# Investigating the neurophysiological underpinnings of higher-order statistical learning in humans

> **NIH NIH F31** · UNIVERSITY OF PENNSYLVANIA · 2020 · $34,833

## Abstract

PROJECT SUMMARY
A diverse range of behaviors including language, event segmentation, and learning to play an instrument involve
parsing complex temporal sequences. Item co-occurrence in a sequence can exist between pairs of items shown
consecutively, as well as triplets, quadruplets and other higher order sets. For example, when learning a
language both humans and natural language processing algorithms make use of first order statistics about which
words tend to follow others, and higher-order statistics about the grammar of sentences and paragraphs. The
statistics of this co-occurrence between sets of different sizes can influence how well people learn the sequence.
We can demonstrate this learning by presenting stimuli in a sequence determined by a walk on a highly clustered
graph where stimuli within a large cluster tend to co-occur more frequently than stimuli in different clusters. When
subjects are shown motor cues generated from a walk on such a graph, they exhibit shorter reaction times and
more similar BOLD activity for successive cues drawn from the same cluster than when they are drawn from
different clusters. Generalizable theories of hippocampal function suggest that its role in learning and predicting
abstract relationships could subserve this behavior. In line with this theory, neuroimaging data implicates the
hippocampus and downstream cortical regions in this behavior. However, direct electrophysiological measures
of neural activity during the performance of this task have not been examined, leaving the description of the
neural underpinnings of the behavior incomplete. We propose to use electrocorticography (ECoG) to test
the hypothesis that activity in the hippocampus reflects behavioral change across clusters, and is
sensitive to higher-order associations between stimuli. Our investigation is an important next step in testing
theories of how the hippocampus aids relational learning, and it will add to knowledge of how information in the
world is represented in the brain and how it aids or hinders learning.

## Key facts

- **NIH application ID:** 9991400
- **Project number:** 1F31MH120925-01A1
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Jennifer Stiso
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $34,833
- **Award type:** 1
- **Project period:** 2020-04-01 → 2021-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9991400

## Citation

> US National Institutes of Health, RePORTER application 9991400, Investigating the neurophysiological underpinnings of higher-order statistical learning in humans (1F31MH120925-01A1). Retrieved via AI Analytics 2026-06-12 from https://api.ai-analytics.org/grant/nih/9991400. Licensed CC0.

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