A Multinomial Processing Tree (MPT) is a directed tree with a probability associated with each arc and partitioned terminal vertices. We consider an additional parameter for each arc, a measure such as time. Each vertex represents a process. An arc descending from a vertex represents selection of a process outcome. A source vertex represents processing beginning with stimulus presentation and a terminal vertex represents a response. An experimental factor selectively influences a vertex if changing the factor level changes parameter values on arcs descending from that vertex and no others. Earlier work shows that if each of two factors selectively influences a different vertex in an arbitrary MPT it is equivalent to one of two simple MPTs. ...
Abstract We introduce MPTinR, a software package devel-oped for the analysis of multinomial processi...
Multinomial processing tree models assume that discrete cognitive states determine observed response...
The original publication can be found at www.springerlink.comIn this paper we introduce a structure ...
Processing trees are widely used to model the accuracy in tasks. For the mental process of a task, p...
Multinomial processing tree model played an important role in human information processing. A method...
Evidence in many experiments indicates that the processes involved in producing re-sponses are arran...
Schweickert and Chen (2008) discussed systematically how to construct a processing tree using experi...
Multinomial processing tree (MPT) models have been widely used by researchers in cognitive psycholog...
Multinomial processing tree (MPT) models are tools for disentangling the contributions of latent cog...
Multinomial processing tree (MPT) models account for observed categorical responses by assuming a fi...
In psychology, multinomial processing tree (MPT) models explain how qualitatively different processe...
Hierarchical multinomial models 2 Multinomial processing tree models are widely used in many areas o...
This paper shows how to develop new multinomial processing tree (MPT) models for discrete choice, an...
<p>A comparison of the performance of different tree inference methods following trimming of realign...
We introduce MPTinR, a software package developed for the analysis of multinomial processing tree (M...
Abstract We introduce MPTinR, a software package devel-oped for the analysis of multinomial processi...
Multinomial processing tree models assume that discrete cognitive states determine observed response...
The original publication can be found at www.springerlink.comIn this paper we introduce a structure ...
Processing trees are widely used to model the accuracy in tasks. For the mental process of a task, p...
Multinomial processing tree model played an important role in human information processing. A method...
Evidence in many experiments indicates that the processes involved in producing re-sponses are arran...
Schweickert and Chen (2008) discussed systematically how to construct a processing tree using experi...
Multinomial processing tree (MPT) models have been widely used by researchers in cognitive psycholog...
Multinomial processing tree (MPT) models are tools for disentangling the contributions of latent cog...
Multinomial processing tree (MPT) models account for observed categorical responses by assuming a fi...
In psychology, multinomial processing tree (MPT) models explain how qualitatively different processe...
Hierarchical multinomial models 2 Multinomial processing tree models are widely used in many areas o...
This paper shows how to develop new multinomial processing tree (MPT) models for discrete choice, an...
<p>A comparison of the performance of different tree inference methods following trimming of realign...
We introduce MPTinR, a software package developed for the analysis of multinomial processing tree (M...
Abstract We introduce MPTinR, a software package devel-oped for the analysis of multinomial processi...
Multinomial processing tree models assume that discrete cognitive states determine observed response...
The original publication can be found at www.springerlink.comIn this paper we introduce a structure ...