MPT Response Times

In my dissertation, I extended multinomial processing tree (MPT) models to include response times. MPT models are very useful to disentangle multiple underlying processes based on observed response frequencies. However, these models are limited to categorical data, which prevents a direct application to continuous variables such as response times. mpt_latent_RTs  

MPT-RT Models

MPT models directly predict that observed RT distributions are mixtures of latent RT distributions. To estimate these latent distributions (e.g., the RT distribution of recognition in memory tasks), we proposed to categorize responses from fast to slow into several bins (similar to a histogram). This results in a new, extended MPT model that allows to estimate the relative speed of the hypothesized cognitive processes. For details, see:

  • Heck, D. W., & Erdfelder, E. (2016). Extending multinomial processing tree models to measure the relative speed of cognitive processes. Psychonomic Bulletin & Review, 23, 1440–1465. doi:10.3758/s13423-016-1025-6

Recognition-Based Decisions

We applied this MPT-RT method with respect to the recognition heuristic in the field of judgment and decision making. In the domain of memory-based inferences, people have to decide which of two options is larger on some criterion (e.g., which city is larger). Often, people choose the city they know. To disentangle whether other information (e.g., whether the known city has an airport or metro) also enters the decision, we extended the r-model (an MPT model) to response times. Thereby, we could show that people were actually faster when considering more, recognition-congruent knowledge. For details, see

Heck, D. W., & Erdfelder, E. (2017). Linking process and measurement models of recognition-based decisions. Psychological Review, 124, 442–471. doi:10.1037/rev0000063

Generalized Processing Tree Models

More recently, we developed an alternative approach that assumes specific parametric distributions (e.g., Gaussian) for the component distributions of MPT-mixtures. These “Generalized Processing Tree (GPT) Models” are explained in:

  • Heck, D. W., Erdfelder, E., & Kieslich, P. J. (2018). Generalized processing tree models: Jointly modeling discrete and continuous variables. Psychometrika, 83, 893–918. doi:10.1007/s11336-018-9622-0