Literature DB >> 7610267

Is human learning rational?

D R Shanks1.   

Abstract

We can predict and control events in the world via associative learning. Such learning is rational if we come to believe that an associative relationship exists between a pair of events only when it truly does. The statistical metric delta P, the difference between the probability of an outcome event in the presence of the predictor and its probability in the absence of the predictor tells us when and to what extent events are indeed related. Contrary to what is often claimed, humans' associative judgements compare very favourably with the delta P metric, even in situations where multiple predictive cues are in competition for association with the outcome. How do humans achieve this judgmental accuracy? I argue that it is not via the application of an explicit mental version of the delta P rule. Instead, accurate judgements are an emergent property of an associationist learning process of the sort that has become common in adaptive network models of cognition. Such an associationist mechanism is the "means" to a normative or statistical "end".

Entities:  

Mesh:

Year:  1995        PMID: 7610267     DOI: 10.1080/14640749508401390

Source DB:  PubMed          Journal:  Q J Exp Psychol A        ISSN: 0272-4987


  18 in total

1.  How two causes are different from one: the use of (un)conditional information in Simpson's paradox.

Authors:  B A Spellman; C M Price; J M Logan
Journal:  Mem Cognit       Date:  2001-03

2.  The role of anticipation and intention in the learning of effects of self-performed actions.

Authors:  Michael Ziessler; Dieter Nattkemper; Peter A Frensch
Journal:  Psychol Res       Date:  2003-11-22

3.  Human causality judgments and response rates on DRL and DRH schedules of reinforcement.

Authors:  Phil Reed
Journal:  Learn Behav       Date:  2003-05       Impact factor: 1.986

Review 4.  Evidence for the role of higher order reasoning processes in cue competition and other learning phenomena.

Authors:  Jan De Houwer; Tom Beckers; Stefaan Vandorpe
Journal:  Learn Behav       Date:  2005-05       Impact factor: 1.986

5.  Nonnormative discounting: there is more to cue interaction effects than controlling for alternative causes.

Authors:  Kelly M Goedert; Barbara A Spellman
Journal:  Learn Behav       Date:  2005-05       Impact factor: 1.986

Review 6.  Comparing associative, statistical, and inferential reasoning accounts of human contingency learning.

Authors:  Oskar Pineño; Ralph R Miller
Journal:  Q J Exp Psychol (Hove)       Date:  2007-03       Impact factor: 2.143

7.  BUCKLE: a model of unobserved cause learning.

Authors:  Christian C Luhmann; Woo-Kyoung Ahn
Journal:  Psychol Rev       Date:  2007-07       Impact factor: 8.934

8.  Highlighting in Early Childhood: Learning Biases Through Attentional Shifting.

Authors:  Joseph M Burling; Hanako Yoshida
Journal:  Cogn Sci       Date:  2016-09-16

9.  The Blicket Within: Preschoolers' Inferences About Insides and Causes.

Authors:  David M Sobel; Caroline M Yoachim; Alison Gopnik; Andrew N Meltzoff; Emily J Blumenthal
Journal:  J Cogn Dev       Date:  2007

10.  Causal and predictive-value judgments, but not predictions, are based on cue-outcome contingency.

Authors:  Miguel A Vadillo; Ralph R Miller; Helena Matute
Journal:  Learn Behav       Date:  2005-05       Impact factor: 1.986

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.