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Research Article

Stimulus valence moderates self-learning

ORCID Icon, , , &
Received 05 Dec 2022, Accepted 11 Mar 2024, Published online: 05 Apr 2024

Figures & data

Figure 1. Examples of the experimental trials.

Figure 1. Examples of the experimental trials.

Table 1. Mean response times (ms) and learning performance (%) as a function of Owner and Valence.

Figure 2. Based on ceiling performance during the PST, Learning curves over the first 20 trials as a function of Owner, Valence, and Stimulus Pair. The raw data points represent the observed learning outcomes, and the modelled curves reflect the results of the generalised estimating equations (GEE) analysis. The GEE analysis examines differences in learning trajectories across trials and experimental conditions.

Figure 2. Based on ceiling performance during the PST, Learning curves over the first 20 trials as a function of Owner, Valence, and Stimulus Pair. The raw data points represent the observed learning outcomes, and the modelled curves reflect the results of the generalised estimating equations (GEE) analysis. The GEE analysis examines differences in learning trajectories across trials and experimental conditions.

Figure 3. Mean posterior parameter distributions as a function of Owner and Valence for learning rates (η), drift rate scaling (vscaling), and boundary separation (a).

Figure 3. Mean posterior parameter distributions as a function of Owner and Valence for learning rates (η), drift rate scaling (vscaling), and boundary separation (a).
Supplemental material

Supplemental Material.docx

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Data availability statement

The data and stimulus materials are available at https://osf.io/5bswv/.