Yoked Criteria Shifts in Decision System Adaptation: Computational and Behavioral Investigations


We describe a theory of decision system adaptation in which yoked criteria shifts serve as a simple but powerful mechanism for rapidly minimizing errors without sacrificing speed. To support our theory, we implemented a connectionist model of lexical decision, wherein the state of a word perception network was “read” by a pair of decision units. The response criteria for these decision units were then subjected to yoked shifts to examine how, in the face of perceived errors, such a response mechanism might adjust performance. We also present the results of a lexical decision experiment that manipulated the truthfulness of the feedback participants received so as to trigger the error correction mechanism while keeping other task parameters constant. The results of the experiment largely parallel those of the simulation, suggesting that yoked decision shifts make an important contribution to error minimization in decision system adaptation.

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