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Neuroanatomical anomalies of dyslexia: Disambiguating the effects of disorder, performance, and maturation.
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A nonparametric Bayesian perspective for machine learning in partially-observed settings ...
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A nonparametric Bayesian perspective for machine learning in partially-observed settings
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Phonological remediation program in students with developmental dyslexia
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| 5 |
HACEK infective endocarditis: characteristics and outcomes from a large, multi-national cohort.
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In: PLoS One, vol. 8, no. 5, pp. e63181 (2013)
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Visual activity predicts auditory recovery from deafness after adult cochlear implantation.
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In: Brain, vol. 136, no. Pt 12, pp. 3682-3695 (2013)
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| 7 |
Is the face-perception system human-specific at birth?
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In: ISSN: 0012-1649 ; Developmental Psychology ; https://hal.archives-ouvertes.fr/hal-00827807 ; Developmental Psychology, American Psychological Association, 2012, 48 (4), pp.1083-90. ⟨10.1037/a0026521⟩ (2012)
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| 8 |
Constructing flexible feature representations using nonparametric Bayesian inference
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In: Austerweil, Joseph Larry. (2012). Constructing flexible feature representations using nonparametric Bayesian inference. UC Berkeley: Psychology. Retrieved from: http://www.escholarship.org/uc/item/7cc9s9hs (2012)
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Abstract:
Representations are a key explanatory device used by cognitive psychologists to account for human behavior. However, little is known about how experience and context affect the representations people use to encode a stimulus. Understanding the effects of context and experience on the representations people use is essential because if two people encode the same stimulus using different representations, their response to that stimulus may be different. First, we present a mathematical framework that can be used to define models that flexibly construct feature representations (where by a feature we mean a part of the image of an object) for a set of observed objects, based on nonparametric Bayesian statistics. An initial model constructed in this framework captures how the distribution of parts and learning categories affects the features people use to represent a set of objects. Next, we build on this work in three ways. First, although people use features that can be transformed on each observation (e.g., translated on the retinal image), many existing feature learning models can only recognize features that are not transformed (occur identically each time). Consequently, we extend the initial model to infer features that are invariant over a set of transformations, and learn different structures of dependence between feature transformations. Second, we compare two possible methods for capturing the manner that categorization affects feature representations. Third, we present a model that learns features incrementally, capturing an effect of the order of object presentation on the features people learn. Finally, we conclude by considering the implications and limitations of our empirical and theoretical results.
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Keyword:
Artificial intelligence; Bayesian modeling; Cognitive psychology; Computational cognitive science; Feature representations; Learning; Nonparametric Bayesian statistics; Perception; Psychology
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URL: http://n2t.net/ark:/13030/m5tb4bfr
http://www.escholarship.org/uc/item/7cc9s9hs
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| 9 |
Constructing flexible feature representations using nonparametric Bayesian inference
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| 10 |
The role of polarity in antonym and synonym conceptual knowledge: evidence from stroke aphasia and multidimensional ratings of abstract words.
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In: Neuropsychologia , 50 (11) 2636 - 2644. (2012) (2012)
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| 12 |
Evolution of non-speech sound memory in postlingual deafness: implications for cochlear implant rehabilitation
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In: ISSN: 0028-3932 ; Neuropsychologia, Vol. 49, No 9 (2011) pp. 2475-82 (2011)
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| 13 |
Clinical psychologist, psychotherapist
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In: http://hal-enpc.archives-ouvertes.fr/docs/00/47/46/46/PDF/PILE_Perceptive.pdf (2010)
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| 15 |
Coagulase-negative staphylococcal prosthetic valve endocarditis--a contemporary update based on the International Collaboration on Endocarditis: prospective cohort study.
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Chu VH, Miro JM, Hoen B, Cabell CH, Pappas PA, Jones P, Stryjewski ME, Anguera I, Braun S, Muñoz P, Commerford P, Tornos P, Francis J, Oyonarte M, Selton-Suty C, Morris AJ, Habib G, Almirante B, Sexton DJ, Corey GR, Fowler VG Jr; International Collaboration on Endocarditis-Prospective Cohort Study Group. Collaborators: Gordon D, Devi U, Spelman D, van der Meer JT, Kauffman C, Bradley S, Armstrong W, Giannitsioti E, Giamarellou H, Lerakis S, del Rio A, Moreno A, Mestres CA, Pare C, Garcia de la Maria C, Claramonte X, Armero Y, De Lazzario E, Marco F, Gatell JM, Miro JM, Almela M, Azqueta M, Jiménez-Expósito MJ, de Benito N, Ramirez J, Perez N, Almirante B, Fernandez-Hidalgo N, Rodriguez de Vera P, Tornos P, Falcó V, Sidani N, Kanj-Sharara S, Kanafani Z, Raglio A, Goglio A, Gnecchi F, Suter F, Valsecchi G, Rizzi M, Ravasio V, Hoen B, Chirouze C, Giannitsioti E, Leroy J, Plesiat P, Bernard Y, Casey A, Lambert P, Watkin R, Elliott T, Patel M, Dismukes W, Pan A, Caros G, Tribouilloy AB, Goissen T, Delahaye A, Delahaye F, Vandenesch F, Vizzotti C, Nacinovich FM, Marin M, Trivi M, Lombardero M, Cortes C, Casabe JH, Altclas J, Kogan S, Clara L, Sanchez M, Commerford A, Hansa C, Deetlefs E, Ntsekhe M, Commerford P, Wray D, Steed LL, Church P, Cantey R, Morris A, Holland D, Murdoch D, Read K, Raymond N, Lang S, Chambers S, Kotsanas D, Korman TM, Peterson G, Purcell J, Southern PM Jr, Shah M, Bedimo R, Reddy A, Levine D, Dhar G, Hanlon-Feeney A, Hannan M, Kelly S, Wang A, Cabell CH, Woods CW, Sexton DJ, Benjamin D Jr, Corey GR, McDonald JR, Federspiel J, Engemann JJ, Reller LB, Drew L, Caram LB, Stryjewski M, Morpeth S, Lalani T, Fowler V Jr, Chu V, Mazaheri B, Neuerburg C, Naber C, Athan E, Henry M, Harris O, Alestig E, Olaison L, Wikstrom L, Snygg-Martin U, Francis J, Venugopal K, Nair L, Thomas V, Chaiworramukkun J, Pachirat O, Chetchotisakd P, Suwanich T, Kamarulzaman A, Tamin SS, Premru MM, Logar M, Lejko-Zupanc T, Orezzi C, Klein J, Bouza E, Moreno M, Rodríguez-Créixems M, Marín M, Fernández M, Muñoz P, Fernández R, Ramallo V, Raoult D, Thuny F, Habib G, Casalta JP, Fournier PE, Chipigina N, Kirill O, Vinogradova T, Kulichenko VP, Butkevich OM, Lion C, Selton-Suty C, Alla F, Coyard H, Doco-Lecompte T, Iarussi D, Durante-Mangoni E, Ragone E, Dialetto G, Tripodi MF, Utili R, Casillo R, Kumar AS, Sharma G, Dickerman SA, Street A, Eisen DP, Grigg L, Abrutyn E, Michelet C, Tattevin P, Donnio PY, Fortes CQ, Edathodu J, Al-Hegelan M, Font B, Anguera I, Guma JR, Cereceda M, Oyonarte MJ, Mella RM, Garcia P, Jones SB, Ramos AI, Paiva MG, Tranchesi RA, Woon LL, Lum LN, Tan RS, Rees D, Kornecny P, Lawrence R, Dever R, Post J, Jones P, Ryan S, Harkness J, Feneley M, Rubinstein E, Strahilewitz J, Ionac A, Mornos C, Dragulescu S, Forno D, Cecchi E, De Rosa F, Imazio M, Trinchero R, Wiesbauer F, Gattringer R, Rubinstein E, Deans G, Andrasevic AT, Barsic B, Klinar I, Vincelj J, Bukovski S, Krajinovic V.. - 2009
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| 16 |
Contrasting patterns of comprehension for superordinate, basic-level, and subordinate names in semantic dementia and aphasic stroke patients.
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In: Cogn Neuropsychol , 25 (4) pp. 582-600. (2008) (2008)
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| 17 |
A genome-wide scan for childhood obesity-associated traits in French families shows significant linkage on chromosome 6q22.31-q23.2.
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In: ISSN: 0012-1797 ; EISSN: 0012-1797 ; Diabetes ; https://hal.archives-ouvertes.fr/hal-00174547 ; Diabetes, American Diabetes Association, 2004, 53 (3), pp.803-11 (2004)
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| 18 |
Design of an individualised measure of the impact of macular disease on quality of life (the MacDQoL)
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In: Quality of Life Research, 13 (6) (2004)
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| 19 |
Design of an individualised measure of the impact of macular disease on quality of life (the MacDQoL)
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In: Quality of Life Research, 13 (6) (2004)
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Design of an individualised measure of the impact of macular disease on quality of life (the MacDQoL)
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In: Quality of Life Research, 13 (6) (2004)
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