Machine learning with a snapshot of data: Spiking neural network 'predicts' reinforcement histories of pigeons' choice behavior.

ABSTRACT: An accumulated body of choice research has demonstrated that choice behavior can be understood within the context of its history of reinforcement by measuring response patterns. Traditionally, work on predicting choice behaviors has been based on the relationship between the history of reinforcement-the reinforcer arrangement used in training conditions-and choice behavior. We suggest an alternative method that treats the reinforcement history as unknown and focuses only on operant choices to accurately predict (more precisely, retrodict) reinforcement histories. We trained machine learning models known as artificial spiking neural networks (SNNs) on previously published pigeon datasets to detect patterns in choices with specific reinforcement histories-seven arranged concurrent variable-i

SUBMITTER: Plessas A 

PROVIDER: S-EPMC9320819 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature