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1. A dynamic neural field model of multimodal merging: application to the ventriloquist effect

Author

Simon Forest, Jean-Charles Quinton, Mathieu Lefort, Statistique pour le Vivant et l’Homme (SVH), Laboratoire Jean Kuntzmann (LJK), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Systèmes Cognitifs et Systèmes Multi-Agents (SyCoSMA), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), Project AMPLIFIER, funded by the French region Auvergne-Rhône-Alpes in the context of the 'Pack Ambition Recherche' initiative., Most of the computations presented in this paper were performed using the GRICAD infrastructure (https://gricad.univ-grenoble-alpes.fr), which is supported by Grenoble research communities., ANR-11-LABX-0025,PERSYVAL-lab,Systemes et Algorithmes Pervasifs au confluent des mondes physique et numérique(2011), and ANR-14-CE25-0006,GAG,Jeux et graphes(2014)

Subjects
Superior Colliculi, Arts and Humanities (miscellaneous), Cognitive Neuroscience, [SCCO.NEUR]Cognitive science/Neuroscience, Visual Perception, Humans, Bayes Theorem, Computer Simulation, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Photic Stimulation, Probability
Abstract

Multimodal merging encompasses the ability to localize stimuli based on imprecise information sampled through individual senses such as sight and hearing. Merging decisions are standardly described using Bayesian models that fit behaviors over many trials, encapsulated in a probability distribution. We introduce a novel computational model based on dynamic neural fields able to simulate decision dynamics and generate localization decisions, trial by trial, adapting to varying degrees of discrepancy between audio and visual stimulations. Neural fields are commonly used to model neural processes at a mesoscopic scale—for instance, neurophysiological activity in the superior colliculus. Our model is fit to human psychophysical data of the ventriloquist effect, additionally testing the influence of retinotopic projection onto the superior colliculus and providing a quantitative performance comparison to the Bayesian reference model. While models perform equally on average, a qualitative analysis of free parameters in our model allows insights into the dynamics of the decision and the individual variations in perception caused by noise. We finally show that the increase in the number of free parameters does not result in overfitting and that the parameter space may be either reduced to fit specific criteria or exploited to perform well on more demanding tasks in the future. Indeed, beyond decision or localization tasks, our model opens the door to the simulation of behavioral dynamics, as well as saccade generation driven by multimodal stimulation.

Published
2022

2. How much does prosody help word segmentation? A simulation study on infant-directed speech

Author

Emmanuel Dupoux, Reiko Mazuka, Alejandrina Cristia, Bogdan Ludusan, RIKEN Center for Brain Science [Wako] (RIKEN CBS), RIKEN - Institute of Physical and Chemical Research [Japon] (RIKEN), Department of Psychology and Neuroscience, Duke University [Durham], Laboratoire de sciences cognitives et psycholinguistique (LSCP), Département d'Etudes Cognitives - ENS Paris (DEC), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École des hautes études en sciences sociales (EHESS)-Centre National de la Recherche Scientifique (CNRS), Apprentissage machine et développement cognitif (CoML), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École des hautes études en sciences sociales (EHESS)-Centre National de la Recherche Scientifique (CNRS)-Département d'Etudes Cognitives - ENS Paris (DEC), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École des hautes études en sciences sociales (EHESS)-Centre National de la Recherche Scientifique (CNRS)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), École des hautes études en sciences sociales (EHESS), The research reported in this paper was partly funded by JSPS Grant-in-Aid for Scientific Research (16H06319, 20H05617) and MEXT Grant-in-aid on Innovative Areas #4903 (Co-creative Language Evolution), 17H06382 to RM. It was also supported by the European Research Council (ERC-2011-AdG-295810 BOOTPHON), the Agence Nationale pour la Recherche (ANR-17-CE28-0007 LangAge, ANR-16-DATA-0004 ACLEW, ANR-14-CE30-0003 MechELex, ANR-17-EURE-0017 Frontcog, ANR-10-IDEX-0001-02 PSL*, ANR-19-P3IA-0001 PRAIRIE 3IA Institute). ED is further grateful to the CIFAR (Learning in Machines and Brain), BL to the Canon Foundation in Europe, and AC to the JS McDonnell Foundation., ANR-17-CE28-0007,LangAge,Différences dans l'apprenabilité du langage selon l'âge(2017), ANR-16-DATA-0004,ACLEW,Analyzing Child Language Experiences Around the World(2016), ANR-14-CE30-0003,MechELex,Méchanismes d'acquisition lexicale précoce(2014), ANR-17-EURE-0017,FrontCog,Frontières en cognition(2017), ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), European Project: 295810,EC:FP7:ERC,ERC-2011-ADG_20110406,BOOTPHON(2012), École normale supérieure - Paris (ENS Paris), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS Paris)

Subjects
Linguistics and Language, Cognitive Neuroscience, Speech recognition, Experimental and Cognitive Psychology, Prosody, Infant-directed speech, 050105 experimental psychology, Language and Linguistics, Speech Acoustics, Task (project management), Infant language acquisition, Developmental and Educational Psychology, Humans, Learning, Speech, 0501 psychology and cognitive sciences, Segmentation, Computer Simulation, ComputingMilieux_MISCELLANEOUS, Computational model, 05 social sciences, Text segmentation, Infant, acquisition, [SCCO.LING]Cognitive science/Linguistics, Word segmentation, [SCCO.PSYC]Cognitive science/Psychology, Speech Perception, Infant language, Cues, Heuristics, Psychology, Word (computer architecture), 050104 developmental & child psychology
Abstract

International audience; Infants come to learn several hundreds of word forms by two years of age, and it is possible this involves carving these forms out from continuous speech. It has been proposed that the task is facilitated by the presence of prosodic boundaries. We revisit this claim by running computational models of word segmentation, with and without prosodic information, on a corpus of infant-directed speech. We use five cognitively-based algorithms, which vary in whether they employ a sub-lexical or a lexical segmentation strategy and whether they are simple heuristics or embody an ideal learner. Results show that providing expert-annotated prosodic breaks does not uniformly help all segmentation models. The sub-lexical algorithms, which perform more poorly, benefit most, while the lexical ones show a very small gain. Moreover, when prosodic information is derived automatically from the acoustic cues infants are known to be sensitive to, errors in the detection of the boundaries lead to smaller positive effects, and even negative ones for some algorithms. This shows that even though infants could potentially use prosodic breaks, it does not necessarily follow that they should incorporate prosody into their segmentation strategies, when confronted with realistic signals.

Published
2022

3. Does Infant-Directed Speech Help Phonetic Learning? A Machine Learning Investigation

Author

Reiko Mazuka, Emmanuel Dupoux, Bogdan Ludusan, RIKEN Center for Brain Science [Wako] (RIKEN CBS), RIKEN - Institute of Physical and Chemical Research [Japon] (RIKEN), Universität Bielefeld = Bielefeld University, Department of Psychology and Neuroscience, Duke University [Durham], Apprentissage machine et développement cognitif (CoML), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire de sciences cognitives et psycholinguistique (LSCP), Département d'Etudes Cognitives - ENS Paris (DEC), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École des hautes études en sciences sociales (EHESS)-Centre National de la Recherche Scientifique (CNRS)-Département d'Etudes Cognitives - ENS Paris (DEC), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École des hautes études en sciences sociales (EHESS)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de sciences cognitives et psycholinguistique (LSCP), CIFAR program in Learning in Machines & Brains CIFAR LMB program, The research reported in this paper was partly funded by JSPS Grant-in-Aid for Scientific Research (16H06319, 20H05617) and MEXT Grant-in-Aid on Innovative Areas #4903 (Co-creative Language Evolution), 17H06382 to R. Mazuka. The work of E. Dupoux in his EHESS role was supported by the European Research Council (ERC-2011-AdG-295810 BOOTPHON) the Agence Nationale pour la Recherche (ANR-10-LABX-0087 IEC, ANR-10-IDEX0001-02 PSL*, ANR-19-P3IA-0001 PRAIRIE 3IA Institute), and CIFAR (Learning in Machines and Brain). Part of the work was conducted while E. Dupoux was a visiting scientist at DeepMind and Facebook. B. Ludusan was also supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement no. 799022., ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), ANR-17-EURE-0017,FrontCog,Frontières en cognition(2017), ANR-10-IDEX-0001,PSL,Paris Sciences et Lettres(2010), European Project: 295810,EC:FP7:ERC,ERC-2011-ADG_20110406,BOOTPHON(2012), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École des hautes études en sciences sociales (EHESS)-Centre National de la Recherche Scientifique (CNRS)-Inria de Paris, and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)

Subjects
Functional role, Adult, Computer science, Cognitive Neuroscience, Experimental and Cognitive Psychology, Machine learning, computer.software_genre, Infant-directed speech, 050105 experimental psychology, Read speech, Machine Learning, 03 medical and health sciences, 0302 clinical medicine, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Artificial Intelligence, Robustness (computer science), Phonetics, Concept learning, Vowel, Humans, Speech, 0501 psychology and cognitive sciences, Speech variability, Hyperarticulation, business.industry, 05 social sciences, Phonetic learning, Infant, Reading, Speech Perception, Artificial intelligence, business, computer, Adult-directed speech, 030217 neurology & neurosurgery
Abstract

A prominent hypothesis holds that by speaking to infants in infant-directed speech (IDS) as opposed to adult-directed speech (ADS), parents help them learn phonetic categories. Specifically, two characteristics of IDS have been claimed to facilitate learning: hyperarticulation, which makes the categories more separable, and variability, which makes the generalization more robust. Here, we test the separability and robustness of vowel category learning on acoustic representations of speech uttered by Japanese adults in ADS, IDS (addressed to 18- to 24-month olds), or read speech (RS). Separability is determined by means of a distance measure computed between the five short vowel categories of Japanese, while robustness is assessed by testing the ability of six different machine learning algorithms trained to classify vowels to generalize on stimuli spoken by a novel speaker in ADS. Using two different speech representations, we find that hyperarticulated speech, in the case of RS, can yield better separability, and that increased between-speaker variability in ADS can yield, for some algorithms, more robust categories. However, these conclusions do not apply to IDS, which turned out to yield neither more separable nor more robust categories compared to ADS inputs. We discuss the usefulness of machine learning algorithms run on real data to test hypotheses about the functional role of IDS. © 2021 The Authors. Cognitive Science published by Wiley Periodicals LLC on behalf of Cognitive Science Society (CSS).

Published
2020

4. Climbing Brain Levels of Organisation from Genes to Consciousness

Author

Jean-Pierre Changeux, CNRS UMR3571, Centre National de la Recherche Scientifique (CNRS), Institut Pasteur [Paris], Collège de France (CdF (institution)), This paper received funding from the European Union's Horizon 2020 research and innovation programmeunder grant agreement No 720270 (HBP SGA1) and SCR 130008 0, 1UE 64102 (HBP ramp up), European Project: 720270,H2020 Pilier Excellent Science,H2020-Adhoc-2014-20,HBP SGA1(2016), and Institut Pasteur [Paris] (IP)

Subjects
0301 basic medicine, Consciousness, Cognitive Neuroscience, media_common.quotation_subject, Brain, Gene Expression Regulation, Developmental, [SDV.NEU.SC]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Cognitive Sciences, Experimental and Cognitive Psychology, 03 medical and health sciences, 030104 developmental biology, 0302 clinical medicine, Neuropsychology and Physiological Psychology, Climbing, Gene Regulatory Networks, Psychology, Neuroscience, Gene, 030217 neurology & neurosurgery, Transcription Factors, media_common, Epigenesis
Abstract

International audience; Given the tremendous complexity of brain organisation, here I propose a strategy that dynamically links stages of brain organisation from genes to consciousness, at four privileged structural levels: genes; transcription factors (TFs)-gene networks; synaptic epigenesis; and long-range connectivity. These structures are viewed as nested and reciprocally inter-regulated, with a hierarchical organisation that proceeds on different timescales during the course of evolution and development. Interlevel bridging mechanisms include intrinsic variation-selection mechanisms, which offer a community of bottom-up and top-down models linking genes to consciousness in a stepwise manner.

Published
2017

5. Advantages and disadvantages of the use of the CSF Amyloid β (Aβ) 42/40 ratio in the diagnosis of Alzheimer’s Disease

Author

Markus Otto, Henrik Zetterberg, Piotr Lewczuk, Oskar Hansson, Sylvain Lehmann, Clinical Memory Research Unit [Malmö, Suède], Department of Clinical Sciences [Malmö, Suède], Lund University [Malmö, Suède]-Lund University [Malmö, Suède], Memory Clinic [Malmö, Suède], Skåne University Hospital [Malmö, Suède], Cellules Souches, Plasticité Cellulaire, Médecine Régénératrice et Immunothérapies (IRMB), Université de Montpellier (UM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier), Department of Neurology [Ulm, Allemagne], Universität Ulm - Ulm University [Ulm, Allemagne], Clinical Neurochemistry Laboratory [Göteborg, Suède], Department of Psychiatry and Neurochemistry [Goteborg], Institute of Neuroscience and Physiology [Göteborg]-Sahlgrenska Academy at University of Gothenburg [Göteborg]-Institute of Neuroscience and Physiology [Göteborg]-Sahlgrenska Academy at University of Gothenburg [Göteborg], Department of Neurodegenerative Disease [Londres, Royaume-Uni], Institute of Neurology [London], University College of London [London] (UCL)-University College of London [London] (UCL), UK Dementia Research Institute (UK DRI), University College of London [London] (UCL), Department of Psychiatry and Psychotherapy [Erlangen, Allemagne], Universitätsklinikum Erlangen [Erlangen, Allemagne], Friedrich-Alexander Universität Erlangen-Nürnberg (FAU)-Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Department of Neurodegeneration Diagnostics [Bialystok, Pologne], Medical University of Bialystok [Bialystok, Pologne], Lab for Clinical Neurochemistry and Neurochemical Dementia Diagnostics [Erlangen, Allemagne], Friedrich-Alexander Universität Erlangen-Nürnberg (FAU)-Friedrich-Alexander Universität Erlangen-Nürnberg (FAU)-Universitätsklinikum Erlangen [Erlangen, Allemagne], Fujirebio Europe sponsored this paper, however, it had no influence on its factual content. PL was supported by the Innovative Medicines Initiative Joint Undertaking under EMIF grant agreement n° 115372, resources of which are composed of financial contribution from the European Union’s Seventh Framework Programme (FP7/2007-2013) and EFPIA companies’ in kind contribution. OH has acquired research support (for the institution) from Roche, GE Healthcare, Biogen, AVID Radiopharmaceuticals, Fujirebio and Euroimmun. HZ’s research is supported by the Swedish Research Council, the European Research Council, the Knut and Alice Wallenberg Foundation, the Olav Thon Foundation, the UK Dementia Research Institute at UCL and Swedish State Support for Clinical Research (ALFGBG)., Lund University [Lund]-Lund University [Lund], Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM), Medical University of Białystok (MUB), and Bodescot, Myriam

Subjects
0301 basic medicine, Oncology, medicine.medical_specialty, Neurology, Amyloid β, Cognitive Neuroscience, [SDV.NEU.NB]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Neurobiology, Diagnostic accuracy, Disease, Review, Aβ 42/40 ratio, Alzheimer's Disease, lcsh:RC346-429, lcsh:RC321-571, 03 medical and health sciences, 0302 clinical medicine, Cerebrospinal fluid, Medizinische Fakultät, Alzheimer Disease, Internal medicine, medicine, Dementia, Humans, ddc:610, Alzheimer’s Disease, Cognitive impairment, lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry, Biochemical markers, lcsh:Neurology. Diseases of the nervous system, Cerebrospinal Fluid, Amyloid beta-Peptides, [SDV.MHEP.GEG] Life Sciences [q-bio]/Human health and pathology/Geriatry and gerontology, business.industry, [SDV.MHEP.GEG]Life Sciences [q-bio]/Human health and pathology/Geriatry and gerontology, [SDV.NEU.NB] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Neurobiology, medicine.disease, Peptide Fragments, 030104 developmental biology, Aβ42/40 ratio, Neurology (clinical), business, 030217 neurology & neurosurgery, Amyloidβ Peptides, Biomarkers
Abstract

International audience; The cerebrospinal fluid (CSF) biochemical markers (biomarkers) Amyloidβ 42 (Aβ42), total Tau (T-tau) and Tau phosphorylated at threonine 181 (P-tau181) have proven diagnostic accuracy for mild cognitive impairment and dementia due to Alzheimer's Disease (AD). In an effort to improve the accuracy of an AD diagnosis, it is important to be able to distinguish between AD and other types of dementia (non-AD). The concentration ratio of Aβ42 to Aβ40 (Aβ42/40 Ratio) has been suggested to be superior to the concentration of Aβ42 alone when identifying patients with AD. This article reviews the available evidence on the use of the CSF Aβ42/40 ratio in the diagnosis of AD. Based on the body of evidence presented herein, it is the conclusion of the current working group that the CSF Aβ42/40 ratio, rather than the absolute value of CSF Aβ42, should be used when analysing CSF AD biomarkers to improve the percentage of appropriately diagnosed patients.

Published
2019