scholarly journals A Moment to Reflect upon Perceptual Synchrony

2006 ◽  
Vol 18 (10) ◽  
pp. 1663-1665 ◽  
Author(s):  
Mark A. Elliott ◽  
Zhuanghua Shi ◽  
Sean D. Kelly
Keyword(s):  
Visual Space ◽  
Temporal Correlation ◽  
Visual Signals ◽  
Direct Result ◽  
Visual Features ◽  
Potential Solution ◽  
Binding Problem ◽  
Stimulus Motion ◽  
Neuronal Synchronization ◽  
Highly Correlated

How does neuronal activity bring about the interpretation of visual space in terms of objects or complex perceptual events? If they group, simple visual features can bring about the integration of spikes from neurons responding to different features to within a few milliseconds. Considered as a potential solution to the “binding problem,” it is suggested that neuronal synchronization is the glue for binding together different features of the same object. This idea receives some support from correlated- and periodic-stimulus motion paradigms, both of which suggest that the segregation of a figure from ground is a direct result of the temporal correlation of visual signals. One could say that perception of a highly correlated visual structure permits space to be bound in time. However, on closer analysis, the concept of perceptual synchrony is insufficient to explain the conditions under which events will be seen as simultaneous. Instead, the grouping effects ascribed to perceptual synchrony are better explained in terms of the intervals of time over which stimulus events integrate and seem to occur simultaneously. This point is supported by the equivalence of some of these measures with well-established estimates of the perceptual moment. However, it is time in extension and not the instantaneous that may best describe how seemingly simultaneous features group. This means that studies of perceptual synchrony are insufficient to address the binding problem.

scholarly journals The feature-weighted receptive field: an interpretable encoding model for complex feature spaces

2017 ◽  
Author(s):  
Ghislain St-Yves ◽  
Thomas Naselaris
Keyword(s):  
Receptive Field ◽  
Frequency Tuning ◽  
Brain Activity ◽  
Receptive Fields ◽  
Visual Space ◽  
Model Complexity ◽  
Visual Features ◽  
Natural Scenes ◽  
Feature Maps ◽  
Feature Map

AbstractWe introduce the feature-weighted receptive field (fwRF), an encoding model designed to balance expressiveness, interpretability and scalability. The fwRF is organized around the notion of a feature map—a transformation of visual stimuli into visual features that preserves the topology of visual space (but not necessarily the native resolution of the stimulus). The key assumption of the fwRF model is that activity in each voxel encodes variation in a spatially localized region across multiple feature maps. This region is fixed for all feature maps; however, the contribution of each feature map to voxel activity is weighted. Thus, the model has two separable sets of parameters: “where” parameters that characterize the location and extent of pooling over visual features, and “what” parameters that characterize tuning to visual features. The “where” parameters are analogous to classical receptive fields, while “what” parameters are analogous to classical tuning functions. By treating these as separable parameters, the fwRF model complexity is independent of the resolution of the underlying feature maps. This makes it possible to estimate models with thousands of high-resolution feature maps from relatively small amounts of data. Once a fwRF model has been estimated from data, spatial pooling and feature tuning can be read-off directly with no (or very little) additional post-processing or in-silico experimentation.We describe an optimization algorithm for estimating fwRF models from data acquired during standard visual neuroimaging experiments. We then demonstrate the model’s application to two distinct sets of features: Gabor wavelets and features supplied by a deep convolutional neural network. We show that when Gabor feature maps are used, the fwRF model recovers receptive fields and spatial frequency tuning functions consistent with known organizational principles of the visual cortex. We also show that a fwRF model can be used to regress entire deep convolutional networks against brain activity. The ability to use whole networks in a single encoding model yields state-of-the-art prediction accuracy. Our results suggest a wide variety of uses for the feature-weighted receptive field model, from retinotopic mapping with natural scenes, to regressing the activities of whole deep neural networks onto measured brain activity.

scholarly journals Multisensory correlation computations in the human brain uncovered by a time-resolved encoding model

2021 ◽  
Author(s):  
Jacques Pesnot Lerousseau ◽  
Cesare Parise ◽  
Marc O. Ernst ◽  
Virginie van Wassenhove
Keyword(s):  
Human Brain ◽  
Causal Inference ◽  
Temporal Order ◽  
Temporal Order Judgment ◽  
Explanatory Power ◽  
Temporal Correlation ◽  
Judgment Task ◽  
Visual Signals ◽  
Complex Scene ◽  
Correlation Detector

ABSTRACTNeural mechanisms that arbitrate between integrating and segregating multisensory information are essential for complex scene analysis and for the resolution of the multisensory correspondence problem. However, these mechanisms and their dynamics remain largely unknown, partly because classical models of multisensory integration are static. Here, we used the Multisensory Correlation Detector, a model that provides a good explanatory power for human behavior while incorporating dynamic computations. Participants judged whether sequences of auditory and visual signals originated from the same source (causal inference) or whether one modality was leading the other (temporal order), while being recorded with magnetoencephalography. To test the match between the Multisensory Correlation Detector dynamics and the magnetoencephalographic recordings, we developed a novel dynamic encoding-model approach of electrophysiological activity, which relied on temporal response functions. First, we confirm that the Multisensory Correlation Detector explains causal inference and temporal order patterns well. Second, we found strong fits of brain activity to the two outputs of the Multisensory Correlation Detector in temporo-parietal cortices, a region with known multisensory integrative properties. Finally, we report an asymmetry in the goodness of the fits, which were more reliable during the causal inference than during the temporal order judgment task. Overall, our results suggest the plausible existence of multisensory correlation detectors in the human brain, which explain why and how causal inference is strongly driven by the temporal correlation of multisensory signals.

scholarly journals A new approach to solving the feature-binding problem in primate vision

2018 ◽  
Vol 8 (4) ◽  
pp. 20180021 ◽  
Author(s):  
James B. Isbister ◽  
Akihiro Eguchi ◽  
Nasir Ahmad ◽  
Juan M. Galeazzi ◽  
Mark J. Buckley ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Subjective Experience ◽  
Temporal Structure ◽  
Visual Stimuli ◽  
Spike Timing ◽  
Feature Binding ◽  
Visual Features ◽  
Binding Problem ◽  
Primate Vision ◽  
Spatio Temporal

We discuss a recently proposed approach to solve the classic feature-binding problem in primate vision that uses neural dynamics known to be present within the visual cortex. Broadly, the feature-binding problem in the visual context concerns not only how a hierarchy of features such as edges and objects within a scene are represented, but also the hierarchical relationships between these features at every spatial scale across the visual field. This is necessary for the visual brain to be able to make sense of its visuospatial world. Solving this problem is an important step towards the development of artificial general intelligence. In neural network simulation studies, it has been found that neurons encoding the binding relations between visual features, known as binding neurons, emerge during visual training when key properties of the visual cortex are incorporated into the models. These biological network properties include (i) bottom-up, lateral and top-down synaptic connections, (ii) spiking neuronal dynamics, (iii) spike timing-dependent plasticity, and (iv) a random distribution of axonal transmission delays (of the order of several milliseconds) in the propagation of spikes between neurons. After training the network on a set of visual stimuli, modelling studies have reported observing the gradual emergence of polychronization through successive layers of the network, in which subpopulations of neurons have learned to emit their spikes in regularly repeating spatio-temporal patterns in response to specific visual stimuli. Such a subpopulation of neurons is known as a polychronous neuronal group (PNG). Some neurons embedded within these PNGs receive convergent inputs from neurons representing lower- and higher-level visual features, and thus appear to encode the hierarchical binding relationship between features. Neural activity with this kind of spatio-temporal structure robustly emerges in the higher network layers even when neurons in the input layer represent visual stimuli with spike timings that are randomized according to a Poisson distribution. The resulting hierarchical representation of visual scenes in such models, including the representation of hierarchical binding relations between lower- and higher-level visual features, is consistent with the hierarchical phenomenology or subjective experience of primate vision and is distinct from approaches interested in segmenting a visual scene into a finite set of objects.

scholarly journals Closed-Loop Learning of Visual Control Policies

2007 ◽  
Vol 28 ◽  
pp. 349-391 ◽  
Author(s):  
S. R. Jodogne ◽  
J. H. Piater
Keyword(s):  
Learning Algorithm ◽  
Visual Space ◽  
Visual Navigation ◽  
Visual Features ◽  
Local Descriptors ◽  
Perceptual Aliasing ◽  
Feature Based ◽  
Flexible Framework ◽  
High Level ◽  
The Hill

In this paper we present a general, flexible framework for learning mappings from images to actions by interacting with the environment. The basic idea is to introduce a feature-based image classifier in front of a reinforcement learning algorithm. The classifier partitions the visual space according to the presence or absence of few highly informative local descriptors that are incrementally selected in a sequence of attempts to remove perceptual aliasing. We also address the problem of fighting overfitting in such a greedy algorithm. Finally, we show how high-level visual features can be generated when the power of local descriptors is insufficient for completely disambiguating the aliased states. This is done by building a hierarchy of composite features that consist of recursive spatial combinations of visual features. We demonstrate the efficacy of our algorithms by solving three visual navigation tasks and a visual version of the classical ``Car on the Hill'' control problem.

scholarly journals DR-Transformer: A Multi-Features Fusion Framework for Tropical Cyclones Intensity Estimation

2021 ◽  
Vol 11 (13) ◽  
pp. 6208
Author(s):  
Yicheng Luo ◽  
Yajing Xu ◽  
Si Li ◽  
Qifeng Qian ◽  
Bo Xiao
Keyword(s):  
Tropical Cyclones ◽  
Estimation Error ◽  
Temporal Correlation ◽  
Visual Features ◽  
Great Success ◽  
Intensity Estimation ◽  
Features Fusion ◽  
The Impact ◽  
Fusion Framework ◽  
Better Than

Convolutional neural networks have achieved great success in analyzing potential features inside tropical cyclones (TCs) using satellite images for intensity estimation. However, due to the high similarity of visual features in TC images, it is still a challenge to learn the accurate mapping between TC images and numerical intensity. Existing works mainly focus on the visual features of a single TC, ignoring the impact of intensity continuity and time evolution among TCs on decision making. Therefore, we propose a DR-transformer framework for temporal TC intensity estimation. Inside DR-transformers, a novel DR-extractor can extract Distance-consistency(DC) and Rotation-invariance (RI) features between TC images, and therefore can better learn the contours, structures, and other visual features of each TC image. DC features can reduce the estimation error between adjacent intensities, and RI features can eliminate feature deviation caused by shooting angles and TC rotation. Additionally, a transformer with a DR-extractor as the backbone is applied to aggregate the temporal correlation in a series of TC images, which can learn the evolution from intensity to the visual features of TC. Experiments show that the final result, an RMSE of 7.76 knots, outperforms the baseline, and is better than any previously reported method trained on the TCIR dataset.

Cross-correlation between Auditory and Visual Signals Promotes Multisensory Integration

2013 ◽  
Vol 26 (3) ◽  
pp. 307-316 ◽  
Author(s):  
Cesare V. Parise ◽  
Vanessa Harrar ◽  
Marc O. Ernst ◽  
Charles Spence
Keyword(s):  
Multisensory Integration ◽  
Cross Correlation ◽  
Temporal Structure ◽  
Temporal Correlation ◽  
Visual Signals ◽  
Lower Sensitivity ◽  
Correspondence Problem ◽  
Temporal Structures ◽  
The Cross

Humans are equipped with multiple sensory channels that provide both redundant and complementary information about the objects and events in the world around them. A primary challenge for the brain is therefore to solve the ‘correspondence problem’, that is, to bind those signals that likely originate from the same environmental source, while keeping separate those unisensory inputs that likely belong to different objects/events. Whether multiple signals have a common origin or not must, however, be inferred from the signals themselves through a causal inference process. Recent studies have demonstrated that cross-correlation, that is, the similarity in temporal structure between unimodal signals, represents a powerful cue for solving the correspondence problem in humans. Here we provide further evidence for the role of the temporal correlation between auditory and visual signals in multisensory integration. Capitalizing on the well-known fact that sensitivity to crossmodal conflict is inversely related to the strength of coupling between the signals, we measured sensitivity to crossmodal spatial conflicts as a function of the cross-correlation between the temporal structures of the audiovisual signals. Observers’ performance was systematically modulated by the cross-correlation, with lower sensitivity to crossmodal conflict being measured for correlated as compared to uncorrelated audiovisual signals. These results therefore provide support for the claim that cross-correlation promotes multisensory integration. A Bayesian framework is proposed to interpret the present results, whereby stimulus correlation is represented on the prior distribution of expected crossmodal co-occurrence.

Vocal-Postural Co-Ordination of a Sexually Dimorphic Display in a Monomorphic Species: the Barbary Dove

Behaviour ◽  
1997 ◽  
Vol 134 (5-6) ◽  
pp. 321-335 ◽  
Author(s):  
R.E. Hutchison ◽  
J.B. Hutchison ◽  
L. Fusani
Keyword(s):  
Repetition Rate ◽  
Temporal Structure ◽  
Movement Pattern ◽  
Visual Signals ◽  
Motor Patterns ◽  
Streptopelia Risoria ◽  
Stereotyped Movement ◽  
Highly Correlated ◽  
The Individual ◽  
Acoustic Pattern

AbstractIn the Barbary dove (Streptopelia risoria L.), communication involves visual and vocal signals. In this species, behavioural interaction between sexual partners leads to changes in the reproductive hormonal condition of both sexes. At the beginning of courtship, male doves perform the bowing display. This display is composed of a stereotyped movement pattern (bowing) combined with an acoustic pattern (bow-call). In this paper, we studied the individual temporal patterning of bowing and the bow-call and how they are integrated in the display. The co-ordination between bowing movements and bow-call was analysed using a digital system for the synchronous analysis of acoustic-visual signals. Bow-calls differ between individuals in both temporal and frequency characteristics, and in their repetition rate. The bowing movements do not differ between individuals in their temporal structure but the repetition rate is individually different. The repetition rates of the vocal and postural motor patterns are highly correlated. However, the two signals are not synchronised and the phase delay between them is individually different. We suggest that in the bowing display the gender and the identity are signalled respectively by the bowing pattern and the bow-call. The integration of the two signals generates a third signal, the integrated bowing display rate. The role of the three signals during male-male encounters and during courtship behaviour is discussed.

scholarly journals Visual-spatial Biases in Ensemble Cognition

2019 ◽  
Author(s):  
Lace Padilla
Keyword(s):  
Health And Safety ◽  
Direct Result ◽  
Visual Features ◽  
Visualization Technique ◽  
Top Down ◽  
Popular Method ◽  
Visual Elements ◽  
Visual Spatial ◽  
Reasoning Errors

Given the widespread use of visualizations and their impact on health and safety, it is important to ensure that viewers interpret visualizations as accurately as possible. Ensemble visualizations are an increasingly popular method for visualizing data, as emerging research demonstrates that ensembles can effectively and intuitively communicate traditionally difficult statistical concepts. While a few studies have identified drawbacks to ensemble visualizations, no studies have identified the sources of reasoning biases that could occur with ensemble visualizations. Our previous work with hurricane forecast simulation ensemble visualizations identified a misunderstanding that could have resulted from the visual features of the display. The current study tested the hypothesis that visual-spatial biases, which are biases that are a direct result of the visualization technique, provide a cognitive mechanism to explain this misunderstanding. In three experiments, we tested the role of the visual elements of ensemble visualizations as well as knowledge about the visualization with novice participants (n = 303). The results suggest that previously documented reasoning errors with ensemble displays can be influenced both by changes to the visualization technique and by top-down knowledge-driven processing.

Temperature Dependence of the Critical Electron Dose for Fatty Acid Monolayers

1982 ◽  
Vol 40 ◽  
pp. 78-79
Author(s):  
Kenneth H. Downing ◽  
Robert M. Glaeser
Keyword(s):  
Electron Beam ◽  
Fatty Acid ◽  
Inelastic Scattering ◽  
Temperature Dependence ◽  
Structural Damage ◽  
Direct Result ◽  
Second Step ◽  
Strong Temperature Dependence ◽  
Electron Dose ◽  
Covalent Bonds

The structural damage of molecules irradiated by electrons is generally considered to occur in two steps. The direct result of inelastic scattering events is the disruption of covalent bonds. Following changes in bond structure, movement of the constituent atoms produces permanent distortions of the molecules. Since at least the second step should show a strong temperature dependence, it was to be expected that cooling a specimen should extend its lifetime in the electron beam. This result has been found in a large number of experiments, but the degree to which cooling the specimen enhances its resistance to radiation damage has been found to vary widely with specimen types.

Response and Reinforcement in Operant Audiometry

1987 ◽  
Vol 52 (3) ◽  
pp. 294-299 ◽  
Author(s):  
Michael A. Primus
Keyword(s):  
Operant Conditioning ◽  
Space Versus ◽  
Visual Space ◽  
Central Processing ◽  
Operant Discrimination ◽  
Conditioning Rate ◽  
Significant Difference ◽  
Simple Detection ◽  
Variable Success ◽  
Response Task

Variable success in audiometric assessment of young children with operant conditioning indicates the need for systematic examination of commonly employed techniques. The current study investigated response and reinforcement features of two operant discrimination paradigms with normal I7-month-old children. Findings indicated more responses prior to the onset of habituation when the response task was based on complex central processing skills (localization and coordination of auditory/visual space) versus simple detection. Use of animation in toy reinforcers resulted in more than a twofold increase in the number of subject responses. Results showed no significant difference in response conditioning rate or consistency for the response tasks and forms of reinforcement examined.

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