Predicting Heuristic Decisions in Child Welfare: A Neural Network Exploration

Ninness, Chris; Yelick, Anna; Ninness, Sharon K.; Cordova, Wilma

doi:10.1007/s42822-021-00047-1

Cited by 3 publications

(1 citation statement)

References 40 publications

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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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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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The Promises and Possibilities of Artificial Intelligence in the Delivery of Behavior Analytic Services

Cox,

Jennings

2023

Behav Analysis Practice

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Artificial intelligence (AI) has begun to impact nearly every aspect of our daily lives and nearly every industry and profession. Many readers of this journal likely work in one or more areas of behavioral health. For readers who work in behavioral health and who are interested in AI, the purpose of this article is to highlight the pervasiveness of AI research being conducted around many facets of behavioral health service delivery. To do this, we first provide a brief overview of some of the areas within AI and the types of problems each area of AI attempts to solve. We then outline the prototypical client journey in behavioral healthcare beginning with diagnosis/assessment and ending with intervention withdrawal or ongoing monitoring. Next, for each stage in the client journey, we highlight several areas that parallel existing behavior analytic practice where researchers have begun to use AI, often to improve the efficiency of service delivery or to learn new things that improve the effectiveness of behavioral health services. Finally, for those whose appetite has been whet for getting involved with AI, we close by describing three roles they might consider trying out and that parallel the three main domains of behavior analysis.These three roles are an AI tool designer (akin to EAB), AI tool implementer (akin to ABA), or AI tool supporter (akin to practice).

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Could Preference Indifference be a Social Behavioral Marker for Foster Youth? A Potential Alternative use for Preference Assessments

et al. 2023

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Predicting Heuristic Decisions in Child Welfare: A Neural Network Exploration

Cited by 3 publications

References 40 publications

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

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

The Promises and Possibilities of Artificial Intelligence in the Delivery of Behavior Analytic Services

Could Preference Indifference be a Social Behavioral Marker for Foster Youth? A Potential Alternative use for Preference Assessments

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