Realism in confidence judgements of performance based on implicit learning

In two experiments we employed calibration methods to investigate the reali sm of participants' confidence ratings of their own classification performa nce based on knowledge acquired after training on an artificial grammar. In Experiment 1 participants showed good realism (but overconfidence) for gra mmatical strings but very poor realism for non-grammatical strings. Method of training (string repetition in writing or mere exposure) did not affect the realism. Furthermore, the participants underestimated their overall per formance. In Experiment 2, using a more complex grammar and controlling for two types of associative chunk-strength, participants showed good realism (but still overconfidence) for both letter and symbol strings, irrespective of grammaticality. Together, these experiments show that implicit learning can give rise to knowledge products that are associated with fairly realis tic meta-knowledge. It is argued that both the zero-correlation criterion a nd the guessing criterion are misplaced when used to define implicit knowle dge; two reasons being that confidence judgements may be affected both by i mplicit knowledge and by inferences.