Dataset Information

The Emergence of Stimulus Relations: Human and Computer Learning.

ABSTRACT: Traditionally, investigations in the area of stimulus equivalence have employed humans as experimental participants. Recently, however, artificial neural network models (often referred to as connectionist models [CMs]) have been developed to simulate performances seen among human participants when training various types of stimulus relations. Two types of neural network models have shown particular promise in recent years. RELNET has demonstrated its capacity to approximate human acquisition of stimulus relations using simulated matching-to-sample (MTS) procedures (e.g., Lyddy & Barnes-Holmes Journal of Speech and Language Pathology and Applied Behavior Analysis, 2, 14-24, 2007). Other newly developed connectionist algorithms train stimulus relations by way of compound stimuli (e.g., Tovar & Chavez The Psychological Record, 62, 747-762, 2012; Vernucio & Debert The Psychological Record, 66, 439-449, 2016). What makes all of these CMs interesting to many behavioral researchers is their apparent ability to simulate the acquisition of diversified stimulus relations as an analogue to human learning; that is, neural networks learn over a series of training epochs such that these models become capable of deriving novel or untrained stimulus relations. With the goal of explaining these quickly evolving approaches to practical and experimental endeavors in behavior analysis, we offer an overview of existing CMs as they apply to behavior-analytic theory and practice. We provide a brief overview of derived stimulus relations as applied to human academic remediation, and we argue that human and simulated human investigations have symbiotic experimental potential. Additionally, we provide a working example of a neural network referred to as emergent virtual analytics (EVA). This model demonstrates a process by which artificial neural networks can be employed by behavior-analytic researchers to understand, simulate, and predict derived stimulus relations made by human participants.

SUBMITTER: Ninness C

PROVIDER: S-EPMC6701726 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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The Emergence of Stimulus Relations: Human and Computer Learning.

Ninness Chris C Ninness Sharon K SK Rumph Marilyn M Lawson David D

Perspectives on behavior science 20171113 1

Traditionally, investigations in the area of stimulus equivalence have employed humans as experimental participants. Recently, however, artificial neural network models (often referred to as connectionist models [CMs]) have been developed to simulate performances seen among human participants when training various types of stimulus relations. Two types of neural network models have shown particular promise in recent years. RELNET has demonstrated its capacity to approximate human acquisition of ...[more]

PMID: 32004361

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Dataset Information

The Emergence of Stimulus Relations: Human and Computer Learning.

Publications

The Emergence of Stimulus Relations: Human and Computer Learning.

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