Comparative Cognition Animal Minds
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Greece, Bulgaria and Albania1 General Introduct
Translating Socio-Cultural Anthropology into Education Educational Anthropology and Anthropology of education in SE European countries: Greece, Bulgaria and Albania1 Ioannis Manos, Gerorgia Sarikoudi University of Macedonia, Thessaloniki, Greece General Introduction Education System I. Greece The education system in Greece is under the central responsibility and supervision of the state administration, and more specifically, the Ministry of Education, Research and Religious Affairs (MERRA). It consists of three levels: primary, secondary (divided in lower and upper-secondary), and tertiary (higher) education. 1. Overview of the Greek education system According to the Greek constitution, the Greek state is bound to provide all Greek citizens with access to free education at all levels of the state education system. The Greek education system2 consists of three levels: primary, secondary (divided in lower and upper-secondary), and higher (tertiary) education. a) Primary Education3 Primary education includes the pre-primary (kindergarten/‘παιδικός σταθμός’/paidikos stathmos) and the primary schools (‘δημοτικό’/dimotiko). Pre-primary education is compulsory4 and 1 DISCLAIMER: The European Commission support for the production of this publication does not constitute an endorsement of the contents which reflects the views only of the authors, and the Commission cannot be held responsible for any use which may be made of the information contained therein. 2 For a summary of the Greek education system, see, https://eacea.ec.europa.eu/national- policies/eurydice/content/greece_en 3 A non-compulsory early childhood education is provided to children from the age of 2 months to 4-years-old by municipal public institutions (Infant Centers, Infant/Child Centers and Child Centers) or private, pre-school education and care centers. -
PSYCHOLOGY DEPARTMENTAL SEMINAR Psychological Approaches to Art Appreciation Professor Helmut Leder Department of Psychological Basic Research University of Vienna
PSYCHOLOGY DEPARTMENTAL SEMINAR Psychological approaches to art appreciation Professor Helmut Leder Department of Psychological Basic Research University of Vienna Helmut Leder is Professor of Cognitive Psychology and Head of the Department of Psychological Basic Research at the University of Vienna. His main fi elds of research are aesthetics, psychology of the arts, design – and face perception. His PhD is from the University of Fribourg. He was a visiting Researcher at the University of Stirling, ATR Japan, USC and UCSD, and at the Languages of Emotion-Cluster, FU Berlin. He is the author or co-author of over 100 scholarly publications and was awarded the Berlyne Award for career contributions to the psychology of aesthetics from the American Psychological Association. Abstract: Art is a unique feature of human experience and several approaches aim to understand what the psychological aspects of this uniqueness are. Art appreciation involves the complex interplay among stimuli, perceiver and contexts, which have been discussed as eliciting a special combination of aesthetic judgments and aesthetic emotions. Based on our model of aesthetic appreciation (Leder et al., 2004), we conducted studies to understand the nature of stylistic processing (Augustin et al., 2008), the dependence of art appreciation of the class of artworks (Belke et al. in press) as well as the complex interplay of the variables involved between these factors. For the latt er, we conducted a study in which we measured diff erences in preferences for classical, abstract, and modern artworks (Leder et al., in press). Using structural equation modeling, we assessed the contribution of emotion, arousal, and comprehension as determining factors of art appreciation. -
The Role of Emotion in Musical Improvisation: an Analysis of Structural Features
The Role of Emotion in Musical Improvisation: An Analysis of Structural Features Malinda J. McPherson1*, Monica Lopez-Gonzalez1,2, Summer K. Rankin1, Charles J. Limb1,2 1 Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America, 2 Peabody Conservatory of The Johns Hopkins University, Baltimore, Maryland, United States of America Abstract One of the primary functions of music is to convey emotion, yet how music accomplishes this task remains unclear. For example, simple correlations between mode (major vs. minor) and emotion (happy vs. sad) do not adequately explain the enormous range, subtlety or complexity of musically induced emotions. In this study, we examined the structural features of unconstrained musical improvisations generated by jazz pianists in response to emotional cues. We hypothesized that musicians would not utilize any universal rules to convey emotions, but would instead combine heterogeneous musical elements together in order to depict positive and negative emotions. Our findings demonstrate a lack of simple correspondence between emotions and musical features of spontaneous musical improvisation. While improvisations in response to positive emotional cues were more likely to be in major keys, have faster tempos, faster key press velocities and more staccato notes when compared to negative improvisations, there was a wide distribution for each emotion with components that directly violated these primary associations. The finding that musicians often combine disparate features together in order to convey emotion during improvisation suggests that structural diversity may be an essential feature of the ability of music to express a wide range of emotion. -
Drawing a Hypothesis Book
Drawing a Edition Angewandte Book Series of the University of Applied Arts Vienna Edited by Gerald Bast, Rector Hypothesis Figures of Thought A Project by Nikolaus Gansterer TABLE OF CONTENTS Nikolaus Gansterer Compiled in the years between 2005 and 2011, while living and working in Maastricht, Nanjing, Rotterdam, Antwerp, Vienna, Mexico City, Los Angeles, Prairie City, Beijing, New York, Berlin and Ghent. This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically those of INDEX OF FIGURES ............................................................................. 9 translation, reprinting, re-use of illustrations, broadcasting, reproduction by photocopying machines or similar means, and storage in data banks. PREFACE ................................................................................................. 21 Product Liability: The publisher can give no guarantee for all the information contained in this Drawing a Hypothesis book. The use of registered names, trademarks, etc. in this publication does not imply, even in the Nikolaus Gansterer absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. © 2011 Springer-Verlag/Wien Printed in Austria SpringerWienNewYork is part of HYPOTHESIS #1 ........................................................................................ 29 Springer Science+Business Media A Line with Variable Direction, which Traces springer.at No -
Expert Face Processing: Specialization and Constraints
Book chapter, will be published in G. Schwarzer & H. Leder, Development of face processing. Göttingen: Hogrefe. 6 Expert Face Processing: Specialization and Constraints Adrian Schwaninger Claus-Christian Carbon Helmut Leder 6.1 Introduction Face processing in adults is the product of innate mechanisms, and is also based on years of experience. There is no doubt that face processing is a human skill at which most adults are real experts. In the present chapter we review theories and hypotheses concerning adults’ face processing skills, as well as what information and processes these are based on. Moreover, we discuss how the high specialization is attained at the cost of being susceptible to specific conditions. Expertise, according to the American Heritage Dictionary is given when a person shows a high degree of skill in or knowledge of a certain subject. This definition implies that an expert is a high-grade specialist. Expertise does not have to be accessible in an explicit way, because an expert does not have to know all the facts of his expertise. The skill humans show in identifying faces is astonishing. According to Bahrick, Bahrick, and Wittlinger (1975) adults are able to recognize familiar faces with an accuracy of 90 per cent or more, even when some of those faces have not been seen for fifty years. Moreover, faces are a class of objects which encourage a special kind of categorization. According to the logic of Roger Brown’s seminal paper “How shall a thing be called” (Brown, 1958), the level of the object name reflects the entry point of the recognition process. -
Machine Learning for Neuroscience Geoffrey E Hinton
Hinton Neural Systems & Circuits 2011, 1:12 http://www.neuralsystemsandcircuits.com/content/1/1/12 QUESTIONS&ANSWERS Open Access Machine learning for neuroscience Geoffrey E Hinton What is machine learning? Could you provide a brief description of the Machine learning is a type of statistics that places parti- methods of machine learning? cular emphasis on the use of advanced computational Machine learning can be divided into three parts: 1) in algorithms. As computers become more powerful, and supervised learning, the aim is to predict a class label or modern experimental methods in areas such as imaging a real value from an input (classifying objects in images generate vast bodies of data, machine learning is becom- or predicting the future value of a stock are examples of ing ever more important for extracting reliable and this type of learning); 2) in unsupervised learning, the meaningful relationships and for making accurate pre- aim is to discover good features for representing the dictions. Key strands of modern machine learning grew input data; and 3) in reinforcement learning, the aim is out of attempts to understand how large numbers of to discover what action should be performed next in interconnected, more or less neuron-like elements could order to maximize the eventual payoff. learn to achieve behaviourally meaningful computations and to extract useful features from images or sound What does machine learning have to do with waves. neuroscience? By the 1990s, key approaches had converged on an Machine learning has two very different relationships to elegant framework called ‘graphical models’, explained neuroscience. As with any other science, modern in Koller and Friedman, in which the nodes of a graph machine-learning methods can be very helpful in analys- represent variables such as edges and corners in an ing the data. -
Talker Variability in Audiovisual Speech Perception
Language Sciences Talker Variability in Audiovisual Speech Perception Shannon Heald and Howard C Nusbaum Journal Name: Frontiers in Psychology ISSN: 1664-1078 Article type: Original Research Article Received on: 02 Apr 2014 Accepted on: 17 Jun 2014 Provisional PDF published on: 17 Jun 2014 www.frontiersin.org: www.frontiersin.org Citation: Heald S and Nusbaum HC(2014) Talker Variability in Audiovisual Speech Perception. Front. Psychol. 5:698. doi:10.3389/fpsyg.2014.00698 /Journal/Abstract.aspx?s=603& /Journal/Abstract.aspx?s=603&name=language%20sciences& name=language%20sciences& ART_DOI=10.3389/fpsyg.2014.00698 ART_DOI=10.3389 /fpsyg.2014.00698: (If clicking on the link doesn't work, try copying and pasting it into your browser.) Copyright statement: © 2014 Heald and Nusbaum. 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. This Provisional PDF corresponds to the article as it appeared upon acceptance, after rigorous peer-review. Fully formatted PDF and full text (HTML) versions will be made available soon. Talker Variability 1 Running Head: Talker Variability in AV Speech Perception Talker Variability in Audiovisual Speech Perception Shannon L.M. Heald a* Howard Nusbaum a a The University of Chicago, Psychology Department *Corresponding Author: Shannon Heald 5848 S. University Ave. – B402 Chicago, IL 60637 Email: [email protected] Phone: 781-492-7568 Talker Variability 3 Statement of Significance A shift between talkers can pose a perceptual challenge to a listener due to an increase in the variability of how acoustic patterns map on to phonetic categories. -
Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning
Deep Neuroevolution: Genetic Algorithms are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning Felipe Petroski Such Vashisht Madhavan Edoardo Conti Joel Lehman Kenneth O. Stanley Jeff Clune Uber AI Labs ffelipe.such, [email protected] Abstract 1. Introduction Deep artificial neural networks (DNNs) are typ- A recent trend in machine learning and AI research is that ically trained via gradient-based learning al- old algorithms work remarkably well when combined with gorithms, namely backpropagation. Evolution sufficient computing resources and data. That has been strategies (ES) can rival backprop-based algo- the story for (1) backpropagation applied to deep neu- rithms such as Q-learning and policy gradients ral networks in supervised learning tasks such as com- on challenging deep reinforcement learning (RL) puter vision (Krizhevsky et al., 2012) and voice recog- problems. However, ES can be considered a nition (Seide et al., 2011), (2) backpropagation for deep gradient-based algorithm because it performs neural networks combined with traditional reinforcement stochastic gradient descent via an operation sim- learning algorithms, such as Q-learning (Watkins & Dayan, ilar to a finite-difference approximation of the 1992; Mnih et al., 2015) or policy gradient (PG) methods gradient. That raises the question of whether (Sehnke et al., 2010; Mnih et al., 2016), and (3) evolution non-gradient-based evolutionary algorithms can strategies (ES) applied to reinforcement learning bench- work at DNN scales. Here we demonstrate they marks (Salimans et al., 2017). One common theme is can: we evolve the weights of a DNN with a sim- that all of these methods are gradient-based, including ES, ple, gradient-free, population-based genetic al- which involves a gradient approximation similar to finite gorithm (GA) and it performs well on hard deep differences (Williams, 1992; Wierstra et al., 2008; Sali- RL problems, including Atari and humanoid lo- mans et al., 2017). -
Complex Internal Representations in Sensorimotor Decision Making: a Bayesian Investigation Doctor of Philosophy, 2014
COMPLEXINTERNALREPRESENTATIONS INSENSORIMOTORDECISIONMAKING: A BAYESIAN INVESTIGATION luigi acerbi I V N E R U S E I T H Y T O H F G E R D I N B U Doctor of Philosophy Doctoral Training Centre for Computational Neuroscience Institute of Perception, Action and Behaviour School of Informatics University of Edinburgh 2014 Luigi Acerbi: Complex internal representations in sensorimotor decision making: A Bayesian investigation Doctor of Philosophy, 2014 supervisors: Prof. Sethu Vijayakumar, Ph.D., FRSE Prof. Daniel M. Wolpert, D.Phil., FRS, FMedSci ABSTRACT The past twenty years have seen a successful formalization of the idea that percep- tion is a form of probabilistic inference. Bayesian Decision Theory (BDT) provides a neat mathematical framework for describing how an ideal observer and actor should interpret incoming sensory stimuli and act in the face of uncertainty. The predictions of BDT, however, crucially depend on the observer’s internal models, represented in the Bayesian framework by priors, likelihoods, and the loss function. Arguably, only in the simplest scenarios (e.g., with a few Gaussian variables) we can expect a real observer’s internal representations to perfectly match the true statistics of the task at hand, and to conform to exact Bayesian computations, but how humans systemati- cally deviate from BDT in more complex cases is yet to be understood. In this thesis we theoretically and experimentally investigate how people represent and perform probabilistic inference with complex (beyond Gaussian) one-dimensional distributions of stimuli in the context of sensorimotor decision making. The goal is to reconstruct the observers’ internal representations and details of their decision- making process from the behavioural data – by employing Bayesian inference to un- cover properties of a system, the ideal observer, that is believed to perform Bayesian inference itself. -
Curriculum Vitae Department of Psychological Sciences University of Connecticut 406 Babbidge Road, Unit 1020 Storrs, CT 06269-1
JAMES STEPHEN MAGNUSON Curriculum Vitae Department of Psychological Sciences University of Connecticut Office: (860) 617-0853 406 Babbidge Road, Unit 1020 [email protected] Storrs, CT 06269-1020 Research interests Psycholinguistics (word recognition, sentence processing, speech perception), neurobiological and genetic bases of typical and atypical language development and function, learning, representation (integration of internal representations with environmental context), probability matching, computational modeling. Teaching interests Psycholinguistics, cognitive science, sensation and perception, judgment and decision making, connectionist modeling, simulation and modeling, laboratory methods, neurobiology of language, behavior genetics of language Education and academic positions 2015 - Associate Director CT Institute for the Brain and Cognitive Sciences Aug. 2014 - Professor University of Connecticut, Department of Psychology 2008-2014 Associate Professor University of Connecticut, Department of Psychology 2012 - Director Neurobiology of Language Training Program, interdisciplinary program involving: Behavioral Neuroscience; Clinical Psychology; Developmental Psychology; Linguistics; Perception-Action-Cognition (Psychological Sciences); Physiology & Neurobiology; Speech, Language & Hearing Sciences 2012 - Director Cognitive Science Shared Electrophysiology Resource Laboratory 2008-2012 Director Psychology Shared Electrophysiology Resource Lab (PSERL) 2004-2008 Assistant Professor University of Connecticut, Department of -
Current Perspectives in Cognitive Processing by Domesticated Animals
EDITORIAL published: 23 August 2021 doi: 10.3389/fpsyg.2021.736717 Editorial: Current Perspectives in Cognitive Processing by Domesticated Animals Katherine Bruce 1†, David A. Leavens 2† and Sarah T. Boysen 3,4*† 1 Department of Psychology, University of North Carolina at Wilmington, Wilmington, NC, United States, 2 School of Psychology, University of Sussex, Brighton, United Kingdom, 3 Center for Animal Welfare Science, Department of Pathobiology, Purdue University, West Lafayette, IN, United States, 4 Comparative Cognition Project, Sunbury, OH, United States Keywords: animal cognition, animals, non-human cognition, cognitive processing, behavior Editorial on the Research Topic Current Perspectives in Cognitive Processing by Domesticated Animals Recently, studies of cognitive processing in domestic animals, especially dogs, seem to have increased exponentially, and research with more typical laboratory animals, such as rats, pigeons, and non-human primates seems to be declining. Funding for behavioral and/or cognitive work with animals has always been challenging, and as costs for animal housing, care, and per diems have increased significantly in the past two decades, researchers have looked to other subject pools that do not require major funding for conducting relevant and important studies that can contribute significantly to our field. Thus, companion animals, notably dogs, have become an important Edited and reviewed by: resource for studies of animal cognition, as well as other accessible and less-studied species like Watanabe Shigeru, goats, horses, and pigs, among others. The Research Topic, entitled “Current Perspectives in Keio University, Japan Cognitive Processing in Domestic Animals,” included 10 papers covering a range of topics and *Correspondence: species, with summaries of each paper provided here. -
Moira Rose (Molly) Dillon
Moira Rose Dillon Department of Psychology, New York University, 6 Washington Place, New York, NY 10003 Email – [email protected] Departmental Website – http://as.nyu.edu/psychology/people/faculty.Moira-Dillon.html Lab Website – https://www.labdevelopingmind.com Employment New York University New York, NY Assistant Professor, Department of Psychology, Faculty of Arts and Sciences (July 2017-present) Concurrent Positions New York University New York, NY Faculty Affiliate, Institute of Human Development and Social Change, Steinhardt School of Culture, Education, and Human Development (May 2019-present) Massachusetts Institute of Technology Cambridge, MA Invited Researcher, Abdul Latif Jameel Poverty Action Lab (J-PAL), Foundations of Learning (April 2021-present) Education Harvard University Cambridge, MA (August 2011-May 2017) Ph.D., Psychology (May 2017) A.M., Psychology (May 2014) Yale University New Haven, CT (August 2004-May 2008) B.A., Cognitive Science; Art (May 2008) Funding 2019-2024 National Science Foundation (PI: $1,718,437) CAREER: Becoming Euclid: Characterizing the geometric intuitions that support formal learning in mathematics (PI: $24,671) CLB: Career-Life Balance Faculty Early Career Development Program Supplement 2019-2023 DARPA (Co-PI; to NYU: $1,703,553; to Dillon: $871,874) Cognitive milestones for Machine Common Sense, Co-PI: Brenden Lake 2019-2021 Jacobs Foundation (PI: 150,000 CHF) Early Career Research Fellowship 2018-2019 Institute of Human Development and Social Change at NYU (PI: $14,848) The arc of geometric