Abstract
We propose that the intriguing bow effects in choice probabilities and response times that are found in unidimensional absolute identification might be a consequence of the mapping process required when a unidimensional psychological representation is mapped to a multidimensional response vector. This idea builds on previous work by Lacouture & Marley (1991) which modeled absolute identification using a connectionist feed-forward network. A formal solution of the so-called "encoder problem" is the basis of the approach, and the inclusion in the model of "noisy" mappings and integrators allows us to model both the responses made and the time to make them. The simulated model reproduces several of the phenomenon in unidimensional absolute identification including bow, range, and some sequential effects.
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