An associative approach to additivity and maximality effects on blocking

Néstor A. Schmajuk, Munir Gunes Kutlu

Research output: Chapter in Book/Report/Conference proceedingChapter

3 Citations (Scopus)

Abstract

Schmajuk, Lam, and Gray (SLG, 1996) introduced an attentional-associative model able to describe a large number of classical paradigms. As other models, the SLG model describes blocking in terms of the competition between the blocker and the blocked conditioned stimulus (CS) to gain association with the unconditioned stimulus (US) or outcome. Recent data suggest, however, other factors together with competition might control the phenomenon. For instance, Beckers et al. (2005) reported that blocking and backward blocking are stronger when participants are informed that (a) the predicted US is submaximal than when it is maximal, and (b) the predictions of the US by the CSs are additive than when they are sub-additive. Submaximality refers to the evidence that the predicted US is weaker than its maximal possible value. Additivity denotes the fact that two CSs, each one independently predicting a given US, predict a stronger US when presented together. Beckers et al. suggested that their results are better explained by inferential accounts, which assume involvement of controlled and effortful reasoning, than by associative views. This chapter shows that a configural version of the SLG attentional-associative model is able to quantitatively approximate submaximality and additivity effects on blocking while providing a mechanistic explanation of the results. In general, the chapter illustrates the potential of associative models to account for newly discovered properties of known psychological phenomena.

Original languageEnglish (US)
Title of host publicationComputational Neuroscience for Advancing Artificial Intelligence
Subtitle of host publicationModels, Methods and Applications
PublisherIGI Global
Pages57-80
Number of pages24
ISBN (Print)9781609600211
DOIs
StatePublished - Dec 1 2010

All Science Journal Classification (ASJC) codes

  • Computer Science(all)

Cite this

Schmajuk, N. A., & Kutlu, M. G. (2010). An associative approach to additivity and maximality effects on blocking. In Computational Neuroscience for Advancing Artificial Intelligence: Models, Methods and Applications (pp. 57-80). IGI Global. https://doi.org/10.4018/978-1-60960-021-1.ch004
Schmajuk, Néstor A. ; Kutlu, Munir Gunes. / An associative approach to additivity and maximality effects on blocking. Computational Neuroscience for Advancing Artificial Intelligence: Models, Methods and Applications. IGI Global, 2010. pp. 57-80
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Schmajuk, NA & Kutlu, MG 2010, An associative approach to additivity and maximality effects on blocking. in Computational Neuroscience for Advancing Artificial Intelligence: Models, Methods and Applications. IGI Global, pp. 57-80. https://doi.org/10.4018/978-1-60960-021-1.ch004

An associative approach to additivity and maximality effects on blocking. / Schmajuk, Néstor A.; Kutlu, Munir Gunes.

Computational Neuroscience for Advancing Artificial Intelligence: Models, Methods and Applications. IGI Global, 2010. p. 57-80.

Research output: Chapter in Book/Report/Conference proceedingChapter

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Schmajuk NA, Kutlu MG. An associative approach to additivity and maximality effects on blocking. In Computational Neuroscience for Advancing Artificial Intelligence: Models, Methods and Applications. IGI Global. 2010. p. 57-80 https://doi.org/10.4018/978-1-60960-021-1.ch004