Modelling Visual Neglect: Computational Insights into Conscious Perception

Background: Visual neglect is an attentional deficit typically resulting from parietal cortex lesion and sometimes frontal lesion. Patients fail to attend to objects and events in the visual hemifield contralateral to their lesion during visual search. Methodology/Principal Finding: The aim of this work was to examine the effects of parietal and frontal lesion in an existing computational model of visual attention and search and simulate visual search behaviour under lesion conditions. We find that unilateral parietal lesion in this model leads to symptoms of visual neglect in simulated search scan paths, including an inhibition of return (IOR) deficit, while frontal lesion leads to milder neglect and to more severe deficits in IOR and perseveration in the scan path. During simulations of search under unilateral parietal lesion, the model’s extrastriate ventral stream area exhibits lower activity for stimuli in the neglected hemifield compared to that for stimuli in the normally perceived hemifield. This could represent a computational correlate of differences observed in neuroimaging for unconscious versus conscious perception following parietal lesion. Conclusions/Significance: Our results lead to the prediction, supported by effective connectivity evidence, that connections between the dorsal and ventral visual streams may be an important factor in the explanation of perceptua

Toward a further understanding of object feature binding: a cognitive neuroscience perspective.

The aim of this thesis is to lead to a further understanding of the neural mechanisms underlying object feature binding in the human brain. The focus is on information processing and integration in the visual system and visual shortterm memory. From a review of the literature it is clear that there are three major competing binding theories, however, none of these individually solves the binding problem satisfactorily. Thus the aim of this research is to conduct behavioural experimentation into object feature binding, paying particular attention to visual short-term memory. The behavioural experiment was designed and conducted using a within-subjects delayed responset ask comprising a battery of sixty-four composite objects each with three features and four dimensions in each of three conditions (spatial, temporal and spatio-temporal).Findings from the experiment,which focus on spatial and temporal aspects of object feature binding and feature proximity on binding errors, support the spatial theories on object feature binding, in addition we propose that temporal theories and convergence, through hierarchical feature analysis, are also involved. Because spatial properties have a dedicated processing neural stream, and temporal properties rely on limited capacity memory systems, memories for sequential information would likely be more difficult to accuratelyr ecall. Our study supports other studies which suggest that both spatial and temporal coherence to differing degrees,may be involved in object feature binding. Traditionally, these theories have purported to provide individual solutions, but this thesis proposes a novel unified theory of object feature binding in which hierarchical feature analysis, spatial attention and temporal synchrony each plays a role. It is further proposed that binding takes place in visual short-term memory through concerted and integrated information processing in distributed cortical areas. A cognitive model detailing this integrated proposal is given. Next, the cognitive model is used to inform the design and suggested implementation of a computational model which would be able to test the theory put forward in this thesis. In order to verify the model, future work is needed to implement the computational model.Thus it is argued that this doctoral thesis provides valuable experimental evidence concerning spatio-temporal aspects of the binding problem and as such is an additional building block in the quest for a solution to the object feature binding problem

Toward a further understanding of object feature binding : a cognitive neuroscience perspective

The aim of this thesis is to lead to a further understanding of the neural mechanisms underlying object feature binding in the human brain. The focus is on information processing and integration in the visual system and visual shortterm memory. From a review of the literature it is clear that there are three major competing binding theories, however, none of these individually solves the binding problem satisfactorily. Thus the aim of this research is to conduct behavioural experimentation into object feature binding, paying particular attention to visual short-term memory. The behavioural experiment was designed and conducted using a within-subjects delayed responset ask comprising a battery of sixty-four composite objects each with three features and four dimensions in each of three conditions (spatial, temporal and spatio-temporal).Findings from the experiment,which focus on spatial and temporal aspects of object feature binding and feature proximity on binding errors, support the spatial theories on object feature binding, in addition we propose that temporal theories and convergence, through hierarchical feature analysis, are also involved. Because spatial properties have a dedicated processing neural stream, and temporal properties rely on limited capacity memory systems, memories for sequential information would likely be more difficult to accuratelyr ecall. Our study supports other studies which suggest that both spatial and temporal coherence to differing degrees,may be involved in object feature binding. Traditionally, these theories have purported to provide individual solutions, but this thesis proposes a novel unified theory of object feature binding in which hierarchical feature analysis, spatial attention and temporal synchrony each plays a role. It is further proposed that binding takes place in visual short-term memory through concerted and integrated information processing in distributed cortical areas. A cognitive model detailing this integrated proposal is given. Next, the cognitive model is used to inform the design and suggested implementation of a computational model which would be able to test the theory put forward in this thesis. In order to verify the model, future work is needed to implement the computational model.Thus it is argued that this doctoral thesis provides valuable experimental evidence concerning spatio-temporal aspects of the binding problem and as such is an additional building block in the quest for a solution to the object feature binding problem.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

A BIASED COMPETITION COMPUTATIONAL MODEL OF SPATIAL AND OBJECT-BASED ATTENTION MEDIATING ACTIVE VISUAL SEARCH

A computational cognitive neuroscience approach was used to examine processes of visual attention in the human and monkey brain. The aim of the work was to produce a biologically plausible neurodynamical model of both spatial and object-based attention that accounted for observations in monkey visual areas V4, inferior temporal cortex (IT) and the lateral intraparietal area (LIP), and was able to produce search scan path behaviour similar to that observed in humans and 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, where attcntional shifts are overt. Therefore, retinal inputs change during the scan path and this approach raised issues, such as memory 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 cellular level. 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-based effects at the cellular level of the model led to systems level behaviour that replicated that observed during active visual search for orientation and colour feature conjunction targets in psychophysical investigations. This provides a valuable insight into the link between cellular and system level behaviour in natural systems. At the systems level, the simulated search 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 in preference to locations containing the target orientation or blank areas. A connection between the ventral and dorsal visual processing streams (Ungerleider & Mishkin. 1982) is suggested to contribute to this selectivity and priority in the featural guidance of search. Such selectivity and avoidance of blank areas has potential application in computer vision applications. 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 insights into the neural correlates of the conscious perception of stimuli The 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 versus endogenous factors in the capture of attention

Brain Computations and Connectivity [2nd edition]

This is an open access title available under the terms of a CC BY-NC-ND 4.0 International licence. It is free to read on the Oxford Academic platform and offered as a free PDF download from OUP and selected open access locations. Brain Computations and Connectivity is about how the brain works. In order to understand this, it is essential to know what is computed by different brain systems; and how the computations are performed. The aim of this book is to elucidate what is computed in different brain systems; and to describe current biologically plausible computational approaches and models of how each of these brain systems computes. Understanding the brain in this way has enormous potential for understanding ourselves better in health and in disease. Potential applications of this understanding are to the treatment of the brain in disease; and to artificial intelligence which will benefit from knowledge of how the brain performs many of its extraordinarily impressive functions. This book is pioneering in taking this approach to brain function: to consider what is computed by many of our brain systems; and how it is computed, and updates by much new evidence including the connectivity of the human brain the earlier book: Rolls (2021) Brain Computations: What and How, Oxford University Press. Brain Computations and Connectivity will be of interest to all scientists interested in brain function and how the brain works, whether they are from neuroscience, or from medical sciences including neurology and psychiatry, or from the area of computational science including machine learning and artificial intelligence, or from areas such as theoretical physics