获取无意识结构知识的机制:主观熟悉感与刺激重复结构在人工语法学习中的作用

The nature of the distinction between conscious and unconscious knowledge is a core issue in the implicit learning field. Furthermore, the phenomenological experience associated with having knowledge is central to the conscious or unconscious status of that knowledge. Consistently, Dienes and Scott (2005) measured the conscious or unconscious status of structure knowledge using subjective measures. Believing that one is purely guessing when in fact one knows indicates unconscious knowledge. But unconscious structural knowledge can also be associated with feelings of intuition or familiarity. In this thesis, we explored whether phenomenological feelings, like familiarity, associated with unconscious structural knowledge could be used, paradoxically, to exert conscious control over the use of the knowledge, and whether people could obtain repetition structure knowledge. We also investigated the neural correlates of awareness of knowing, as measured phenomenologically. In study one, subjects were trained on two grammars and then asked to endorse strings from only one of the grammars. Subjects also rated how familiar each string they felt and reported whether or not they used familiarity to make their grammaticality judgment. We found subjects could endorse the strings of just one grammar and ignore the strings from the other. Importantly, when subjects said they were using familiarity, the rated familiarity for test strings consistent with their chosen grammar was greater than that for strings from the other grammar. Familiarity, subjectively defined, is sensitive to intentions and can play a key role in strategic control. In study two, we manipulated the structural characteristic of stings and explored whether participants could learn repetition structures in the grammatical strings. We measured phenomenology again and also ERPs. Deviant letters of ungrammatical strings violating the repetition structure elicited the N2 component; we took this to be an indication of knowledge, whether conscious or not. Strings which were attributed to conscious categories (rules and recollection) rather than phenomenology associated with unconscious structural knowledge (guessing, intuition and familiarity) elicited the P300 component. Different waveforms provided evidence for the neural correlates of different phenomenologies associated with knowledge of an artificial grammar.

Clemaps: Multiple Alignment Of Protein Structures Based On Conformational Letters

CLEMAPS is a tool for multiple alignment of protein structures. It distinguishes itself from other existing algorithms for multiple structure alignment by the use of conformational letters, which are discretized states of 3D segmental structural states. A letter corresponds to a cluster of combinations of three angles formed by C-alpha pseudobonds of four contiguous residues. A substitution matrix called CLESUM is available to measure the similarity between any two such letters. The input 3D structures are first converted to sequences of conformational letters. Each string of a fixed length is then taken as the center seed to search other sequences for neighbors of the seed, which are strings similar to the seed. A seed and its neighbors form a center-star, which corresponds to a fragment set of local structural similarity shared by many proteins. The detection of center-stars using CLESUM is extremely efficient. Local similarity is a necessary, but insufficient, condition for structural alignment. Once center-stars are found, the spatial consistency between any two stars are examined to find consistent star duads using atomic coordinates. Consistent duads are later joined to create a core for multiple alignment, which is further polished to produce the final alignment. The utility of CLEMAPS is tested on various protein structure ensembles.