Speeded decision tasks are usually modeled within the evidence accumulation framework, enabling inferences on latent cognitive parameters, and capturing dependencies between the observed response times and accuracy. An example is the speed-accuracy trade-off, where people sacrifice speed for accuracy (or vice versa). Different views on this phenomenon lead to the idea that participants may not be able to control this trade-off on a continuum, but rather switch between distinct states (Dutilh et al., Cognitive Science 35(2):211–250, 2010). Hidden Markov models are used to account for switching between distinct states. However, combining evidence accumulation models with a hidden Markov structure is a challenging problem, as evidence accumulation models typically come with identification and computational issues that make them challenging on their own. Thus, an integration of hidden Markov models with evidence accumulation models has still remained elusive, even though such models would allow researchers to capture potential dependencies between response times and accuracy within the states, while concomitantly capturing different behavioral modes during cognitive processing. This article presents a model that uses an evidence accumulation model as part of a hidden Markov structure. This model is considered as a proof of principle that evidence accumulation models can be combined with Markov switching models. As such, the article considers a very simple case of a simplified Linear Ballistic Accumulation. An extensive simulation study was conducted to validate the model’s implementation according to principles of robust Bayesian workflow. Example reanalysis of data from Dutilh et al. (Cognitive Science 35(2):211–250, 2010) demonstrates the application of the new model. The article concludes with limitations and future extensions or alternatives to the model and its application.

Speeded decision tasks are usually modeled within the evidence accumulation framework, enabling inferences on latent cognitive parameters, and capturing dependencies between the observed response times and accuracy. An example is the speed-accuracy trade-off, where people sacrifice speed for accuracy (or vice versa). Different views on this phenomenon lead to the idea that participants may not be able to control this trade-off on a continuum, but rather switch between distinct states (Dutilh, et al., 2010).Hidden Markov models are used to account for switching between distinct states. However, combining evidence accumulation models with a hidden Markov structure is a challenging problem, as evidence accumulation models typically come with identification and computational issues that make them challenging on their own. Thus, hidden Markov models have not used the evidence accumulation framework, giving up on the inference on the latent cognitive parameters, or capturing potential dependencies between response times and accuracy within the states.This article presents a model that uses an evidence accumulation model as part of a hidden Markov structure. This model is considered as a proof of principle that evidence accumulation models can be combined with Markov switching models. As such, the article considers a very simple case of a simplified Linear Ballistic Accumulation. An extensive simulation study was conducted to validate the model's implementation according to principles of robust Bayesian workflow. Example reanalysis of data from Dutilh, et al. (2010) demonstrates the application of the new model. The article concludes with limitations and future extensions or alternatives to the model and its application.

A popular approach to recommender systems is to factor the ratingmatrix into two low rank matrices. Although this has proven to be agood way to predict ratings, the component scores in these matrix fac-torisation methods don’t serve as estimates of the underlying causesof the ratings. We propose to model the rating matrix using Multidi-mensional Item Response Theory (MIRT). We show that MIRT doesallow for a causal interpretation of the latent scores, and additionally,allows to control the exploration-exploitation trade off using comput-erized adaptive testing (CAT). We adapt MIRT to large proportionsof missing data using elastic net regularisation. Our model is vali-dated in parameter recovery simulations, and is applied to a binarizedversion of the movielens 1M dataset. Results show that parameterscan be recovered accurately for models with up to three dimensions,for various proportions of missing data. Performance on the movielensdataset with a three dimensional MIRT model–performing is on parwith matrix factorisation methods of up to 20 dimensions. In a secondsimulation study, we show how MIRT can be combined with CAT totackle the exploration-exploitation tradeoff, resulting in more accurateitem rankings. We conclude that MIRT can provide both more inter-pretability and more control over the exploration-exploitation tradeoff.We discuss directions for future work on MIRT based recommendation.

No abstract