EDF5684 Chap.10 Derived Stimulus Relations
Derived Stimulus Relations
Derived stimulus relations extend generative learning beyond sameness. This chapter introduces arbitrary applicable relational responding, mutual entailment, combinatorial entailment and transformation of function.
Directional maps protect against common errors in which relational words are reversed incorrectly, while contextual cues explain why the same stimuli can participate in comparison, opposition, hierarchy, spatial or temporal relations.
What this chapter covers
- 01
Equivalence and non-equivalence relations
- 02
Arbitrary applicable relational responding
- 03
Contextual control of the relevant relation
- 04
Mutual entailment and reciprocal relational terms
- 05
Combinatorial entailment across linked relations
- 06
Transformation of function through relational networks
Worked example · free
Combine an ordered comparison network
- +1Reverse L larger than M to derive M smaller than L.
- +1Reverse M larger than N to derive N smaller than M.
- +1Combine the trained links to derive L larger than N.
- +1State the reciprocal combined relation, N smaller than L.
- +1Confirm that these test directions were not directly trained.
- +1Use neutral test trials without feedback that teaches the proposed derived relations.
Key terms
- Relational Responding
- Behaviour that relates stimuli according to contextual cues and learning history.
- Mutual Entailment
- A derived reciprocal relation that follows from another established relation.
- Combinatorial Entailment
- Derived relations formed by combining two or more established relational links.
- Transformation of Function
- A change in stimulus function that follows from its place in a relational network.
- Contextual Cue
- A cue that specifies which relation or stimulus function is relevant in the current context.
Derived Stimulus Relations FAQ
How do non-equivalence relations differ from equivalence?
Equivalence involves sameness, so reversing the relation preserves the term. Comparison and spatial relations require reciprocal terms: if A is larger than B, B is smaller than A. Their derived logic depends on direction and contextual cues.
What is mutual entailment?
It is a derived relation in the reverse direction, adjusted to the relation involved. From A above B, the mutually entailed relation is B below A. Simply repeating the same directional word would contradict the original statement.
What does transformation of function add?
It explains how the behavioural function of one stimulus can change because of its relation to another, without direct conditioning of every stimulus. The predicted direction depends on the network, and the actual behavioural effect still needs to be measured.
How should I check a relational-network answer?
Begin by listing only the trained relations and the contextual cue that determines the relevant dimension. Draw directional arrows, write the correct reciprocal term for each reversal, and combine only paths that share a compatible middle node. Mark every proposed test as trained, instructed or untrained. For transformation questions, separate the predicted change in function from the observed response.
A correct conclusion should identify the specific derived relation, preserve its direction and acknowledge when baseline accuracy, test independence or behavioural evidence is not supplied.
Assessment move
Translate every relational sentence into an arrow and write the reciprocal term beside it. Reverse arrows for mutual entailment, then combine compatible paths for combinatorial entailment. Keep physical similarity separate from learned arbitrary relations. When reviewing transformation of function, state the network prediction and the observed behavioural effect on different lines so prediction is not mistaken for evidence.
Use novel symbols to test the relation rather than relying on familiar knowledge. Create separate practice decks for coordination, comparison, opposition, temporal order and containment. On each card, write the contextual cue, the trained relation and the correct reciprocal operator.
Reverse larger than to smaller than rather than copying the original wording, and do the same for before and after or contains and is contained by. Then combine only compatible paths and write the derived relation between the outer terms. This prevents a generic symmetry rule from replacing relational precision. For transformation-of-function questions, use a three-line answer. First name the function trained to one stimulus.
Second specify the relational network and contextual cue. Third predict the changed response to each related stimulus. On a fourth line, record the observed outcome or mark it as not supplied. The separation keeps a theoretical prediction from being reported as a result. Use arbitrary shapes or syllables when generating examples so familiar world knowledge cannot solve them independently of the programmed history.
Add one control question that asks whether the relation was directly trained, verbally instructed or genuinely untrained in the described context. Finish by changing the contextual cue while holding the stimuli constant and recomputing the predictions.
This exercise shows why stimulus properties alone do not determine the applicable relation and why derived performance remains contextual rather than permanently attached to the objects.
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