Download Free On The Role Of Correspondence Noise In Human Visual Motion Perception Book in PDF and EPUB Free Download. You can read online On The Role Of Correspondence Noise In Human Visual Motion Perception and write the review.

One of the major goals of this thesis is to investigate the extent to which correspondence noise, (i.e., the false pairing of dots in adjacent frames) limits motion detection performance in random dot kinematograms (RDKs). The performance measures of interest are Dmax and Dmin i.e., the largest and smallest inter-frame dot displacement, respectively, for which motion can be reliably detected. Dmax and threshold coherence (i.e., the smallest proportion of dots that must be moved between frames for motion to be reliably detected) in RDKs are known to be affected by false pairing or correspondence noise. Here the roles of correspondence noise and receptive field geometry in limiting performance are investigated. The range of Dmax observed in the literature is consistent with the current information-limit based interpretation. Dmin is interpreted in the light of correspondence noise and under-sampling. Based on the psychophysical experiments performed in the early parts of the dissertation, a model for correspondence noise based on the principle of receptive field scaling is developed for Dmax. Model simulations provide a good account of psychophysically estimated Dmax over a range of stimulus parameters, showing that correspondence noise and receptive field geometry have a major influence on displacement thresholds.
The Cambridge Handbook of Applied Perception Research covers core areas of research in perception with an emphasis on its application to real-world environments. Topics include multisensory processing of information, time perception, sustained attention, and signal detection, as well as pedagogical issues surrounding the training of applied perception researchers. In addition to familiar topics, such as perceptual learning, the Handbook focuses on emerging areas of importance, such as human-robot coordination, haptic interfaces, and issues facing societies in the twenty-first century (such as terrorism and threat detection, medical errors, and the broader implications of automation). Organized into sections representing major areas of theoretical and practical importance for the application of perception psychology to human performance and the design and operation of human-technology interdependence, it also addresses the challenges to basic research, including the problem of quantifying information, defining cognitive resources, and theoretical advances in the nature of attention and perceptual processes.
Noise has been widely used to investigate the processing properties of various visual functions (e.g. detection, discrimination, attention, perceptual learning, averaging, crowding, face recognition), in various populations (e.g. older adults, amblyopes, migrainers, dyslexic children), using noise along various dimensions (e.g. pixel noise, orientation jitter, contrast jitter). The reason to use external noise is generally not to characterize visual processing in external noise per se, but rather to reveal how vision works in ordinary conditions when performance is limited by our intrinsic noise rather than externally added noise. For instance, reverse correlation aims at identifying the relevant information to perform a given task in noiseless conditions and measuring contrast thresholds in various noise levels can be used to understand the impact of intrinsic noise that limits sensitivity to noiseless stimuli. Why use noise? Since Fechner named it, psychophysics has always emphasized the systematic investigation of conditions that break vision. External noise raises threshold hugely and selectively. In hearing, Fletcher used noise in his famous critical-band experiments to reveal frequency-selective channels in hearing. Critical bands have been found in vision too. More generally, the big reliable effects of noise give important clues to how the system works. And simple models have been proposed to account for the effects of visual noise. As noise has been more widely used, questions have been raised about the simplifying assumptions that link the processing properties in noiseless conditions to measurements in external noise. For instance, it is usually assumed that the processing strategy (or mechanism) used to perform a task and its processing properties (e.g. filter tuning) are unaffected by the addition of external noise. Some have suggested that the processing properties could change with the addition of external noise (e.g. change in filter tuning or more lateral masking in noise), which would need to be considered before drawing conclusions about the processing properties in noiseless condition. Others have suggested that different processing properties (or mechanisms) could be solicited in low and high noise conditions, complicating the characterization of processing properties in noiseless condition based on processing properties identified in noise conditions. The current Research Topic probes further into what the effects of visual noise tell us about vision in ordinary conditions. Our Editorial gives an overview of the articles in this special issue.
In everyday experience, visual motion is an extremely important source of information about the world. Motion cues are vital to our perception of where objects are and where they are moving. Biological motion cues give us the information from which to build the fine-grained, almost subconscious understanding of another's emotions and intentions that is so often necessary in social interactions.
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.
The major reference work for a rapidly advancing field synthesizes central themes, reports on current findings, and offers a blueprint for future research. Scientists' attempts to understand the physiology underlying our apprehension of the physical world was long dominated by a focus on the individual senses. The 1980s saw the beginning of systematic efforts to examine interactions among different sensory modalities at the level of the single neuron. And by the end of the 1990s, a recognizable and multidisciplinary field of "multisensory processes" had emerged. More recently, studies involving both human and nonhuman subjects have focused on relationships among multisensory neuronal ensembles and their behavioral, perceptual, and cognitive correlates. The New Handbook of Multisensory Processing synthesizes the central themes in this rapidly developing area, reports on current findings, and offers a blueprint for future research. The contributions, all of them written for this volume by leading experts, reflect the evolution and current state of the field. This handbook does more than simply review the field. Each of the volume's eleven sections broadly surveys a major topic, and each begins with a substantive and thought-provoking commentary by the section editor that identifies the major issues being explored, describes their treatment in the chapters that follow, and sets these findings within the context of the existing body of knowledge. Together, the commentaries and chapters provide an invaluable guide to areas of general agreement, unresolved issues, and topics that remain to be explored in this fast-moving field.
"Visual neurons are known to exhibit a significant degree of variability in their response to the same visual input. From the perspective of the experimenter, "noise" refers to any intrinsic source of neural and behavioural variability. Noise in visual neurons is an important contributor of trial-to-trial variability in the behavioural response. Studies in non-human primates have often associated intrinsic noise with significant impairments in perceptual judgment. In this thesis, I explore the underlying neural mechanisms behind multi-element motion perception, in the presence of intrinsic ("noise") and extrinsic (stimulus-driven) correlations. We look at the timecourse of correlated activity in MT neurons, and examine its relationship to the visual stimulus and behavioural outcomes. We show that very short timescale "noise" correlations between MT neurons can account for a significant degree of behavioural variability in a motion coherence detection task (Chapter 2). We also look at feed-forward mechanisms that could give rise to the link between short timescale correlations in area MT and behavioural outcomes. And explore the timescales in which visual inputs can dynamically alter correlations, in a motion correlation detection task. In Chapter 3, we demonstrate that unlike "noise" correlations, stimulus-driven correlations in MT neurons can enhance performance, depending on their timescale, and the computational demands of our task. Finally, we carried out a motion direction change detection task in humans (Chapter 4) and showed that integration of remote motion change signals benefits from the degree to which they reverse in the same global direction. " --