Variable Ratio
Overview
The variable ratio (VR) schedule delivers reinforcement after an unpredictable number of responses that varies around a specified mean. For example, VR5 requires an average of five responses per reinforcer, but individual ratios might be 2, 7, 3, 8, 1, 9 — drawn from a distribution centered on 5. This unpredictability produces the highest, most stable response rates of any simple reinforcement schedule.
VR schedules generate persistent, extinction-resistant responding because the subject cannot predict which response will produce reinforcement. This is the schedule underlying gambling behavior in humans and is used extensively in addiction research. The absence of post-reinforcement pauses distinguishes VR from fixed ratio performance.
ConductMaze implements VR schedules using either Fleshler-Hoffman distributions or custom ratio lists. The software randomizes ratio sequences across sessions to prevent pattern learning, logs every response with sub-second precision, and supports within-session VR value changes for parametric studies.
Trial Flow
Session Start
House light ON, levers extended
Active Lever Press
Subject presses the active lever
Ratio Check
Response count >= current variable ratio value?
Reinforcer Delivery
Pellet dispensed, cue light activated
New Ratio Drawn
Next ratio sampled from distribution around mean
Session Limit Check
Max reinforcers or time limit reached?
Session End
Levers retracted, data saved
Parameters
| Parameter | Type | Default | Description |
|---|---|---|---|
| Mean Ratio | integer | 5 | Average number of responses required per reinforcer |
| Distribution Type | enum | Fleshler-Hoffman | Distribution algorithm for generating ratio values (Fleshler-Hoffman, arithmetic, geometric) |
| Max Reinforcers | integer | 50 | Maximum reinforcers before session ends |
| Max Session Time | seconds | 3600 | Absolute session time limit |
| Active Lever Side | enum | Right | Which lever is reinforced (Left, Right, Counterbalanced) |
| Cue Light Duration | seconds | 3 | Duration of cue light paired with reinforcer delivery |
| Reinforcer Type | enum | Sucrose Pellet | Type of reinforcement delivered |
Metrics
| Metric | Unit | Description |
|---|---|---|
| Total Active Presses | count | Total presses on the active lever |
| Total Inactive Presses | count | Total presses on the inactive lever |
| Reinforcers Earned | count | Total reinforcers delivered |
| Response Rate | presses/min | Mean rate of active lever pressing |
| Inter-Response Time | seconds | Mean time between successive active presses |
| Actual Mean Ratio | ratio | Observed mean ratio across the session (should approximate programmed mean) |
| Latency to First Press | seconds | Time from session start to first active press |
Sample Data
| Trial | Ratio_Required | Active_Presses | Inactive_Presses | IRT_s | Rate_ppm |
|---|
Representative data for illustration purposes. Actual values will vary by species, strain, and experimental conditions.
Applications
- 1Gambling and impulsivity research — VR schedules model the unpredictable reinforcement underlying problem gambling
- 2Drug self-administration — VR schedules maintain stable intake patterns for pharmacological studies
- 3Extinction resistance studies — VR-trained responses are more resistant to extinction than FR-trained responses
- 4Behavioral economics — measuring demand elasticity under variable reinforcement contingencies
- 5Comparative schedule analysis — contrasting VR and FR performance on response rate and patterning
Related Protocols
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