PCS_convergence_McCandR

The iteration to consider -- in most cases, this will be the
current iteration during a simulation run, however, the check can also be
applied to a model output retroactively, and the iteration specified
manually.

iteration

The current energy level within the network

current_energy

A matrix of iteration, energy and node states (in
columns, in that order), across all previous iterations (in rows).

memory.matrix

Criterion for stability. Changes below this value
are no longer considered significant, and ten iterations without significant changes
to the energy level in succession will trigger the convergence check.

stability_criterion

Either <code>'qualitative'</code> (default), in which case the check
returns a boolean value representing whether it has passed or not, or
<code>'quantitative'</code>, in which case the number of checked trials for which
the convergence criterion was met is returned. This last option is of most
value for debugging convergence.

output


This function applies the convergence criterion defined by McClelland and
Rumelhart to a given network, and returns either a (qualitative) boolean
value that represents the convergence state, or a (quantitative) value that
represents the number of iterations (of the last 10) that have met the
convergence threshold.


Parallel Constraint Satisfaction (PCS) models are an increasingly
common class of models in Psychology, with applications to reading and word
recognition (McClelland & Rumelhart, 1981), judgment and decision making
(Glöckner & Betsch, 2008; Glöckner, Hilbig, & Jekel, 2014), and several
other fields (e.g. Read, Vanman, & Miller, 1997). 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.

PCS_convergence_McCandR: Check a PCS network for convergence

Description

Usage

Arguments

Details