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.


45th Annual Convention; Chicago, IL; 2019

Event Details

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Symposium #422
CE Offered: BACB
Token Reinforcement: An Examination of Token Function and Application
Monday, May 27, 2019
9:00 AM–9:50 AM
Hyatt Regency West, Ballroom Level, Regency Ballroom C
Area: AUT/PCH; Domain: Translational
Chair: Jonathan W. Ivy (The Pennsylvania State University - Harrisburg )
CE Instructor: Jonathan W. Ivy, Ph.D.

A token economy is a complex system of reinforcement in which a token is delivered (or removed) contingent upon target behavior(s) and can be later exchanged for back-up reinforcers. The complexity of a token economy is derived from three-interconnected schedules of reinforcement. Following the pioneering research of Ayllon and Azrin (1965; 1968), token economies and token reinforcement has become a common component of behavioral programming. Despite the broad success of this behavioral technology, the mechanics of token reinforcement have not been thoroughly studied (Hackenberg, 2009; 2018). Further, token economy literature often contains vague or incomplete procedural descriptions (Ivy, Meindl, Overly, & Robson, 2017). The purpose of this symposium is to present and synthesize token reinforcement and token economy literature. The first presentation will examine the methods to condition token reinforcers. The second presentation will explore assessment strategies to evaluate the function of tokens. Finally, the third presentation examines the application of token economies for individuals with Autism Spectrum Disorder.

Instruction Level: Intermediate
Keyword(s): token economy, token reinforcement
Target Audience:

Practitioners and researchers who use token economies or token reinforcement.

Learning Objectives: The audience will be able to label and describe four methods to condition a token reinforcer. The audience will be able to describe strategies to access the function of tokens. The audience will be able to discuss the state of token economy research for individuals with Autism Spectrum Disorder.
Methods to Condition Token Reinforcers
(Service Delivery)
JONATHAN W. IVY (The Pennsylvania State University - Harrisburg ), Kathryn Glodowski (The Pennsylvania State University - Harrisburg)
Abstract: A token reinforcer is a type of conditioned reinforcer that can be exchanged for other, already established reinforcers (i.e., terminal reinforcers; Skinner, 1953, p.79). Tokens are unique among other conditioned reinforcers (c.f., attention) in that contact with the terminal reinforcer requires tokens be accrued and exchanged. Since the pioneering work of Ayllon and Azrin (1965; 1968), token reinforcement has become a common component of behavioral research and practice. Despite the large body of empirical evidence supporting the use of token reinforcement, the process to condition a token reinforcer has not undergone thorough evaluation. The purpose of this presentation is to identify and describe methods to condition token reinforcers from research and the conceptual analysis of behavior. The author will describe four primary methods of token conditioning: a) verbal description of token-reinforcer relation, b) token-reinforcer (i.e., stimulus-stimulus) pairing, c) response-independent token delivery with exchange, and d) response-dependent token delivery with exchange. Common procedural variations, implications for practice, and areas of future research will be discussed.
Measuring the Stimulus Functions of Tokens: Assessment Strategies for Clinicians
(Service Delivery)
Abstract: Tokens are traditionally referenced as functioning as generalized reinforcers when used in clinical settings (Kazdin & Bootzin, 1976). However, evidence from basic research demonstrate tokens functioning as S-deltas and actually suppress early-component responding within a token schedule. (Foster, Hackenberg, Vaidya, 2001). Given that the clinical utility of tokens rests of the expectation that tokens will maintain behavior in the absence of or in the face of long delays to primary reinforcement, it is essential that they function as conditioned or generalized conditioned reinforcers. Likewise, avoiding token economy arrangements that facilitate S-delta effects is equally as important. This talk will focus on assessment strategies for measuring the stimulus function of tokens that are practical to implement in a clinical setting as well as provide suggestions for how to optimally arrange a token economy given varying stimulus functions.

A Systematic Review of the Token Economy With Individuals With Autism Spectrum Disorder

(Applied Research)
STEPHANIE ORTIZ (Caldwell University), Ruth M. DeBar (Caldwell University), Jenny-Lee Alisa Aciu (Caldwell University )

Token reinforcement systems are widely used in the field of applied behavior analysis to promote behavior change across settings, behaviors, and populations (e.g., individuals of typical development and with developmental delays). While previous literature reviews on token reinforcement have assessed staff training, selection of backup reinforcers, programed consequences, and generalization procedures across diverse populations, none have explicitly evaluated these procedures with individuals diagnosed with autism spectrum disorder (ASD). The purpose of this systematic literature review was to extend previous analyses by evaluating applications of the token economy with individuals with ASD. The included studies were summarized across (a) participant demographics (age, gender, and diagnoses), (b) experimental setting, (c) token conditioning (d) target behavior defined, (e) inverse of target behavior, (f) programed consequences, (g) individual delivering tokens, (h) training on token delivery, (i) token production schedule, (j) exchange-production schedule, (k) token-exchange schedule, (l) token economies implemented, (m) token boards, (n) conditioned reinforcer, (o) token exchange, (p) backup reinforcer selection, (q) designated backup reinforcers, (r) opportunity to select backup reinforcer, (s) faded token economy, (t) maintenance, (u) generalization, (v) social validity, (w) procedural integrity, (x) interobserver agreement (IOA), and (y) outcomes.




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