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Formal Psychological Models of Categorization and Learning

Formal psychological models of categorization and learning, independently-replicated data sets against which to test them, and simulation archives.

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catlearn

Formal Psychological Models of Categorization and Learning

Catlearn is an archive of formal models of categorization and learning, plus benchmark datasets to test them against. It's also an archive of simulations using those models. It's free and open source software ... and always will be.

Catlearn is a package for R, and is very easy to install within that environment. For a tutorial introduction to catlearn, and the Open Models initiative more generally, see Wills et al. (2017). There is also lots of information about catlearn on its website.

Installing the stable version

Make sure you're running the latest version of R and then type:

install.packages('catlearn')

and then

library(catlearn).

Installing the latest (unstable) version

If you're not sure what "unstable" means in this context, you probably want to install the stable version instead. If you're sure you want the unstable version, first install devtools and its dependencies. Then run:

devtools::install_github("ajwills72/catlearn")

Functions in catlearn

Name Description
krus96exit Simulation of AP krus96 with EXIT model
medin87train Input representation of Exp. 1 in Medin et al. (1987) for models input-compatible with slpALCOVE or slpSUSTAIN.
catlearn-package Formal Modeling for Psychology.
convertSUSTAIN Convert nominal-dimension input representation into a 'padded' (slpSUSTAIN) format
act2probrat Convert output activation to a rating of outcome probability
krus96train Input representation of krus96 for models input-compatible with slpEXIT
nosof88exalcove Simulation of CIRP nosof88 with ex-ALCOVE model
nosof88 Instantiation frequency CIRP
krus96 Inverse Base-rate Effect AP
homa76 Category breadth CIRP
nosof88train Input representation of nosof88 for models input-compatible with slpALCOVE.
nosof94 Type I-VI category structure CIRP
nosof94exalcove Simulation of CIRP nosof94 with ex-ALCOVE model
nosof94bnalcove Simulation of CIRP nosof94 with BN-ALCOVE model
nosof88protoalcove Simulation of CIRP nosof88 with proto-ALCOVE model
nosof88protoalcove_opt Parameter optimization of proto-ALCOVE model with nosof88 CIRP
nosof88oat Ordinal adequacy test for simulations of nosof88 CIRP
nosof88exalcove_opt Parameter optimization of ex-ALCOVE model with nosof88 CIRP
nosof94sustain Simulation of CIRP nosof94 with the SUSTAIN model
nosof94plot Plot Nosofsky et al. (1994) data / simulations
shin92exalcove_opt Parameter optimization of ex-ALCOVE model with shin92 CIRP
shin92exalcove Simulation of CIRP shin92 with ex-ALCOVE model
shin92oat Ordinal adequacy test for simulations of shin92 CIRP
shin92protoalcove Simulation of CIRP shin92 with proto-ALCOVE model
nosof94exalcove_opt Parameter optimization of ex-ALCOVE model with nosof94 CIRP
shin92protoalcove_opt Parameter optimization of proto-ALCOVE model with shin92 CIRP
shin92train Input representation of shin92 for models input-compatible with slpALCOVE.
nosof94oat Ordinal adequacy test for simulations of nosof94 CIRP
nosof94train Input representation of nosof94 for models input-compatible with slpALCOVE or slpSUSTAIN
shin92 Category size CIRP
slpRW Rescorla-Wagner (1972) associative learning model.
slpMBMF MB/MF reinforcement learning model
slpMack75 Mackintosh (1975) associative learning model
slpEXIT EXIT Category Learning Model
slpLMSnet Gluck & Bower (1988) network model
slpCOVIS COVIS category learning model
slpDIVA DIVA category learning model
slpALCOVE ALCOVE category learning model
slpBM Bush & Mosteller (1951) simple associative learning model
slpSUSTAIN SUSTAIN Category Learning Model
ssecl Sum of squared errors
thegrid Ordinal adequacy results for all catlearn simulations
stsimGCM Generalized Context Model
No Results!

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Details

Type Package
Date 2020-09-16
Encoding UTF-8
License GPL (>= 2)
LinkingTo Rcpp, RcppArmadillo
LazyData true
NeedsCompilation yes
Packaged 2020-09-16 09:07:13 UTC; andy
Repository CRAN
Date/Publication 2020-09-16 09:50:07 UTC

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