Prior knowledge of relational structure allows people to quickly make sense of and respond to new experiences. When awareness of such structure is not necessary to support learning, however, it is unclear when and why individuals “spontaneously discover” an underlying relational schema. The present study examines the determinants of such discovery in discrimination-based transitive inference (TI), whereby people learn about a hierarchy of interrelated premises and are tested on their ability to draw inferences that bridge studied relations. Experiencing “chained” sequences of overlapping premises during training was predicted to facilitate the discovery of relational structure. Among individuals without prior knowledge of the hierarchy, chaining improved relational learning and was most likely to result in explicit awareness of the underlying relations between items. Observation of chained training sequences was also more effective than the self-generation of training sequences. These findings add to growing evidence that the temporal dynamics of training, including successive presentation of overlapping associations, are key to understanding spontaneous relational discovery during learning.
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Random pairing was necessary to ensure that stage 1 choices were not predictive of reinforcement in stage 2 (e.g., if the selected item was always paired with the immediately superordinate item, participants could learn a simple rule to choose the new option in stage 2 without learning the premises themselves). A consequence of this design is that some trials in the Passive-Adjacent condition did not involve chaining, since selecting an adjacent item in stage 1 sometimes led to the repetition of the same premise from the previous trial or a non-overlapping premise in stage 2. Thus, the Passive-Adjacent condition maximizes the frequency of chaining (overlapping premises in successive trials) under the constraint of random pairing in stage 2.
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The author thanks Shaina Glass and Mitra Mostafavi for their assistance with response coding.
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A preliminary report of these findings was presented at the 2020 Annual Meeting of the Cognitive Science Society (see http://psyarxiv.com/cp8bm/).
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Post-task awareness questionnaire
Questions were adapted from a questionnaire used in past studies of awareness in transitive inference (Kumaran & Ludwig, 2013; Moses, Villate, Binns, Davidson, & Ryan, 2008). Responses consistent with awareness of the hierarchy or a logical ordering of the items were coded as aware (1); all other responses were coded as unaware (0). In the questions, “Phase 1” refers to the initial training phase whereas “Phase 2” refers to the final test phase which included both recall trials (premises experienced during study) and inference trials (novel pairings of non-adjacent items).
Q1: “Do you think there was a correct answer for all of the pairs that you experienced during Phase 2?”
Not sure (0)
Q2: “In Phase 2 when you were presented with different pairs of cards, what reason did you have for choosing one as opposed to the other?”
There is a logically correct choice. (1)
One just seemed right but I can’t explain why. (0)
I guessed. There may be a correct answer but I don’t know what it is. (0)
I made a random choice because there is no correct choice. (0)
Q3: “What strategy (if any) did you use in Phase 1 to learn which card was correct in each pairing?”
I tried to figure out the correct ordering of all the cards. (1)
I memorized the right choice for each pair. (0)
I just chose randomly and eventually got it. (0)
No strategy. (0)
The following question also appeared on the awareness questionnaire but was not included in the analysis because it did not assess understanding of the relationship between items:
Q4: “In Phase 2 you were asked to make choices between pairs of cards. Were all of the pairs in Phase 2 the same as the pairs you had already experienced during Phase 1?”
A questionnaire was used to identify participants who were not engaged in the task, who were not fluent in English, and other invalid respondents such as bots which may affect data quality on Amazon Mechanical Turk (Chmielewski & Kucker, 2019). Following the training phase, participants were instructed to respond to a set of naturalistic decision making prompts (see examples below). For each situation, participants generated a possible course of action. Two research assistants coded responses based on whether they were meaningfully related to the prompt. Participants were excluded when both raters agreed that the generated course of action was not responsive to the prompt (proportion of agreement was 99%). Participants were not excluded based on the validity/feasibility of responses or writing quality (e.g., spelling or grammar errors).
Imagine you find yourself in the following situation. What is a potential course of action that you could take in this situation?
On the morning of an important job interview, you wake up with fever and chills.
You have procrastinated on starting a term paper and the due date is tomorrow.
You stop by a store to grab an item you need. You are in a hurry but the cashier is nowhere to be seen.
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Markant, D.B. Chained study and the discovery of relational structure. Mem Cogn (2021). https://doi.org/10.3758/s13421-021-01201-1
- Transitive inference
- Relational learning
- Relational discovery