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This research investigated two aspects of intuition: preference and process. The underlying basis of preference for intuition defined by Jung in his theory of psychological types and measured by the sensing-intuition (SN) scale of the Myers- Briggs Type Indicator was explored in two areas: performance on ability tests and individual differences in use of intuition. Process of intuition is defined as the use of unconscious associations to guide decision making. A thinking aloud protocol technique was used to investigate differences in strategy between sensing and intuitive types on two ability tests. Test instructions and conditions were varied to investigate whether preference or ability underlies this difference. Results indicated that the SN difference is best characterised as a focus on different types of information - concrete reality vs. looking beyond reality to patterns, connections and possibilities. The finding that sensing types could modify their style suggested that this is due to a personality preference that can be overridden rather than an underlying ability difference. The nature of the SN difference was further explored by examining the differences predicted by type theory between the types in the use of intuition. This prediction contrasts with some process theories of intuition which expect few or no individual differences. Results indicated that intuitive types were more accurate and more likely to choose to use intuition than sensing types. Results suggested that preference for different types of information led to use of different strategies on the tasks. Intuitive types tended to focus on feelings of familiarity, which resulted in their accessing intuition in the form of unconsciously learnt associations. Sensing types preferred to focus on concrete information such as conscious memory of prior experience. The research has made contributions by evaluating the theory of psychological types, validating the sensing-intuition scale, and also by demonstrating the existence of individual differences in certain measures of intuition.

No description available.

This thesis provides a conceptual and empirical analysis of implicit concept formation. A review of concept formation studies highlights the need for improving existing methodology in establish- ing the claim for implicit concept formation. Eight experiments are reported that address this aim. A review of theoretical issues highlights the need for computational modelling to elucidate the nature of implicit learning. Two chapters address the feasibility of different exemplar and Connectionist models in accounting for how subjects perform on tasks typically employed in the implicit learn- ing literature. The first five experiments use a concept formation task that involves classifying "computer people" as belonging to a particular town or income category. A number of manipulations are made of the underlying rule to be learned and of the cover task given subjects. In all cases, the knowledge underlying classification performance can be elicited both by free recall and by forced choice tasks. The final three experiments employ Reber's (e.g., 1989) grammar learning paradigm. More rigorous methods for eliciting the knowledge underlying classification performance are employed than have been used previously by Reber. The knowledge underlying clas- sification performance is not elicited by free recall, but is elicited by a forced-choice measure. The robustness of the learning in this paradigm is investigated by using a secondary task methodol- ogy. Concurrent random number generation interferes with all knowledge measures. A number of parameter-free Connectionist and exemplar models of artificial grammar learning are tested against the experimental data. The importance of different assumptions regarding the coding of features and the learning rule used is investigated by determin- ing the performance of the model with and without each assumption. Only one class of Connectionist model passes all the tests. Fur- ther, this class of model can simulate subject performance in a different task domain. The relevance of these empirical and theoretical results for understanding implicit learning is discussed, and suggestions are made for future research.

The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file. Title from PDF of title page (University of Missouri--Columbia, viewed on March 19, 2010). Thesis advisor: Dr. Steven A. Hackley. Includes bibliographical references.

Thesis (Ph. D.)--UCLA, 2009. / Vita. Description based on print version record. Includes bibliographical references.

Thesis (Ph. D.)--University of Washington, 2002. / Vita. Includes bibliographical references (leaves 110-142).

Thesis (M.Sc.)--Memorial University of Newfoundland, 2003. / Bibliography: leaves 54-60. Also available online.

Doctorat en sciences psychologiques / info:eu-repo/semantics/nonPublished

The conscious or unconscious acquirement of knowledge in implicit category learning was examined in accordance with predictions made by the COVIS theory of categorization (Ashby & Maddox, 2011). COVIS assumes separate category learning systems. The explicit system relies on easily verbalized rules while the implicit system requires integration of more than one stimulus dimension. Participants in this experiment categorized lines varying in length and orientation as belonging to one of two categories; in the rule-based (RB) condition only length was relevant, while participants in the information integration (II) condition needed to integrate both dimensions. Corrective feedback was provided during training. In test phases, participants were asked to attribute their responses to one of four criteria (guess, intuition, memory, or rule), a measure adapted from Dienes and Scott (2005). Neural activity in dorsolateral prefrontal cortex (DLPFC) was recorded with a 20-optode fNIRS system. We found that in the implicit (II) learning condition, participants who reported guessing less than half the time were learning but were unconscious to the structures driving that learning, reflected by accuracy, attribution self-report and neural activation. Our results substantiate the claim that implicit category learning is mediated unconsciously and evidence the dual-system model of categorization postulated by COVIS, furthering our understanding of category learning and thus, the ways in which to improve it.