The prefrontal cortex and the executive control of attention

Rossi, Andrew F.; Pessoa, Luiz; Desimone, Robert; Ungerleider, Leslie G.

doi:10.1007/s00221-008-1642-z

Cited by 293 publications

(190 citation statements)

References 55 publications

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“…Studies in monkeys and humans have recorded fluctuations in ongoing activity levels that at least partially reflect these sequence-based ("predictive") facilitatory and suppressive modulatory effects (23,25). Those studies support the view that cortical areas at all system levels are subject to continuous "topdown" biasing as a function of ongoing prediction.…”

Section: Discussionmentioning

confidence: 73%

“…Human studies have documented complex receptive biasing in behavioral and imaging studies in our great sensory/perceptual systems (vision/reading, audition/aural language, and somesthesia) (21)(22)(23)(24). Studies in monkeys and humans have recorded fluctuations in ongoing activity levels that at least partially reflect these sequence-based ("predictive") facilitatory and suppressive modulatory effects (23,25).…”

Section: Discussionmentioning

confidence: 99%

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Successive-signal biasing for a learned sound sequence

Zhou

Villers-Sidani

Panizzutti

et al. 2010

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

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Adult rats were trained to detect the occurrence of a two-element sound sequence in a background of nine other nontarget sound pairs. Training resulted in a modest, enduring, static expansion of the cortical areas of representation of both target stimulus sounds. More importantly, once the initial stimulus A in the target A-B sequence was presented, the cortical "map" changed dynamically, specifically to exaggerate further the representation of the "anticipated" stimulus B. If B occurred, it was represented over a larger cortical area by more strongly excited, more coordinated, and more selectively responding neurons. This biasing peaked at the expected time of B onset with respect to A onset. No dynamic biasing of responses was recorded for any sound presented in a nontarget pair. Responses to nontarget frequencies flanking the representation of B were reduced in area and in response strength only after the presentation of A at the expected time of B onset. This study shows that cortical areas are not representationally static but, to the contrary, can be biased moment by moment in time as a function of behavioral context. cortical representation | perceptual training | plasticity | primary auditory cortex S uccessive-signal biasing ("prediction"), which is manifested in many behavioral studies in psychoacoustics and linguistics, very significantly contributes to the reception and production of rapidly successive inputs or actions (1-5). This study was designed to begin to reveal fundamental auditory system processes that could account for that biasing. Our primary initial goal was to apply a strategy in training by which an adult rat would be listening for the occurrence of specific stimuli in the context of (cued by the presentation of) other specific stimuli. In this initial study, we trained adult rats to respond to mark their recognition of the occurrence of a specific two-sound sequence, with the onsets of those sounds separated by 300 ms. We reasoned that in the context of this training, the rat might be biased for ("listen for" or "expect") the first sound stimulus, A. If and only if it occurred, cortical networks would hypothetically be biased to favor the reception of the second component of the target-pair stimulus, B.These studies show that training resulted in a modest static expansion of the representation of both sound elements of the target pair and, if and only if the first sound element of the target sound pair was delivered, in significant response biasing selective for the second sound stimulus in the target sequence. ResultsBehavioral Training. Rats in an experimental group (n = 6) were trained to identify a two-element auditory stimulus target presented with nine nontarget pairs (Fig. 1A) to receive food rewards. All initial stimuli in each pair were tones 200 ms in duration presented at 1.5, 3, 7, or 10 kHz. The initial stimulus in a target stimulus pair was 3 kHz (referred to as A). The second stimulus in each pair could again be 1.5, 3, 7, or 10 kHz. Second stimuli were 50 ms in duratio...

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Section: Discussionmentioning

confidence: 73%

Section: Discussionmentioning

confidence: 99%

Successive-signal biasing for a learned sound sequence

Zhou

Villers-Sidani

Panizzutti

et al. 2010

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

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“…What the present results point to is the severe capacity limit of this adaptive coding system in implementing more than one task set at a time, thereby impeding our ability to consciously perceive, and appropriately respond to, successive events in the world. [21][22][23][24][25][26][27][28][29][30][31][32][33] performed an auditory-vocal (AV) sound discrimination task, a visualmanual (VM) face discrimination task, or both tasks simultaneously (dualtask, DT). A black fixation marker (0.1°of visual angle) on a gray background was continuously presented at screen center to facilitate fixation.…”

Section: Discussionmentioning

confidence: 99%

“…A number of investigations have implicated posterior lateral PFC in both the PRP (22,(24)(25)(26) and the AB (27)(28)(29), which has led to the suggestion that this brain region may correspond to the neural substrates of a unified attentional bottleneck (30). In recent support of this hypothesis, the same brain region has been shown to play a general role in the control of attention (31,32). Moreover, several studies point to a key role for PFC, perhaps in consortium with parietal regions, in general attentional capacity limitations (33), decision-making (34), and in both visual-spatial processing and action selection (35), leaving it well suited to act as a coordination center for behavior and executive control (36)(37)(38).…”

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confidence: 99%

A Unified attentional bottleneck in the human brain

Tombu

Asplund²,

Dux³

et al. 2011

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

189

146

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Human information processing is characterized by bottlenecks that constrain throughput. These bottlenecks limit both what we can perceive and what we can act on in multitask settings. Although perceptual and response limitations are often attributed to independent information processing bottlenecks, it has recently been suggested that a common attentional limitation may be responsible for both. To date, however, evidence supporting the existence of such a "unified" bottleneck has been mixed. Here, we tested the unified bottleneck hypothesis using time-resolved fMRI. Experiment 1 isolated brain regions involved in the response selection bottleneck that limits speeded dual-task performance. These same brain regions were not only engaged by a perceptual encoding task in Experiment 2, their activity also tracked delays to a speeded decision-making task caused by concurrent perceptual encoding (Experiment 3). We conclude that a unified attentional bottleneck, including the inferior frontal junction, superior medial frontal cortex, and bilateral insula, temporally limits operations as diverse as perceptual encoding and decision-making.attention | attentional blink | psychological refractory period A lthough the human brain computes a rich representation of the sensory world, it does not have sufficient processing power to fully analyze all of the information it receives (1). Attentional mechanisms must therefore select important aspects of the environment for additional processing while filtering out less salient information (e.g., ref.2). Although attention makes the vast amount of sensory information impinging on our senses manageable, it does so at a cost, for it results in bottlenecks that can both block awareness of, and disrupt decision-making for, behaviorally relevant events. These limitations are most evident in dual-task settings, as the concurrent performance of two or more tasks usually leads to impairment in at least one of the tasks.Evidence supporting the view that attention to one event can block awareness of another comes from the attentional blink (AB) paradigm, in which participants report two-typically visual-targets (T1 and T2) presented in a rapid stream of distractors. The AB refers to the profound deficit in the explicit perception of T2 when that target follows T1 by ≈200-500 ms (3, 4). Although a number of hypotheses have been proposed to explain the AB (5), several lines of evidence suggest that deploying attention to consciously encode an initial target can constrain awareness of additional targets (6). Moreover, the duration of the attentional blink is modulated by the encoding load of T1 (7). Based on such findings, it has been proposed that the attentional demands of encoding information into working memory constitutes a bottleneck in information processing (henceforth, the encoding bottleneck; ref. 8).Unlike the AB, where capacity limitations manifest as a failure of awareness, the psychological refractory period (PRP) paradigm exposes the serial nature of decisional processes. In it, p...

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“…For what concerns β and γ waves the electrodes that better perform in our classification are AF3 (β) and AF4 together with F8 and F3. All these electrodes are located in the pre-frontal cortex which is related to control of attention [26] add to top-down mechanisms.…”

Section: Optimal Electrodes With Respect To the Brain Wavementioning

confidence: 99%

Auditory Saliency Classification Using EEG Data

Corchs¹,

Gasparini²

2017

FSP

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We investigate if starting from EEG signals it is possible to classify salient audio events. We set up an experiment and collected EEG data with proper audio stimuli. We trained several kNN classifiers. As the EEG signals under different frequency bands are related to different brain activities, we analyzed EEG data from each single electrode and for each single frequency band, as well as EEG data from combined electrodes and frequency bands. From our preliminary analysis we found that for each frequency band there is a set of electrodes that permits to achieve better classification results and these electrodes are related to specific brain regions. We propose a cross-subject approach, where different classifiers are trained on each single individual, and tested on data collected from all the others. This initial investigation suggests several hints to improve this classification task.

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The prefrontal cortex and the executive control of attention

Cited by 293 publications

References 55 publications

Successive-signal biasing for a learned sound sequence

Successive-signal biasing for a learned sound sequence

A Unified attentional bottleneck in the human brain

Auditory Saliency Classification Using EEG Data

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