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      Perceiving individuality in harpsichord performance

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          Abstract

          Can listeners recognize the individual characteristics of unfamiliar performers playing two different musical pieces on the harpsichord? Six professional harpsichordists, three prize-winners and three non prize-winners, made two recordings of two pieces from the Baroque period (a variation on a Partita by Frescobaldi and a rondo by François Couperin) on an instrument equipped with a MIDI console. Short (8 to 15 s) excerpts from these 24 recordings were subsequently used in a sorting task in which 20 musicians and 20 non-musicians, balanced for gender, listened to these excerpts and grouped together those that they thought had been played by the same performer. Twenty-six participants, including 17 musicians and nine non-musicians, performed significantly better than chance, demonstrating that the excerpts contained sufficient information to enable listeners to recognize the individual characteristics of the performers. The grouping accuracy of musicians was significantly higher than that observed for non-musicians. No significant difference in grouping accuracy was found between prize-winning performers and non-winners or between genders. However, the grouping accuracy was significantly higher for the rondo than for the variation, suggesting that the features of the two pieces differed in a way that affected the listeners’ ability to sort them accurately. Furthermore, only musicians performed above chance level when matching variation excerpts with rondo excerpts, suggesting that accurately assigning recordings of different pieces to their performer may require musical training. Comparisons between the MIDI performance data and the results of the sorting task revealed that tempo and, to a lesser extent, note onset asynchrony were the most important predictors of the perceived distance between performers, and that listeners appeared to rely mostly on a holistic percept of the excerpts rather than on a comparison of note-by-note expressive patterns.

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          Statistics is a subject of many uses and surprisingly few effective practitioners. The traditional road to statistical knowledge is blocked, for most, by a formidable wall of mathematics. The approach in An Introduction to the Bootstrap avoids that wall. It arms scientists and engineers, as well as statisticians, with the computational techniques they need to analyze and understand complicated data sets.
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            Thinking the voice: neural correlates of voice perception.

            The human voice is the carrier of speech, but also an "auditory face" that conveys important affective and identity information. Little is known about the neural bases of our abilities to perceive such paralinguistic information in voice. Results from recent neuroimaging studies suggest that the different types of vocal information could be processed in partially dissociated functional pathways, and support a neurocognitive model of voice perception largely similar to that proposed for face perception.
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              Models of object recognition.

              Understanding how biological visual systems recognize objects is one of the ultimate goals in computational neuroscience. From the computational viewpoint of learning, different recognition tasks, such as categorization and identification, are similar, representing different trade-offs between specificity and invariance. Thus, the different tasks do not require different classes of models. We briefly review some recent trends in computational vision and then focus on feedforward, view-based models that are supported by psychophysical and physiological data.
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                Author and article information

                Journal
                Front Psychol
                Front Psychol
                Front. Psychol.
                Frontiers in Psychology
                Frontiers Media S.A.
                1664-1078
                24 February 2014
                2014
                : 5
                : 141
                Affiliations
                [1] 1Goldsmiths College, University of London London, UK
                [2] 2Department of Cognitive Biology, University of Vienna Vienna, Austria
                Author notes

                Edited by: Eddy J. Davelaar, Birkbeck College, UK

                Reviewed by: Eddy J. Davelaar, Birkbeck College, UK; Sarah Creel, University of California at San Diego, USA

                *Correspondence: Bruno Gingras, Department of Cognitive Biology, University of Vienna, Althanstrasse 14, Vienna A-1090, Austria e-mail: brunogingras@ 123456gmail.com

                This article was submitted to Cognitive Science, a section of the journal Frontiers in Psychology.

                Article
                10.3389/fpsyg.2014.00141
                3932517
                ea13f8aa-6447-4072-89bc-116cccee51ea
                Copyright © 2014 Koren and Gingras.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 01 September 2013
                : 04 February 2014
                Page count
                Figures: 3, Tables: 4, Equations: 0, References: 58, Pages: 13, Words: 0
                Categories
                Psychology
                Original Research Article

                Clinical Psychology & Psychiatry
                music performance,individuality,harpsichord,categorization,musical expertise

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