# Contextual modulation of visual decision-making across the visual hierarchy > **NIH NIH R34** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2023 · $711,000 ## Abstract Project Summary/Abstract In sensory decision-making, choices are influenced by non-sensory factors such as motivation, attention, and recent trial history. We seek to incorporate these influences into a drift diffusion model (DDM), by modeling non-sensory variables as deterministic modulators of the starting point or drift rate of sensory evidence accumulation. However, decision-making models are subject to confounds due to the non-stationarity and correlations in long-term behavioral data. More work is needed to quantify these properties and develop new statistical approaches to overcome them. Existing datasets have proven inadequate, so new datasets must be collected. To gain insight into the neural mechanisms of contextual modulation in decision-making, our goal is to compare non-sensory influences on sensory decision- making across levels of the visual hierarchy and between parallel visual streams. We have evidence there are differences, which could be leveraged to identify where in the brain non-visual information enters into visual decision-making. To establish feasibility for an R01, we need to establish a new collaboration with a statistician, develop methods for training single animals in multiple visual tasks; show that visual tasks differ in their sensitivity to non- sensory modulation; and show that we can obtain the amount of trial data required to fit and compare models within subject. We propose to train individual animals in grating orientation, random-dot motion, object identity, spatial location of luminance or contrast, as well as piloting two new tasks (stochastic drifting grating, spatial location of motion). These visual features are thought to be extracted in different brain areas: in primary visual cortex (V1); in different higher visual areas (HVAs) in ventral or dorsal streams; or in a V1-independent collicular pathway. We will collect long-term data on the interleaved tasks using automated high-throughput in-cage testing, and validate that these data meet statistical requirements for model fitting. Rats are ideal for this study because individual rats can learn multiple visual tasks, and we are able to obtain 105-106 behavioral trials per rat without water or food restriction. Rats are also suitable for viral vector targeting strategies and high-density electrophysiology with optogenetics in freely behaving animals. At the end of this 2-year R34 project, the lab will have assembled a new multi-disciplinary research team poised for dissection of underlying circuit mechanisms, with validated visual tasks, training protocols, statistical approaches, and model-fitting methods. These preparations will support a BRAIN Initiative: Targeted BCP R01 application aimed at dissecting neural representations and circuit mechanisms of contextual modulation of choice. ## Key facts - **NIH application ID:** 10658176 - **Project number:** 1R34NS132037-01 - **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO - **Principal Investigator:** PAMELA REINAGEL - **Activity code:** R34 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2023 - **Award amount:** $711,000 - **Award type:** 1 - **Project period:** 2023-06-01 → 2026-05-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10658176 ## Citation > US National Institutes of Health, RePORTER application 10658176, Contextual modulation of visual decision-making across the visual hierarchy (1R34NS132037-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10658176. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*