# The emergence of abstract structure knowledge across learning and sleep

> **NIH NIH R21** · UNIVERSITY OF PENNSYLVANIA · 2022 · $219,813

## Abstract

In any given cognitive domain, representations of individual elements are not independent but are organized by
means of structured relations. Representations of this underlying structure are powerful because they can allow
generalization and inference in novel environments. In the semantic domain, structure captures associations
between different semantic features or concepts (e.g., green, wings, can fly) and is known to influence the
development and deterioration of semantic knowledge. We recently found that humans find it easier to learn
novel categories that contain clusters of reliably co-occurring features, revealing an influence of structure on
novel category formation. However, a critical unknown is whether learned representations of structure are closely
tied to category-specific elements, or whether they become abstract to some extent, transformed away from the
experienced features. Further, if abstract structural representations do emerge, prior work provides intriguing
hints that they may require offline consolidation during awake rest or sleep. We have developed a paradigm in
which carefully designed graph structures govern the pattern of feature co-occurrences within individual
categories. Here we implement a "structure transfer'' extension of this paradigm in order to determine whether
learning one structured category facilitates learning of a second identically structured category defined by a new
set of features. This facilitation would provide evidence that structure representations are abstract to some
degree. Aim 1 will use these methods to evaluate whether abstract structural representations emerge
immediately during learning. Aim 2 will determine whether these representations persist, or emerge, over a delay,
and whether sleep-based consolidation in particular is needed. The role of replay of recent experience during
sleep will be evaluated using electroencephalography (EEG) paired with closed-loop targeted memory
reactivation (TMR), a technique that enables causal influence over the consolidation of recently learned
information in humans. This work will inform and constrain theories of semantic learning as well as theories of
structure learning and representation more broadly.

## Key facts

- **NIH application ID:** 10527095
- **Project number:** 1R21MH128788-01A1
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Anna C Schapiro
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $219,813
- **Award type:** 1
- **Project period:** 2022-09-01 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10527095

## Citation

> US National Institutes of Health, RePORTER application 10527095, The emergence of abstract structure knowledge across learning and sleep (1R21MH128788-01A1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10527095. Licensed CC0.

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