Spatiotemporal Segregation in Visual Search: Evidence From Parietal Lesions.

Olivers, Christian N. L.; Humphreys, Glyn W.

doi:10.1037/0096-1523.30.4.667

Cited by 25 publications

(46 citation statements)

References 142 publications

(222 reference statements)

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“…This negative colour carry-over effect is consistent with the inhibition of the features as well as the locations of the old items (cf. Humphreys et al, 2004). The result also fits with the idea of spreading suppression, as put forward by Duncan and Humphreys (1989); in this case, there is a spread of suppression from inhibited old distractors to new items carrying the inhibited properties -the result is that reaction times are slowed targets with these properties.…”

Section: Visual Search Over Timesupporting

confidence: 69%

“…This indicates that the SPL/precuneus activation is not tied to the search operation but it is consistent with these brain regions being linked to the inhibitory processing of distractors. The activation of the SPL/precuneus may reflect the operations of inhibitory neurons or some initial attention being paid to the old distractors in order to then inhibit them (see Humphreys et al, 2004, for evidence consistent with this from probe-dot procedures).…”

Section: The Neural Basis Of Inhibitory and Excitatory Biasesmentioning

confidence: 90%

“…However, as we have noted, search involves multiple processes (positive activation for targets, inhibitory suppression of distractors, the maintenance of target templates and so forth), so it is useful to explore paradigms such as preview search which can enable different processes to be isolated. There have now been several studies of preview search using functional brain imaging, and it has been consistently found that, relative to search when all the items appear together, preview search is associated with increased activation of several regions of posterior parietal cortex (the superior parietal lobe [SPL] and the precuneus; Allen et al, 2008;Humphreys et al, 2004;Olivers et al, 2005;Pollmann et al, 2003). This is interesting because preview search can be more efficient than baseline search conditions when all the items appear simultaneously, so the increased activation does not simply reflect the general difficulty of search.…”

Section: The Neural Basis Of Inhibitory and Excitatory Biasesmentioning

confidence: 99%

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Using biologically plausible neural models to specify the functional and neural mechanisms of visual search

Humphreys¹,

Allen²,

Mavritsaki³

2009

Progress in Brain Research

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Abstract:We AU : 2 review research from our laboratory that attempts to pull apart the functional and neural mechanisms of visual search using converging, inter-disciplinary evidence from experimental studies with normal participants, neuropsychological studies with brain lesioned patients, functional brain imaging and computational modelling. The work suggests that search is determined by excitatory mechanisms that support the selection of target stimuli, and inhibitory mechanisms that suppress irrelevant distractors. These mechanisms operate through separable though overlapping neural circuits which can be functionally decomposed by imposing model-based analyses on brain imaging data. The chapter highlights the need for inter-disciplinary research for understanding complex cognitive processes at several levels.

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Section: Visual Search Over Timesupporting

confidence: 69%

Section: The Neural Basis Of Inhibitory and Excitatory Biasesmentioning

confidence: 90%

Section: The Neural Basis Of Inhibitory and Excitatory Biasesmentioning

confidence: 99%

See 1 more Smart Citation

Using biologically plausible neural models to specify the functional and neural mechanisms of visual search

Humphreys¹,

Allen²,

Mavritsaki³

2009

Progress in Brain Research

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“…In contrast, patients with damage to more medial and superior parietal regions (including the precuneus) ought to have problems in suppressing irrelevant distractors. While damage to posterior parietal cortex has been shown to disrupt preview search [25], the precise factors involved, and whether they might differ across patients, has not been explored. The analysis with sSoTS predicts that differences should emerge as finer-grained analyses of patient sub-groups is undertaken.…”

Section: Discussionmentioning

confidence: 99%

Model Based Analysis of fMRI-Data: Applying the sSoTS Framework to the Neural Basic of Preview Search

Mavritsaki

Allen

Humphreys

2009

Attention in Cognitive Systems

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A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription.For more information, please contact eprints@nottingham.ac.ukModel based analysis of fMRI-data: Applying the sSoTS framework to the neural basic of preview search.Eirini Mavritsaki, Harriet Allen, and Glyn Humphreys Behavioural Brain Sciences Centre, School of Psychology, University of Birmingham, Edgbaston, B15 2TT, UK Abstract. The current work aims to unveil the neural circuits underlying visual search over time and space by using a model-based analysis of behavioural and fMRI data. It has been suggested by Watson and Humphreys [31] that the prioritization of new stimuli presented in our visual field can be helped by the active ignoring of old items, a process they termed visual marking. Studies using fMRI link the marking process with activation in superior parietal areas and the precuneus [4,18,27,26]. Marking has been simulated previously using a neural-level account of search, the spiking Search over Time and Space (sSoTS) model, which incorporates inhibitory as well as excitatory mechanisms to guide visual selection. Here we used sSoTS to help decompose the fMRI signals found in a preview search procedure, when participants search for a new target whilst ignoring old distractors. The time course of activity linked to inhibitory and excitatory processes in the model was used as a regressor for the fMRI data. The results showed that different neural networks were correlated with top-down excitation and top-down inhibition in the model, enabling us to fractionate brain regions previously linked to visual marking. We discuss the contribution of model-based analysis for decomposing fMRI data.

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“…According to this account, a deficit for contralesional targets in preview search may arise because patients have difficulty disengaging attention from ipsilesional distractors in the preview. Olivers and Humphreys (2004) discarded a spatial disengagement account, however. This account predicts that performance should be most disrupted when the preview items fall in the ipsilesional field and the new search items in the contralesional field.…”

Section: Time Course After Lesioningmentioning

confidence: 99%

Bridging the gap between physiology and behavior: Evidence from the sSoTS model of human visual attention.

Mavritsaki¹,

Heinke²,

Allen³

et al. 2011

Psychological Review

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We present the case for a role of biologically plausible neural network modeling in bridging the gap between physiology and behavior. We argue that spiking-level networks can allow "vertical" translation between physiological properties of neural systems and emergent "whole-system" performanceenabling psychological results to be simulated from implemented networks and also inferences to be made from simulations concerning processing at a neural level. These models also emphasize particular factors (e.g., the dynamics of performance in relation to real-time neuronal processing) that are not highlighted in other approaches and that can be tested empirically. We illustrate our argument from neural-level models that select stimuli by biased competition. We show that a model with biased competition dynamics can simulate data ranging from physiological studies of single-cell activity (Study 1) to whole-system behavior in human visual search (Study 2), while also capturing effects at an intermediate level, including performance breakdown after neural lesion (Study 3) and data from brain imaging (Study 4). We also show that, at each level of analysis, novel predictions can be derived from the biologically plausible parameters adopted, which we proceed to test (Study 5). We argue that, at least for studying the dynamics of visual attention, the approach productively links single-cell to psychological data. Keywords: visual search, integrate-and-fire neurons, computational model, fMRI, neuropsychologyUnderstanding human cognition is difficult. One of the difficulties is that cognition can be described at many different levelsfrom the high-level computational principles that may shape the landscape within which processes must function, through to the physiological principles by which neurons operate. How can these different levels best be related? Marr (1982) provided an influential framework in which different levels of analysis were described. This framework separated out, on the one hand, theories dealing with the computational principles and the neural hardware, from, on the other, "midlevel" theories that deal with abstract algorithms, which could operate on a variety of platforms (computers as well as human brains). However, what was not specified in this framework was how we move between the different levels; indeed, the levels were described as operating in a quasi-independent manner. This means that, within the framework, it is possible to have an algorithmic account of cognition that is separated from constraints based on neural-level functions, as well as neural-level accounts unconstrained by data derived from the operation of component cognitive processes. This leaves unspecified how we may link theories dealing with abstract psychological mechanisms (attention, memory, decision making) to theories that are concerned with how neurons communicate. It also means that the benefits that might be gained from this linkage are unexploited; theoretical insights at one level do not infiltrate other levels. The aim of t...

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Spatiotemporal Segregation in Visual Search: Evidence From Parietal Lesions.

Cited by 25 publications

References 142 publications

Using biologically plausible neural models to specify the functional and neural mechanisms of visual search

Using biologically plausible neural models to specify the functional and neural mechanisms of visual search

Model Based Analysis of fMRI-Data: Applying the sSoTS Framework to the Neural Basic of Preview Search

Bridging the gap between physiology and behavior: Evidence from the sSoTS model of human visual attention.

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