Previous research has demonstrated that EEG data can be used to identify and remove unintentional responses from a data set (guesses and slips). This study sought to determine if removing this error variance has a significant impact on the interpretation of a trainee's performance. Participants were taught to recognize tank silhouettes. Multiple linear regression models were built for each participant based on three sets of their data: 1) all trials of their performance data, 2) only trials that were learned according to a state space analysis, and 3) their intentional data as identified by EEG. When compared to an expert model, each of the three models for every participant yielded a different diagnosis, indicating that filtering performance data with EEG data changes the interpretation of a participant's competence.
Spoken word recognition involves brief activation of candidate words. Six experiments examined whether words semantically related to phonologically activated words would be falsely recognized. Experiments 1 and 2 involved homophones as test words; Experiment 3 used strong associates for the semantic mediation link. Experiment 4 approximated lists of "strong" converging associates. Experiment 5 expanded the real time needed for word identification by using a gating procedure during study. In Experiment 6, the goal was to create a more sensitive test by requiring participants to indicate which of two lures (mediated or control) was "most likely" to be new. Recognition errors were sensitive to separate phonetic and semantic stages in the mediated chain; however, there was little evidence of mediated false recognition, despite expectations derived from common models of spreading activation.
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