LEADER 05195oam 2200565 450 001 9910807277403321 005 20190911112729.0 010 $a981-4458-84-8 035 $a(OCoLC)869343252 035 $a(MiFhGG)GVRL8RCZ 035 $a(EXLCZ)992550000001191455 100 $a20130930h20142014 uy 0 101 0 $aeng 135 $aurun|---uuuua 181 $ctxt 182 $cc 183 $acr 200 10$aComputational models of cognitive processes $eproceedings of the 13th Neural Computation and Psychology Workshop, San Sebastian, Spain, 12-14 July 2012 /$feditors, Julien Mayor, University of Geneva, Switzerland, Pablo Gomez, De Paul University, USA 210 1$aNew Jersey :$cWorld Scientific,$d[2014] 210 4$d?2014 215 $a1 online resource (ix, 276 pages) $cillustrations (some color) 225 1 $aProgress in neural processing ;$vvolume 21 300 $aDescription based upon print version of record. 311 $a981-4458-83-X 311 $a1-306-39627-1 320 $aIncludes bibliographical references and index. 327 $aPreface; Contents; Language; Modelling Language - Vision Interactions in the Hub and Spoke Framework; 1. Introduction; 2. Virtues of the Hub & Spoke Framework; 3. A Hub & Spoke Model of Language Mediated Visual Attention; 3.1. Language Mediated Visual Attention & The Visual World Paradigm; 3.2. Method; 3.2.1. Network; 3.2.2. Artificial Corpus; 3.2.3. Training; 3.2.4. Pre-Test; 3.3. Results; 3.3.1. Simulation of Phonological Effects; 3.3.2. Simulation of Visual Effects; 3.3.3. Simulation of Semantic Effects; 4. Discussion; References 327 $aModelling Letter Perception: The Effect of Supervision and Top-Down Information on Simulated Reaction Times1. Introduction; 2. Method; 2.1. Simulations; 2.2. Neural Network Algorithms; 2.2.1. Restricted Boltzmann Machines; 2.2.2. Training a Deep-Belief Network; 2.2.3. Delta-Rule and Back-Propagation; 2.2.4. Simulating Reaction Times; 2.3. Human Reaction Time Data; 3. Results; 4. Conclusions; References; Encoding Words into a Potts Attractor Network; 1. Introduction; 2. BLISS: The Training Language; 3. Potts Attractor Network: a Simplified Model of the Cortex 327 $a4. Implementation of Word Representation in the Potts Network4.1. Semantic Representation; 4.2. Syntactic Representation; 5. Discussion; References; Unexpected Predictability in the Hawaiian Passive; 1. Introduction; 2. Data; 3. Methods; 3.1. Pre-processing; 3.2. The model; 3.3. Error measures; 3.3.1. Mean Squared Error; 3.3.2. Classification Error; 3.4. Baseline estimates; 3.4.1. Random guess: adaptation to the range of target values; 3.4.2. Weighted guess: adaptation to the distribution of target values; 4. Results; 5. Conclusion; Acknowledgements; References 327 $aDifference Between Spoken and Written Language Based on Zipf 's Law Analysis1. Introduction; 2. Methods; 3. Results; 3.1. Log- log frequency vs. rank plots; 3.2. Five most frequent words in 1-, 2-, and 3-grams; 3.3. Exponent of rank; 4. Discussion; Acknowledgments; References; Reading Aloud is Quicker than Reading Silently: A Study in the Japanese Language Demonstrating the Enhancement of Cognitive Processing by Action; 1. Introduction; 2. Material and Methods; 3. Results; 4. Discussion; References; Development; Testing a Dynamic Neural Field Model of Children's Category Labelling 327 $a1. Introduction2. Simulation; 2.1. Dynamic Neural Fields; 2.2. Categorisation by Shared Features; 2.3. Method; 2.3.1. Architecture; 2.3.2. Stimuli; 2.3.3. Design and Procedure; 2.3.4. Results and Discussion; 3. Experiment; 3.1. Method; 3.1.1. Participants; 3.1.2. Stimuli; 3.1.3. Procedure and Design; 3.2. Results and Discussion; 4. General Discussion; References; Theoretical and Computational Limitations in Simulating 3- to4-Month-Old Infants' Categorization Processes; 1. Introduction; 2. Simulation 1. Reproduction of the asymmetric categorization effect; 2.1. Stimuli 327 $a2.2. Neural network procedure 330 $aComputational Models of Cognitive Processes collects refereed versions of papers presented at the 13th Neural Computation and Psychology Workshop (NCPW13) that took place July 2012, in San Sebastian (Spain). This workshop series is a well-established and unique forum that brings together researchers from such diverse disciplines as artificial intelligence, cognitive science, computer science, neurobiology, philosophy and psychology to discuss their latest work on models of cognitive processes. 410 0$aProgress in neural processing ;$vvol. 21. 606 $aNeural networks (Neurobiology)$vCongresses 606 $aCognition$vCongresses 606 $aNeural stimulation$vCongresses 615 0$aNeural networks (Neurobiology) 615 0$aCognition 615 0$aNeural stimulation 676 $a612.8/233 702 $aMayor$b Julien 702 $aGomez$b Pablo$g(Pablo Alegria), 712 12$aNeural Computation and Psychology Workshop 801 0$bMiFhGG 801 1$bMiFhGG 906 $aBOOK 912 $a9910807277403321 996 $aComputational models of cognitive processes$93932897 997 $aUNINA