title: Knowing that you know: an essay on mechanistic metaknowledge.
abstract: Humans are adept at providing accurate statements of confidence in their perceptual identification and recall memory responses. In spite of this, mechanical pattern-recognition systems and other artificial intelligence devices seldom express response certainty. The purpose of this paper is to show how useful confidence ratings can be in integrating the results of a variety of pattern-recognition systems to produce a single, optimal decision concerning the target to be recognized. We outline several ways neural network pattern-recognition systems could be modified to issue confidence ratings with each classification response. In sketching a mechanical system of confidence ratings we find we have also provided a preliminary framework for understanding human confidence judgments and human metacognition.
authors.affiliation: Department of Psychology, Okalahoma State University, Stillwater 74078.
authors.forename: L
authors.initials: L
authors.lastname: Hochhaus
authors.forename: J S
authors.initials: JS
authors.lastname: Gordon
mesh_terms: D001185:Artificial Intelligence; D006801:Humans; D011939:Mental Recall; D016571:Neural Networks, Computer; D010364:Pattern Recognition, Visual; D012718:Set, Psychology