# Neural and behavioral mechanisms of abstraction in humans

> **NIH NIH K08** · NEW YORK STATE PSYCHIATRIC INSTITUTE DBA RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC · 2020 · $197,640

## Abstract

Fundamental to human intelligence is the ability to abstract a general rule from prior experiences and then
apply this rule to new stimuli so as to infer likely outcomes. The processes of abstraction and inference work in
tandem to inform the expectations that drive both behavior (e.g., choosing the best option) and affect (e.g.,
excitement for a reward). Dysfunction in these processes leads to distorted expectations, and in turn, the
maladaptive behavior and emotions that are a hallmark of psychiatric disease. Inferred expectations can be
inflated positively, as in substance use and mania, or negatively, as in depression, PTSD and generalized
anxiety, leading to avoidance behavior and dysphoric or anxious affect. The critical role of abstraction and
inference in healthy and pathological behavior belies our limited understanding of their neural basis. While past
work has shown neural representations change with abstract learning (e.g., increased representational
similarity), the link between the specific format of neural representation and behavioral function (i.e., inference)
remains untested. Moreover, most existing tasks focus on abstract learning from reward, leaving open
questions about abstraction during aversive outcomes, which is fundamental to most mental illness. Here we
propose to develop a theoretical framework for how the brain represents past stimuli in a format that reflects
abstract knowledge and a mechanism for using this structured representation to infer the properties of novel
stimuli. This framework will be coupled with a behavioral task in humans that captures the essential elements
of real-world abstraction, including appetitive and aversive outcomes, and an analysis approach for fMRI that
tests the functional link between the format of representation in the human brain and inference behavior. To fill
these gaps, the proposed work in humans leverages recent findings in the monkey showing that populations of
single neurons represent stimuli in an abstract format that supports inference. Translating this work will
advance understanding of the neural basis of abstraction in humans. I propose to accomplish this with two
specific aims. First, I will develop a novel behavioral task in which human subjects learn an implicit rule from
prior experience and use this rule to infer rewarded actions during concurrent fMRI. Using a novel
computational method, I will test whether the format of representations of experienced stimuli supports
inference about unexperienced stimuli. I will further validate the link between brain and behavior by testing the
predictions that the neural format emerges with learning and that it explains individual variation in inference.
Second, I will compare the roles of appetitive and aversive outcomes on abstract rule learning and on the
formation of neural representations that support inference. This work will lay the foundation for studying the
neural basis of abstraction in humans and, more generally, will...

## Key facts

- **NIH application ID:** 10017328
- **Project number:** 5K08MH121775-02
- **Recipient organization:** NEW YORK STATE PSYCHIATRIC INSTITUTE DBA RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC
- **Principal Investigator:** Daniel Landay Kimmel
- **Activity code:** K08 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $197,640
- **Award type:** 5
- **Project period:** 2019-09-16 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10017328

## Citation

> US National Institutes of Health, RePORTER application 10017328, Neural and behavioral mechanisms of abstraction in humans (5K08MH121775-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10017328. Licensed CC0.

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