# Cognitive and Neural Strategies for Latent Feature Inference > **NIH NIH K99** · UNIVERSITY OF COLORADO · 2023 · $130,354 ## Abstract PROJECT SUMMARY The world around us has a statistical structure that we can use to improve our choices. Learning the underlying structure by identifying key features, such as the rate of change, is useful for adapting and optimizing our decision-making strategies. However, learning these features requires accumulating evidence across multiple timescales: a short timescale that considers explicit evidence for the current decision, and a long timescale that supports latent environmental feature inference. In the brain, evidence accumulation across timescales necessary for flexible decision-making should therefore engage contextual memory in regions such as hippocampus (HC). This proposal aims to identify cognitive strategies and neural mechanisms humans use to accumulate evidence across timescales for adaptive decision-making. Using an interdisciplinary approach that utilizes computational modeling to develop and validate human behavioral and human electrophysiological experiments, I will 1) identify the variety of decision strategies humans use to support multi-timescale inference, 2) model plausible neural mechanisms of human cognitive strategies, and 3) define HC’s role in implementing multi-timescale inference. This work is in line with the BRAIN initiative’s mission to link behavior and function and priority research areas 5 (Theory and Data Analysis tools) and 6 (Human Neuroscience). With my outstanding mentor team, who have combined expertise in theory and experimental work, the mentored phase of this grant will provide me with 1) additional research skills in both static inference models and neural-circuit modeling and 2) career development through personalized mentorship, writing, and scientific communication training. The University of Colorado Boulder offers an ideal environment for this work, with numerous resources between the departments of Applied Math, Psychology and Institute for Cognitive Science. Additionally, with the availability of many programs and seminars online, resources at co-mentor institutions University of Pennsylvania and University of Houston are also accessible. The independent phase research will combine this additional training with my previous experience in human electrophysiology and signal processing to study the role of HC in flexible decision-making, analyzing human neural recordings from epilepsy patients while they perform a multi-timescale decision-making task recorded by my collaborators at University of Utah. My long term goals are to launch my own lab that applies a multimodal approach of theory, human behavior, and human neural electrophysiology to identifying the cognitive and neural strategies associated with flexible decision-making and the impacts that pathological disruptions have on these processes. ## Key facts - **NIH application ID:** 10662877 - **Project number:** 1K99NS127855-01A1 - **Recipient organization:** UNIVERSITY OF COLORADO - **Principal Investigator:** Tahra Eissa - **Activity code:** K99 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2023 - **Award amount:** $130,354 - **Award type:** 1 - **Project period:** 2023-07-01 → 2025-06-30 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10662877 ## Citation > US National Institutes of Health, RePORTER application 10662877, Cognitive and Neural Strategies for Latent Feature Inference (1K99NS127855-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10662877. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*