z-logo
Premium
RELATIONAL LEARNING IN A CONTEXT OF TRANSPOSITION: A REVIEW
Author(s) -
Lazareva Olga F.
Publication year - 2012
Publication title -
journal of the experimental analysis of behavior
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.75
H-Index - 61
eISSN - 1938-3711
pISSN - 0022-5002
DOI - 10.1901/jeab.2012.97-231
Subject(s) - transposition (logic) , context (archaeology) , computer science , cognitive science , psychology , data science , world wide web , natural language processing , artificial intelligence , biology , paleontology
In a typical transposition task, an animal is presented with a single pair of stimuli (for example, S3+ S4—, where plus and minus denote reward and nonreward and digits denote stimulus location on a sensory dimension such as size). Subsequently, an animal is presented with a testing pair that contains a previously reinforced or nonreinforced stimulus and a novel stimulus (for example, S2‐S3 and S4–S5). Does the choice of a novel S2 instead of previously reinforced S3 in a testing pair S2–S3 indicate that the animal has learned a relation (i.e., “select smaller”)? This review of empirical evidence and theoretical accounts shows that an organism's behavior in a transposition task is undoubtedly influenced by prior reinforcement history of the training stimuli (Spence, 1937). However, it is also affected by two other factors that are relational in nature—a similarity of two testing stimuli to each other and an overall similarity of the testing pair as a whole to the training pair as a whole. The influence of the two latter factors is especially evident in studies that use multiple pairs of training stimuli and a wide range of testing pairs comprising nonadjacent stimuli (Lazareva, Miner, Young, & Wasserman, 2008; Lazareva, Wasserman, & Young, 2005). In sum, the evidence suggests that both prior reinforcement history and relational information affect an animal's behavior in a typical transposition task.

This content is not available in your region!

Continue researching here.

Having issues? You can contact us here