article · Figshare · 2018
Making Instance-based Learning Theory usable and understandable: The Instance-based Learning Tool
Dutt, Varun, Gonzalez, Cleotilde
Cited by 0Open access
Abstract
This paper focuses on the creation and presentation of a user-friendly experience for developing computational models of human behavior. Although computational models of human behavior have enjoyed a rich history in cognitive psychology, they have lacked widespread impact, partly due to the technical knowledge and programming required in addition to the complexities of the modeling process. We describe a modeling tool called IBLTool that is a computational implementation of the Instance-based Learning Theory (IBLT). IBLT is a theory that represents how decisions are made from experience in dynamic tasks. The IBLTool makes IBLT usable and understandable to a wider community of cognitive and behavioral scientists. The tool uses graphical user interfaces that take a modeler step-by-step through several IBLT processes and help the modeler derive predictions of human behavior in a particular task. A task would connect and interact with the IBLTool and store the decision-making data while the tool collects statistical data from the execution of a model for the task. We explain the functioning of the IBLTool and demonstrate a concrete example of the design and execution of a model for the Iowa Gambling task. The example is intended to provide a concrete demonstration of the capabilities of the IBLTool.
Cite this paper
Dutt, V., & Gonzalez, C. (2018). Making Instance-based Learning Theory usable and understandable: The Instance-based Learning Tool. Figshare. https://doi.org/10.1184/r1/6571250.v1
Dutt, Varun, and Cleotilde Gonzalez. “Making Instance-based Learning Theory usable and understandable: The Instance-based Learning Tool.” Figshare, 2018, https://doi.org/10.1184/r1/6571250.v1.
Dutt, Varun, and Cleotilde Gonzalez. 2018. “Making Instance-based Learning Theory usable and understandable: The Instance-based Learning Tool.” Figshare. https://doi.org/10.1184/r1/6571250.v1.
Dutt, V. and Gonzalez, C. (2018) ‘Making Instance-based Learning Theory usable and understandable: The Instance-based Learning Tool’, Figshare. Available at: https://doi.org/10.1184/r1/6571250.v1.
@article{dutt2018making,
title = {{Making Instance-based Learning Theory usable and understandable: The Instance-based Learning Tool}},
author = {Dutt, Varun and Gonzalez, Cleotilde},
journal = {Figshare},
year = {2018},
doi = {10.1184/r1/6571250.v1}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.