token-economies

name	token-economies
description	Use when designing, implementing, and troubleshooting token economy systems including token selection, backup reinforcer pricing, exchange schedules, response cost, and fading procedures.

Token Economies

A token economy is a systematic reinforcement program in which tokens are delivered contingent on target behavior and exchanged later for backup reinforcers. Token economies are among the most versatile and widely researched behavior management systems in ABA.

Conceptual Foundation

Tokens function as conditioned (generalized) reinforcers. Their effectiveness depends on:

• Pairing: Tokens must be reliably paired with backup reinforcers to acquire conditioned reinforcing value.
• Variety: Multiple backup reinforcers prevent satiation on any single reinforcer.
• Contingency: Tokens are delivered immediately contingent on the target behavior, bridging the delay to the backup reinforcer.

Components of a Token Economy

1. Target Behaviors

• Clearly defined, observable, measurable behaviors.
• Can be behaviors to increase (skill use, compliance, on-task) or behaviors to decrease (via differential reinforcement—tokens earned for the absence of problem behavior).
• Start with 2–3 target behaviors. Add more once the system is running smoothly.
• Ensure the client can discriminate which behaviors earn tokens.

2. Tokens

Types of tokens based on client needs and context:

Token Type	Population	Advantages	Considerations
Poker chips	Older children, adults	Tangible, easy to count	Choking hazard for young children
Stickers on a chart	Young children	Visual progress tracking	Cannot be removed for response cost
Stars/stamps	Classrooms	Efficient for groups	Limited tactile feedback
Points (written)	Adolescents, adults	Flexible, no physical tokens	Requires literacy
Digital points (app)	Tech-comfortable clients	Automated, visual, portable	Requires device access
Velcro icons	Children with developmental disabilities	Tangible, visual, portable	Easy to lose

Token Properties

• Easy to deliver quickly (immediate reinforcement).
• Difficult to counterfeit or steal.
• Easy to accumulate and count.
• Age-appropriate and contextually appropriate.

3. Backup Reinforcers

• Identified through preference assessment (not assumed).
• Include a variety: tangible items, activities, privileges, social reinforcers.
• Rotate regularly to prevent satiation.
• Include at least 5–10 options at any given time.
• Organize in a "store" or "menu" format.

4. Exchange Ratio

The number of tokens required to purchase each backup reinforcer.

• Initial ratio: Set low enough that the client contacts the backup reinforcer quickly and frequently. Early success builds buy-in.
• Differential pricing: More preferred or larger reinforcers cost more tokens.
• Pricing should be achievable: If a client earns 10 tokens per session, at least some items should be available within one session.

Exchange Schedule Options

Schedule	Description	When to Use
End of session	Exchange at the end of each session	Early implementation, young children
End of day	Exchange at the end of the school/therapy day	Classrooms, longer sessions
Accumulated	Save tokens across sessions for larger items	Building delay tolerance, older clients
On-demand	Client can request exchange at any time	Initial stages to increase motivation

Establishing Tokens as Conditioned Reinforcers

Pairing Procedure

1. Give a token and immediately provide a backup reinforcer (no exchange required).
2. Repeat across multiple opportunities with varied backup reinforcers.
3. Gradually introduce the exchange requirement: first, exchange 1 token → reinforcer.
4. Progressively increase the exchange ratio as the tokens acquire conditioned reinforcing value.

Signs That Tokens Have Conditioned Value

• Client works for tokens without immediate backup reinforcer access.
• Client protests when tokens are removed or lost.
• Client counts or monitors their token balance.

Response Cost

Response cost involves removing tokens contingent on specified undesirable behavior.

Implementation Guidelines

• Define which behaviors result in token loss and how many tokens are lost.
• Do not remove more tokens than the client can earn in a session—net negative balances are demoralizing and counterproductive.
• Response cost should be smaller than the earning rate: the client should always be able to earn more than they lose.
• Combine response cost with high rates of token delivery for appropriate behavior.
• Introduce response cost only after the token economy is well established and the client values the tokens.

Cautions

• Response cost can produce emotional side effects (frustration, aggression) in some clients.
• Monitor emotional responses when first introducing response cost.
• If side effects are significant, reduce response cost magnitude or remove it.
• Never use response cost as the primary behavior change strategy—reinforcement must predominate.

Group Token Economies

Considerations

• Can all group members earn independently (individual contingencies) or does the group earn collectively (group contingency)?
• Independent: Each client earns and exchanges their own tokens.
• Interdependent: Group earns tokens together; all access backup reinforcers when the group reaches the criterion.
• Dependent: One or a few members' behavior determines the group's access to reinforcers. Use cautiously to avoid scapegoating.

Advantages of Group Systems

• Efficient for classrooms and residential settings.
• Natural peer reinforcement for appropriate behavior.
• Teaches cooperation and collective responsibility.

Fading the Token Economy

The goal is always to fade the token economy so behavior is maintained by natural contingencies.

Fading Steps

1. Thin the reinforcement schedule: Increase the number of responses required per token.
2. Increase the exchange delay: Move from end-of-session exchange to end-of-day, then end-of-week.
3. Shift to intermittent token delivery: Move from FR-1 to VR schedules.
4. Introduce self-monitoring: Client tracks their own behavior and delivers their own tokens.
5. Shift to natural reinforcers: Social praise, privileges, and natural consequences replace tokens.
6. Phase out tokens entirely: Replace with verbal feedback and self-management.
7. Monitor maintenance: Continue to assess whether the behavior maintains without tokens.

Fading Timeline

• Do not rush fading. Move to the next step only when behavior is stable at the current level.
• If behavior deteriorates during fading, step back to the previous level and stabilize before attempting again.

Troubleshooting

Problem	Possible Cause	Solution
Client not interested in tokens	Tokens not paired; backup reinforcers not preferred	Re-pair tokens; conduct new preference assessment
Hoarding tokens	Client prefers collecting to exchanging	Set maximum savings; ensure exchange items are desirable
Counterfeiting	Tokens are easy to reproduce	Use tokens that are harder to counterfeit; monitor inventory
Loss of motivation over time	Satiation; reinforcers not rotated	Rotate backup reinforcers; conduct new preference assessments monthly
Disruption during exchange	Exchange is unstructured	Create a structured exchange routine with clear expectations
Token delivery is too slow	Too many target behaviors; complex system	Simplify; reduce targets; train staff to deliver quickly

Applications

• Classrooms: Schoolwide positive behavior support, individual student plans.
• Residential settings: Level systems, daily living skill programs.
• Clinical ABA: Session-based reinforcement systems for skill acquisition and behavior reduction.
• Self-management: Adults using point systems for personal goals (exercise, medication adherence).
• Organizational behavior management: Employee incentive programs.

Key References

• Kazdin, A. E. (1977). The Token Economy: A Review and Evaluation. Plenum Press.
• Cooper, J. O., Heron, T. E., & Heward, W. L. (2020). Applied Behavior Analysis (3rd ed.). Pearson.
• Matson, J. L., & Boisjoli, J. A. (2009). The token economy for children with intellectual disability and/or autism: A review. Research in Developmental Disabilities, 30, 240–248.
• Hackenberg, T. D. (2009). Token reinforcement: A review and analysis. JEAB, 91, 257–286.