What Is a Token Economy?
A token economy is a reinforcement system in which individuals earn tokens (conditioned reinforcers) contingent on specified target behaviors. Tokens are later exchanged for backup reinforcers, which are the items, activities, or privileges that have value to the individual. Token economies are grounded in the principle of conditioned reinforcement, where a neutral stimulus (the token) acquires reinforcing properties through repeated pairing with established reinforcers.
Token economies were first systematically implemented and researched in psychiatric settings by Ayllon and Azrin (1968), who demonstrated dramatic improvements in adaptive behavior among institutionalized patients. Since then, token economies have been adapted for virtually every population and setting in applied behavior analysis.
Core Components of a Token Economy
Tokens
Tokens can be any object, symbol, or mark that is easy to deliver, durable, portable, and difficult to counterfeit. Common examples include poker chips, stickers on a chart, points on a card, tally marks, digital points in an app, or stamps on a card. The ideal token is one that can be delivered immediately following the target behavior without interrupting ongoing activity.
Target Behaviors
Clearly define which behaviors earn tokens. These should be operationally defined so that all implementers (teachers, parents, clinicians) deliver tokens consistently. Start with a small number of clearly defined target behaviors and expand as the system becomes established.
Backup Reinforcers
The backup reinforcer menu should include a variety of items and activities at different "prices" to maintain motivation across time. Conduct regular preference assessments to update the menu. Include both small, immediately accessible reinforcers and larger, aspirational items that require saving tokens across sessions.
Exchange Schedule
Determine when and how tokens are exchanged for backup reinforcers. Initially, allow frequent exchange opportunities (e.g., at the end of each activity or session). As the individual becomes accustomed to the system, gradually extend the exchange interval to build tolerance for delayed reinforcement.
Designing a Simple Token Economy
A simple token economy targets one to three behaviors, uses a single type of token, offers a small menu of backup reinforcers, and provides frequent exchange opportunities.
Example: Classroom Token Board
A student earns star stickers for completing assigned work, raising their hand before speaking, and remaining in their seat during instruction. The token board holds 10 stars. When the board is full, the student selects a backup reinforcer from a menu (5 minutes of computer time, a small prize from the prize box, or a homework pass). This system is straightforward, easy to implement, and effective for building foundational skills.
Designing a Complex Token Economy
Complex token economies may target multiple behaviors at different reinforcement rates, incorporate response cost (token loss for specified behaviors), use tiered pricing for different backup reinforcers, include group contingencies alongside individual contingencies, and involve savings or banking systems.
Response Cost
Response cost is a punishment procedure in which tokens are removed contingent on specified undesirable behaviors. When adding response cost to a token economy, ensure that the individual earns more tokens than they lose overall. A system where the individual frequently loses all tokens will undermine motivation and may lead to emotional responses or behavioral escalation. Research by Iwata and Bailey (1974) demonstrated that response cost is most effective when it is combined with a rich token earning schedule.
Tiered Pricing
Offering backup reinforcers at different token costs allows individuals to choose between immediate, small reinforcers (2 tokens for 5 minutes of free time) and larger, delayed reinforcers (20 tokens for a special outing). This pricing structure teaches saving behavior and decision-making, both valuable functional skills.
Implementation Guidelines
- Explain the system clearly: Before starting, teach the individual how the token economy works. Show them the tokens, demonstrate how they are earned, and let them practice exchanging tokens for backup reinforcers.
- Start lean and build: Begin with a simple system and add complexity only as the individual demonstrates understanding and success.
- Maintain consistency: All implementers must deliver tokens using the same criteria. Inconsistency undermines the contingency.
- Pair tokens with social reinforcement: Always deliver tokens with praise or other social reinforcement. This pairing helps maintain the conditioned reinforcing value of the tokens and facilitates later fading.
- Monitor and adjust: Track data on target behaviors and token earning/exchange patterns. Adjust the system based on data, not assumptions.
Fading the Token Economy
A token economy should not be permanent. Plan for fading from the beginning:
- Gradually increase the number of tokens required for exchange (leaning the schedule).
- Extend the time between exchange opportunities.
- Shift from tangible backup reinforcers to more natural reinforcers (social praise, privileges, self-monitoring).
- Introduce self-management components where the individual tracks their own behavior and self-delivers tokens.
- Fade the tokens entirely, relying on natural reinforcement contingencies and self-regulation.
Common Mistakes
- Making the exchange ratio too lean from the start: If the individual has to earn too many tokens before accessing a backup reinforcer, motivation drops quickly.
- Not updating the backup reinforcer menu: Satiation is a real concern. Regularly rotate and refresh the available reinforcers.
- Using response cost without sufficient earning opportunities: The individual should always be able to earn significantly more tokens than they lose.
- Failing to plan for generalization and fading: Without a fading plan, the individual becomes dependent on the artificial token system.
BCBA Exam Relevance
Token economies are a frequently tested topic on the BCBA exam. Be prepared to design a token economy from a clinical vignette, identify components (tokens, target behaviors, backup reinforcers, exchange schedule), troubleshoot implementation problems, explain the role of conditioned reinforcement, and discuss response cost as a component of the system.