Literature DB >> 23475820

Learning and extrapolating a periodic function.

Michael L Kalish1.   

Abstract

How people learn continuous functional relationships remains a poorly understood capacity. In this article, I argue that the mere presence of nonmonotonic extrapolation of periodic functions neither threatens existing theories of function learning nor distinguishes between them. However, I show that merely learning periodic functions is extremely difficult. It is only when stimuli are presented numerically, rather than as numberless quantities, that participants learn anything like a periodic function. In addition, I show that even then, people do not regularly extrapolate periodically. The lesson is that careful methodologies will be required to understand a psychological capacity that is as idiosyncratic as the learning of complex functions appears to be.

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Year:  2013        PMID: 23475820     DOI: 10.3758/s13421-013-0306-9

Source DB:  PubMed          Journal:  Mem Cognit        ISSN: 0090-502X


  10 in total

1.  A multidimensional scaling approach to mental multiplication.

Authors:  Thomas L Griffiths; Michael L Kalish
Journal:  Mem Cognit       Date:  2002-01

2.  Generalization, similarity, and Bayesian inference.

Authors:  J B Tenenbaum; T L Griffiths
Journal:  Behav Brain Sci       Date:  2001-08       Impact factor: 12.579

3.  The conceptual basis of function learning and extrapolation: comparison of rule-based and associative-based models.

Authors:  Mark A McDaniel; Jerome R Busemeyer
Journal:  Psychon Bull Rev       Date:  2005-02

4.  Why people underestimate y when extrapolating in linear functions.

Authors:  Peter J Kwantes; Andrew Neal
Journal:  J Exp Psychol Learn Mem Cogn       Date:  2006-09       Impact factor: 3.051

5.  Function learning: induction of continuous stimulus-response relations.

Authors:  K Koh; D E Meyer
Journal:  J Exp Psychol Learn Mem Cogn       Date:  1991-09       Impact factor: 3.051

6.  Attention, similarity, and the identification-categorization relationship.

Authors:  R M Nosofsky
Journal:  J Exp Psychol Gen       Date:  1986-03

7.  Extrapolation: the sine qua non for abstraction in function learning.

Authors:  E L DeLosh; J R Busemeyer; M A McDaniel
Journal:  J Exp Psychol Learn Mem Cogn       Date:  1997-07       Impact factor: 3.051

8.  Population of linear experts: knowledge partitioning and function learning.

Authors:  Michael L Kalish; Stephan Lewandowsky; John K Kruschke
Journal:  Psychol Rev       Date:  2004-10       Impact factor: 8.934

9.  Nonmonotonic extrapolation in function learning.

Authors:  Lewis Bott; Evan Heit
Journal:  J Exp Psychol Learn Mem Cogn       Date:  2004-01       Impact factor: 3.051

10.  Estimation of the correlation coefficient using the Bayesian Approach and its applications for epidemiologic research.

Authors:  Enrique F Schisterman; Kirsten B Moysich; Lucinda J England; Malla Rao
Journal:  BMC Med Res Methodol       Date:  2003-03-25       Impact factor: 4.615

  10 in total
  1 in total

Review 1.  A rational model of function learning.

Authors:  Christopher G Lucas; Thomas L Griffiths; Joseph J Williams; Michael L Kalish
Journal:  Psychon Bull Rev       Date:  2015-03-03
  1 in total

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