Statistical Models

A Bayesian hierarchical measurement model for assessing repetition learning effects in empirical data on the level of individual participants. Crucially, this model is based on recent evidence that repetition learning effects depends on participants' ability to recognize what is being repeated to them. As long as repeating stimuli are not identified as such, no learning effects are observed. To account for this, the model is set up as a mixture model, which allows to classify if a participant produced a learning effect or not. Furthermore, it contains a free parameter for assessing the onset point of a learning effect throughout a time series of repeated practice trials. This parameter allows to delay the onset of any learning effects, as long as repetitions are not noticed.

A vast body of research suggests that the primary motor cortex is involved in motor imagery. This raises the issue of inhibition: how is it possible for motor imagery not to lead to motor execution? The motor execution threshold may be "upregulated" during motor imagery to prevent execution. Alternatively, it has been proposed that, in parallel to excitatory mechanisms, inhibitory mechanisms may be actively suppressing motor output during motor imagery. These theories are verbal in nature, with well-known limitations. We introduced a toy-model of the inhibitory mechanisms thought to be at play during motor imagery to disentangle predictions from competing hypotheses. The toy model provides a simplified overarching description of how the motor system is involved over time during motor imagery and has been shown to predict well mental chronometry data (reaction times and imagined movement times).