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Investigating operation-specific learning effects in the Raven's Advanced Progressive Matrices: A linear logistic test modeling approach

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  • Lozano, José H.
  • Revuelta, Javier

Abstract

The present study aimed to investigate practice effects associated with the abstract rules involved in the Raven's Advanced Progressive Matrices (RAPM) under standard administration conditions. To that end, a linear logistic test modeling approach was used in combination with Carpenter, Just, and Shell's (1990) taxonomy of rules. Several operation-specific learning models were used in order to test different contingent and non-contingent learning hypotheses. The models were fitted to a sample of responses from 293 participants to Sets I and II of the RAPM. A Bayesian framework was adopted for model estimation and evaluation. The perceptual variables involved in the items were included in the analyses in order to control their influence on performance on the RAPM. The results did not provide evidence of rule learning during the RAPM. Instead, they suggested the existence of fatigue effects associated with each of the rules. Interestingly, the results revealed the existence of learning effects associated with the items' perceptual properties.

Suggested Citation

  • Lozano, José H. & Revuelta, Javier, 2020. "Investigating operation-specific learning effects in the Raven's Advanced Progressive Matrices: A linear logistic test modeling approach," Intelligence, Elsevier, vol. 82(C).
    • Handle: RePEc:eee:intell:v:82:y:2020:i:c:s0160289620300465
    DOI: 10.1016/j.intell.2020.101468
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    References listed on IDEAS

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    1. William Mollenkopf, 1950. "An experimental study of the effects on item-analysis data of changing item placement and test time limit," Psychometrika, Springer;The Psychometric Society, vol. 15(3), pages 291-315, September.
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    Cited by:

    1. José H. Lozano & Javier Revuelta, 2021. "A Bayesian Generalized Explanatory Item Response Model to Account for Learning During the Test," Psychometrika, Springer;The Psychometric Society, vol. 86(4), pages 994-1015, December.
    2. Gignac, Gilles E. & Szodorai, Eva T., 2024. "Defining intelligence: Bridging the gap between human and artificial perspectives," Intelligence, Elsevier, vol. 104(C).

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