Catlearn is an archive of formal models of categorization and associative learning in psychology.
Some introductory materials on catlearn:
Join the catlearn-package e-mail list to get occasional information on updates to the catlearn package.
Installation instructions are here.
The latest stable version of catlearn contains the following:
ALCOVE (slpALCOVE), see also our description of ALCOVE
Gluck & Bower (1988) (slpLMSnet)
MBMF: model-based, model-free hybrid (slpMBMF)
Mackintosh (1975) (slpMack75)
Simulations of several dataset-model combinations (e.g. krus96exit is a simulation of the krus96 dataset with the slpEXIT model). In some cases:
the optimization routines are available (functions ending opt).
functions to generate input representations for the models are separately available, to facilitate re-use (functions ending train).
functions to automatically test the ordinal adequacy of the model fit are included (functions ending oat). These functions also produce summary output for the relevant simulation.
functions to plot model predictions are included (functions ending plot).
act2probrat (convert output model activation to a predicted rating).
convertSUSTAIN (convert nominal-dimension input representation to a ‘padded’ format)
medin87train (input representation of Exp. 1 of Medin et al., 1987)
If you’d like to contribute to this project by adding models, datasets, or simulations to the catlearn package, contact Andy Wills.
The Catlearn Research Group are keen to talk about the catlearn project to any interested party (academic or non-academic). Why not invite us to give a talk or run a workshop where you are? We do not charge an appearance fee, but if you would like us to be physically present (at an appropriate social distance), we would prefer it if you were able to reimburse our travel expenses, including accommodation.
The Catlearn Research Group are based in the United Kingdom, Plymouth University
Previous talks and workshops have included:
The OpenModels project Andy Wills. 8th September 2020. UK network of Open Research Working Groups (virtual meeting), MRC-CBU, Cambridge, UK Talk available on youtube.
Benchmark phenomena in category learning. Andy Wills. 22nd June 2019. Annual Summer Interdisciplinary Conference (ASIC), Seefeld, Austria.
The OpenModels project in category learning. Stuart Spicer and Angus Inkster. 14th June 2019. Open Science symposium. Plymouth University, UK.
Progress in modelling through distributed collaboration. Andy Wills. 16th April 2019. Tagung Experimentell Arbeitender Psychologen (TEAP) conference, London, UK
Progress in modelling through distributed collaboration. Andy Wills. 21st March 2019. Open Science symposium. University of Ghent, Belgium.
Progress in modelling through distributed collaboration. Andy Wills. 7th March 2019. PRomoting Open Science PRactices (PROSPR) seminar series, Lancaster University, UK
Progress in modelling through distributed collaboration. Andy Wills. 19th June 2018. International Category Learning Symposium, Plymouth, UK.
An introduction to the Open Models project. Andy Wills. 28th March 2018. Associative Learning Symposium, Gregynog, Wales.
An introduction to the Open Models project. Andy Wills. 27th October 2017. School of Psychology, Cardiff University, UK.
A Practical Introduction to Distributed Collaboration for Formal Modeling. Andy Wills and Charlotte Edmunds, 22nd July 2017. 50th Annual Meeting of the Society for Mathematical Psychology, University of Warwick, UK.
Progress in learning theory through distributed collaboration. Andy Wills, 11th April 2017. Associative Learning Symposium, Gregynog, Wales.
A Practical Introduction to Distributed Collaboration for Formal Modeling. Andy Wills, 23rd March 2017. International Convention of Psychological Science, Vienna, Austria.
Progress in modelling through distributed collaboration. Andy Wills, Jan 2017. Experimental Psychology Society, London, UK.
Introduction to catlearn. Andy Wills. 26th Jan 2017. School of Psychology, Plymouth University, UK.
We aim to release version 0.9 to CRAN by 19th March 2021.
Contributions of working, tested, Rd-documented code are welcome for consideration at any time. Where code is ready for inclusion into catlearn, it will first be released to the community as an unstable point release of catlearn on github. On 5th March 2021, the latest unstable release on github will be used to check and build stable version 0.9 for release to CRAN.
Dates of CRAN releases, along with email-list announcements (from 0.7.2, see CHANGELOG for more detail):
Version 0.9.x (“Incredible Icing”) future
Version 0.8.x (“Harmonious honey”) CURRENT
Version 0.7.x (“Gooey chocolate”)
2020-08-06: Version 0.7.5: unstable release: slpLMSnet added, implements Gluck & Bower (1988).
2020-07-01: Version 0.7.4: fix for slpSUSTAIN bug introduced in 0.7.2.
2020-05-15: Version 0.7.3: slpALCOVE upgraded to include some checks for user errors in model specification.
2020-05-13: Version 0.7.2: slpSUSTAIN upgraded to: (a) improve implementation of cluster recruitment in edge cases not covered in Love et al. (2004), and (b) add basic checking of common user errors.
2019-10-10: Version 0.7.1: Stable release on CRAN. announcement.
Version 0.6.x (“Fried chicken”)
2019-10-03: Version 0.6.5: slpSUSTAIN uprgaded to include unsupervised category learning.
2019-10-02: Version 0.6.4: slpMack75 added.
2019-05-23: Version 0.6.3: slpEXIT converted to C++ for speed.
2019-03-18: Version 0.6.2: Minor patch so packages tests work on R 3.6.0.
2019-02-18: Version 0.6.1: Stable release on CRAN. Minor maintenance release. announcement.
2018-07-17: Version 0.6: Stable release on CRAN. slpEXIT and slpSUSTAIN added. announcement.
Version 0.5 (“Excellent bacon”)
Version 0.4 (“Dinky doughnut”)
Version 0.3 (“Cream cake”)
Versions 0.1, 0.2 (not named)
Wills, A.J., & Pothos, E.M. (2012). On the adequacy of currennt empirical evaluations of formal models of categorization. Psychological Bulletin, 138, 102-125.
Wills, A.J., O’Connell, G., Edmunds, C.E.R., & Inkster, A.B.(2017). Progress in modeling through distributed collaboration: Concepts, tools, and category-learning examples. The Psychology of Learning and Motivation.