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Transfer of a conditional ordering response through conditional equivalence classes.

E Wulfert, S C Hayes

    Journal of the Experimental Analysis of Behavior
    |September 1, 1988
    PubMed
    Summary
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    This study demonstrates how teaching simple conditional discriminations can establish complex equivalence classes in humans. These classes enable untrained ordering and sequencing of stimuli, suggesting a basis for language acquisition.

    Area of Science:

    • Behavioral Psychology
    • Cognitive Science
    • Experimental Psychology

    Background:

    • Equivalence classes are formed through conditional discrimination training.
    • Stimulus functions can generalize across members of an equivalence class.
    • Understanding emergent behavior is key to understanding complex cognition.

    Purpose of the Study:

    • To investigate the emergence of ordered and conditionally ordered sequences within equivalence classes.
    • To examine the transfer of ordering and conditional ordering functions to untrained stimuli.
    • To explore the implications for behavior-analytic accounts of language.

    Main Methods:

    • Adult humans were trained on matching-to-sample tasks to form two four-member equivalence classes.
    • Subjects were taught to order stimuli within classes, and this response was brought under conditional control.

    Related Experiment Videos

  • Higher-order conditional control was implemented to form four conditional equivalence classes.

    • Main Results:

    • Participants demonstrated ordering of all class members after training on only a subset.
    • Conditional ordering also transferred to untrained stimuli within the equivalence classes.
    • A significant number of untrained sequences (120) emerged from trained sequences (8).

    Conclusions:

    • Equivalence class formation supports the emergence of complex sequential behavior.
    • Generalization of ordering and conditional ordering functions highlights the power of stimulus equivalence.
    • These findings offer insights into behavior-analytic models of semantics and generative grammar.