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Learning the Abstract General Task Structure in a Rapidly Changing Task Content Cover

Learning the Abstract General Task Structure in a Rapidly Changing Task Content

Open Access
|Jul 2021

Figures & Tables

joc-4-1-176-g1.png
Figure 1

Trial sequence in the NEXT paradigm. Each mini-block consisted of two novel stimulus-response mapping rules (e.g., X-RIGHT and Y-LEFT). On each mini-block, participants are first instructed towards performance in the GO task, in which the stimuli appear in green color and only performed twice. After the instructions and prior to the GO task, a number of targets in red color require a fixed NEXT response (right/left, counterbalanced between participants and constant throughout the experiment).

joc-4-1-176-g2.png
Figure 2

Left panel: RT as function of Miniblock in Experiment 1. Grey dots illustrate the individual variance around the mean. Right panel: RT as a function of Block, error bars represent 95% Bayesian credible interval.

joc-4-1-176-g3.png
Figure 3

Left panel: RT as function of miniblock in the conceptual replication condition in Experiment 2. Individual data are shown in dots, and the mean can be seen in the line. Right panel: RT as a function of Block, error bars represent 95% Bayesian credible interval.

joc-4-1-176-g4.png
Figure 4

Left panel: RT as function of miniblock in Experiment 3, different conditions are marked with different colors. Individual data are shown in dots, and the mean per condition can be seen in the lines. Right panel: RT as a function of Block and Condition, error bars represent 95% Bayesian credible interval.

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Figure 5

Left panel: RT as function of miniblock in Experiment 4, different conditions are marked with different colors. Individual data are shown in dots, and the mean per condition can be seen in the lines. Right panel: RT as a function of Block and Condition, error bars represent 95% Bayesian credible interval.

Table 1

BIC values for fixed (no individual differences)/mixed (individual differences) models of a linear/non-linear function.

# MODELMODEL DEFINITIONARE EXPERIMENTAL CONDITIONS ALLOWED TO DIFFER IN TERMS OF PARAMETER VALUESBIC
1Fixed linearno172,514.4
2yes172,198.8
3Mixed linearno168,962.0
4yes168,786.0
5Fixed non-linearno172,374.1
6SP+A+LR a171,989.7
7SP172,011.4
8A172,151.0
9LR172,007.9
10SP+A171,981.2
11SP+LR172,016.7
12A+LR171,983.7
13Mixed non-linear bno168,265.3
14SP+A+LR168,275.0
15SP168,242.3
16A168,282.7
17LRDid not converge c
18SP+A168,264.4
19SP+LR168,263.8
20A+LRDid not converge

[i] a SP = Starting point; A = asymptote; LR = learning rate.

b For simplicity, in this set of models, the random effect was estimated for all three parameters. This will be further tested for the best fitting model.

c Models 17 and 20 did not converge after exceeding the number of maximal iterations, (which was set to 10,000), suggesting that these models are unsuitable for the data.

Table 2

BIC values for models comparing different random effects in the mixed non-linear model.

# MODELMODEL DEFINITIONRANDOM EFFECTSBIC
15Mixed non-linear, SP differs between the experimental conditionsSP+A+LR168,242.3
21SP168,823.7
22A169,392.7
23LRDid not converge
24SP+A168,375.8
25SP+LRDid not converge
26A+LR168,545.7
joc-4-1-176-g6.png
Figure 6

Trial sequence in the “without NEXT_delay” condition.

joc-4-1-176-g7.png
Figure 7

Left panel: RT as function of mini-block in Experiments 3 (with NEXT condition) and 5 (without NEXT conditions), different conditions are marked with different colors. Individual data are shown in dots, and the mean per condition can be seen in the lines. Right panel: RT as a function of Block and Condition, error bars represent 95% Bayesian credible interval.

DOI: https://doi.org/10.5334/joc.176 | Journal eISSN: 2514-4820
Language: English
Submitted on: Dec 23, 2020
Accepted on: Jun 17, 2021
Published on: Jul 7, 2021
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2021 Maayan Pereg, Danielle Harpaz, Katrina Sabah, Mattan S. Ben-Shachar, Inbar Amir, Gesine Dreisbach, Nachshon Meiran, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.