Proportions of errors of different types across training sessions.

Proportions of errors of different types across training sessions.</p

Supplementary algorithms.

(PDF)</p

Fitted values of the strength <i>α</i> (left) and forgetting rate λ (middle) parameters, as well as their joint effect on prediction (right), using the constrained prior that places the model in a forgetful regime, described in S1 Table.

A context of n previous events corresponds to level n in the HCRP. Lower values of α and λ imply a greater contribution from the context to the prediction of behavior. The context gain for context length n is the decrease in the KL divergence between the predictive distribution of the complete model and a partial model upon considering n previous elements, compared to considering only n-1 previous elements. Note that the scale of the context gain is reversed and higher values signify more gain. (TIFF)</p

Repeated measures ANOVAs in session 9 and 10.

In the left set of columns, the trial type is defined as Pold(trigram) and in the right set of columns it is defined as Pnew(trigram).</p

Repeated measures ANOVAs in sessions 1–8.

In the left set of columns, the trial type is defined as the state and in the right set of columns it is defined as P(trigram).</p

Modeling strategy.

We adopted a model-based approach, fitting the hyperparameters θ of an internal sequence model (upper box), together with low level effects (the spatial distance between subsequent response locations, response repetition, error and post-error trials; lower box) to participants’ response times. The contribution of the sequence model is the scaled log of the predictive probability of each key press k (one of the four keys, marked as transparent square), given the context u (previous events, marked as a string of colored squares). The sequence model makes predictions by flexibly combining information from deepening windows onto the past, considering fewer or more previous stimuli.</p

Negative log likelihood loss of HCRP models fitted to 10.000 ASRT data points.

(a) Negative log likelihood as a function of the maximum number of previous events considered. (b) Negative log likelihood as a function of the prior importance of two previous events, i.e. trigrams (b). In (b), lower values of α2 imply higher prior importance. The vertical dashed line in (a) marks the n that was used for fitting the human data in the Manuscript. (TIFF)</p

Predicting the latency of errors.

(a) Pattern errors. (b) Recency errors. In the case of HCRPf, the hyperparameter priors were adjusted to express more forgetfulness. The error bars represent the 95%CI.</p

Mixed effects model with random intercepts for participants and several low-level predictors, sorted by their absolute fitted slope B (in ms).

Due to the large data set, all factors are significant. However, we made an arbitrary cut-off at the horizontal line for the low-level effects included in the response model because of the small effect sizes. (PDF)</p

Correlation between the fitted HCRP parameters and working memory.

(a)(b) Pearson correlation matrices of the working memory test scores and the strength parameters α and decay parameters λ of the HCRP model, respectively. Correlations that met the significance criterion of p p < .05. Bands represent the 95% CI.</p