Tree inference with factors selectively influencing processes in a processing tree

DOI10.1016/J.JMP.2008.01.004zbMath1162.91534OpenAlexW1988051422MaRDI QIDQ1023422

Shengbao Chen, Richard Schweickert

Publication date: 11 June 2009

Published in: Journal of Mathematical Psychology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmp.2008.01.004

Mathematics Subject Classification ID

Graph theory (including graph drawing) in computer science (68R10) Psychophysics and psychophysiology; perception (91E30)

Related Items (10)

A multinomial processing tree inferred from age-related memory-error probabilities: possibility of inferring more if response times were available ⋮ Two topics in tree inference: locating a phonological network effect in immediate recall and arborescence partitive set form ⋮ A context-free language for binary multinomial processing tree models ⋮ Multinomial processing trees with response times: changing speed and accuracy by selectively influencing a vertex ⋮ Selecting amongst multinomial models: an apologia for normalized maximum likelihood ⋮ Multiplicatively interacting factors selectively influencing parameters in multiple response class processing and rate trees ⋮ Tree inference: selective influence in multinomial processing trees with supplementary measures such as response time ⋮ Tree inference: uniqueness of multinomial processing trees representing response time when two factors selectively influence processes ⋮ Beta-MPT: multinomial processing tree models for addressing individual differences ⋮ Tree inference: response time in multinomial processing trees, representation and uniqueness of parameters

Uses Software

• HMMTree

Cites Work

• Processing time predictions of current models of perception in the classic additive factors paradigm
• A Bayesian procedure for separately estimating storage and retrieval components of forgetting
• Representing parametric order constraints in multi-trial applications of multinomial processing tree models
• The statistical analysis of general processing tree models with the EM algorithm
• How to assess a model's testability and identifiability
• Selective influence through conditional independence
• The statistical analysis of a model for storage and retrieval processes in human memory
• Extending general processing tree models to analyze reaction time experiments.
• Unnamed Item

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