ABSTRACT
Experimental cognitive psychology often involves recording two quite distinct kinds of data. The first is whether the computation itself is done correctly or incorrectly and the second records how long it took to get an answer. Neural network computations are often loosely described as being “brain-like.” This suggests that it should be possible to generate “reaction time” data simply by seeing how long it takes for the network to generate the answer, and error data by looking at the computed results in the same system. Simple feedforward nets usually do not give direct computation time data. However, network models realizing dynamical systems can generate “reaction times” directly by noting the time required for the network computation to be completed. In some cases genuine random processes are necessary to generate differing reaction times, but in other cases deterministic, noise free systems can generate distributions of reaction times.
