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Long-term Contingency Learning Depends on Contingency Awareness Cover

Long-term Contingency Learning Depends on Contingency Awareness

Open Access
|Feb 2025

Figures & Tables

joc-8-1-433-g1.png
Figure 1

The alternating blocks paradigm: Two non-overlapping sets of color-word contingencies are presented in alternating blocks of the experiment. Responding to the first occurrences of a word within each block constitutes a pure measure of long-term contingency learning. Responding to later occurrences of a word within a block reflects a mixture of long-term learning and short-term retrieval.

Table 1

Color-word contingency manipulations in the color classification task. Numbers indicate frequencies of occurrence for color-word combinations for different parts of each block (initial part of a block [1st occurrences], central part [2nd–5th occurrences], and final part of a block [6th occurrence]) across the 20 blocks of the task; numbers in parentheses indicate the frequency of occurrences within a single example block.

COLORWORD SET 1WORD SET 2
OFFENWEICHKLARRUNDWARMLEICHTGANZKLEIN
1st occurrences
    red10 (1)10 (0)10 (1)10 (0)////
    yellow10 (0)10 (1)10 (0)10 (1)////
    blue////10 (1)10 (0)10 (1)10 (0)
    green////10 (0)10 (1)10 (0)10 (1)
2nd–5th occurrences
    red60 (3)60 (3)20 (1)20 (1)////
    yellow20 (1)20 (1)60 (3)60 (3)////
    blue////60 (3)60 (3)20 (1)20 (1)
    green////20 (1)20 (1)60 (3)60 (3)
6th occurrences
    red10 (0)10 (1)10 (0)10 (1)////
    yellow10 (1)10 (0)10 (1)10 (0)////
    blue////10 (0)10 (1)10 (0)10 (1)
    green////10 (1)10 (0)10 (1)10 (0)
Overall
    red80 (hc)80 (hc)40 (lc)40 (lc)////
    yellow40 (lc)40 (lc)80 (hc)80 (hc)////
    blue////80 (hc)80 (hc)40 (lc)40 (lc)
    green////40 (lc)40 (lc)80 (hc)80 (hc)

[i] Note. hc, high contingency color-word combinations; lc, low contingency color-word combinations. The specific assignment of words to colors represents only one instance of the counterbalanced design.

Table 2

Results of a stepwise multi-level regression analysis predicting RT based on contingencies (CL, step 1), contingency awareness (CA) and its interaction with CL (step 2).

PREDICTORMODEL 1MODEL 2
Intercept403***
[393.5, 411.5]
403***
[393.6, 411.6]
CL (hc vs. lc)–5**
[–7.8, –1.5]
–5**
[–7.7, –1.4]
CA (correct vs. incorrect)–4*
[–7.3, –0.2]
CL × CA–9**
[–14.9, –2.1]
BIC9242592407
∆ BIC–18

[i] Note. *p < 0.05, **p < 0.01, ***p < 0.001. CL, Contingency learning; hc/lc: high/low contingency trials. CA, contingency awareness; correct/incorrect: identification of the typical word/color combination. BIC, Bayesian information criterion. We implemented a person specific intercept to control for individual differences in RTs. All other variables were implemented on a trial level. Values in brackets indicate the 95% confidence interval (lower and upper limit) for each regression weight. Regression weights (ß) reflect the difference in milliseconds between the conditions that define a contrast.

Table 3

Results of a stepwise multi-level regression analysis predicting RT based on contingencies (CL, step 1), episodic retrieval (ER, step 2), contingency awareness (CA) and its interaction with CL (step 3).

PREDICTORMODEL 1MODEL 2MODEL 3
Intercept393***
[385.3, 401.6]
393***
[385.3, 401.6]
393***
[385.3, 401.6]
CL (hc vs. lc)–8***
[–9.2, –6.3]
–2
[–3.2, +0.1]
–2
[–3.2, +0.1]
ER (matching vs mismatching)–13***
[–14.3, –11.3]
–13***
[–14.3, –11.3]
CA (correct vs. incorrect)–0
[–1.8, +1.2]
CL × CA–5***
[–8.4, –2.5]
BIC451971451687451670
∆ BIC–284–17

[i] Note. *p < 0.05, **p < 0.01, ***p < 0.001. CL, Contingency learning; hc/lc: high/low contingency trials. ER, episodic retrieval; matching/mismatching response during the last occurrence of the word; CA, contingency awareness; correct/incorrect: identification of the typical word/color combination. BIC, Bayesian information criterion. We implemented a person specific intercept to control for individual differences in RTs. All other variables were implemented on a trial level. Values in brackets indicate the 95% confidence interval (lower and upper limit) for each regression weight. Regression weights (ß) reflect the difference in milliseconds between the conditions that define a contrast.

DOI: https://doi.org/10.5334/joc.433 | Journal eISSN: 2514-4820
Language: English
Submitted on: Nov 9, 2024
Accepted on: Jan 31, 2025
Published on: Feb 11, 2025
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2025 Klaus Rothermund, Lennart Kapinos, Jan De Houwer, James R. Schmidt, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.