Sensory Cue Integration

This book is concerned with sensory cue integration both within and between sensory modalities, and focuses on the emerging way of thinking about cue combination in terms of uncertainty. These probabilistic approaches derive from the realization that our sensors are noisy and moreover are often affected by ambiguity. For example, mechanoreceptor outputs are variable and they cannot distinguish if a perceived force is caused by the weight of an object or by force we are producing ourselves. The probabilistic approaches elaborated in this book aim at formalizing the uncertainty of cues. They describe cue combination as the nervous system's attempt to minimize uncertainty in its estimates and to choose successful actions. Some computational approaches described in the chapters of this book are concerned with the application of such statistical ideas to real-world cue-combination problems. Others ask how uncertainty may be represented in the nervous system and used for cue combination. Importantly, across behavioral, electrophysiological and theoretical approaches, Bayesian statistics is emerging as a common language in which cue-combination problems can be expressed.

This book is concerned with sensory cue integration both within and between sensory modalities, and focuses on the emerging way of thinking about cue combination in terms of uncertainty. These probabilistic approaches derive from the realization that our sensors are noisy and moreover are often affected by ambiguity. For example, mechanoreceptor outputs are variable and they cannot distinguish if a perceived force is caused by the weight of an object or by force we are producing ourselves. The probabilistic approaches elaborated in this book aim at formalizing the uncertainty of cues. They describe cue combination as the nervous system's attempt to minimize uncertainty in its estimates and to choose successful actions. Some computational approaches described in the chapters of this book are concerned with the application of such statistical ideas to real-world cue-combination problems. Others ask how uncertainty may be represented in the nervous system and used for cue combination. Importantly, across behavioral, electrophysiological and theoretical approaches, Bayesian statistics is emerging as a common language in which cue-combination problems can be expressed

It has become accepted in the neuroscience community that perception and performance are quintessentially multisensory by nature. Using the full palette of modern brain imaging and neuroscience methods, The Neural Bases of Multisensory Processes details current understanding in the neural bases for these phenomena as studied across species, stages of development, and clinical statuses. Organized thematically into nine sub-sections, the book is a collection of contributions by leading scientists in the field. Chapters build generally from basic to applied, allowing readers to ascertain how fundamental science informs the clinical and applied sciences. Topics discussed include: Anatomy, essential for understanding the neural substrates of multisensory processing Neurophysiological bases and how multisensory stimuli can dramatically change the encoding processes for sensory information Combinatorial principles and modeling, focusing on efforts to gain a better mechanistic handle on multisensory operations and their network dynamics Development and plasticity Clinical manifestations and how perception and action are affected by altered sensory experience Attention and spatial representations The last sections of the book focus on naturalistic multisensory processes in three separate contexts: motion signals, multisensory contributions to the perception and generation of communication signals, and how the perception of flavor is generated. The text provides a solid introduction for newcomers and a strong overview of the current state of the field for experts.

Head direction (HD) cells fire as a function of the animal's heading direction, with each cell responding to a specific head orientation. This thesis describes the findings from single cell recordings of HD cells in rats. In particular, the thesis focuses on the manner in which these cells integrate landmark and path integration cues. The first experimental chapter aimed to assess the degree to which HD cell firing is influenced by the geometry of an environment. The findings from this experiment suggest that the reliability of the path integration signal affects the degree to which HD cells integrate geometric cues. As the stability of the path integration signal increased (rat was oriented) the weighting of geometric cues decreased. Conversely, when the stability of the path integration signal decreased (rat was disoriented) the weighting of geometric cues increased. This finding, that the influence of a cue is inversely proportional to the variability of that cue, supports a Bayesian account of cue integration. The second experiment therefore directly tested a Bayesian model of cue integration. HD cells and behaviour were simultaneously recorded during a conflict between path integration signals and landmark information. Behavioural findings did not support a Bayesian model, but recordings of HD cells did show evidence of cue weighting based on cue reliability. Interestingly, the reliability of the cues was not expressed in the cells' firing rates or tuning widths and therefore the reliability coding could occur before the signal is sent to the HD system. The final experiment had three main objectives: To establish when HD cells switch from primarily using landmark information to mostly using path integration information. The second aim was to reveal whether HD cells in different areas of the HD circuit respond to these two sensory cues in a similar manner. The third aim was to provide an insight into how activity in the HD network propagates from one preferred firing direction to another. HD cell firing demonstrated cue integration across the system with no differences in brain region. At small conflicts, HD cell firing suggested landmark dominance, whilst larger conflicts demonstrated a greater weighting of path integration. Preliminary findings also suggest that activity can sweep from one preferred firing direction to the next.

Multisensory Flavor Perception: From Fundamental Neuroscience Through to the Marketplace provides state-of-the-art coverage of the latest insights from the rapidly-expanding world of multisensory flavor research. The book highlights the various types of crossmodal interactions, such as sound and taste, and vision and taste, showing their impact on sensory and hedonic perception, along with their consumption in the context of food and drink. The chapters in this edited volume review the existing literature, also explaining the underlying neural and psychological mechanisms which lead to crossmodal perception of flavor. The book brings together research which has not been presented before, making it the first book in the market to cover the literature of multisensory flavor perception by incorporating the latest in psychophysics and neuroscience. Authored by top academics and world leaders in the field Takes readers on a journey from the neurological underpinnings of multisensory flavor perception, then presenting insights that can be used by food companies to create better flavor sensations for consumers Offers a wide perspective on multisensory flavor perception, an area of rapidly expanding knowledge

Philosophers and cognitive scientists address the relationships among the senses and the connections between conscious experiences that form unified wholes. In this volume, cognitive scientists and philosophers examine two closely related aspects of mind and mental functioning: the relationships among the various senses and the links that connect different conscious experiences to form unified wholes. The contributors address a range of questions concerning how information from one sense influences the processing of information from the other senses and how unified states of consciousness emerge from the bonds that tie conscious experiences together. Sensory Integration and the Unity of Consciousness is the first book to address both of these topics, integrating scientific and philosophical concerns. A flood of recent work in both philosophy and perception science has challenged traditional conceptions of the sensory systems as operating in isolation. Contributors to the volume consider the ways in which perceptual contact with the world is or may be “multisensory,” discussing such subjects as the modeling of multisensory integration and philosophical aspects of sensory modalities. Recent years have seen a similar surge of interest in unity of consciousness. Contributors explore a range of questions on this topic, including the nature of that unity, the degree to which conscious experiences are unified, and the relationship between unified consciousness and the self. Contributors Tim Bayne, David J. Bennett, Berit Brogaard, Barry Dainton, Ophelia Deroy, Frederique de Vignemont, Marc Ernst, Richard Held, Christopher S. Hill, Geoffrey Lee, Kristan Marlow, Farid Masrour, Jennifer Matey, Casey O'Callaghan, Cesare V. Parise, Kevin Rice, Elizabeth Schechter, Pawan Sinha, Julia Trommershaeuser, Loes C. J. van Dam, Jonathan Vogel, James Van Cleve, Robert Van Gulick, Jonas Wulff

We perceive and understand our environment using many sensory systems-vision, touch, hearing, taste, smell, and proprioception. These multiple sensory modalities give us complementary sources of information about the environment. This book explores how we develop the ability to integrate our senses.

Experimental and theoretical neuroscientists use Bayesian approaches to analyze the brain mechanisms of perception, decision-making, and motor control.

A comprehensive Introduction to the world of brain and behavior computational models This book provides a broad collection of articles covering different aspects of computational modeling efforts in psychology and neuroscience. Specifically, it discusses models that span different brain regions (hippocampus, amygdala, basal ganglia, visual cortex), different species (humans, rats, fruit flies), and different modeling methods (neural network, Bayesian, reinforcement learning, data fitting, and Hodgkin-Huxley models, among others). Computational Models of Brain and Behavior is divided into four sections: (a) Models of brain disorders; (b) Neural models of behavioral processes; (c) Models of neural processes, brain regions and neurotransmitters, and (d) Neural modeling approaches. It provides in-depth coverage of models of psychiatric disorders, including depression, posttraumatic stress disorder (PTSD), schizophrenia, and dyslexia; models of neurological disorders, including Alzheimer’s disease, Parkinson’s disease, and epilepsy; early sensory and perceptual processes; models of olfaction; higher/systems level models and low-level models; Pavlovian and instrumental conditioning; linking information theory to neurobiology; and more. Covers computational approximations to intellectual disability in down syndrome Discusses computational models of pharmacological and immunological treatment in Alzheimer's disease Examines neural circuit models of serotonergic system (from microcircuits to cognition) Educates on information theory, memory, prediction, and timing in associative learning Computational Models of Brain and Behavior is written for advanced undergraduate, Master's and PhD-level students—as well as researchers involved in computational neuroscience modeling research.