Attention Systems and the Organization of the Human Parietal Cortex

Rushworth, Matthew F. S.; Paus, T.; Sipilä, Perttu

doi:10.1523/jneurosci.21-14-05262.2001

Cited by 288 publications

(148 citation statements)

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“…Rushworth and colleagues (2001) concluded that modulation of the SPL and the medial parietal cortex are related to changes of the response rules, whereas the core visual selection areas are located further in the intraparietal sulcus. The present findings dovetail with those of colleagues (1997, 2001), especially in that both studies showed minimal overlap between areas involved in visual selection versus response conflict, and support their conclusion that SPL has a role in maintaining and applying the correct SR mappings (or visuomotor rules, as they are called in Rushworth et al, 2001). …”

Section: Theoretical Implicationssupporting

confidence: 92%

“…In line with this contention, previous studies have shown that posterior parietal areas are involved in governing SR-mappings (e.g., Rushworth et al, 1997Rushworth et al, , 2001Wager et al, 2005). For instance, Rushworth and colleagues (2001) examined effects of changing visual selection rules (attend to shape/color) versus response rules.…”

Section: Theoretical Implicationsmentioning

confidence: 88%

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Distinct neural networks for target feature versus dimension changes in visual search, as revealed by EEG and fMRI

2014

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Publisher's copyright statement: NOTICE: this is the author's version of a work that was accepted for publication in NeuroImage. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be re ected in this document. Changes may have been made to this work since it was submitted for publication. A de nitive version was subsequently published in NeuroImage, 102, Part 2, 15 November 2014, 10.1016/j.neuroimage.2014.08.058. Additional information:Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-pro t purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. AbstractIn visual search, responses are slowed, from one trial to the next, both when the target dimension changes (e.g., from a color target to a size target) and when the target feature changes (e.g., from a red target to a green target) relative to being repeated across trials. The present study examined whether such feature and dimension switch costs can be attributed to the same underlying mechanism(s). Contrary to this contention, an EEG study showed that feature changes influenced visual selection of the target (i.e., delayed N2pc onset), whereas dimension changes influenced the later process of response selection (i.e., delayed s-LRP onset). An fMRI study provided convergent evidence for the two-system view: Compared with repetitions, feature changes led to increased activation in the occipital cortex, and superior and inferior parietal lobules, which have been implicated in spatial attention. By contrast, dimension changes led to activation of a frontoposterior network that is primarily linked with response selection (i.e., pre-motor cortex, supplementary motor area and frontal areas). Taken together, the results suggest that feature and dimension switch costs are based on different processes. Specifically, whereas target feature changes delay attention shifts to the target, target dimension changes interfere with later response selection operations.

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Section: Theoretical Implicationssupporting

confidence: 92%

Section: Theoretical Implicationsmentioning

confidence: 88%

Distinct neural networks for target feature versus dimension changes in visual search, as revealed by EEG and fMRI

2014

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“…These areas have been shown to activate when simulated driving is contrasted with a passive viewing task in previous studies (Calhoun et al, 2002). The parietal areas which show a decrease here have been implicated in not only the types of spatial processing associated with driving, but also in the allocation of visual spatial attention (Rushworth et al, 2001). The decreased parietal activation in the dual-task condition may therefore be a reflection of both a decrease in the spatial computations associated with driving as well as a decrease in spatial attention.…”

Section: Discussionsupporting

confidence: 49%

A decrease in brain activation associated with driving when listening to someone speak

2008

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Behavioral studies have shown that engaging in a secondary task, such as talking on a cellular telephone, disrupts driving performance. This study used functional magnetic resonance imaging (fMRI) to investigate the impact of concurrent auditory language comprehension on the brain activity associated with a simulated driving task. Participants steered a vehicle along a curving virtual road, either undisturbed or while listening to spoken sentences that they judged as true or false. The dual-task condition produced a significant deterioration in driving accuracy caused by the processing of the auditory sentences. At the same time, the parietal lobe activation associated with spatial processing in the undisturbed driving task decreased by 37% when participants concurrently listened to sentences. The findings show that language comprehension performed concurrently with driving draws mental resources away from the driving and produces deterioration in driving performance, even when it does not require holding or dialing a phone. IntroductionAn enduring question about the human mind concerns the ability to do two things at the same time. As technological and informational capabilities of our environment increase, the number of available information streams increases, and hence the opportunities for complex multitasking increase. In particular, multitasking of driving and conversing on a cell phone is technologically available, but intuitively seems dangerous in some circumstances. Although driving becomes sufficiently cognitively automated (Schneider, 1999) to permit experienced drivers to perform other tasks at the same time, such as carrying on a conversation, a large number of behavioral studies have now shown that performing another cognitive task while driving an actual or virtual car substantially degrades driving performance Nilsson, 1994, 1995;Anttila and Luoma, 2005;Beede and Kass, 2006;Brookhuis et al., 1991;Consiglio et al., 2003;Drory, 1985;Engström et al., 2005;Haigney et al., 2000;Hancock et al., 2003;Horberry et al., 2006;Horrey and Wickens, 2004;Hunton and Rose, 2005;Jamson and Merat, 2005;Kubose et al., 2006;Lamble et al., 1999;Lesch and Hancock, 2004;Liu and Lee, 2005;Matthews et al., 2003;McKnight and McKnight, 1993;Patten et al., 2004;Ranney et al., 2005; Nunes, 2000, 2003;Santos et al., 2005;Shinar et al., 2005; Drews, 2004, 2007;Strayer et al., 2003Strayer et al., , 2006Strayer and Johnston, 2001; Bolling, 2005, 2006;Treffner and Barrett, 2004). Although some of these studies show that some aspects of driving are unaffected by a secondary task (e.g., Haigney et al., 2000) and in some cases certain aspects improve (e.g., Brookhuis et al., 1991;Engström et al., 2005), a recent meta-analysis of the literature suggests a large overall decrement in driving performance when a secondary task is added (Horey and Wickens, 2006).

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“…In a second experiment, we employ fMRI to assess whether the pattern of neural recruitment varies depending on the type of switch performed. Rather than focusing on particular subregions (Rushworth, et al, 2001(Rushworth, et al, , 2002, we examined regions that were differentially modulated by the interaction of shifting and type of task (rule or perceptual). To preview, we observed greater activity in the dorsolateral prefrontal cortex (DLPFC; note that we define this as Brodmann areas 9 and 46) for rule shifting that perceptual shifting whereas the superior parietal cortex exhibited the opposite pattern.…”

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confidence: 99%

Shifting set about task switching: Behavioral and neural evidence for distinct forms of cognitive flexibility

2008

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Task switching is an important aspect of cognitive control and understanding its underlying mechanisms is the focus of considerable research. In this paper, we contrast two different kinds of task switching paradigms and provide evidence that different cognitive mechanisms underlie switching behavior depending on whether the switch is between sets of rules (rule switch) or sets of features presented simultaneously (perceptual switch). In two experiments, we demonstrate that behavioral effects (Experiment 1) and neural recruitment (Experiment 2) vary with the type of switch performed. While perceptual switch costs occurred when the alternative feature set was physically present, rule switch costs were observed even in their absence. Rule switching was also characterized by larger target repetition effects and by greater engagement of the dorsolateral prefrontal cortex. In contrast, perceptual switching was associated with greater recruitment of the parietal cortex. These results provide strong evidence for multiple forms of switching and suggest the limitations of generalizing results across shift types. KeywordsExecutive function; cognitive control; dorsolateral prefrontal cortex; superior parietal cortex; fMRI The ability to switch tasks rapidly is important for responding flexibly in a changing environment (Miller & Cohen, 2001). Both behavioral and neuroscientific investigators have sought to understand how people disengage from one task in order to undertake another (see Monsell, 2003, for a review). In this set of experiments, we seek to understand the behavioral and neural consequences associated with different forms of task switching. Specifically, we examine whether there are qualitative differences in the cognitive mechanisms used to switch between tasks that require the reorienting of visuospatial attention (e.g., to what or where should I attend?) and switches that involve the ability to deal with changing goal-related information such as rules (e.g., what should I do?). We propose that the "task" used to investigate task switching (or "set" in set shifting) has important implications for understanding the nature of © 2008 Elsevier Ltd. All rights reserved.Corresponding address: Dr. Susan Ravizza, Dept. of Psychology, Michigan State University, East Lansing, MI, 48824 Email: E-mail: ravizzas@msu.edu Phone: 517−432−3366. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. NIH Public Access Author ManuscriptNeuropsychologia. Author manuscript; available in PMC 2009 October 1. Published in final edited form as:Neuropsychologia. NIH-PA Author ManuscriptNIH-PA ...

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Attention Systems and the Organization of the Human Parietal Cortex

Cited by 288 publications

References 65 publications

Distinct neural networks for target feature versus dimension changes in visual search, as revealed by EEG and fMRI

Distinct neural networks for target feature versus dimension changes in visual search, as revealed by EEG and fMRI

A decrease in brain activation associated with driving when listening to someone speak

Shifting set about task switching: Behavioral and neural evidence for distinct forms of cognitive flexibility

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