Parameterization of connectionist models.
We present a method for estimating parameters of connectionist models that allows the model's output to fit as closely as possible to empirical data. The method minimizes a cost function that measures the difference between statistics computed from the model's output and statistics computed from the subjects' performance. An optimization algorithm finds the values of the parameters that minimize the value of this cost function. The cost function also indicates whether the model's statistics are significantly different from the data's. In some cases, the method can find the optimal parameters automatically. In others, the method may facilitate the manual search for optimal parameters. The method has been implemented in Matlab, is fully documented, and is available for free download from the Psychonomic Society Web archive at www.psychonomic.org/archive/.
Bogacz
Rafal
R
Princeton University, Princeton, New Jersey, USA. r.bogacz@bristo.ac.uk
Cohen
Jonathan D
JD
D000465:Algorithms; D001288:Attention; D006801:Humans; D008960:Models, Psychological; D016571:Neural Networks, Computer; D011584:Psychology / Q000379:methods; D011930:Reaction Time