The role of the lateral intraparietal area in orienting attention and its implications for visual search

Bisley, James W.; Mirpour, Koorosh; Arcizet, Fabrice; Ong, Wei Song

doi:10.1111/j.1460-9568.2011.07700.x

Cited by 49 publications

(39 citation statements)

References 104 publications

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“…A second common interpretation of the LIP target selection response is in terms of a "priority" map that ranks competing visual cues for saccades or attention (14,33,(46)(47)(48)(49)(50)(51)(52). Similar to the value interpretation, the priority hypothesis describes LIP as encoding a common currency for visual selection.…”

Section: Discussionmentioning

confidence: 99%

Parietal neurons encode expected gains in instrumental information

Foley

Kelly

Mhatre

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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In natural behavior, animals have access to multiple sources of information, but only a few of these sources are relevant for learning and actions. Beyond choosing an appropriate action, making good decisions entails the ability to choose the relevant information, but fundamental questions remain about the brain's information sampling policies. Recent studies described the neural correlates of seeking information about a reward, but it remains unknown whether, and how, neurons encode choices of instrumental information, in contexts in which the information guides subsequent actions. Here we show that parietal cortical neurons involved in oculomotor decisions encode, before an information sampling saccade, the reduction in uncertainty that the saccade is expected to bring for a subsequent action. These responses were distinct from the neurons' visual and saccadic modulations and from signals of expected reward or reward prediction errors. Therefore, even in an instrumental context when information and reward gains are closely correlated, individual cells encode decision variables that are based on informational factors and can guide the active sampling of action-relevant cues.information sampling | saccades | attention | decisions | reward I n natural behavior, animals have access to multiple sources of information, but few of these sources are relevant for learning or action. Making good decisions therefore entails not only the selection of the ultimate action but, more primarily, the decision of which source of information to sample. Decisions about information sampling are central for tasks as diverse as making a medical diagnosis (which is the best test to prescribe?), making categorization decisions (which is the most informative feature?) (1, 2), and guiding skilled actions (what should I keep my eyes on while driving?). Despite the ubiquity and significance of active sampling mechanisms, few studies have been devoted to understanding these mechanisms and their importance for decision theories. Evidence accumulation has been extensively examined in decision research (3) but has been portrayed as a passive process, in the sense that decision makers rely on predetermined (experimenter selected) sources of information but cannot themselves determine which source to consult to guide a future action.Recent studies begin to shed light on this question by showing that animals (including pigeons, monkeys, and humans) prefer to observe cues that are predictive rather than nonpredictive about a future reward, and that the value of informative cues is encoded in the orbital frontal cortex and midbrain dopamine (DA) cells (4, 5). These investigations, however, have been limited to noninstrumental contexts in which animals seek to obtain information about a reward merely in order to know, but cannot act based on the information. Very little is known about the much more common scenario in which animals sample instrumental information to make decisions and guide future actions (6).Understanding instrumental sampling poses...

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Section: Discussionmentioning

confidence: 99%

Parietal neurons encode expected gains in instrumental information

Foley

Kelly

Mhatre

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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“…Further results indicate that pFC, including dorsolateral pFC in humans, may be crucial in suppressing the response of more posterior regions to an irrelevant but salient stimulus, suggesting an important way through which topdown mechanisms can modulate AC effects (Bisley, Mirpour, Arcizet, & Ong, 2011;Talsma et al, 2009;de Fockert, Rees, Frith, & Lavie, 2004;Nobre et al, 1997). More generally, pFC is supposed to provide both inhibitory and excitatory input to distributed neural circuits required to support performance in diverse selective attention tasks (Knight, Staines, Swick, & Chao, 1999).…”

Section: Introductionmentioning

confidence: 99%

Disentangling the Role of Cortico-Basal Ganglia Loops in Top–Down and Bottom–Up Visual Attention: An Investigation of Attention Deficits in Parkinson Disease

Tommasi

Fiorio

Yelnik

et al. 2015

Journal of Cognitive Neuroscience

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Abstract■ It is solidly established that top-down (goal-driven) and bottom-up (stimulus-driven) attention mechanisms depend on distributed cortical networks, including prefrontal and frontoparietal regions. On the other hand, it is less clear whether the BG also contribute to one or the other of these mechanisms, or to both. The current study was principally undertaken to clarify this issue. Parkinson disease (PD), a neurodegenerative disorder primarily affecting the BG, has proven to be an effective model for investigating the contribution of the BG to different brain functions; therefore, we set out to investigate deficits of top-down and bottom-up attention in a selected cohort of PD patients. With this objective in mind, we compared the performance on three computerized tasks of two groups of 12 parkinsonian patients (assessed without any treatment), one otherwise pharmacologically treated and the other also surgically treated, with that of a group of controls. The main behavioral tool for our study was an attentional capture task, which enabled us to tap the competition between top-down and bottom-up mechanisms of visual attention. This task was suitably combined with a choice RT and a simple RT task to isolate any specific deficit of attention from deficits in motor response selection and initiation. In the two groups of patients, we found an equivalent increase of attentional capture but also comparable delays in target selection in the absence of any salient distractor (reflecting impaired top-down mechanisms) and movement initiation compared with controls. In contrast, motor response selection processes appeared to be prolonged only in the operated patients. Our results confirm that the BG are involved in both motor and cognitive domains. Specifically, damage to the BG, as it occurs in PD, leads to a distinct deficit of top-down control of visual attention, and this can account, albeit indirectly, for the enhancement of attentional capture, reflecting weakened ability of top-down mechanisms to antagonize bottom-up control. ■

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“…In the brain, the lateral intraparietal area (LIP) contains a representation of the relative stimulus saliency across the entire visual field [3]. In our model, the LIP component calculates the overall saliency of each object in the 2D image and determines the most salient stimulus.…”

Section: Saliency Calculationmentioning

confidence: 99%

A Computational Model of Emotional Attention for Autonomous Agents

Barriga

Rodríguez

Ramos

et al. 2013

Transactions on Computational Science XVIII

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Abstract.A major challenge in artificial intelligence has been the development of autonomous agents (AAs) capable of displaying very believable behaviors. In order to achieve such objective, the underlying architectures of these intelligent systems have been designed to incorporate biologically inspired components. It is expected that through the interaction of this type of components, AAs are able to implement more intelligent and believable behavior. Although the literature reports several computational models of attention and emotions developed to be included in cognitive agent architectures, these have been implemented as two separated processes, disregarding essential interactions between these two human functions whose modeling and computational implementation may increase the believability of behaviors developed by AAs. In this paper, we propose a biologically inspired computational model of emotional attention. This model is designed to provide AAs with adequate mechanisms to attend and react to emotionally salient elements in the environment. The results of four types of simulations performed to evaluate the behavior of AAs implementing the proposed model are presented.

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The role of the lateral intraparietal area in orienting attention and its implications for visual search

Cited by 49 publications

References 104 publications

Parietal neurons encode expected gains in instrumental information

Parietal neurons encode expected gains in instrumental information

Disentangling the Role of Cortico-Basal Ganglia Loops in Top–Down and Bottom–Up Visual Attention: An Investigation of Attention Deficits in Parkinson Disease

A Computational Model of Emotional Attention for Autonomous Agents

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