# Bayesian updating as a framework to predict the cognitive, neural and physiological mechanisms underlying social status

> **NIH NIH R35** · UNIVERSITY OF CALIFORNIA AT DAVIS · 2024 · $428,390

## Abstract

Social status is a major determinant of individual success. Status is gained or lost over the course
of repeated interactions generating hierarchies within groups. These dominance hierarchies are
key features of all animal societies, including our own. While hierarchies are often stable,
predicting an individual’s position within the hierarchy can be very difficult. This is in part because
status can be influenced by numerous factors such as nepotism, genetic variation and perhaps
most importantly, previous experience. Considerable evidence highlights how an individual’s
current success is strongly determined by their previous success: winners keep winning and
losers keep losing. Our understanding of how and why these winner/loser effects occur is still
limited, preventing our ability to explain and more importantly, predict why some interactions lead
to entrenched status and others lead to upsets in contest outcomes. Here I propose to use a novel
framework, Bayesian updating, to describe how individuals respond to dominance interactions
throughout their lives. Bayesian updating is a computational mechanism whereby individuals can
update their beliefs about the likelihood of a given outcome based on their previous (prior)
information and the current information they are receiving. Bayesian updating mimics the inherent
path-dependency of changes in social status and offers a longer-term perspective on phenotypic
change than current models. Here I will use this framework to make predictions about behavioral,
neurological and physiological responses to accumulated social defeats and successes over the
lifetime. I will do this using a novel animal system, the Amazon molly. This naturally clonal
vertebrate gives birth to independent offspring providing a unique opportunity to fully isolate the
effects of previous experience on behavior, neural activation and hormonal pathways while
controlling for genetic and inherited factors that also influence dominance and status. This work
will improve our understanding of how previous experiences can ripple forward to influence
current behavior which has implications for our ability to predict responses to behavioral and
pharmacological interventions, for pathological or maladaptive behavior (e.g. bullying, PTSD) and
can help highlight how inequities can perpetuate throughout the lifetime of individuals.

## Key facts

- **NIH application ID:** 10841352
- **Project number:** 1R35GM153309-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA AT DAVIS
- **Principal Investigator:** Kate Laskowski
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $428,390
- **Award type:** 1
- **Project period:** 2024-06-01 → 2029-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10841352

## Citation

> US National Institutes of Health, RePORTER application 10841352, Bayesian updating as a framework to predict the cognitive, neural and physiological mechanisms underlying social status (1R35GM153309-01). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10841352. Licensed CC0.

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