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Shifting the spotlight of attention: evidence for discrete computations in cognition.

Buschman TJ, Miller EK - Front Hum Neurosci (2010)

Bottom Line: Therefore, by understanding the neural mechanisms of attention we hope to understand a core component of cognition.We generalize these findings to present a hypothesis that cognition relies on neural mechanisms that operate in discrete, periodic computations, as reflected in ongoing oscillations.We discuss the advantages of the model, experimental support, and make several testable hypotheses.

View Article: PubMed Central - PubMed

Affiliation: Department of Brain and Cognitive Sciences and The Picower Institute for Learning and Memory, Massachusetts Institute of Technology Cambridge, MA, USA.

ABSTRACT
Our thoughts have a limited bandwidth; we can only fully process a few items in mind simultaneously. To compensate, the brain developed attention, the ability to select information relevant to the current task, while filtering out the rest. Therefore, by understanding the neural mechanisms of attention we hope to understand a core component of cognition. Here, we review our recent investigations of the neural mechanisms underlying the control of visual attention in frontal and parietal cortex. This includes the observation that the neural mechanisms that shift attention were synchronized to 25 Hz oscillatory brain rhythms, with each shift in attention falling within a single cycle of the oscillation. We generalize these findings to present a hypothesis that cognition relies on neural mechanisms that operate in discrete, periodic computations, as reflected in ongoing oscillations. We discuss the advantages of the model, experimental support, and make several testable hypotheses.

No MeSH data available.


(A) Synchrony between frontal and parietal cortex during visual pop-out (top) and visual search (bottom) Both tasks emphasize a “beta” band (18–34 Hz) and a “gamma” band (35–55 Hz) over baseline. However, there is a greater emphasis on gamma band activity during pop-out and beta band during search. (B) Direct comparison of synchrony between frontal and parietal cortex across frequency. This comparison highlights the increase in synchrony between frontal and parietal cortex in the beta band during top-down, internally guided visual search. Likewise, an increase in gamma band synchrony is observed during bottom-up, externally driven pop-out.
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Figure 3: (A) Synchrony between frontal and parietal cortex during visual pop-out (top) and visual search (bottom) Both tasks emphasize a “beta” band (18–34 Hz) and a “gamma” band (35–55 Hz) over baseline. However, there is a greater emphasis on gamma band activity during pop-out and beta band during search. (B) Direct comparison of synchrony between frontal and parietal cortex across frequency. This comparison highlights the increase in synchrony between frontal and parietal cortex in the beta band during top-down, internally guided visual search. Likewise, an increase in gamma band synchrony is observed during bottom-up, externally driven pop-out.

Mentions: A further prediction of this model would be that the flexibility to switch between different behaviors will be reflected in changes in synchrony. To test this we examined synchrony between frontal and parietal cortex in our visual search paradigm. Although both frontal and parietal regions were involved in both top-down and bottom-up search, they seemed to make different contributions. Thus, there might be differences in the synchrony between the two regions during the two tasks that support their changing roles. This is exactly what we found: the frequency of coherence between prefrontal cortex and posterior parietal cortex depended on whether attention was top-down or bottom-up (see Figure 3, Buschman and Miller, 2007). When attention was automatically drawn to a target, coherence between parietal and frontal cortex was increased in the “gamma” band (35–55 Hz). However, when the animal internally directed its attention, coherence in the parietal-frontal network was increased in the “beta” band (22–34 Hz). Von Stein et al. (2000) found similar results in cats – synchrony between primary visual cortex and multimodal areas was stronger at lower frequencies when stimuli were associated with behavioral responses (suggesting a “top-down” component) and stronger at higher frequencies with behaviorally irrelevant novel stimuli (suggesting “bottom-up”). Changes in inter-areal synchrony for different behavioral tasks was also found by Pesaran et al. (2008), who showed an increase in synchrony between the parietal reach region and premotor cortex (frontal) around 15 Hz when the animal was allowed to move through a display freely compared to an instructed version of the same task.


Shifting the spotlight of attention: evidence for discrete computations in cognition.

Buschman TJ, Miller EK - Front Hum Neurosci (2010)

(A) Synchrony between frontal and parietal cortex during visual pop-out (top) and visual search (bottom) Both tasks emphasize a “beta” band (18–34 Hz) and a “gamma” band (35–55 Hz) over baseline. However, there is a greater emphasis on gamma band activity during pop-out and beta band during search. (B) Direct comparison of synchrony between frontal and parietal cortex across frequency. This comparison highlights the increase in synchrony between frontal and parietal cortex in the beta band during top-down, internally guided visual search. Likewise, an increase in gamma band synchrony is observed during bottom-up, externally driven pop-out.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2990535&req=5

Figure 3: (A) Synchrony between frontal and parietal cortex during visual pop-out (top) and visual search (bottom) Both tasks emphasize a “beta” band (18–34 Hz) and a “gamma” band (35–55 Hz) over baseline. However, there is a greater emphasis on gamma band activity during pop-out and beta band during search. (B) Direct comparison of synchrony between frontal and parietal cortex across frequency. This comparison highlights the increase in synchrony between frontal and parietal cortex in the beta band during top-down, internally guided visual search. Likewise, an increase in gamma band synchrony is observed during bottom-up, externally driven pop-out.
Mentions: A further prediction of this model would be that the flexibility to switch between different behaviors will be reflected in changes in synchrony. To test this we examined synchrony between frontal and parietal cortex in our visual search paradigm. Although both frontal and parietal regions were involved in both top-down and bottom-up search, they seemed to make different contributions. Thus, there might be differences in the synchrony between the two regions during the two tasks that support their changing roles. This is exactly what we found: the frequency of coherence between prefrontal cortex and posterior parietal cortex depended on whether attention was top-down or bottom-up (see Figure 3, Buschman and Miller, 2007). When attention was automatically drawn to a target, coherence between parietal and frontal cortex was increased in the “gamma” band (35–55 Hz). However, when the animal internally directed its attention, coherence in the parietal-frontal network was increased in the “beta” band (22–34 Hz). Von Stein et al. (2000) found similar results in cats – synchrony between primary visual cortex and multimodal areas was stronger at lower frequencies when stimuli were associated with behavioral responses (suggesting a “top-down” component) and stronger at higher frequencies with behaviorally irrelevant novel stimuli (suggesting “bottom-up”). Changes in inter-areal synchrony for different behavioral tasks was also found by Pesaran et al. (2008), who showed an increase in synchrony between the parietal reach region and premotor cortex (frontal) around 15 Hz when the animal was allowed to move through a display freely compared to an instructed version of the same task.

Bottom Line: Therefore, by understanding the neural mechanisms of attention we hope to understand a core component of cognition.We generalize these findings to present a hypothesis that cognition relies on neural mechanisms that operate in discrete, periodic computations, as reflected in ongoing oscillations.We discuss the advantages of the model, experimental support, and make several testable hypotheses.

View Article: PubMed Central - PubMed

Affiliation: Department of Brain and Cognitive Sciences and The Picower Institute for Learning and Memory, Massachusetts Institute of Technology Cambridge, MA, USA.

ABSTRACT
Our thoughts have a limited bandwidth; we can only fully process a few items in mind simultaneously. To compensate, the brain developed attention, the ability to select information relevant to the current task, while filtering out the rest. Therefore, by understanding the neural mechanisms of attention we hope to understand a core component of cognition. Here, we review our recent investigations of the neural mechanisms underlying the control of visual attention in frontal and parietal cortex. This includes the observation that the neural mechanisms that shift attention were synchronized to 25 Hz oscillatory brain rhythms, with each shift in attention falling within a single cycle of the oscillation. We generalize these findings to present a hypothesis that cognition relies on neural mechanisms that operate in discrete, periodic computations, as reflected in ongoing oscillations. We discuss the advantages of the model, experimental support, and make several testable hypotheses.

No MeSH data available.