# Neural foundations of learning, reasoning, and surprise in human infants

> **NIH NIH F32** · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · 2020 · $64,554

## Abstract

How is the human brain organized to learn and reason about the world during development? Although 
young infants show behavioral signs of surprise (e.g. longer looking) at unexpected stimuli (e.g. 
upon hearing a completely new word, seeing an object float in midair, or seeing a person perform an 
inefficient action) (Gergely & Csibra, 2003; Saffran, Aslin, & Newport, 1996; Spelke, Breinlinger, 
Macomber, & Jacobson, 1992; Spelke & Kinzler, 2007), the neural systems that support their 
expectation and surprise are not accessible through measurements of behavior. In this proposal, we 
use an infant-friendly, non-invasive neuroimaging technique, functional near-infrared spectroscopy 
(fNIRS), to investigate the neural systems that allow infants and adults to form expectations and 
detect violations of those expectations across different situations. We test the hypotheses that 
sensory cortex encode task- and modality specific information during learning, that lateral 
prefrontal cortex (LPFC) detects subsequent violations of those expectations, and that LPFC drives 
attention towards the surprising stimulus. If so, we should find differentiated sensory activity 
during learning across tasks, but overlapping LPFC activity during surprise in those tasks, which 
should in turn predict behavior. Aim 1 tests these predictions using learning tasks, wherein people 
form new expectations in the lab (e.g. about which word, 'doti' or 'lado', is more likely to come 
from a newly learned artificial language) (Marcus, 1999; Saffran et al., 1996). Aim 2 investigates 
the same predictions in tasks requiring prior expectations about objects (that they're solid), 
people (that they behave rationally), and probability (that randomly drawn samples tend to be 
representative of the population) (Gergely & Csibra, 2003; Spelke et al., 1992; Spelke & Kinzler, 
2007; Xu & Garcia, 2008). Aim 3 compares the neural systems that allow people to learn new 
expectations (Aim 1) and reason using prior expectations (Aim 2). Across all aims, we will test 
infants and adults in multiple tasks (e.g. one task involving people, another involving objects), 
compare neural activity during learning and surprise in those tasks, and use this neural activity 
to predict behavior. The proposed research takes an innovative, cross-disciplinary approach to push 
the frontiers of our theories of how the brain is organized to enable such rapid and rich learning 
across development. Its findings will reveal (1) which regions of the brain are implicated in 
forming expectations and having those expectations violated, (2) whether those cortical regions 
support a general, broad process of belief formation or task- and modality-specific learning, and 
(3) how they modulate behavior in infants and adults. Thus, this work has the potential to 
transform our understanding of how infants learn and reason in and out of the lab. To know how to 
nurture learning and reasoning, during both typical and atypic...

## Key facts

- **NIH application ID:** 10068684
- **Project number:** 1F32HD103363-01
- **Recipient organization:** MASSACHUSETTS INSTITUTE OF TECHNOLOGY
- **Principal Investigator:** Shari Liu
- **Activity code:** F32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $64,554
- **Award type:** 1
- **Project period:** 2020-09-01 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10068684

## Citation

> US National Institutes of Health, RePORTER application 10068684, Neural foundations of learning, reasoning, and surprise in human infants (1F32HD103363-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10068684. Licensed CC0.

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