PCSinR: Parallel Constraint Satisfaction Networks in R
Parallel Constraint Satisfaction (PCS) models are an increasingly
common class of models in Psychology, with applications to reading and word
recognition (McClelland & Rumelhart, 1981;
\doi{10.1037/0033-295X.88.5.375}), judgment and decision making (Glöckner &
Betsch, 2008 \doi{10.1017/S1930297500002424}; Glöckner, Hilbig, &
Jekel, 2014 \doi{10.1016/j.cognition.2014.08.017}), and several other
fields. In each of these fields, they provide a quantitative model of
psychological phenomena, with precise predictions regarding choice
probabilities, decision times, and often the degree of confidence. This
package provides the necessary functions to create and simulate basic
Parallel Constraint Satisfaction networks within R.
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[aut, cre],
Daniel W. Heck