# LINKING BRAIN, BEHAVIOR, AND GENES ACROSS SPECIES AND DEVELOPMENT: EVALUATION OF INTEGRATIVE CATEGORY LEARNING MODELS

> **NIH NIH P01** · OHIO STATE UNIVERSITY · 2020 · $184,672

## Abstract

Summary
Judging a person as a friend or foe, a mushroom as edible or poisonous, or a sound as an \l\ or \r\ are
examples of categorization problems. Because people never encounter the same stimulus twice, they
must develop categorization schemes that capture the useful regularities in their environment. Key
research challenges include how humans acquire and represent categories. This project tackles the
broader challenge of elucidating the nature of the learning system or systems, these systems' neural
underpinnings, how such systems develop, how they differ across species, and how they interact. To
answer these fundamental questions, a space of category learning models is defined to allow for
formal evaluation of the theories these models encode. Although no one study can explicate the
nature, developmental trajectory, evolution, and neural underpinnings of all category learning
mechanisms, the results from numerous studies can when coupled with powerful analysis techniques.
By defining a space of models, data from numerous studies (developmental - Project 1, as well as
comparative and neuroscientific - Project 2) can be jointly evaluated to determine the most likely
theories given the data. This approach incorporates key task variables, such as proposed
relationships between formal mechanisms and brain regions, and how various system capacities and
biases can vary across development and evolution. Thus, the developed theories (in the form of
formal models) not only specify computational mechanisms, but how these mechanisms change over
development, vary across species, their neural underpinnings, how genetic variations shape
individual differences, and how task variables (e.g., secondary task load) affect their operation.
Bayesian Model Selection (BMS) procedures will evaluate candidate models on a vast array of data
collected within Projects 1 and 2 to determine which models are most likely to be valid (i.e.,
generalize to novel studies). Preliminary results will help guide efforts in Projects 1 and 2 to determine
the most theoretically informative study designs. Model fits may prove useful for gauging what
constitutes normal development and for directing interventions for populations suffering from disease
or other difficulties.

## Key facts

- **NIH application ID:** 9932478
- **Project number:** 5P01HD080679-05
- **Recipient organization:** OHIO STATE UNIVERSITY
- **Principal Investigator:** BRADLEY C LOVE
- **Activity code:** P01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $184,672
- **Award type:** 5
- **Project period:** — → 2022-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9932478

## Citation

> US National Institutes of Health, RePORTER application 9932478, LINKING BRAIN, BEHAVIOR, AND GENES ACROSS SPECIES AND DEVELOPMENT: EVALUATION OF INTEGRATIVE CATEGORY LEARNING MODELS (5P01HD080679-05). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9932478. Licensed CC0.

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