Association for Behavior Analysis International

The Association for Behavior Analysis International® (ABAI) is a nonprofit membership organization with the mission to contribute to the well-being of society by developing, enhancing, and supporting the growth and vitality of the science of behavior analysis through research, education, and practice.


43rd Annual Convention; Denver, CO; 2017

Event Details

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Symposium #217
CE Offered: BACB
Stimulus Equivalence-Based Instruction: Recent Advances in Training Parameters and Content Domains
Sunday, May 28, 2017
10:00 AM–10:50 AM
Convention Center Mile High Ballroom 2C
Area: PRA/EDC; Domain: Applied Research
Chair: Leif Albright (Caldwell University)
CE Instructor: Bryan J. Blair, M.S.
Abstract: This symposium will present recent data from studies that assessed the efficacy of stimulus-equivalence-based instruction for academic skills. Stimulus equivalence-based instruction might be an effective alternative to more traditional teaching formats (e.g., lecture, independently reading texts, memorization, answering study guide questions etc.). Stimulus equivalence is demonstrated when untrained relations are shown to emerge after training with multiple exemplars and a series of conditional discriminations. The acquisition of skills might be quicker with stimulus equivalence-based instruction given the fact that untrained skills emerge (Fienup & Critchfield, 2010). Recently, stimulus equivalence based instruction has been used to teach an array of skills to learners with varied abilities (e.g., Rehfeldt, 2011). This symposium will present three studies on the use of stimulus equivalence-based instruction to teach the identification of reinforcement schedules, the identification of behavior disorders, and the visual analysis of behavior analytic line graphs. All three studies demonstrated the emergence of untrained relations and skills, as well as the generalization of those skills to novel stimuli and the maintenance of the untrained skills over time.
Instruction Level: Intermediate
Keyword(s): Multiple Exemplar, Stimulus Equivalence
Using Stimulus Equivalence-Based Instruction to Teach Schedules of Reinforcement to College Students
LEIF ALBRIGHT (Caldwell University), Meg Lipper (Caldwell University), Kenneth F. Reeve (Caldwell University), Sharon A. Reeve (Caldwell University), April N. Kisamore (Caldwell University), Antonios Varelas (Hostos Community College, City University of New York)
Abstract: We evaluated the use of computerized equivalence-based instruction (EBI) to teach classes representing schedules of reinforcement to college students. Four, 5-member classes representing fixed-ratio, fixed-interval, variable-ratio, and variable-interval schedules of reinforcement were taught. A pretest-training-posttest design with a between-subjects comparison was used to evaluate the effects of EBI on participants’ performance during both a computer-based test and a written multiple-choice test. Participants in the control group were only exposed to pretest and posttests. All participants in the experimental group acquired the baseline trained relations during match-to-sample instruction. Test scores improved from pretest to posttest and derived (untrained) relations emerged across all participants in the experimental group following training but did not change for the participants in the control group. In addition, participants maintained the learned relations one week after EBI was completed. The present study demonstrated that EBI is an effective teaching procedure to teach schedules of reinforcement to college-level learners.

Contextual Control of Behavioral or Medical Pediatric Stimulus Classes Taught With Equivalence-Based Instruction

JESSICA DAY-WATKINS (Caldwell University), Kenneth F. Reeve (Caldwell University), Sharon A. Reeve (Caldwell University), Jason C. Vladescu (Caldwell University), James E. Connell (Drexel University)

Using equivalence-based instruction (EBI) and multiple-exemplar training, college nursing students were taught three, 4-member classes of pediatric behavior problems that had properties of both medical and behavioral origin. After equivalence classes were established, membership to either a behavioral or medical alternate class was tested with the presentation of multiple probe vignettes (short case histories) not used during training. These vignettes served as supplemental contextual cues that shifted membership between behavioral or medical alternate classes. The results extended the applied contextual control literature by demonstrating generalization across contextual cues using multiple exemplars.

Using Stimulus Equivalence-Based Instruction to Teach Visual Analysis of Graphs
BRYAN J. BLAIR (Endicott College), Lesley A. Shawler (Endicott College), Samantha Russo (Endicott College), Jonathan J. Tarbox (FirstSteps for Kids; Endicott College), Michael F. Dorsey (Endicott College)
Abstract: Previous research has demonstrated alarmingly low rater agreement when visually inspecting trends in single-subject designs. Typical didactic instruction of visual analysis among behavior analysts yields poor and unreliable results. As such, a refined technology to improve the reliability among behavior analysts is warranted. Recently developing research has focused on the emergence of untrained and novel responding by using Equivalence-Based Instruction in a variety of complex human behaviors. The current study used Equivalence-Based Instruction to teach the visual analysis of single-subject graphs to college students. The specific skill was the identification of functional relations in single-subject reversal design research graphs. Given a pre-test/post-test/generalization/maintenance design, and with the use of computer-based training, initial data suggest that Equivalence-Based Instruction might be an effective method for teaching this complex and extremely relevant skill. All participants were explicitly taught specific labels and rules (five three-member classes) for the different graphs. The initial data suggest that untrained relations emerged and that those relations can be used to label and analyze novel graphs.



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