# Neural dynamics underlying spatiotemporal cognitive integration

> **NIH NIH R01** · BOSTON CHILDREN'S HOSPITAL · 2022 · $416,320

## Abstract

Project Summary
 Our ability to visually interpret the world around us depends on rapid bottom-up computations that
extract relevant information from the sensory inputs, but it also depends on our accumulated core knowledge
about the world providing top-down signals based on prior experience. The goal of this proposal is to study the
mechanisms by which visual information is integrated spatially and temporally to combine bottom-up and top-
down knowledge. Towards this goal, we combine behavioral measurements, invasive neurophysiological
recordings, invasive electrical stimulation, and computational models. We focus on the ubiquitous challenge of
visual search, exemplified by searching for your phone using exclusively visual cues. The behavioral data will
provide critical constraints about human integrative abilities, particularly through eye movements and the
dynamics of recognition and object location. The invasive neurophysiological data will provide high
spatiotemporal resolution of neural activity along the inferior temporal cortex and the interactions with the pre-
frontal cortex, which are hypothesized to be critical for conveying the type of top-down signals required for
recognition and attention modulation during visual search. Ultimately, a central goal of our proposal is to
formalize our understanding of these integrative processes via a quantitative computational model. This
computational model should be able to capture the behavioral and physiological results and provide testable
predictions. During the current award, we have made progress towards elucidating the mechanisms underlying
pattern completion used by the visual system to infer the identity of objects from partial information, the effects
of contextual information during object recognition, and computational models of visual search. We have strong
preliminary evidence that suggests that state-of-the-art purely bottom-up theories of recognition instantiated by
deep convolutional networks cannot explain human behavior and physiology. Therefore, the proposed work
aims to establish a strong computational, behavioral and physiological framework that merges bottom-up and
top-down processing. Furthermore, we will move beyond correlative measures by using electrical stimulation to
stress test the models and establish causal links between key nodes in the circuitry and visual search
behavior. Understanding the neural mechanisms by which core knowledge is incorporated into sensory
processing is arguably one of the greatest challenges in Cognitive Science and may have important
implications for many neurological and psychiatric conditions that are characterized by dysfunctional top-down
signaling and remain poorly understood.

## Key facts

- **NIH application ID:** 10442811
- **Project number:** 2R01EY026025-07
- **Recipient organization:** BOSTON CHILDREN'S HOSPITAL
- **Principal Investigator:** Gabriel Kreiman
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $416,320
- **Award type:** 2
- **Project period:** 2016-03-01 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10442811

## Citation

> US National Institutes of Health, RePORTER application 10442811, Neural dynamics underlying spatiotemporal cognitive integration (2R01EY026025-07). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10442811. Licensed CC0.

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