Graduation Year
2019
Document Type
Dissertation
Degree
Ph.D.
Degree Name
Doctor of Philosophy (Ph.D.)
Degree Granting Department
Child and Family Studies
Major Professor
Andrew Samaha, Ph.D.
Committee Member
Phyllis Jones, Ph.D.
Committee Member
Kwang-Sun Blair, Ph.D.
Committee Member
Raymond Miltenberger, Ph.D.
Keywords
conditioned reinforcement, developmental disabilities, education, observational conditioning, video modeling
Abstract
Prior research has supported the use of reinforcer-based methods in school settings. Video based modeling methods for establishing conditioned reinforcers without the need for explicit pairing with primary reinforcers can help to extend the use of these resources into new contexts. The use of video based conditioning has potential applications in school settings to increase academic skills without the use of more costly-to-implement reinforcer systems. However, conditioning of this kind might be restricted by the need to individually condition stimuli with different participants. The current study evaluated effects of video based conditioning on relative rate of sight word reading across two experiments. In Experiment 1, token preference was conditioned via individual video presentation. In Experiment 2, video presentation was evaluated in a small group format. Participants included children between the ages of 4-12, and responding was evaluated using a concurrent-choice assessment embedded within a multiple baseline across participants design. The results of Experiment 1 indicated only marginal differentiation of responding to the different tokens. Results of Experiment 2 found differentiation of preference for 2 of the 3 participants. A consistent preference hierarchy was obtained across both participants, which slightly favored Video Observational Conditioning over Video Modeling and an unconditioned (neutral) token.
Scholar Commons Citation
Gauert, Spencer B., "A Comparison of Different Modeling Techniques to Establish Token Reinforcers in Classroom Settings" (2019). USF Tampa Graduate Theses and Dissertations.
https://digitalcommons.usf.edu/etd/8030