The University of Southampton
University of Southampton Institutional Repository
Warning ePrints Soton is experiencing an issue with some file downloads not being available. We are working hard to fix this. Please bear with us.

Preprint Glautier (2017) Revised Non-local influences on associative learning: new data and further model evaluation

Preprint Glautier (2017) Revised Non-local influences on associative learning: new data and further model evaluation
Preprint Glautier (2017) Revised Non-local influences on associative learning: new data and further model evaluation
Previous work (Glautier, 2013) showed that the responses made by humans on trial n in simple associative learning tasks were influenced by events that took place on trial n−1 and a simple extension of the Rescorla-Wagner Model (RWM Rescorla & Wagner, 1972), the Memory Environment Cue Array (MECA) model, was presented to account for those results. In the current work further evidence of non-local influences on responding during associative learning tasks is presented. The Rescorla-Wagner model and the MECA model are evaluated as models for the observed data using qualitative, näive maximum likelihood, and Akaike weight analyses. In two experiments the Akaike weight analyses strongly supported the simpler Rescorla-Wagner model over the MECA model but the qualitive and näive maximum likelihood analyses strongly supported the MECA model model over the simpler Rescorla-Wagner model. In Experiment 2 this apparent conflict was resolved using a generalisation criterion test (Ahn, Busemeyer, Wagenmakers, & Stout, 2008; Busemeyer & Wang, 2000) which gave clear support to the MECA model over the Rescorla-Wagner model. These results demonstrate the superiority of model selection using predictive validity, where possible, over selection using statistical adjustments for model complexity.
akaike weight analysis, associative learning, generalisation criterion, MECA model, model selection, non-local, Rescorla-Wagner
Glautier, Steven
964468b2-3ad7-40cc-b4be-e35c7dee518f
Glautier, Steven
964468b2-3ad7-40cc-b4be-e35c7dee518f

Glautier, Steven (2017) Preprint Glautier (2017) Revised Non-local influences on associative learning: new data and further model evaluation. (doi:10.31219/osf.io/4nx2v).

Record type: Other

Abstract

Previous work (Glautier, 2013) showed that the responses made by humans on trial n in simple associative learning tasks were influenced by events that took place on trial n−1 and a simple extension of the Rescorla-Wagner Model (RWM Rescorla & Wagner, 1972), the Memory Environment Cue Array (MECA) model, was presented to account for those results. In the current work further evidence of non-local influences on responding during associative learning tasks is presented. The Rescorla-Wagner model and the MECA model are evaluated as models for the observed data using qualitative, näive maximum likelihood, and Akaike weight analyses. In two experiments the Akaike weight analyses strongly supported the simpler Rescorla-Wagner model over the MECA model but the qualitive and näive maximum likelihood analyses strongly supported the MECA model model over the simpler Rescorla-Wagner model. In Experiment 2 this apparent conflict was resolved using a generalisation criterion test (Ahn, Busemeyer, Wagenmakers, & Stout, 2008; Busemeyer & Wang, 2000) which gave clear support to the MECA model over the Rescorla-Wagner model. These results demonstrate the superiority of model selection using predictive validity, where possible, over selection using statistical adjustments for model complexity.

This record has no associated files available for download.

More information

Published date: 1 December 2017
Related URLs:
Keywords: akaike weight analysis, associative learning, generalisation criterion, MECA model, model selection, non-local, Rescorla-Wagner

Identifiers

Local EPrints ID: 431694
URI: http://eprints.soton.ac.uk/id/eprint/431694
PURE UUID: 08fef94b-5384-481f-9641-b003f41c7b78
ORCID for Steven Glautier: ORCID iD orcid.org/0000-0001-8852-3268

Catalogue record

Date deposited: 13 Jun 2019 16:30
Last modified: 10 Nov 2021 02:53

Export record

Altmetrics

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×