# Neural Mechanisms for Feature-Based Attention

> **NIH NIH R01** · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · 2020 · $498,039

## Abstract

We must often search cluttered scenes for items of immediate behavioral relevance – e.g. our food, our car
keys, our friend, and so on. In each case, we use a memory of the target object’s features to efficiently guide
our search to objects that share some of its features, so that we are not forced to inspect every object in a
crowded scene individually. Efficient visual search is critical for efficient visually guided behavior. Although
much is known about the biological mechanisms underlying the selection of objects based on spatial location,
much less is known about the mechanisms underlying the selection of objects based on features. To design
an effective neural prosthesis or to treat people with sensory or attentional impairments, we need a better
understanding of feature attention at the systems level. A better understanding of feature attention will also
give us more insight into the mechanisms underlying visual working memory and visual memory recall, as
these related functions seem to involve at least partially overlapping neural circuits. Until recently, it was
unclear if there was any specific brain structure that stored the information about attended features and used it
to guide visual processing in the cortex through top-down feedback. We recently obtained evidence for such a
site in prefrontal cortex, in a region that we have termed VPA. Our Aims are focused on a better
understanding of VPA and the mechanisms by which it interacts with other visual areas during attention to
features. In Aim 1, we will use electrical stimulation paired with fMRI to densely map the projections of a wide
expanse of lateral prefrontal cortex, including VPA. This prefrontal “connectome” will show us how VPA relates
to other prefrontal circuits, and it will give us the neural wiring diagram for how VPA interacts with other
functional regions throughout the brain. The published connectome will also serve as valuable resource for the
neuroscience community. Preliminary results from the connectome are already being used to guide our other
two Aims. In Aim 2, we will use pharmacological methods to reversibly deactivate VPA, to test our hypotheses
that VPA is the source of feedback that modulates processing in area V4 during attention to features such as
shape and color. A positive result would be strong evidence in favor of VPAs feedback control of the ventral
stream for object recognition. In Aim 3, we will test our hypotheses about the role of VPA in the dorsal stream,
during attention to objects based on their direction of motion. Cells in VPA, MT, MST, FST, and LIP will be
recorded simultaneously, to test whether neural activity in VPA has the temporal properties needed to support
VPA’s causal role in attention to motion. We will then use new technology we have developed to
optogenetically suppress VPA and test whether it impairs attention to motion and reduces or eliminates the
effects of attention to motion on the responses of cells in areas MT, MST, ...

## Key facts

- **NIH application ID:** 9842893
- **Project number:** 5R01EY029666-02
- **Recipient organization:** MASSACHUSETTS INSTITUTE OF TECHNOLOGY
- **Principal Investigator:** Robert Desimone
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $498,039
- **Award type:** 5
- **Project period:** 2019-01-01 → 2023-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9842893

## Citation

> US National Institutes of Health, RePORTER application 9842893, Neural Mechanisms for Feature-Based Attention (5R01EY029666-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9842893. Licensed CC0.

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