Modeling relational responding with artificial neural networks.

Janelle Mendoza, Stefano Ghirlanda
Author Information
  1. Janelle Mendoza: Department of Psychology, CUNY Graduate Center, USA. Electronic address: jmendoza1@gradcenter.cuny.edu.
  2. Stefano Ghirlanda: Department of Psychology, CUNY Graduate Center, USA; Department of Psychology, Brooklyn College, USA; Center for the Study of Cultural Evolution, Stockholm University, Sweden; Department of Biology, CUNY Graduate Center, USA. Electronic address: sghirlanda@brooklyn.cuny.edu.

Abstract

Relational responding refers to behavior that conforms to a rule for com- paring stimuli. Lazareva et al. (2014) trained pigeons to choose either the smaller or the larger of two circles, using 1-3 pairs of circles for training and 17-19 new pairs for testing. The pigeons showed relational responding on some test pairs and systematic failures on others. We present a simple artificial neural network model that reproduces the animals' behavior well, similarly to Lazareva et al.'s (2014) statistical model based on stimulus features and stimulus relationships. We analyze how the network model gener- alizes from training to test stimuli, and show that it can reconcile contrasting ideas about relational responding from the seminal works by Köhler (1929, 1918/1938, 1924), positing that animals can learn relational rules such as "choose the larger stimulus," and Spence (1937), positing that relational re- sponding can be explained based on stimulus generalization.

Keywords

MeSH Term

Animals
Discrimination Learning
Learning
Behavior, Animal
Generalization, Stimulus
Columbidae

Word Cloud

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