The Experts below are selected from a list of 232293 Experts worldwide ranked by ideXlab platform
Riitta Salmelin - One of the best experts on this subject based on the ideXlab platform.
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Statistical Models of morphology predict eye-tracking measures during visual word recognition
Memory & Cognition, 2019Co-Authors: Minna Lehtonen, Matti Varjokallio, Henna Kivikari, Annika Hultén, Sami Virpioja, Tero Hakala, Mikko Kurimo, Krista Lagus, Riitta SalmelinAbstract:We studied how Statistical Models of morphology that are built on different kinds of representational units, i.e., Models emphasizing either holistic units or decomposition, perform in predicting human word recognition. More specifically, we studied the predictive power of such Models at early vs. late stages of word recognition by using eye-tracking during two tasks. The tasks included a standard lexical decision task and a word recognition task that assumedly places less emphasis on postlexical reanalysis and decision processes. The lexical decision results showed good performance of Morfessor Models based on the Minimum Description Length optimization principle. Models which segment words at some morpheme boundaries and keep other boundaries unsegmented performed well both at early and late stages of word recognition, supporting dual- or multiple-route cognitive Models of morphological processing. Statistical Models based on full forms fared better in late than early measures. The results of the second, multi-word recognition task showed that early and late stages of processing often involve accessing morphological constituents, with the exception of short complex words. Late stages of word recognition additionally involve predicting upcoming morphemes on the basis of previous ones in multimorphemic words. The Statistical Models based fully on whole words did not fare well in this task. Thus, we assume that the good performance of such Models in global measures such as gaze durations or reaction times in lexical decision largely stems from postlexical reanalysis or decision processes. This finding highlights the importance of considering task demands in the study of morphological processing.
David W Albrecht - One of the best experts on this subject based on the ideXlab platform.
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predictive Statistical Models for user modeling
User Modeling and User-adapted Interaction, 2001Co-Authors: Ingrid Zukerman, David W AlbrechtAbstract:The limitations of traditional knowledge representation methods for modeling complex human behaviour led to the investigation of Statistical Models. Predictive Statistical Models enable the anticipation of certain aspects of human behaviour, such as goals, actions and preferences. In this paper, we motivate the development of these Models in the context of the user modeling enterprise. We then review the two main approaches to predictive Statistical modeling, content-based and collaborative, and discuss the main techniques used to develop predictive Statistical Models. We also consider the evaluation requirements of these Models in the user modeling context, and propose topics for future research.
Mihai Datcu - One of the best experts on this subject based on the ideXlab platform.
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A Comparative Study of Statistical Models for Multilook SAR Images
IEEE Geoscience and Remote Sensing Letters, 2014Co-Authors: Gottfried Schwarz, Mihai DatcuAbstract:In this letter, we carry out a comparative study of Statistical Models for multilook synthetic aperture radar amplitude images. Ten state-of-the-art Statistical Models are selected for comparison. To achieve a fair evaluation, we estimate all model parameters using the method of log-cumulants and apply the method to an image pyramid with varying pixel spacing (and resolution). The pyramid is created by different image product generation options. In addition to pixel spacing and resolution, we also consider the homogeneity of a scene for performance evaluation and we apply three performance measures. Through this study, it was found out that some Models perform well for all resolutions, while the performance of other Models depends heavily on the image content.
Minna Lehtonen - One of the best experts on this subject based on the ideXlab platform.
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Statistical Models of morphology predict eye-tracking measures during visual word recognition
Memory & Cognition, 2019Co-Authors: Minna Lehtonen, Matti Varjokallio, Henna Kivikari, Annika Hultén, Sami Virpioja, Tero Hakala, Mikko Kurimo, Krista Lagus, Riitta SalmelinAbstract:We studied how Statistical Models of morphology that are built on different kinds of representational units, i.e., Models emphasizing either holistic units or decomposition, perform in predicting human word recognition. More specifically, we studied the predictive power of such Models at early vs. late stages of word recognition by using eye-tracking during two tasks. The tasks included a standard lexical decision task and a word recognition task that assumedly places less emphasis on postlexical reanalysis and decision processes. The lexical decision results showed good performance of Morfessor Models based on the Minimum Description Length optimization principle. Models which segment words at some morpheme boundaries and keep other boundaries unsegmented performed well both at early and late stages of word recognition, supporting dual- or multiple-route cognitive Models of morphological processing. Statistical Models based on full forms fared better in late than early measures. The results of the second, multi-word recognition task showed that early and late stages of processing often involve accessing morphological constituents, with the exception of short complex words. Late stages of word recognition additionally involve predicting upcoming morphemes on the basis of previous ones in multimorphemic words. The Statistical Models based fully on whole words did not fare well in this task. Thus, we assume that the good performance of such Models in global measures such as gaze durations or reaction times in lexical decision largely stems from postlexical reanalysis or decision processes. This finding highlights the importance of considering task demands in the study of morphological processing.
Ingrid Zukerman - One of the best experts on this subject based on the ideXlab platform.
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predictive Statistical Models for user modeling
User Modeling and User-adapted Interaction, 2001Co-Authors: Ingrid Zukerman, David W AlbrechtAbstract:The limitations of traditional knowledge representation methods for modeling complex human behaviour led to the investigation of Statistical Models. Predictive Statistical Models enable the anticipation of certain aspects of human behaviour, such as goals, actions and preferences. In this paper, we motivate the development of these Models in the context of the user modeling enterprise. We then review the two main approaches to predictive Statistical modeling, content-based and collaborative, and discuss the main techniques used to develop predictive Statistical Models. We also consider the evaluation requirements of these Models in the user modeling context, and propose topics for future research.
