Temporal Uncertainty During Overshadowing

Abstract

Copyright © 2011 by IGI Global. All rights reserved. No part of this publication may be reproduced, stored or distributed in any form or by any means, electronic or mechanical, including photocopying, without written permission from the publisher. Product or company names used in this set are for identification purposes only. Inclusion of the names of the products or companies does not indicate a claim of ownership by IGI Global of the trademark or registered trademark. Library of Congress Cataloging-in-Publication DataComputational neuroscience for advancing artificial intelligence : models, methods and applications / Eduardo Alonso and Esther Mondragon, editors. p. cm. Summary: "This book argues that computational models in behavioral neuroscience must be taken with caution, and advocates for the study of mathematical models of existing theories as complementary to neuropsychological models and computational models"--Provided by publisher.Includes bibliographical references and index. ISBN 978-1-60960-021-1 (hardcover) --ISBN 978-1-60960-023-5 (ebook) 1.

The effect of stimulus distribution form on the acquisition and rate of conditioned responding: Implications for theory.

“…As outlined in the beginning of this article, although some studies had previously examined differences in learning about fixed and variable duration CSs, their results were generally inconsistent. Nonetheless, some of these studies showed higher asymptotic rates to fixed duration stimuli (Jennings et al, 2006, 2011; Kirkpatrick & Church, 1998; but see Kamin, 1960); the present results confirm these findings, while at the same time ruling out several potentially artifactual explanations.…”

“…Intuitions notwithstanding, empirical studies examining these issues have not provided evidence for a consistent difference in either the rate of responding or the speed of acquisition to fixed and variable CSs. With respect to rate of conditioned responding, Kirkpatrick and Church (1998) reported a “subtle” (and statistically significant) superiority in conditioned responding to a fixed CS (Kirkpatrick & Church, 1998; see also Jennings, Alonso, Mondragón, & Bonardi, 2006, 2011) although others have reported no difference (Kamin, 1960; Low & Low, 1962; Patterson, 1970), and the results of a series of studies by Libby and Church (1975) were inconclusive. With respect to rate of acquisition of the CR, to our knowledge only one study has compared learning about these different distribution forms (Ward et al, 2012 Experiments 1 and 3); no difference in acquisition to fixed and variable CSs was found (although I/T ratio manipulations did have the predicted effect on CR acquisition).…”

Interactions of timing and prediction error learning

“…Here, the more salient CS usually results in more robust conditioning. With regard to temporal properties, it appears that variability of the CS may affect overshadowing, with weaker overshadowing by variable than by fixed CSs (Jennings et al 2011). In addition, overshadowing has been reported to be more robust with shorter CSs than with longer CSs (Kehoe 1983; but see Jennings et al 2007; McMillan and Roberts 2010; Hancock 1982; Fairhurst, Gallistel, and Gibbon 2003), consistent with the idea that both shorter and less variable CSs may be more salient due to their higher information value in predicting the US (Balsam, Drew, and Gallistel 2010).…”

“…The temporal representation in TD models is a series of discrete units within the time course of a CS, so one difference between TD models and other theories of timing is the nature of the perception of time (discrete in TD models vs. continuous in most timing models). Because TD models incorporate timing into a prediction error model, they perform reasonably well in predicting at least some aspects of CR timing (Ludvig, Sutton, and Kehoe 2012) and can also predict at least some elements of the effects of temporal variables on conditioning (e.g., Jennings et al 2011). Altering aspects of the stimulus representation can improve performance of CR timing by incorporating scalar variance into the temporal representation associated with the different CS components (Ludvig, Sutton, and Kehoe 2008).…”