# Temporal prediction and social function:  Modeling neural and behavioral correlates of making predictions in time across typically-developing and austistic adults

> **NIH NIH F32** · VANDERBILT UNIVERSITY MEDICAL CENTER · 2024 · $78,328

## Abstract

Due to the broad etiological heterogeneity of autistic phenotypes, unitary theories of autism often fall short of
describing the entirety, or even the majority, of the autism spectrum. Recent predictive coding hypotheses
avoid this core issue by arguing autism is caused by altered parameters for updating mental models of the
world, while leaving the details of those changes open. Attempts at building falsifiable predictive coding
theories by specifying these details, such as the slow-updating and high-precision hypotheses of predictive
coding in autism, have led to conflicting empirical results, implying they encounter the same dilemma as other
unitary theories. Different prediction strategies may appear in different subsets of the heterogenous autistic
population or be more relevant in specific tasks or contexts. In addition, there may be dissociations between
prediction strategies and behavior in autism that lead to behavioral differences between autism and typical
development without an altered underlying predictive mechanism. This project aims to assess multiple theories
of predictive coding in a task-driven, non-social context and a task-free, social context. We use multiple
measurement paradigms, including phenotypic characterization, eye-tracking, and functional neuroimaging, to
obtain as complete a picture as possible of the entire cognitive mechanism, spanning multiple constructs
across several domains of human functioning. This evidence will allow us to disentangle both individual-level
heterogeneity in coding impairments from task- or context-level heterogeneity and dissociation between
behavioral outcomes and neural correlates of predictive coding. Our work will contribute to our understanding
of both the "how" and the "why" of temporal prediction by measuring how typical and atypical temporal
prediction are encoded in the brain and variation in the links between temporal prediction and social behaviors
across social behavioral phenotypes. Focusing on autism, which is typified by impairments in social function,
allows us to determine how much temporal prediction is a direct factor in the ease with which individuals attain
their desired social connectedness, or whether it is largely mediated through other cognitive constraints. More
generally, this project will provide insight on what temporal prediction is for. In clinical knowledge and practice,
this research will lead to improvements in our ability to precisely target interventions, particularly those
involving structured sensory experiences, to specific patients by building on existing predictive coding
capabilities to scaffold the development of social behaviors. It will also lead to assessments of these
interventions' effects on cognitive mechanisms, rather than relying purely on behavioral or phenotypic
outcomes. This will allow clinicians to more effectively capitalize on autistic individuals' existing skills to achieve
their own social and relational goals. Such work is crucia...

## Key facts

- **NIH application ID:** 10903042
- **Project number:** 1F32MH134612-01A1
- **Recipient organization:** VANDERBILT UNIVERSITY MEDICAL CENTER
- **Principal Investigator:** Noah Roy Fram
- **Activity code:** F32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $78,328
- **Award type:** 1
- **Project period:** 2024-08-01 → 2027-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10903042

## Citation

> US National Institutes of Health, RePORTER application 10903042, Temporal prediction and social function:  Modeling neural and behavioral correlates of making predictions in time across typically-developing and austistic adults (1F32MH134612-01A1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10903042. Licensed CC0.

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