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A Refined Neuronal Population Measure of Visual Attention.

Mayo JP, Cohen MR, Maunsell JH - PLoS ONE (2015)

Bottom Line: Neurophysiological studies of cognitive mechanisms such as visual attention typically ignore trial-by-trial variability and instead report mean differences averaged across many trials.Here, we refine this method to eliminate problems that can cause bias in estimates of attentional state in certain scenarios.We demonstrate the sources of these problems using simulations and propose an amendment to the previous formulation that provides superior performance in trial-by-trial assessments of attentional state.

View Article: PubMed Central - PubMed

Affiliation: Department of Neurobiology, Harvard Medical School, 220 Longwood Avenue, Boston, Massachusetts, United States of America.

ABSTRACT
Neurophysiological studies of cognitive mechanisms such as visual attention typically ignore trial-by-trial variability and instead report mean differences averaged across many trials. Advances in electrophysiology allow for the simultaneous recording of small populations of neurons, which may obviate the need for averaging activity over trials. We recently introduced a method called the attention axis that uses multi-electrode recordings to provide estimates of attentional state of behaving monkeys on individual trials. Here, we refine this method to eliminate problems that can cause bias in estimates of attentional state in certain scenarios. We demonstrate the sources of these problems using simulations and propose an amendment to the previous formulation that provides superior performance in trial-by-trial assessments of attentional state.

No MeSH data available.


Behavioral performance as a function of attention axis position.Left, Projections on the original attention axis for each stimulus location (Fig 2F from [5]). Horizontal line indicates mean proportion correct across trials. Right, Same data combined across stimulus locations plotted on the original attention axis (red line) and re-plotted on the revised attention axis (blue line).
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pone.0136570.g004: Behavioral performance as a function of attention axis position.Left, Projections on the original attention axis for each stimulus location (Fig 2F from [5]). Horizontal line indicates mean proportion correct across trials. Right, Same data combined across stimulus locations plotted on the original attention axis (red line) and re-plotted on the revised attention axis (blue line).

Mentions: The attention axis measures the attentional state of the subject during a brief period (here, 200 ms) based on the average modulation of neuronal responses between different attention conditions (e.g., attend-left vs. attend-right). Below, we illustrate potential problems with this measure as it was originally applied [5, 6]. The first two issues (Figs 1 and 2) can generate different projections on Hit and Miss trials even when there is no meaningful change in the underlying neuronal activity. These issues can be eliminated by using the modified analysis described below. We verify this refined approach using simulations (Fig 3) and discuss limitations of the sensitivity of attention axis measurements. Finally, we re-plot central results from our previous work using the refined attention axis (Figs 4–6).


A Refined Neuronal Population Measure of Visual Attention.

Mayo JP, Cohen MR, Maunsell JH - PLoS ONE (2015)

Behavioral performance as a function of attention axis position.Left, Projections on the original attention axis for each stimulus location (Fig 2F from [5]). Horizontal line indicates mean proportion correct across trials. Right, Same data combined across stimulus locations plotted on the original attention axis (red line) and re-plotted on the revised attention axis (blue line).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4546609&req=5

pone.0136570.g004: Behavioral performance as a function of attention axis position.Left, Projections on the original attention axis for each stimulus location (Fig 2F from [5]). Horizontal line indicates mean proportion correct across trials. Right, Same data combined across stimulus locations plotted on the original attention axis (red line) and re-plotted on the revised attention axis (blue line).
Mentions: The attention axis measures the attentional state of the subject during a brief period (here, 200 ms) based on the average modulation of neuronal responses between different attention conditions (e.g., attend-left vs. attend-right). Below, we illustrate potential problems with this measure as it was originally applied [5, 6]. The first two issues (Figs 1 and 2) can generate different projections on Hit and Miss trials even when there is no meaningful change in the underlying neuronal activity. These issues can be eliminated by using the modified analysis described below. We verify this refined approach using simulations (Fig 3) and discuss limitations of the sensitivity of attention axis measurements. Finally, we re-plot central results from our previous work using the refined attention axis (Figs 4–6).

Bottom Line: Neurophysiological studies of cognitive mechanisms such as visual attention typically ignore trial-by-trial variability and instead report mean differences averaged across many trials.Here, we refine this method to eliminate problems that can cause bias in estimates of attentional state in certain scenarios.We demonstrate the sources of these problems using simulations and propose an amendment to the previous formulation that provides superior performance in trial-by-trial assessments of attentional state.

View Article: PubMed Central - PubMed

Affiliation: Department of Neurobiology, Harvard Medical School, 220 Longwood Avenue, Boston, Massachusetts, United States of America.

ABSTRACT
Neurophysiological studies of cognitive mechanisms such as visual attention typically ignore trial-by-trial variability and instead report mean differences averaged across many trials. Advances in electrophysiology allow for the simultaneous recording of small populations of neurons, which may obviate the need for averaging activity over trials. We recently introduced a method called the attention axis that uses multi-electrode recordings to provide estimates of attentional state of behaving monkeys on individual trials. Here, we refine this method to eliminate problems that can cause bias in estimates of attentional state in certain scenarios. We demonstrate the sources of these problems using simulations and propose an amendment to the previous formulation that provides superior performance in trial-by-trial assessments of attentional state.

No MeSH data available.