Identifying Accurate and Inaccurate Stimulus Relations: Human and Computer Learning

Ninness, Chris; Rehfeldt, Ruth Anne; Ninness, Sharon K.

doi:10.1007/s40732-019-00337-6

Cited by 6 publications

(3 citation statements)

References 44 publications

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“…Notably, they have developed a research agenda for extending the application of EVA to analyzing both theoretical and applied aspects of stimulus equivalence and derived stimulus control. For example, they have used EVA to explore the basic training requirements for human participants to derive stimulus relations and generalize to other training sets in a task directed at establishing stimulus relations between algebraic expressions (Ninness et al, 2019), and more recently they have discussed the implications of simulating more challenging human performances in neural networks (Ninness & Ninness, 2020). Particularly, they have included more hidden layers in the EVA architecture, in a way that the final network includes four layers (one input layer, two hidden layers, and one output layer) instead of the typical three-layer architecture, which provides the network with additional computational power, approaching the methods used in deep neural networks (where "deep" indicates the addition of processing layers).…”

Section: Feedforward Network Using Compound Stimulimentioning

confidence: 99%

Computational models of stimulus equivalence: An intersection for the study of symbolic behavior

Tovar

Torres-Chávez

Mofrad

et al. 2023

J Exper Analysis Behavior

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Stimulus equivalence is a central paradigm in the analysis of symbolic behavior, language, and cognition. It describes emergent relations between stimuli that were not explicitly trained and cannot be explained by primary stimulus generalization. In recent years, researchers have developed computational models to simulate the learning of equivalence relations. These models have been used to address primary theoretical and methodological issues in this field, such as exploring the underlying mechanisms that explain emergent equivalence relations and analyzing the effects of training and testing protocols on equivalence outcomes. Nonetheless, although these models build upon general learning principles, their operation is usually obscure for nonmodelers, and in the field of stimulus equivalence computational models have been developed with a variety of approaches, architectures, and algorithms that make it difficult to understand the scope and contributions of these tools. In this paper, we present the state of the art in computational modeling of stimulus equivalence. We seek to provide concise and accessible descriptions of the models' functioning and operation, highlight their main theoretical and methodological contributions, identify the existing software available for researchers to run experiments, and suggest future directions in the emergent field of computational modeling of stimulus equivalence.

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Section: Feedforward Network Using Compound Stimulimentioning

confidence: 99%

Computational models of stimulus equivalence: An intersection for the study of symbolic behavior

Tovar

Torres-Chávez

Mofrad

et al. 2023

J Exper Analysis Behavior

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“…A good overview of existing CMs is provided in (Ninness et al, 2018) along with a working example of a neural network called emergent virtual analytics (EVA) (see Ninness et al, 2019, for more simulations with EVA). Through EVA, the process of applying neural network simulations in behavior-analytic research is demonstrated.…”

Section: Computational Models Of Formation Of Stimulus Equivalence CLmentioning

confidence: 99%

Equivalence Projective Simulation as a Framework for Modeling Formation of Stimulus Equivalence Classes

et al. 2020

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Stimulus equivalence (SE) and projective simulation (PS) study complex behavior, the former in human subjects and the latter in artificial agents. We apply the PS learning framework for modeling the formation of equivalence classes. For this purpose, we first modify the PS model to accommodate imitating the emergence of equivalence relations. Later, we formulate the SE formation through the matching-to-sample (MTS) procedure. The proposed version of PS model, called the equivalence projective simulation (EPS) model, is able to act within a varying action set and derive new relations without receiving feedback from the environment. To the best of our knowledge, it is the first time that the field of equivalence theory in behavior analysis has been linked to an artificial agent in a machine learning context. This model has many advantages over existing neural network models. Briefly, our EPS model is not a black box model, but rather a model with the capability of easy interpretation and flexibility for further modifications. To validate the model, some experimental results performed by prominent behavior analysts are simulated. The results confirm that the EPS model is able to reliably simulate and replicate the same behavior as real experiments in various settings, including formation of equivalence relations in typical participants, nonformation of equivalence relations in language-disabled children, and nodal effect in a linear series with nodal distance five. Moreover, through a hypothetical experiment, we discuss the possibility of applying EPS in further equivalence theory research.

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“…Some of the computational analogs informed by RFT have been drawn from machine learning expert systems and artificial intelligence neural networks. Examples include the use of Deep Neural Networks (DNN) to detect effects in multiple baseline single case design graphed data ( Lanovaz and Bailey, 2020 ) and forecast human participant learning of trigonometry ( Ninness et al, 2019 ; Ninness and Ninness, 2020 ); the use of Kohonen Self-Organizing Maps (SOM: Kohonen, 1988 ) for behavioral pattern detection in legislature voting, breast cancer diagnosis ( Ninness et al, 2012 ), visual symmetry detection ( Dresp-Langley and Wandeto, 2021 ), and surgical expertise detection ( Dresp-Langley et al, 2021 ); and blends of DNN and SOM architectures to model decision making in child welfare systems ( Ninness et al, 2021 ). Additional work with Connectionist Models (CM) has provided confirmatory validation of methodological nuances in relational training sequencing for humans ( Lyddy and Barnes-Holmes, 2007 ).…”

Section: Introductionmentioning

confidence: 99%

An Open-Source Relational Network Derivation Script in R for Modeling and Visualizing Complex Behavior for Scientists and Practitioners

Smith

Hayes²

2022

Front. Psychol.

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Relational models of cognition provide parsimonious and actionable models of generative behavior witnessed in humans. They also inform many current computational analogs of cognition including Deep Neural Networks, Reinforcement Learning algorithms, Self-Organizing Maps, as well as blended architectures that are outperforming traditional semantic models. The black box nature of these computer models artificially limits scientific and applied progress and human computer interaction. This paper presents a first in the field attempt to model relational processes using logical derivation scripts and network graph visualizations written in the open-source R language. These tools are presented as a way for researchers and practitioners to begin to explore more complex relational models in a manner that can advance the theory and empirical science, as well as prepare the field for future collaborations with advanced computational models of cognition.

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Identifying Accurate and Inaccurate Stimulus Relations: Human and Computer Learning

Cited by 6 publications

References 44 publications

Computational models of stimulus equivalence: An intersection for the study of symbolic behavior

Computational models of stimulus equivalence: An intersection for the study of symbolic behavior

Equivalence Projective Simulation as a Framework for Modeling Formation of Stimulus Equivalence Classes

An Open-Source Relational Network Derivation Script in R for Modeling and Visualizing Complex Behavior for Scientists and Practitioners

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