produce search scan path behaviour similar to that observed in humansand monkeys.Of particular interest currently in the visual attention literature is the biased competition hypothesis (Desimone &Duncan. 1995). The model presented here is the first active vision implementation of biased competition, whereattcntional shifts are overt. Therefore, retinal inputs change during the scan path and this approach raised issues, such asmemory for searched locations across saccades, not addressed bv previous models with static retinas.This is the first model to examine the different time courses associated with spatial and object-based effects at the cellularlevel. Single cell recordings in areas V4 (Luck et al., 1997; Chelazzi et al., 2001) and IT (Chelazzi ct al., 1993, 1998)were replicated such that attentional effects occurred at the appropriate time after onset of the stimulus. Object-basedeffects at the cellular level of the model led to systems level behaviour that replicated that observed during active visualsearch for orientation and colour feature conjunction targets in psychophysical investigations. This provides a valuableinsight into the link between cellular and system level behaviour in natural systems. At the systems level, the simulatedsearch process showed selectivity in its scan path that was similar to that observed in humans (Scialfa & Joffe, 1998;Williams & Reingold, 2001) and monkeys (Motter & Belky. 1998b), being guided to target coloured locations inpreference to locations containing the target orientation or blank areas. A connection between the ventral and dorsalvisual processing streams (Ungerleider & Mishkin. 1982) is suggested to contribute to this selectivity and priority in thefeatural guidance of search. Such selectivity and avoidance of blank areas has potential application in computer visionapplications.Simulation of lesions within the model and comparison with patient data provided further verification of the model.Simulation of visual neglect due to parietal cortical lesion suggests that the model has the capability to provide insightsinto the neural correlates of the conscious perception of stimuliThe biased competition approach described here provides an extendable framework within which further "bottom-up"stimulus and "top-down" mnemonic and cognitive biases can be added, in order to further examine exogenous versusendogenous factors in the capture of attention.
展开▼
