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\n\n \n \n Anvari, A.; Maldonado, M.; and Ruiz, A. S.\n\n\n \n \n \n \n \n The Puzzle of Reflexive Belief Construction in Spanish.\n \n \n \n \n\n\n \n\n\n\n In
Proceedings of Sinn Und Bedeutung, volume 23, pages 57–74, 2019. \n
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@inproceedings{AnvariEtAl2019,\n title = {The Puzzle of {{Reflexive Belief Construction}} in {{Spanish}}},\n booktitle = {Proceedings of {{Sinn}} Und {{Bedeutung}}},\n author = {Anvari, Amir and Maldonado, Mora and Ruiz, Andr{\\'e}s Soria},\n year = {2019},\n volume = {23},\n pages = {57--74},\n copyright = {All rights reserved},\n url={Paper=https://ling.auf.net/lingbuzz/004470}\n}\n\n
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\n\n \n \n Maldonado, M.; and Culbertson, J.\n\n\n \n \n \n \n Something about Us: Learning First Person Pronoun Systems.\n \n \n \n\n\n \n\n\n\n In
41st Annual Meeting of the Cognitive Science Society, pages 7, Montreal, 2019. \n
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@inproceedings{MaldonadoCulbertson2019,\n title = {Something about Us: {{Learning}} First Person Pronoun Systems},\n booktitle = {41st {{Annual Meeting}} of the {{Cognitive Science Society}}},\n author = {Maldonado, Mora and Culbertson, Jennifer},\n year = {2019},\n pages = {7},\n address = {{Montreal}},\n abstract = {Languages partition semantic space into linguistic categories in systematic ways. In this study, we investigate a semantic space which has received sustained attention in theoretical linguistics: person. Person systems convey the roles entities play in the conversational context (i.e., speaker(s), addressee(s), other(s)). Like other linguistic category systems (e.g. color and kinship terms), not all ways of partitioning the person space are equally likely. We use an artificial language learning paradigm to test whether typological frequency correlates with learnability of person paradigms. We focus on first person systems (e.g., `I' and `we' in English), and test the predictions of a set of theories which posit a universal set of features ({$\\pm$}exclusive, and {$\\pm$}minimal) to capture this space. Our results provide the first experimental evidence for feature-based theories of person systems.},\n copyright = {All rights reserved},\n langid = {english},\n file = {/Users/mmaldona/Zotero/storage/5F7YYTA3/Maldonado et al. - Something about us Learning first person pronoun s.pdf}\n}\n\n
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\n Languages partition semantic space into linguistic categories in systematic ways. In this study, we investigate a semantic space which has received sustained attention in theoretical linguistics: person. Person systems convey the roles entities play in the conversational context (i.e., speaker(s), addressee(s), other(s)). Like other linguistic category systems (e.g. color and kinship terms), not all ways of partitioning the person space are equally likely. We use an artificial language learning paradigm to test whether typological frequency correlates with learnability of person paradigms. We focus on first person systems (e.g., `I' and `we' in English), and test the predictions of a set of theories which posit a universal set of features ($±$exclusive, and $±$minimal) to capture this space. Our results provide the first experimental evidence for feature-based theories of person systems.\n
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\n\n \n \n Maldonado, M.; and Culbertson, J.\n\n\n \n \n \n \n Learnability as a Window into Universal Constraints on Person Systems.\n \n \n \n\n\n \n\n\n\n In
Proceedings of the 22nd Amsterdam Colloquium, pages 484–493, 2019. \n
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@inproceedings{MaldonadoCulbertson2019a,\n title = {Learnability as a Window into Universal Constraints on Person Systems},\n booktitle = {Proceedings of the 22nd {{Amsterdam Colloquium}}},\n author = {Maldonado, Mora and Culbertson, Jennifer},\n year = {2019},\n pages = {484--493},\n copyright = {All rights reserved},\n file = {/Users/mmaldona/Zotero/storage/NRFXRAKN/Maldonado and Culbertson - Learnability as a window into universal constraint.pdf}\n}\n\n
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\n\n \n \n Maldonado, M.; Chemla, E.; and Spector, B.\n\n\n \n \n \n \n \n Revealing Abstract Semantic Mechanisms through Priming: The Distributive/Collective Contrast.\n \n \n \n \n\n\n \n\n\n\n
Cognition, 182: 171–176. January 2019.\n
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@article{MaldonadoEtAl2019,\n title = {Revealing Abstract Semantic Mechanisms through Priming: {{The}} Distributive/Collective Contrast},\n shorttitle = {Revealing Abstract Semantic Mechanisms through Priming},\n author = {Maldonado, Mora and Chemla, Emmanuel and Spector, Benjamin},\n year = {2019},\n month = jan,\n journal = {Cognition},\n volume = {182},\n pages = {171--176},\n issn = {00100277},\n doi = {10.1016/j.cognition.2018.09.009},\n abstract = {Sentences such as The bags are light allow both collective (they are light together) and distributive interpretations (each bag is light). We report the results of two experiments showing that this collective/distributive contrast gives rise to priming effects. These findings suggest that collective and distributive readings involve different interpretative mechanisms, which are at play during real comprehension and can be targeted by priming, independently of the specific verification strategy associated with each interpretation.},\n copyright = {All rights reserved},\n url_Paper = {https://semanticsarchive.net/Archive/zVhYTdjY/MaldonadoChemlaSpector2018_paper.pdf},\n langid = {english},\n}\n\n
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\n Sentences such as The bags are light allow both collective (they are light together) and distributive interpretations (each bag is light). We report the results of two experiments showing that this collective/distributive contrast gives rise to priming effects. These findings suggest that collective and distributive readings involve different interpretative mechanisms, which are at play during real comprehension and can be targeted by priming, independently of the specific verification strategy associated with each interpretation.\n
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\n\n \n \n Maldonado, M.; Dunbar, E.; and Chemla, E.\n\n\n \n \n \n \n Mouse Tracking as a Window into Decision Making.\n \n \n \n\n\n \n\n\n\n
Behavior research methods, 51(3): 1085–1101. 2019.\n
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@article{MaldonadoEtAl2019a,\n title = {Mouse Tracking as a Window into Decision Making},\n author = {Maldonado, Mora and Dunbar, Ewan and Chemla, Emmanuel},\n year = {2019},\n journal = {Behavior research methods},\n volume = {51},\n number = {3},\n pages = {1085--1101},\n abstract = {Mouse tracking promises to be an efficient method to investigate the dynamics of cognitive processes: It is easier to deploy than eyetracking, yet in principle it is much more fine-grained than looking at response times. We investigated these claimed benefits directly, asking how the features of decision processes\\textemdash notably, decision changes\\textemdash might be captured in mouse movements. We ran two experiments, one in which we explicitly manipulated whether our stimuli triggered a flip in decision, and one in which we replicated more ecological, classical mouse-tracking results on linguistic negation (Dale \\& Duran, Cognitive Science, 35, 983\\textendash 996, 2011). We concluded, first, that spatial information (mouse path) is more important than temporal information (speed and acceleration) for detecting decision changes, and we offer a comparison of the sensitivities of various typical measures used in analyses of mouse tracking (area under the trajectory curve, direction flips, etc.). We do so using an ``optimal'' analysis of our data (a linear discriminant analysis explicitly trained to classify trajectories) and see what type of data (position, speed, or acceleration) it capitalizes on. We also quantify how its results compare with those based on more standard measures.},\n copyright = {All rights reserved},\n file = {/Users/mmaldona/Zotero/storage/3ZL7Z7FS/Maldonado et al. - 2019 - Mouse tracking as a window into decision making.pdf;/Users/mmaldona/Zotero/storage/CTQQJW36/s13428-018-01194-x.html}\n}\n\n\n
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\n Mouse tracking promises to be an efficient method to investigate the dynamics of cognitive processes: It is easier to deploy than eyetracking, yet in principle it is much more fine-grained than looking at response times. We investigated these claimed benefits directly, asking how the features of decision processes— notably, decision changes— might be captured in mouse movements. We ran two experiments, one in which we explicitly manipulated whether our stimuli triggered a flip in decision, and one in which we replicated more ecological, classical mouse-tracking results on linguistic negation (Dale & Duran, Cognitive Science, 35, 983– 996, 2011). We concluded, first, that spatial information (mouse path) is more important than temporal information (speed and acceleration) for detecting decision changes, and we offer a comparison of the sensitivities of various typical measures used in analyses of mouse tracking (area under the trajectory curve, direction flips, etc.). We do so using an ``optimal'' analysis of our data (a linear discriminant analysis explicitly trained to classify trajectories) and see what type of data (position, speed, or acceleration) it capitalizes on. We also quantify how its results compare with those based on more standard measures.\n
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