Beyond the simple contrastive analysis [[electronic resource] ] : appropriate experimental approaches for unraveling the neural basis of conscious experience / / edited by Jaan Aru and Talis Bachmann

• Record Nr.: UNINA9910137532803321
• Pubbl/distr/stampa: Lausanne, Switzerland : , : Frontiers Media SA, , 2015; ©2015
• Descrizione fisica: 1 online resource (129 pages) : illustrations, charts; digital, PDF file(s)
• Collana: Frontiers Research Topics
• Soggetti: Consciousness - Physiological aspects; Neurology - Research; Psychology; Social sciences; Social Sciences
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Bibliographic Level Mode of Issuance: Monograph
• Nota di bibliografia: Includes bibliographical references.
• Sommario/riassunto: Contrasting conditions with and without conscious experience has served consciousness research well. However, research based on this simple contrast has led to controversies about the neural basis of conscious experience. One key reason for these ongoing debates seems to be that the simple contrast between conditions with and without consciousness is not specific for unraveling the neural basis of conscious experience, but rather also leads to other processes that precede or follow it. Acknowledging this methodological problem implies that some of the previous research findings about the neural underpinnings of conscious experience are actually reflecting the prerequisites and consequences rather than the direct correlates of conscious perception. Thus, it is required to re-evaluate the previous results to find out which of them are telling us anything about the neural basis of consciousness. But first and foremost, to overcome this methodological problem we need new experimental paradigms that go beyond the simple contrastive analysis or find the ways how some older ; but well forgotten paradigms may foster a new look at this emerging problem. Accordingly, this research topic is looking for empirical and theoretical contributions that: 1) envision new and suitable experimental approaches to study consciousness that are free from the limitations of the simple contrastive analysis; 2) provide empirical data that help to separate the neural correlates of conscious experience from the prerequisites and consequences of it; 3) help to re-assess previous research findings about the neural correlates of conscious perception in the light of the methodological problems with the traditional contrastive analysis. We hope that the theoretical insights and experimental approaches collected within this Research Topic help us to gain a more refined understanding of the neural basis of conscious experience.

Artificial Intelligence in Music, Sound, Art and Design : 11th International Conference, EvoMUSART 2022, Held as Part of EvoStar 2022, Madrid, Spain, April 20–22, 2022, Proceedings / / edited by Tiago Martins, Nereida Rodríguez-Fernández, Sérgio M. Rebelo

• Record Nr.: UNINA9910561297303321
• Pubbl/distr/stampa: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022
• ISBN: 3-031-03789-8
• Edizione: [1st ed. 2022.]
• Descrizione fisica: 1 online resource (427 pages)
• Collana: Lecture Notes in Computer Science, , 1611-3349 ; ; 13221
• Disciplina: 700.28563
• Soggetti: Computer science; Computers; Image processing - Digital techniques; Computer vision; Software engineering; Computer engineering; Computer networks; Theory of Computation; Computing Milieux; Computer Imaging, Vision, Pattern Recognition and Graphics; Software Engineering; Computer Engineering and Networks
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Intro -- Preface -- Organization -- Contents -- Long Talks -- SonOpt: Sonifying Bi-objective Population-Based Optimization Algorithms -- 1 Introduction -- 2 Related Work -- 3 Methodology and System Overview -- 4 Experimental Study -- 4.1 Experimental Setup -- 4.2 Experimental Analysis -- 5 Conclusion and Future Work -- References -- A Systematic Evaluation of GPT-2-Based Music Generation -- 1 Introduction -- 2 Background -- 2.1 GPT-2 Models -- 2.2 Representing Music Data for GPT-2 Input -- 2.3 Statistical Analysis of Generative Music Models -- 3 Musical Metrics -- 4 Dataset Curation -- 5 Evaluating Generative Model Output -- 5.1 Varying the Training Level -- 5.2 Varying the Training Corpus -- 6 Web Application -- 7 Conclusions and Future Work -- References -- Expressive Aliens - Laban Effort Factors for Non-anthropomorphic Morphologies -- 1 Introduction -- 2 Background -- 2.1 Movement Qualities -- 2.2 Motion Synthesis -- 2.3 Anthropomorphic versus Non-anthropomorphic Characters -- 2.4 Physical Validity versus Expressivity -- 3 Implementation -- 3.1 Morphologies -- 3.2 SAC Algorithm -- 3.3 Observation Vector -- 3.4 Rewards -- 4 Training -- 5 Results -- 6 Discussion -- 7 Conclusion and Outlook -- References -- Painting with Evolutionary Algorithms -- 1 Introduction -- 2 Rearranging Brush Strokes -- 3 Algorithms -- 4 Experiment and Results -- 5 Extrapolation -- 6 Some Final Remarks -- References -- Evolutionary Construction of Stories that Combine Several Plot Lines -- 1 Introduction -- 2 Related Work -- 2.1 Plot Line Combination -- 2.2 Computational Metrics for Stories -- 2.3 Evolutionary Construction of Narratives -- 3 An Evolutionary Multiplot Story Composer -- 3.1 The Knowledge Resources -- 3.2 Character Fusion and Discourse Planning -- 3.3 Representing Multiplot Stories for Evolutionary Construction -- 3.4 Constructing an Initial Population.; 3.5 Evolutionary Operators -- 3.6 Fitness Functions -- 4 Discussion -- 4.1 Results -- 4.2 Relation with Previous Work -- 5 Conclusions -- References -- Fashion Style Generation: Evolutionary Search with Gaussian Mixture Models in the Latent Space -- 1 Introduction -- 2 Related Work -- 2.1 GANs -- 2.2 Fashion Styles -- 2.3 Evolutionary Search of GANs' Latent Space -- 3 Dataset -- 4 Model -- 4.1 Generative Model -- 4.2 Style Model -- 4.3 Evolutionary Search -- 5 Results -- 6 Discussion -- 7 Conclusion and Future Work -- References -- Classification of Guitar Effects and Extraction of Their Parameter Settings from Instrument Mixes Using Convolutional Neural Networks -- 1 Introduction -- 2 Method and Materials -- 2.1 Dataset for Guitar Effect Parameter Extraction -- 2.2 Dataset for Guitar Effect Classification -- 2.3 Time-Frequency Representations -- 2.4 Convolutional Neural Networks -- 2.5 Training and Evaluation -- 2.6 Baseline -- 2.7 Robustness Analysis -- 3 Results -- 3.1 Effect Classification -- 3.2 Effect Parameter Extraction -- 3.3 Robustness to Noise and Pitch Shifts -- 4 Discussion -- 4.1 Guitar Effect Classification -- 4.2 Guitar Effect Parameter Extraction -- 4.3 Robustness -- 4.4 Limitations -- 5 Conclusion -- References -- Aesthetic Evaluation of Experimental Stimuli Using Spatial Complexity and Kolmogorov Complexity -- 1 Introduction -- 2 Conceptual Model -- 3 Spatial Complexity Measure -- 4 Kolmogorov Complexity of 2D Patterns -- 5 ; Experiment and Results -- 5.1 Method -- 5.2 Material -- 5.3 Procedure -- 5.4 Results -- 5.5 Procedure for the Extended Study -- 5.6 Results and Analysis -- 6 Discussions -- References -- Towards the Generation of Musical Explanations with GPT-3 -- 1 Introduction -- 2 Background -- 2.1 Communication in Human-Machine Music Interactions -- 2.2 Transformer-Based Approaches in Music.; 2.3 Transformer-Based Approaches in Explainable AI -- 3 Musical Capability of GPT-3 -- 3.1 Extracting the Key from a Sequence of Notes -- 3.2 Providing Explanations for a Fictional Song -- 3.3 Extracting Musical Information Using MusicABC Notation -- 4 Explaining Musical Decisions -- 4.1 Methodology -- 4.2 Results -- 5 Discussion -- 6 Conclusion -- References -- Lamuse: Leveraging Artificial Intelligence for Sparking Inspiration -- 1 Introduction -- 2 The Creative Process -- 2.1 Context -- 2.2 General Assumptions and Process -- 2.3 Data Requirements -- 3 The Artificial Intelligence -- 3.1 Artwork Decomposition -- 3.2 Recomposing with a Visual Universe and Projection -- 3.3 Style Transfer -- 4 Results Analysis and Discussion -- 4.1 E. Potier -- 4.2 Rarès-Victor -- 4.3 N. Varoqui -- 4.4 O. Masmonteil -- 5 Conclusion -- References -- EvoDesigner: Towards Aiding Creativity in Graphic Design -- 1 Introduction -- 2 Related Work -- 3 Approach -- 3.1 Evolutionary Engine -- 4 Experimental Setup and Results -- 5 Conclusion -- References -- Conditional Drums Generation Using Compound Word Representations -- 1 Introduction -- 2 Related Work -- 3 Data Encoding Representation -- 3.1 Encoder Representation - Conditional Information -- 3.2 Decoder Representation - Generated Drum Sequences -- 4 Proposed Architecture -- 4.1 Encoder - Decoder -- 4.2 Implementation Details -- 5 Experimental Setup -- 5.1 Dataset and Preprocessing -- 5.2 Evaluation Metrics -- 6 Results -- 6.1 Objective Evaluation -- 6.2 Subjective Evaluation -- 7 Conclusions -- References -- Music Style Transfer Using Constant-Q Transform Spectrograms -- 1 Introduction -- 2 Related Work -- 3 Experimental Setup -- 4 Experiments and Results -- 4.1 CycleGAN Hyperparameter Experiment -- 4.2 CQTGAN Sample Rates Experiment -- 4.3 Unseen Audio Examples Experiment -- 5 Survey Evaluation -- 6 Discussion.; 7 Conclusions and Future Work -- References -- SpeechTyper: From Speech to Typographic Composition -- 1 Introduction -- 2 Related Work -- 3 SpeechTyper -- 3.1 Extracting Speech Data -- 3.2 Designing Glyphs -- 3.3 Creating Typographic Compositions Based on Speech -- 4 Experimentation -- 4.1 Setup -- 4.2 Results/Discussion -- 5 Conclusion and Future Work -- References -- A Creative Tool for the Musician Combining LSTM and Markov Chains in Max/MSP -- 1 Introduction -- 2 Data Representation -- 2.1 Pitch -- 2.2 Time -- 2.3 MIDI Analyzer -- 3 LSTM Data Encoding -- 3.1 Pitch -- 3.2 Rhythm -- 3.3 Machine Learning Datasets -- 4 Scramble -- 5 User Interface -- 6 Experimental Results -- 7 Conclusions and Future Work -- References -- Translating Emotions from EEG to Visual Arts -- 1 Introduction -- 2 Background and Related Works -- 3 Preparation of Datasets -- 4 Pipeline -- 4.1 Extra Losses -- 5 Experiments and Results -- 5.1 Example Experiment -- 6 Results Assessment: Online Survey -- 7 Discussion -- 8 Conclusions -- References -- Emotion-Driven Interactive Storytelling: Let Me Tell You How to Feel -- 1 Introduction -- 2 Background -- 3 Methodology -- 3.1 Emotion Recognition -- 3.2 Interface Design -- 3.3 System's Assessment -- 4 Results -- 5 Discussion -- 6 Conclusion -- References -- Modern Evolution Strategies for Creativity: Fitting Concrete Images and Abstract Concepts -- 1 Introduction -- 2 Background -- 3 Modern Evolution Strategies for Creativity -- 4 Fitting Concrete Target Image -- 5 Fitting Abstract ; Concept with CLIP -- 6 Discussion and Conclusion -- References -- Co-creative Product Design with Interactive Evolutionary Algorithms: A Practice-Based Reflection -- 1 Introduction -- 2 Background -- 3 Related Work -- 4 Design Study -- 4.1 Design Task -- 4.2 Study Method -- 4.3 Participant: The Designer -- 4.4 Software Tools and Algorithm.; 4.5 Findings: Introspective Design Reflections -- 5 Discussion -- 5.1 Early Constraining of the Design Space -- 5.2 Support in Problem-Solution Co-evolution -- 5.3 Escaping and Falling in a Fixation Trap -- 5.4 The IGA as Creative Partner -- 5.5 Study Limitations -- 6 Conclusion and Future Work -- References -- Sound Model Factory: An Integrated System Architecture for Generative Audio Modelling -- 1 Background and Motivation -- 1.1 Previous Work -- 2 Architecture -- 2.1 System Overview -- 2.2 System Components -- 3 Connecting the GAN and the RNN -- 3.1 Parameter Linearization -- 4 Evaluation -- 4.1 Human Evaluation Adaptively Smoothed Latent Space -- 4.2 Parameter Response Time -- 4.3 Sound Quality Evaluation Based on Audio Classification -- 4.4 Continuous Interpolation of Pitch and Timbre -- 5 Conclusion -- References -- Short Talks -- An Application of Neural Embedding Models for Representing Artistic Periods -- 1 Introduction -- 2 Data -- 3 Methodology -- 3.1 word2vec -- 3.2 t-SNE -- 4 Results -- 4.1 Salvador Dalí -- 4.2 Vincent van Gogh -- 4.3 Pablo Picasso -- 5 Discussion -- 6 Conclusion -- References -- MusIAC: An Extensible Generative Framework for Music Infilling Applications with Multi-level Control -- 1 Introduction -- 2 Related Work -- 3 Proposed Model and Representation -- 3.1 Adding Control Features -- 3.2 Data Representation -- 3.3 Model Architecture -- 4 Experimental Setup -- 4.1 Dataset -- 4.2 Model Configuration and Training -- 4.3 Inference Strategy -- 5 Evaluation -- 5.1 Objective Evaluation Using Selected Metrics -- 5.2 The Interactive Interface and Controllability -- 6 Conclusion -- References -- A Study on Noise, Complexity, and Audio Aesthetics -- 1 Introduction -- 2 Conceptual over Perceptual -- 2.1 Japanoise -- 3 Complexity -- 3.1 Complexity and Computational Aesthetics -- 4 Proposed Aesthetic Metrics -- 5 Experiment.; 6 Discussion.
• Sommario/riassunto: This book constitutes the refereed proceedings of the 10th European Conference on Artificial Intelligence in Music, Sound, Art and Design, EvoMUSART 2022, held as part of Evo* 2022, in April 2022, co-located with the Evo* 2022 events, EvoCOP, EvoApplications, and EuroGP. The 20 full papers and 6 short papers presented in this book were carefully reviewed and selected from 66 submissions. They cover a wide range of topics and application areas, including generative approaches to music and visual art, deep learning, and architecture.