Study guide
12.4 Procedures That Promote Emergent Relations and Generative Performance
Board Certified Assistant Behavior Analyst BCaBA
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1 Introduction
About the BCaBA Exam: 2026 Format, Fees, Blueprint, and Study Plan
2 Chapter 1: Behaviorism and Philosophical Foundations
1.1 Goals of Behavior Analysis & Foundational Assumptions 1.2 Radical Behaviorism vs. Methodological Behaviorism & Rejection of Mentalism 1.3 Seven Dimensions of Applied Behavior Analysis & Branches of Behavior Analysis
3 Chapter 2: Core Behavioral Principles & Contingencies
2.1 Behavior, Response Classes, Stimulus Classes, and Conditioning Paradigms 2.2 Positive and Negative Reinforcement & Schedules of Reinforcement 2.3 Positive and Negative Punishment & Unwanted Effects of Aversives 2.4 Unconditioned, Conditioned, and Generalized Reinforcers and Punishers
4 Chapter 3: Operant Mechanisms & Complex Principles
3.1 Operant and Respondent Extinction Processes 3.2 Motivating Operations vs. Discriminative Stimuli 3.3 Stimulus Control, Equivalence, and Elementary Verbal Operants 3.4 Rule-Governed vs. Contingency-Shaped Behavior; Instructions, Rules, and Modeling
5 Chapter 4: Measurement Systems & Interobserver Agreement
4.1 Operational Definitions and Direct Continuous Measurement 4.2 Discontinuous Measurement Systems & Measurement Artifacts 4.3 Measurement Quality: Validity, Accuracy, Reliability, and IOA Calculations
6 Chapter 5: Data Display & Visual Analysis
5.1 Graphing Conventions: Line Graphs, Cumulative Records, and Standard Celeration Charts 5.2 Visual Analysis of Graphed Data: Level, Trend, Variability, and Immediacy of Effect 5.3 Baseline Logic and Data-Based Clinical Decision Making
7 Chapter 6: Experimental Design & Internal Validity
6.1 Characteristics of Single-Case Experimental Designs & Experimental Control 6.2 Core Single-Case Designs: Reversal, Multiple Baseline, Alternating Treatments, and Changing Criterion 6.3 Threats to Internal Validity & Social/External Validity
8 Chapter 7: Ethics in Behavior Analysis: Core Responsibilities & Client Rights
7.1 BACB Ethics Code Structure, Core Principles, and Professional Competence 7.2 Responsibility to Clients: Confidentiality, Informed Consent, and Client Dignity 7.3 Multiple Relationships, Conflicts of Interest, and Gift-Giving Prohibitions 7.4 Risks of Unethical Conduct, Public Statements, and Transitioning Clients and Supervisors
9 Chapter 8: Ethics in Assessment, Intervention, & Regulatory Compliance
8.1 Ethical Standards in Behavior Assessment and Intervention Selection 8.2 Mandated Reporting, Legal Obligations, and Documentation/Billing Integrity 8.3 Cultural Responsiveness, Interprofessional Collaboration, and Disciplinary Reporting 8.4 Interpersonal Skills for Professional Relationships and Ongoing Professional Development
10 Chapter 9: Behavioral Assessment & Functional Assessment
9.1 Indirect Assessment Methods and Review of Records 9.2 Descriptive Functional Behavior Assessment: Direct Observation and Data Collection 9.3 Experimental Functional Analysis (Analog FA) and Specialized Variations 9.4 Preference and Reinforcer Assessments & Social Validity 9.5 When Services Are Warranted, Skills Assessment, and Contextual Fit
11 Chapter 10: Foundational Behavior-Change Procedures
10.1 Differential Reinforcement Procedures (DRA, DRI, DRO, DRL, DRH) 10.2 Prompting Strategies, Prompt Hierarchies, and Systematic Prompt Fading 10.3 Shaping (Successive Approximations) and Chaining Procedures 10.4 Token Economies, Contingency Contracts, and Group Contingencies
12 Chapter 11: Antecedent & Function-Based Interventions
11.1 Functional Communication Training (FCT) and Noncontingent Reinforcement (NCR) 11.2 Antecedent Manipulations: High-Probability Sequences, Premack Principle, and Choice Architecture 11.3 Programming for Generalization and Maintenance: Stokes and Baer (1977) Tactics
13 Chapter 12: Teaching New Skills: Acquisition Arrangements, Discriminations, Verbal Behavior, and Self-Management
12.1 Discrete-Trial, Free-Operant, and Naturalistic Teaching Arrangements 12.2 Teaching Simple and Conditional Discriminations 12.3 Teaching Elementary Verbal Operants: Mands, Echoics, Tacts, and Intraverbals 12.4 Procedures That Promote Emergent Relations and Generative Performance 12.5 Self-Management Procedures
14 Chapter 13: Intervention Development, Fidelity, & Monitoring
13.1 Writing Socially Valid, Observable, and Measurable Behavioral Goals 13.2 Designing Behavior Support Plans: Function-Based Multi-Component Architecture 13.3 Treatment Integrity Monitoring, Fidelity Measurement, and Data-Based Plan Revision 13.4 Managing Behavioral Relapse, Transition Planning, and Systematic Discharge Criteria
15 Chapter 14: Supervisory Roles, Behavioral Skills Training, & BACB Requirements
14.1 The BCaBA Role: Practicing Under Ongoing BCBA Supervision and Professional Boundaries 14.2 Behavioral Skills Training (BST) for Direct Care Staff and RBTs 14.3 Objective Performance Monitoring, Performance Feedback, and Supervision Documentation 14.4 Promoting Cultural Humility, Equity, and Ethical Supervision in Behavioral Healthcare 14.5 Effective Supervisory Relationships: Practices, Expectations, and Supervision Goals 14.6 Performance Management, Performance Diagnostics (PDC-HS), and Evaluating Supervision
12.4 Procedures That Promote Emergent Relations and Generative Performance
Key Takeaways Explain This Topic AI
Equivalence-based instruction directly teaches a few conditional relations (for example, A-B and B-C) and then tests, without reinforcement, for untaught symmetry, transitivity, and equivalence relations.
In a three-member class, teaching 2 relations (A-B and B-C) can produce 4 untaught relations (B-A, C-B, A-C, and C-A) plus reflexivity, which makes instruction efficient.
Training structures differ: linear series (A-B, B-C), one-to-many or sample-as-node (A-B, A-C), and many-to-one or comparison-as-node (B-A, C-A); each can establish equivalence classes.
Generative performance means responding correctly to novel combinations or situations without direct teaching; matrix training produces recombinative generalization, and multiple-exemplar instruction builds generalized operants.
Last updated: October 2026
Teaching Less, Learning More
Tasks B.15 and G.11 concern emergent relations (responses that appear without being directly taught or reinforced) and generative performance (correct responding to new combinations or situations). Section 3.3 defined reflexivity, symmetry, and transitivity. This section turns those properties into instructional procedures. The practical payoff is efficiency: carefully chosen teaching produces many more skills than were taught.
Equivalence-Based Instruction (EBI)
Equivalence-based instruction uses matching-to-sample (section 12.2) to teach a small set of arbitrary relations among stimuli and then tests for the relations that should emerge if the stimuli form an equivalence class.
Designing an EBI Program
Choose the stimulus sets. Example for early reading: A = spoken word ("cat"), B = picture of a cat, C = printed word cat . Example for college statistics: A = a term, B = its definition, C = an example graph.
Choose a training structure.
Linear series : teach A-B and B-C.
One-to-many (sample-as-node) : teach A-B and A-C.
Many-to-one (comparison-as-node) : teach B-A and C-A.
All three can produce equivalence; programs choose based on which relations are easiest to teach and which matter most for the learner.
Teach the baseline relations to mastery with reinforcement and error correction, mixing classes so the learner must attend to the sample (for example, cat, dog, and pig sets taught together).
Test without reinforcement. Present probe trials for the untaught relations (symmetry: B-A, C-B; transitivity: A-C; equivalence: C-A) without feedback . Reinforcing test trials would teach the relation directly and you could no longer tell whether it emerged. Keep baseline trials (with reduced reinforcement) mixed in so the learner stays engaged.
Remediate if needed. If a relation fails to emerge, review the baseline relations, add training on a missing relation, or change the training structure, then retest.
Counting the Payoff
In the reading example, teaching 2 relations (A-B and B-C) can yield 4 untaught relations (B-A, C-B, A-C, C-A) plus reflexivity for each stimulus. As class size grows, the number of emergent relations grows much faster than the number taught: adding a fourth member (D = sign) by teaching just C-D can produce relations between D and every other member.
Sidman (1971) worked with an adolescent with severe intellectual disability who could already match spoken words to pictures. After he was taught to match spoken words to printed words, he matched printed words to pictures and read the words aloud, relations that were never directly taught. Reviews of EBI with college students have found it efficient for teaching course concepts such as statistics and neuroanatomy.
Related Generative Repertoires
Naming (bidirectional naming) : when hearing an object's name, saying the name, and selecting the object become integrated, a learner who is taught only to tact an object can also select it when asked, or vice versa. Multiple-exemplar instruction across listener and speaker responses is used to establish this repertoire.
Derived relations beyond sameness : relational frame theory extends equivalence to other relations (opposite, more than, before and after). If "A is more than B" and "B is more than C," the learner can derive "C is less than A."
Matrix Training and Recombinative Generalization
Recombinative generalization is responding correctly to new combinations of previously learned components. Matrix training arranges teaching to produce it:
- push roll drop
- car taught probe probe
- ball probe taught probe
- block probe probe taught
Teaching only the diagonal (push car, roll ball, drop block) can lead the learner to perform or name the untaught combinations ("roll car," "drop ball"). Matrix training has been used for actions with objects, early grammar, play, and preliteracy skills (for example, Axe & Sainato, 2010). Overlapping arrangements (teaching a few more cells) help when the diagonal alone does not produce full recombination.
Multiple-Exemplar Instruction and Generalized Operants
Multiple-exemplar instruction (MEI) teaches a response across many varied examples so that a general repertoire emerges, not just specific stimulus-response pairs. Teaching imitation of many different actions produces generalized imitation (section 3.4); teaching many "Show me ___" items across materials produces generalized instruction following. MEI is also how generative performance is built: after enough exemplars, the learner responds correctly to examples never taught.
Example: Teaching Coin Values
A teacher wants a student to name coin values, select coins by value, and match written prices to coins. Instead of teaching every combination, she defines three stimulus sets for each coin: A = the coin, B = the spoken coin name ("quarter"), and C = the written value ("25 cents"). She teaches B-A (select the quarter when hearing "quarter") and B-C (select "25 cents" when hearing "quarter") with reinforcement until both are mastered, mixing pennies, nickels, dimes, and quarters so the student must attend to each sample. She then runs unreinforced probes for A-C (match the coin to "25 cents") and C-A (match "25 cents" to the coin). Because this one-to-many structure uses the spoken name as the node, the student can now show coin-to-value relations that were never taught directly, saving many teaching sessions.
Practical Guidelines
Choose stimuli that are socially meaningful (reading, money, safety signs, course concepts).
Teach baseline relations to a high criterion before testing.
Run test probes in extinction and mix in maintenance trials.
Graph emergent relations separately from taught relations so you can see what the instruction produced.
If relations do not emerge, change the procedure; do not simply keep testing.
Common Exam Traps
Reinforcing test trials. Then the relation was taught, not derived.
Calling a taught relation emergent. Only relations never directly reinforced count.
Confusing transitivity and symmetry. Symmetry reverses a taught relation (B-A after A-B); transitivity links two taught relations through a shared stimulus (A-C after A-B and B-C).
Confusing stimulus generalization with recombinative generalization. Stimulus generalization is responding to physically similar stimuli; recombinative generalization is new combinations of learned parts.