Relevant bibliographies by topics / Multisensory processing

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Multisensory processing'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Multisensory processing"

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Keil, Julian, and Daniel Senkowski. "Neural Oscillations Orchestrate Multisensory Processing." Neuroscientist 24, no. 6 (2018): 609–26. http://dx.doi.org/10.1177/1073858418755352.

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At any given moment, we receive input through our different sensory systems, and this information needs to be processed and integrated. Multisensory processing requires the coordinated activity of distinct cortical areas. Key mechanisms implicated in these processes include local neural oscillations and functional connectivity between distant cortical areas. Evidence is now emerging that neural oscillations in distinct frequency bands reflect different mechanisms of multisensory processing. Moreover, studies suggest that aberrant neural oscillations contribute to multisensory processing defici
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Brang, David, Zack Taich, Steven A. Hillyard, and Vilayanur S. Ramachandran. "Task dependent anatomical connections underlie multisensory processing." Seeing and Perceiving 25 (2012): 8. http://dx.doi.org/10.1163/187847612x646316.

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Our senses interact in daily life through multisensory integration, facilitating perceptual processes and behavioral responses. Numerous multisensory regions have been identified in humans and animals, raising the question of whether a single mechanism can support the dynamic range of experiences and behaviors multisensory processing engenders. The most common neural mechanisms proposed to underlie multisensory processing include anatomical connections directly linking early sensory areas, indirect connections to higher-order multisensory regions, and functional connectivity between cortical a
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Schubert, Teresa. "Multisensory processing without vision." Nature Reviews Psychology 1, no. 2 (2022): 71. http://dx.doi.org/10.1038/s44159-022-00021-7.

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Yau, Jeffrey M., Gregory C. DeAngelis, and Dora E. Angelaki. "Dissecting neural circuits for multisensory integration and crossmodal processing." Philosophical Transactions of the Royal Society B: Biological Sciences 370, no. 1677 (2015): 20140203. http://dx.doi.org/10.1098/rstb.2014.0203.

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We rely on rich and complex sensory information to perceive and understand our environment. Our multisensory experience of the world depends on the brain's remarkable ability to combine signals across sensory systems. Behavioural, neurophysiological and neuroimaging experiments have established principles of multisensory integration and candidate neural mechanisms. Here we review how targeted manipulation of neural activity using invasive and non-invasive neuromodulation techniques have advanced our understanding of multisensory processing. Neuromodulation studies have provided detailed charac
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McCracken, Heather S., Bernadette A. Murphy, James J. Burkitt, Cheryl M. Glazebrook, and Paul C. Yielder. "Audiovisual Multisensory Processing in Young Adults With Attention-Deficit/Hyperactivity Disorder." Multisensory Research 33, no. 6 (2020): 599–623. http://dx.doi.org/10.1163/22134808-20191472.

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Abstract Multisensory integration is a fundamental form of sensory processing that is involved in many everyday tasks. Those with Attention-Deficit/Hyperactivity Disorder (ADHD) have characteristic alterations to various brain regions that may influence multisensory processing. The overall aim of this work was to assess how adults with ADHD process audiovisual multisensory stimuli during a complex response time task. The paradigm used was a two-alternative forced-choice discrimination task paired with continuous 64-electrode electroencephalography, allowing for the measurement of response time
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Berthiaume, Maxine, Giulia Corno, Kevin Nolet, and Stéphane Bouchard. "A Novel Integrated Information Processing Model of Presence." PRESENCE: Virtual and Augmented Reality 27, no. 4 (2018): 378–99. http://dx.doi.org/10.1162/pres_a_00336.

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Abstract The objective of this article is to conduct a narrative literature review on multisensory integration and propose a novel information processing model of presence in virtual reality (VR). The first half of the article introduces basic multisensory integration (implicit information processing) and the integration of coherent stimuli (explicit information processing) in the physical environment, offering an explanation for people's reactions during VR immersions and is an important component of our model. To help clarify these concepts, examples are provided. The second half of the arti
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Simon, S. A., I. E. de Araujo, J. R. Stapleton, and M. A. L. Nicolelis. "Multisensory Processing of Gustatory Stimuli." Chemosensory Perception 1, no. 2 (2008): 95–102. http://dx.doi.org/10.1007/s12078-008-9014-4.

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Dunham, Kacie, Alisa Zoltowski, Jacob I. Feldman, et al. "Neural Correlates of Audiovisual Speech Processing in Autistic and Non-Autistic Youth." Multisensory Research 36, no. 3 (2023): 263–88. http://dx.doi.org/10.1163/22134808-bja10093.

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Abstract Autistic youth demonstrate differences in processing multisensory information, particularly in temporal processing of multisensory speech. Extensive research has identified several key brain regions for multisensory speech processing in non-autistic adults, including the superior temporal sulcus (STS) and insula, but it is unclear to what extent these regions are involved in temporal processing of multisensory speech in autistic youth. As a first step in exploring the neural substrates of multisensory temporal processing in this clinical population, we employed functional magnetic res
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Johnston, Phillip R., Claude Alain, and Anthony R. McIntosh. "Individual Differences in Multisensory Processing Are Related to Broad Differences in the Balance of Local versus Distributed Information." Journal of Cognitive Neuroscience 34, no. 5 (2022): 846–63. http://dx.doi.org/10.1162/jocn_a_01835.

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Abstract The brain's ability to extract information from multiple sensory channels is crucial to perception and effective engagement with the environment, but the individual differences observed in multisensory processing lack mechanistic explanation. We hypothesized that, from the perspective of information theory, individuals with more effective multisensory processing will exhibit a higher degree of shared information among distributed neural populations while engaged in a multisensory task, representing more effective coordination of information among regions. To investigate this, healthy
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Allman, Brian L., and M. Alex Meredith. "Multisensory Processing in “Unimodal” Neurons: Cross-Modal Subthreshold Auditory Effects in Cat Extrastriate Visual Cortex." Journal of Neurophysiology 98, no. 1 (2007): 545–49. http://dx.doi.org/10.1152/jn.00173.2007.

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Historically, the study of multisensory processing has examined the function of the definitive neuron type, the bimodal neuron. These neurons are excited by inputs from more than one sensory modality, and when multisensory stimuli are present, they can integrate their responses in a predictable manner. However, recent studies have revealed that multisensory processing in the cortex is not restricted to bimodal neurons. The present investigation sought to examine the potential for multisensory processing in nonbimodal (unimodal) neurons in the retinotopically organized posterolateral lateral su
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Dissertations / Theses on the topic "Multisensory processing"

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Steven, M. S. "Neuroimaging of multisensory processing and synaesthesia." Thesis, University of Oxford, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.410663.

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Thesen, Thomas. "Multisensory processing in the human brain." Thesis, University of Oxford, 2005. http://ora.ox.ac.uk/objects/uuid:e644c5d7-1cf6-42d5-b073-86f1f70a48b6.

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Perception has traditionally been studied as a modular function where different sensory systems operate as separate and independent modules. However, multisensory integration is essential for the perception of a coherent and unified representation of the external world that we experience phenomenologically. Mounting evidence suggests that the senses do not operate in isolation but that the brain processes and integrates information across modalities. A standing debate is at what level in the processing hierarchy the sensory streams converge, for example, if multisensory speech information conv
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Hammond-Kenny, Amy J. "Multisensory processing in the ferret auditory cortex." Thesis, University of Oxford, 2017. http://ora.ox.ac.uk/objects/uuid:4e9a731e-4001-4e88-8ffb-7c0c7317050a.

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Our perception of events depends on the integration of information derived from different sensory modalities. Functional imaging and electrophysiological studies have shown that multisensory interactions occur even at the level of primary sensory cortices; however their functional significance remains elusive. Therefore, to explore the relationship between multisensory interactions in early auditory cortical processing areas and behaviour, we recorded activity from the auditory cortex during the performance of different auditory-visual tasks. Ferrets were trained by positive operant-conditioni
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Roa, Romero Yadira [Verfasser]. "Neural correlates of multisensory processing / Yadira Roa Romero." Berlin : Freie Universität Berlin, 2016. http://d-nb.info/1099952263/34.

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Harper, Ross E. F. "'Time flies' : multisensory processing by circadian clocks in Drosophila melanogaster." Thesis, University College London (University of London), 2017. http://discovery.ucl.ac.uk/10037959/.

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Periodic changes of environmental signals are sufficient to synchronise circadian rhythms across species. Circadian time, then, is a concept tethered to a diverse spread of different sensory modalities. In spite of this fact, circadian systems have historically been studied in a unimodal fashion investigating the processing of singular cues, while keeping others constant. My research sought to challenge this dogma via exploration of multisensory cue combination in the circadian clock of Drosophila melanogaster. Systematic behavioural analysis in wild type flies showed that misalignments betwee
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Kawakami, Sayaka. "Atypical Multisensory Integration and the Temporal Binding Window in Autism Spectrum Disorder." Doctoral thesis, Kyoto University, 2021. http://hdl.handle.net/2433/263586.

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Regener, Paula. "Investigation of multisensory processing and structural brain differences in Autism Spectrum Disorder." Thesis, University of Glasgow, 2015. http://theses.gla.ac.uk/7432/.

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This thesis is an investigation of structural brain abnormalities, as well as multisensory and unisensory processing deficits in autistic traits and Autism Spectrum Disorder (ASD). To achieve this, structural and functional magnetic resonance imaging (fMRI) and psychophysical techniques were employed. ASD is a neurodevelopmental condition which is characterised by the social communication and interaction deficits, as well as repetitive patterns of behaviour, interests and activities. These traits are thought to be present in a typical population. The Autism Spectrum Quotient questionnaire (AQ)
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Jennings, Michael. "Effect of Attentional Capture and Cross-Modal Interference in Multisensory Cognitive Processing." ScholarWorks, 2018. https://scholarworks.waldenu.edu/dissertations/4793.

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Despite considerable research, the effects of common types of noise on verbal and spatial information processing are still relatively unknown. Three experiments, using convenience sampling were conducted to investigate the effect of auditory interference on the cognitive performance of 24 adult men and women during the Stroop test, perception of object recognition and spatial location tasks, and the perception of object size, shape, and spatial location tasks. The data were analyzed using univariate analysis of variance and 1-way multivariate analysis of variance. The Experiment 1 findings ind
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Nierula, Birgit. "Multisensory processing and agency in VR embodiment: Interactions through BCI and their therapeutic applications." Doctoral thesis, Universitat de Barcelona, 2017. http://hdl.handle.net/10803/461771.

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Body ownership refers to the experience that this body is my body and is closely linked to consciousness. Multisensory integration processes play an important role in body ownership as shown in the rubber hand illusion, which induces the illusory experience that a rubber hand is part of one's own body. Illusions of body ownership can also be experienced in immersive virtual reality (VR), which was used in all three experiments of this thesis. The first experiment of this thesis aimed at investigating some of the underlying mechanisms of body ownership. Specifically we were interested whether
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Balz, Johanna [Verfasser]. "Neurophysiological and biochemical markers of multisensory processing in schizophrenia and healthy adults / Johanna Balz." Berlin : Freie Universität Berlin, 2018. http://d-nb.info/117670897X/34.

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Books on the topic "Multisensory processing"

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Gemma, Calvert, Spence Charles, and Stein Barry E, eds. The handbook of multisensory processes. MIT Press, 2004.

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NATO Advanced Study Institute on Multisensor Data and Information Processing for Rapid and Robust Situation and Threat Assessment (2005 Albena, Bulgaria). Advances and challenges in multisensor data and information processing. IOS Press, 2007.

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Klein, Lawrence A. Millimeter-wave and infrared multisensor design and signal processing. Artech House, 1997.

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Yaakov, Bar-Shalom, and University of California, Los Angeles. University Extension., eds. Multitarget-multisensor tracking: Applications and advances. Artech House, 1990.

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Zhu, Yunmin. Multisensor Decision And Estimation Fusion. Springer US, 2003.

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Hall, David L. Mathematical techniques in multisensor data fusion. Artech House, 1992.

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L, Hall David. Mathematical techniques in multisensor data fusion. Artech House, 1992.

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Clark, James Joseph. Data fusion for sensory information processing systems. Kluwer Academic Publishers, 1990.

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1954-, Docampo D., Figueiras-Vidal A. R, and Pérez-González F. 1967-, eds. Intelligent methods in signal processing and communications. Birkhäuser, 1997.

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Foresti, Gian Luca. Multisensor Surveillance Systems: The Fusion Perspective. Springer US, 2003.

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Book chapters on the topic "Multisensory processing"

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Spence, Charles, and Cristy Ho. "Multisensory information processing." In APA handbook of human systems integration. American Psychological Association, 2015. http://dx.doi.org/10.1037/14528-027.

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Wallraven, Christian. "Multisensory Shape Processing." In Shape Perception in Human and Computer Vision. Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5195-1_32.

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King, Andrew J., Amy Hammond-Kenny, and Fernando R. Nodal. "Multisensory Processing in the Auditory Cortex." In Multisensory Processes. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-10461-0_6.

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Lee, Adrian K. C., Ross K. Maddox, and Jennifer K. Bizley. "An Object-Based Interpretation of Audiovisual Processing." In Multisensory Processes. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-10461-0_4.

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King, Andrew J., Amy Hammond-Kenny, and Fernando R. Nodal. "Correction to: Multisensory Processing in the Auditory Cortex." In Multisensory Processes. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-10461-0_13.

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Baum Miller, Sarah H., and Mark T. Wallace. "Multisensory Processing Differences in Individuals with Autism Spectrum Disorder." In Multisensory Processes. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-10461-0_12.

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Rohe, Tim, and Marc L. Zeise. "Inputs, Outputs, and Multisensory Processing." In Neuroscience for Psychologists. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47645-8_6.

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Zuo, Yanfang, and Zuoren Wang. "Neural Oscillations and Multisensory Processing." In Advances in Experimental Medicine and Biology. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-7611-9_8.

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Wang, He, Wen-Hao Zhang, K. Y. Michael Wong, and Si Wu. "Encoding Multisensory Information in Modular Neural Networks." In Neural Information Processing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70093-9_70.

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Barone, Pascal, and Olivier Deguine. "Multisensory Processing in Cochlear Implant Listeners." In Auditory Prostheses. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9434-9_15.

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Conference papers on the topic "Multisensory processing"

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Belyaeva, Irina, Yu-Ping Wang, Tony W. Wilson, Vince D. Calhoun, Julia M. Stephen, and Tülay Adali. "Assessing Pediatric Cognitive Development via Multisensory Brain Imaging Analysis." In 2024 32nd European Signal Processing Conference (EUSIPCO). IEEE, 2024. http://dx.doi.org/10.23919/eusipco63174.2024.10714926.

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Saud, Sean, Connar Hite, Ashley Diehl, and Tarek Taha. "Leveraging hierarchical methods for multisensor fusion." In Signal Processing, Sensor/Information Fusion, and Target Recognition XXXIV, edited by Lynne L. Grewe, Erik P. Blasch, and Ivan Kadar. SPIE, 2025. https://doi.org/10.1117/12.3053479.

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Li, Tongyang, and Yuqi Huang. "Stereo vision and UWB-based multisensor fusion SLAM." In Fifth International Conference on Signal Processing and Computer Science (SPCS 2024), edited by Haiquan Zhao and Lei Chen. SPIE, 2025. https://doi.org/10.1117/12.3052925.

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Lim, H. K., L. P. Keniston, J. H. Shin, C. Nguyen, M. A. Meredith, and K. J. Cios. "A Neuronal Multisensory Processing Simulator." In 2010 International Joint Conference on Neural Networks (IJCNN). IEEE, 2010. http://dx.doi.org/10.1109/ijcnn.2010.5596777.

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Nadimi Shahraki, Amirreza, Maida Toumaian, Jian Liu, Nikolaos Smyrnis, and Ioannis Delis. "Neurocomputational characterisation of differences in multisensory processing in Autism and Schizophrenia." In 2023 Conference on Cognitive Computational Neuroscience. Cognitive Computational Neuroscience, 2023. http://dx.doi.org/10.32470/ccn.2023.1545-0.

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"A MULTISENSORY MULTIMEDIA MODEL TO SUPPORT DYSLEXIC CHILDREN IN LEARNING." In International Conference on Signal Processing and Multimedia Applications. SciTePress - Science and and Technology Publications, 2010. http://dx.doi.org/10.5220/0002885901930202.

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Kawahara, Misako, Disa Sauter, and Akihiro Tanaka. "Impact of Culture on the Development of Multisensory Emotion Perception." In The 14th International Conference on Auditory-Visual Speech Processing. ISCA, 2017. http://dx.doi.org/10.21437/avsp.2017-21.

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Ichihashi, Kazuyoshi, and Hideki Ichihashi. "Multisensory images evoked by olfactory stimuli: Crossmodal correspondences in human information processing." In 2014 15th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD). IEEE, 2014. http://dx.doi.org/10.1109/snpd.2014.6888735.

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Kawase, Marina, Ikuma Adachi, and Akihiro Tanaka. "Multisensory Perception of Emotion for Human and Chimpanzee Expressions by Humans." In The 14th International Conference on Auditory-Visual Speech Processing. ISCA, 2017. http://dx.doi.org/10.21437/avsp.2017-22.

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Hamacher, Kevin, and Rüdiger Buchkremer. "Sensory-Marketing-Evaluation of E-Commerce Websites with Artificial Intelligence." In Digital Support from Crisis to Progressive Change. University of Maribor Press, 2021. http://dx.doi.org/10.18690/978-961-286-485-9.51.

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Multisensory consumer engagement on e-commerce websites is technically limited to visual, acoustic, and written elements. Consumers communicate, buy, and share products and services via digital environments in which sensory information is limited. To improve consumers' online sensory experience, media types and the content need to be quantitatively assessed and adapted. This project aims to develop a quantitative model, an Online Sensory Marketing Index (OSMI), which assesses ecommerce websites in multisensory communication quality. The OSMI will be supported by an automatic procedure that is
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Reports on the topic "Multisensory processing"

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Navarro, Luke, Shea Hammond, and Richard Johansen. Sensor fusion for aerial robotic system. Engineer Research and Development Center (U.S.), 2025. https://doi.org/10.21079/11681/49701.

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As uncrewed aerial vehicle (drone) use expands across industries so also does the complexity of sensor payloads. At present, there are no commercially available products for the management and fusion of multisensor data. Sensor Fusion for Aerial Robotic Systems (SFARS) is a sensor agnostic, modular platform for intelligent multisensor data fusion and processing. At the time of writing, SFARS exists as a root codebase, a PC application for processing of previously collected drone data and as a prototype hardware platform for real-time drone deployment. This report serves as a technical users gu
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Vann, Laura D., Kevin M. Cuomo, Jean E. Piou, and Joseph T. Mayhan. Multisensor Fusion Processing for Enhanced Radar Imaging. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada376545.

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Kashyap, Rangasami L. Integrated Detection and Estimation Using Multisensor and Distributed Processing. Defense Technical Information Center, 1999. http://dx.doi.org/10.21236/ada368042.

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Amin, Moeness G. Blind Time-Frequency Analysis for Source Discrimination in Multisensor Array Processing. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada390472.

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Tugnait, Jitendra K. Multichannel/Multisensors Signal Processing In Uncertain Environments With Application To Multitarget Tracking. Defense Technical Information Center, 1999. http://dx.doi.org/10.21236/ada363949.

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Tugnait, Jitendra K. Multichannel/Multisensor Signal Processing In Uncertain Environments With Application To Multitarget Tracking. Defense Technical Information Center, 1998. http://dx.doi.org/10.21236/ada345218.

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Tugnait, Jitendra K. Multichannel/Multisensor Signal Processing in Uncertain Environments With Application To Multitarget Tracking. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada381457.

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Miller, J., B. Barrow, T. Bell, D. Keiswetter, and I. J. Won. Processing Techniques for Discrimination Between Buried UXO and Clutter Using Multisensor Array Data. Defense Technical Information Center, 1999. http://dx.doi.org/10.21236/ada379880.

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