# Neural dynamics underlying rule-based decision-making

> **NIH NIH K08** · NEW YORK STATE PSYCHIATRIC INSTITUTE DBA RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC · 2022 · $197,640

## Abstract

Flexible cognition requires working memory (WM), the ability to form and manipulate mental representations.
The contents of working memory include internally-generated factors required to determine which of many
possible behavioral contingencies, or rules, should be applied under varying circumstances. Such an ability to
flexibly invoke behavioral contingencies underlies many crucial functions, for example the cognitive regulation of
emotion and context-dependent decision-making. In primates, the retention and manipulation of WM
representations depends on the prefrontal cortex (PFC), and in humans, dysfunction of the PFC is associated
with range of symptoms in psychiatric illness such as the neurocognitive deficits in schizophrenia. Neurons in
PFC produce persistent spiking activity during behaviors that require WM, suggesting a mechanism by which
mnemonic mental representations are maintained across time in the absence of a stimulus to drive activity.
Although many PFC neurons respond most vigorously during WM of a specific stimulus feature to which they
are specialized, a large proportion of PFC neurons actually exhibit mixed selectivity: heterogenous and time-varying responses to complex mixtures of remembered stimulus features. Nonlinear mixing is theorized to serve
a pivotal role in flexible cognition by enabling high-dimensional representations from which simple linear readouts
can extract many more task-related variables than if the neurons were highly specialized. How both the degree
of nonlinearity and the dimensionality of representations are dynamically related to factors such as cognitive
demand or learning remains larely unexplored. I propose to evaluate the proposition that nonlinear mixed-selectivity neurons give rise to distributed, high-dimensional representations suited to the higher cognitive
functions for which they are invoked. This hypothesis asserts that substantial information exists in population-level structure which would be evident in the joint activity of a large number of neurons, and most apparent in an
animal performing a cognitively demanding task for which successful completion necessitates formation of a
high-dimensional representation. To test this assertion, I will use arrays of microelectrodes chronically implanted
in the PFC of monkeys to record, in parallel, the activity of many single units while monkeys perform a delayed
match to sample (DMS) task in which matches are based on conjunctions of features of the probe and sample
stimuli. Because the matches are based on conjunctions, the decision rule can be made more or less complex
and hence would require a representation of higher or lower dimension. I will examine how dimensionality of
representations and the degree of nonlinear mixing is dynamically related to learning and task performance, and
I will test the hypothesis that the complexity of the decision rule predicts the dimensionality of a neural
representation during performance of the task. I will ...

## Key facts

- **NIH application ID:** 10402283
- **Project number:** 5K08MH120434-04
- **Recipient organization:** NEW YORK STATE PSYCHIATRIC INSTITUTE DBA RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC
- **Principal Investigator:** Lee Phipps Lovejoy
- **Activity code:** K08 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $197,640
- **Award type:** 5
- **Project period:** 2019-06-14 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10402283

## Citation

> US National Institutes of Health, RePORTER application 10402283, Neural dynamics underlying rule-based decision-making (5K08MH120434-04). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10402283. Licensed CC0.

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