Fundamental tools for developing likelihood functions within ACT-R
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Publication:2116079
DOI10.1016/j.jmp.2021.102636zbMath1484.91351OpenAlexW4205947349MaRDI QIDQ2116079
Joseph W. Houpt, Christopher R. Fisher, Glenn Gunzelmann
Publication date: 16 March 2022
Published in: Journal of Mathematical Psychology (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jmp.2021.102636
Cognitive psychology (91E10) Memory and learning in psychology (91E40) Applications of statistics to psychology (62P15)
Uses Software
Cites Work
- Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC
- The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo
- The lognormal race: a cognitive-process model of choice and latency with desirable psychometric properties
- Developing memory-based models of ACT-R within a statistical framework
- Estimating the dimension of a model
- Tutorial on maximum likelihood estimation
- Approximate Bayesian computation with differential evolution
- On the Foundations of Statistical Inference
- A Simplex Method for Function Minimization
- A new look at the statistical model identification
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