An Introduction to Relational Frame Theory

Lesson 1: Introduction
1.1 How does this work?
1.2 Who is this for?
1.3 What will I learn?
Lesson 2: Language
2.1 Public and Private Language
2.2 Uses of Language
2.3 The Dark Side of Language
2.4 Language and Suffering
2.5 The Bright Side of Language
2.6 Symbolism and Language
2.7 Knowledge Check
2.8 Generativity and Language
2.9 Generativity Example
2.10 Knowledge Check
2.11 Theories of Language
2.12 Traditional Theories of Language
2.13 RFT Approach to Language
2.14 Review
Lesson 3: Functional Contextualism
3.1 Concept Map
3.2 Concept Map: Functional Contextualism
3.3 Analytic Goal of Functional Contextualism
3.4 Defining Psychological Event
3.5 Functional Contextualism vs. Traditional Psychology
3.6 Prediction AND Influence
3.7 Focus on Manipulable Variables
3.8 Defining Manipulable Variable
3.9 Context
3.10 Interventions are Part of the Context
3.11 Correlations Between Psychological Events
3.12 Limitations of Correlations
3.13 Importance of Contextual Variables
3.14 Attitudes and Smoking
3.15 Do Attitudes Cause Smoking?
3.16 Attitudes Before Behavior or After?
3.17 Prediction without Influence
3.18 Depression Example #1
3.19 Depression Example #1: Analysis
3.20 Depression Example #2
3.21 Depression Example #2: Analysis
3.22 Birth Control Example #1
3.23 Birth Control Example #1: Analysis
3.24 Birth Control Example #2
3.25 Birth Control Example #2: Analysis
3.26 Knowledge Check
3.27 Traditional Theories of Language
3.28 Lack of Focus on Context
3.29 RFT Focus on the Context of Language
3.30 Review
Lesson 4: Derived Stimulus Relations
4.1 Concept Map
4.2 Derived Stimulus Relations
4.3 Definition of Derived Stimulus Relation
4.4 Derived Stimulus Relation Example
4.5 Directly Trained Relations
4.6 Derived Stimulus Relation
4.7 Stimulus Equivalence Example Part 1
4.8 Stimulus Equivalence Example Part 2
4.9 Stimulus Equivalence Example Part 3
4.10 Trained and Derived Equivalence Relations
4.11 Foxy Equivalence Part 1
4.12 Foxy Equivalence Part 2
4.13 Foxy Equivalence Part 3
4.14 Foxy Equivalence Part 4
4.15 Foxy Equivalence Part 5
4.16 Foxy Equivalence Part 6
4.17 Foxy Equivalence Part 7
4.18 Foxy Equivalence Part 8
4.19 Foxy Equivalence Part 9
4.20 Multiple Types of Stimulus Relations
4.21 Comparative Speed Relation Example
4.22 Knowledge Check
4.23 Networks of Derived Stimulus Relations
4.24 Sampson Network Part 1
4.25 Sampson Network Part 2
4.26 Sampson Network Part 3
4.27 Sampson Network Part 4
4.28 Sampson Network Part 5
4.29 Six Degrees of Kevin Bacon
4.30 Words and Relational Networks
4.31 Words and Expanded Relational Networks
4.32 Knowledge Check
4.33 Derived Stimulus Relations and Symbolism
4.34 Derived Stimulus Relations and Generativity
4.35 Derived Stimulus Relations and the Properties of Language
4.36 Derived Stimulus Relations and Non-Human Animals
4.37 Derived Stimulus Relations and Verbal Ability
4.38 Derived Stimulus Relations and Language Development
4.39 Knowledge Check
4.40 Review
Lesson 5: Human Language and Cognition
5.1 Concept Map
5.2 Does Language Mediate Derived Stimulus Relations?
5.3 Derived Stimulus Relations = Language
5.4 Review
Lesson 6: Relational Frames
6.1 Concept Map
6.2 Relational Frames and Psychological Events
6.3 Derived Stimulus Relations as Learned Behavior
6.4 A Relational Frame
6.5 Relational Framing
6.6 Relational Frames Are Patterns of Responding
6.7 Review
Lesson 7: Relational Responding
7.1 Concept Map
7.2 Relational Responding Definition
7.3 Relational Responding Oreo Example
7.4 Relational Responding Pigeon Non-Example Part 1
7.5 Relational Responding Pigeon Non-Example Part 2
7.6 Relational Responding Pigeon Example
7.7 Sample Relational Responses
7.8 Knowledge Check
7.9 Relational Frame
7.10 Relational Response Example: Fruit Part 1
7.11 Relational Response Example: Fruit Part 2
7.12 Relational Response Example: Balls Part 1
7.13 Relational Response Example: Balls Part 2
7.14 Relational Response Example: Stars Part 1
7.15 Relational Response Example: Stars Part 2
7.16 Relational Response Example: Stars Part 3
7.17 Responding Based on Relative Size
7.18 Relational Response Example: Stars
7.19 Bidirectional Relations
7.20 Mutual Entailment
7.21 Combining Relations
7.22 Relation Between A and B
7.23 Relation Between B and C
7.24 Relation Between A and C
7.25 Combinatorial Entailment
7.26 Stimulus Functions Based on Relation of Size
7.27 Stimulus Functions Based on Relation of Size
7.28 Transformation of Stimulus Functions
7.29 Features of Relational Responding
7.30 Non-Arbitrary Relational Responding
7.31 Arbitrary Relational Responding
7.32 Arbitrary Relations Example: Coins
7.33 Features of Relational Frames
7.34 Arbitrary Relational Response Example: Triangles
7.35 Clue for Arbitrary Relational Response
7.36 Stimulus Functions for Arbitrary Relational Response
7.37 Arbitrary Relational Response
7.38 Features of Arbitrary Relational Response
7.39 Clue for Arbitrary Relational Response
7.40 Contextual Cues for Arbitrary Relational Responses
7.41 Arbitrarily Applicable Relational Responding and Derived Stimulus Relations
7.42 Types of Relational Frames
7.43 Families of Relational Frames
7.44 Knowledge Check
7.45 Review
Lesson 8: Generalized Operants
8.1 Concept Map
8.2 Definition of Operant
8.3 Operant Example #1
8.4 Operant Example #1 – Common Function
8.5 Operant Example #1 – Similar Forms
8.6 Operant Example #2
8.7 Definition of Generalized Operant
8.8 Generalized Operant Example #1 Part 1
8.9 Generalized Operant Example #1 Part 2
8.10 Generalized Operant Example #2
8.11 Generalized Operant Example #3
8.12 Knowledge Check
8.13 Relational Responding as a Generalized Operant
8.14 Dragging Triangles as an Operant
8.15 Dragging Triangles as an Operant: Context
8.16 Dragging Triangles as an Operant: Response
8.17 Dragging Triangles as an Operant: Effect
8.18 Dragging Triangles as a Generalized Operant
8.19 Sample Relational Frames
8.20 Knowledge Check
8.21 Review
Lesson 9: Multiple Exemplar Training
9.1 Concept Map
9.2 Multiple Exemplar Training and Generalized Operants
9.3 Feature #1 of Multiple Exemplar Training
9.4 Feature #2 of Multiple Exemplar Training
9.5 Feature #3 of Multiple Exemplar Training
9.6 Multiple Exemplar Training Example: Imitation
9.7 Multiple Exemplar Training Example: Imitation
9.8 Knowledge Check
9.9 Multiple Exemplar Training for Relational Responding
9.10 MET Example Part 1: Bigger/Smaller
9.11 MET Example Part 2: Bigger/Smaller
9.12 MET Example Part 3: Bigger/Smaller
9.13 MET Example Part 4: Reinforcing Mutual Entailment
9.14 MET Example Part 5: More Examples
9.15 MET Example Part 6: Context
9.16 MET Example Part 7: Arbitrary Relation
9.17 MET Example Part 8: Arbitrary Relation
9.18 MET Example Part 9: Derived and Arbitrary Relation
9.19 MET Example Part 10: Context Predicts Reinforcement
9.20 MET Example Part 11: Arbitrarily Applicable Relational Response
9.21 MET Example Part 12: Class of Responses
9.22 MET Example Part 13: Same Effect
9.23 MET Example Part 14: Given Context
9.24 MET Example Part 15: Relational Response as Generalized Operant
9.25 Naming and Equivalence Classes
9.26 Families of Relational Frames
9.27 Knowledge Check
9.28 Review
Lesson 10: Mutual Entailment
10.1 Concept Map
10.2 Definition of Mutual Entailment
10.3 Sample Bidirectional Relations
10.4 Bidirectional Relations
10.5 Arbitrarily Applicable Mutual Entailment
10.6 Mutual Entailment Example
10.7 Mutual Entailment Cues Example
10.8 Knowledge Check
10.9 Review
Lesson 11: Combinatorial Entailment
11.1 Concept Map
11.2 Definition of Combinatorial Entailment
11.3 Combinatorial Entailment Example Part 1
11.4 Combinatorial Entailment Example Part 2
11.5 Combinatorial Entailment Example Part 3
11.6 Combinatorial Entailment Example Part 4
11.7 Combinatorial Entailment Example Part 5
11.8 Combinatorial Entailment Example Part 6
11.9 Precision of Combinatorially Entailed Relations
11.10 Combining Many Stimulus Relations Part 1
11.11 Combining Many Stimulus Relations Part 2
11.12 Combining Many Stimulus Relations Part 3
11.13 Deriving Vast Networks of Relations
11.14 Knowledge Check
11.15 Review
Lesson 12: Transformation of Stimulus Functions
12.1 Concept Map
12.2 Definition of Transformation of Stimulus Functions
12.3 Sample Psychological Functions of Stimuli
12.4 Perceptual Functions of Stimuli
12.5 Multiple Psychological Functions of Stimuli
12.6 Gorilla at the Zoo Stimulus Functions
12.7 Gorilla in Your Bathroom Stimulus Functions
12.8 Bell and Dog Before Conditioning
12.9 Bell and Dog After Conditioning
12.10 Setting and History Affect Function
12.11 Stimulus Relations Affect Functions
12.12 Relation of Opposition Example with Salt and Sugar
12.13 Mutually Entailed Relations of Opposition
12.14 Additional Stimulus Functions for Salt
12.15 Transformed Stimulus Functions for Sugar
12.16 Transformation of Stimulus Functions Example
12.17 Transformation of Stimulus Functions Based on Arbitrary Relations
12.18 Reinforcing Functions for Arbitrary Stimulus
12.19 Arbitrary Relation of More/Less Established
12.20 Transformed Functions Based on More/Less
12.21 Arbitrary Relation of Opposition Established
12.22 Transformed Functions Based on Opposition
12.23 Arbitrary Relation of Equivalence Established
12.24 Transformed Functions Based on Equivalence
12.25 Transformation of Functions is Part of the Relational Response
12.26 Example: Fear of Dogs and Chiens Part 1
12.27 Example: Fear of Dogs and Chiens Part 2
12.28 Example: Fear of Dogs and Chiens Part 3
12.29 Example: Fear of Dogs and Chiens Part 4
12.30 Example: Fear of Dogs and Chiens Part 5
12.31 Example: Fear of Dogs and Chiens Part 6
12.32 Example: Fear of Dogs and Chiens Part 7
12.33 Example: Fear of Dogs and Chiens Part 8
12.34 Example: Fear of Dogs and Chiens Part 9
12.35 Example: Fear of Dogs and Chiens Part 10
12.36 Example: Fear of Dogs and Chiens Part 11
12.37 Example: Sally and CavityJuice Part 1
12.38 Example: Sally and CavityJuice Part 2
12.39 Example: Sally and CavityJuice Part 3
12.40 Example: Sally and CavityJuice Part 4
12.41 Example: Sally and CavityJuice Part 5
12.42 Example: Sally and CavityJuice Part 6
12.43 Example: Sally and CavityJuice Part 7
12.44 Knowledge Check
12.45 Review
Lesson 13: Contextual Control
13.1 Concept Map
13.2 Relational Responding Based on Physical Properties
13.3 Relational Responding Based on Features of the Context
13.4 Different Cues for Different Frames
13.5 Many Cues Are Words or Phrases
13.6 Sample Contextual Cues
13.7 Cues Often Used for Equivalence Relations
13.8 Contextual Cues Acquire Function via Multiple Exemplar Training
13.9 Contextual Cues Example: Bigger/Smaller
13.10 Contextual Cues Example: Arbitrary Bigger/Smaller
13.11 Derived Contextual Cues Part 1
13.12 Derived Contextual Cues Part 2
13.13 Derived Contextual Cues Part 3
13.14 Crel Definition
13.15 Knowledge Check
13.16 Cfunc Definition
13.17 Transforming All Stimulus Functions Example
13.18 Multiple Stimulus Functions Example
13.19 Cfunc Example: Picture a Banana
13.20 Cfunc Example: Taste of a Banana
13.21 Cfunc Example: Bananarama
13.22 Cfunc Example: Salt and Sugar Opposites
13.23 Common Functions for Equivalence Relations
13.24 Using Cfuncs to Change Functions for Equivalence Relations
13.25 Knowledge Check
13.26 Review
Lesson 14: Arbitrarily Applicable
14.1 Concept Map
14.2 Arbitrarily Applicable Definition and Example
14.3 Arbitrary Applicability and Contextual Control
14.4 Relational Frames Are Not Always Arbitrarily Applied
14.5 Example of Non-Arbitrary Application Part 1
14.6 Example of Non-Arbitrary Application Part 2
14.7 Example of Non-Arbitrary Application Part 3
14.8 Example of Non-Arbitrary Application Part 4
14.9 Example of Non-Arbitrary Application Part 5
14.10 Example of Non-Arbitrary Application Part 6
14.11 Vast Relational Networks
14.12 Vast Relational Networks and Physical Properties
14.13 Vast Relational Networks and Deriving Novel Solutions
14.14 Review
Lesson 15: Implications and Applications
15.1 Concept Map
15.2 Derived Stimulus Relations and Language Development
15.3 Applications of RFT
15.4 Language Units and Relational Networks
15.5 Sentences as Relational Networks Part 1
15.6 Sentences as Relational Networks Part 2
15.7 Sentences as Relational Networks Part 3
15.8 Sentences as Relational Networks Part 4
15.9 Stories as Relational Networks
15.10 Other Language Units as Relational Networks
15.11 Relating Sets of Relations
15.12 Analogies as Relating Relations Example Part 1
15.13 Analogies as Relating Relations Example Part 2
15.14 Analogies as Relating Relations Example Part 3
15.15 Analogies as Relating Relations Example Part 4
15.16 Metaphors as Relating Relations Example Part 1
15.17 Metaphors as Relating Relations Example Part 2
15.18 Metaphors as Relating Relations Example Part 3
15.19 Metaphors as Relating Relations Example Part 4
15.20 Metaphors as Relating Relations Example Part 5
15.21 Problem Solving
15.22 Problem Solving Example Part 1
15.23 Problem Solving Example Part 2
15.24 Problem Solving Example Part 3
15.25 Problem Solving Example Part 4
15.26 Problem Solving Example Part 5
15.27 Problem Solving Example Part 6
15.28 Problem Solving Example Part 7
15.29 Problem Solving Example Part 8
15.30 Problem Solving Example Part 9
15.31 Problem Solving Example Part 10
15.32 Problem Solving Example Part 11
15.33 Problem Solving Example Part 12
15.34 Rules and Contingency-Shaped Behavior
15.35 Contingency-Shaped Behavior Example
15.36 Rule-Governed Behavior Example
15.37 Challenge of Understanding Rule-Governed Behavior
15.38 RFT and Rule-Governed Behavior Example Part 1
15.39 RFT and Rule-Governed Behavior Example Part 2
15.40 RFT and Rule-Governed Behavior Example Part 3
15.41 RFT and Rule-Governed Behavior Example Part 4
15.42 RFT and Rule-Governed Behavior Example Part 5
15.43 The Power of Rules
15.44 RFT and the Dark Side of Language
15.45 Harmful Effects of Language
15.46 Dominance of Verbal Relations
15.47 Bidirectional Transformation of Stimulus Functions
15.48 Bidirectional Transformation of Aversive Functions
15.49 Escape and Avoidance of Aversive Words and Thoughts
15.50 Example: Avoiding Anxiety Part 1
15.51 Example: Avoiding Anxiety Part 2
15.52 Example: Avoiding Anxiety Part 3
15.53 Example: Avoiding Anxiety Part 4
15.54 Example: Avoiding Anxiety Part 5
15.55 Rampant Rule Following
15.56 Rule Following and Behavioral Rigidity
15.57 Acceptance and Commitment Therapy (ACT)
15.58 ACT: Loosen or Weaken Verbal Relations
15.59 ACT: Weaken Transformation of Stimulus Functions
15.60 ACT: Limit Rule Following
15.61 Other RFT Applications and Implications
15.62 Review
Final Quiz for An Introduction to Relational Frame Theory
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14.9 Example of Non-Arbitrary Application Part 5

An Introduction to Relational Frame Theory Lesson 14: Arbitrarily Applicable 14.9 Example of Non-Arbitrary Application Part 5
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…providing a context in which the child is likely to respond to these combinatorially entailed relations of equivalence between being bigger and terms like more popular.

A bulldog and a dog with the same bigger size.

arbitrarily applicable ≠ always arbitrarily applied

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3 Comments
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Linda Dittmar Linda Dittmar December 16, 2024 at 4:47 pm

I’m hoping this will be answered in future slide: The child has entailed a FALSE equivalency between “bigger” and “more popular” – similar to the FALSE response that the white duck is “smaller” than the yellow duck. I would consider these formations of relational frames to be a result of instructor error . . .????

Anh-Tram Newcomb Anh-Tram Newcomb February 8, 2025 at 1:09 pm

I think the purpose of these examples is to demonstrate how relationships are derived arbitrarily based on history of reinforcement and contextual control (white/yellow ducks). This is sort of discussed when the lesson mentioned that relational frames are not always arbitrarily applied as we do often use physical attributes to derive relations. This is my understanding of this page. This specific example of the dogs is more focused on Cfuncs, at some point the child may have derived relations that bigger is more popular (i.e., think of it like deriving a relationship- Basketball players are bigger than others, NBA Basketball players are famous and popular, therefore the child may have applied that relationship to this situation). I think you comment is on point and it does show that this specific relationship that is being derived is based on each individual’s learning history as previously mentioned in Generalized Operants (I think)
since your specific learning history there is no correlation between bigger and popular.

Linda Dittmar Linda Dittmar February 8, 2025 at 5:31 pm

Thank you so much for your response!

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