New York

Empirical Research - Collaborative Research: A Bayesian Approach To Number Reasoning

Principal Investigator:

Co-Investigator:

Project Overview

Background & Purpose:

This project will develop and test a formal Bayesian model of the basic number sense that supports many of our arithmetic concepts. This formal approach can explain how a student’s confidence relates to the precision of their number knowledge and can test these predictions at the level of single neurons. These connections build on cutting-edge neuroscience, including recent evidence revealing the Bayesian characteristics of the activations of individual neurons in the brain.

Setting:

University of Rochester
Johns Hopkins University
Cold Spring Harbor Laboratory

Research Design:

The project uses a comparative research design and will generate evidence that is descriptive [design research] and causal [experimental]. Original data on children and young adults will be collected using imaging and behavioral testing in the laboratory. CRT screens with keyboards will be used to record the responses and reaction times. We will use standard statistical tests to analyze behavioral data, and computational modelling of behavior and its neural basis.

Findings:

Findings will be posted as they become available, e.g. see http://jn.physiology.org/content/early/2015/02/27/jn.00787.2014.

Publications & Presentations:

http://www.bcs.rochester.edu/people/alex/
http://churchlandlab.labsites.cshl.edu/publications/
http://caoslab.bcs.rochester.edu/
http://www.bcs.rochester.edu/people/daphne/
http://www.psy.jhu.edu/~halberda/

Discipline:

State:

Target Population: