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Dissociation between Dorsal and Ventral Posterior Parietal Cortical Responses to Incidental Changes in Natural Scenes  [PDF]
Lorelei R. Howard, Dharshan Kumaran, H. Freyja ólafsdóttir, Hugo J. Spiers
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0067988
Abstract: Background The posterior parietal cortex (PPC) is thought to interact with the medial temporal lobe (MTL) to support spatial cognition and topographical memory. While the response of medial temporal lobe regions to topographical stimuli has been intensively studied, much less research has focused on the role of PPC and its functional connectivity with the medial temporal lobe. Methodology/Principle Findings Here we report a dissociation between dorsal and ventral regions of PPC in response to different types of change in natural scenes using an fMRI adaptation paradigm. During scanning subjects performed an incidental target detection task whilst viewing trial unique sequentially presented pairs of natural scenes, each containing a single prominent object. We observed a dissociation between the superior parietal gyrus and the angular gyrus, with the former showing greater sensitivity to spatial change, and the latter showing greater sensitivity to scene novelty. In addition, we observed that the parahippocampal cortex has increased functional connectivity with the angular gyrus, but not superior parietal gyrus, when subjects view change to the scene content. Conclusions/Significance Our findings provide support for proposed dissociations between dorsal and ventral regions of PPC and suggest that the dorsal PPC may support the spatial coding of the visual environment even when this information is incidental to the task at hand. Further, through revealing the differential functional interactions of the SPG and AG with the MTL our results help advance our understanding of how the MTL and PPC cooperate to update representations of the world around us.
Categorization of Natural Dynamic Audiovisual Scenes  [PDF]
Olli Rummukainen, Jenni Radun, Toni Virtanen, Ville Pulkki
PLOS ONE , 2014, DOI: 10.1371/journal.pone.0095848
Abstract: This work analyzed the perceptual attributes of natural dynamic audiovisual scenes. We presented thirty participants with 19 natural scenes in a similarity categorization task, followed by a semi-structured interview. The scenes were reproduced with an immersive audiovisual display. Natural scene perception has been studied mainly with unimodal settings, which have identified motion as one of the most salient attributes related to visual scenes, and sound intensity along with pitch trajectories related to auditory scenes. However, controlled laboratory experiments with natural multimodal stimuli are still scarce. Our results show that humans pay attention to similar perceptual attributes in natural scenes, and a two-dimensional perceptual map of the stimulus scenes and perceptual attributes was obtained in this work. The exploratory results show the amount of movement, perceived noisiness, and eventfulness of the scene to be the most important perceptual attributes in naturalistically reproduced real-world urban environments. We found the scene gist properties openness and expansion to remain as important factors in scenes with no salient auditory or visual events. We propose that the study of scene perception should move forward to understand better the processes behind multimodal scene processing in real-world environments. We publish our stimulus scenes as spherical video recordings and sound field recordings in a publicly available database.
Cortical Sensitivity to Visual Features in Natural Scenes  [PDF]
Gidon Felsen,Jon Touryan,Feng Han,Yang Dan
PLOS Biology , 2012, DOI: 10.1371/journal.pbio.0030342
Abstract: A central hypothesis concerning sensory processing is that the neuronal circuits are specifically adapted to represent natural stimuli efficiently. Here we show a novel effect in cortical coding of natural images. Using spike-triggered average or spike-triggered covariance analyses, we first identified the visual features selectively represented by each cortical neuron from its responses to natural images. We then measured the neuronal sensitivity to these features when they were present in either natural images or random stimuli. We found that in the responses of complex cells, but not of simple cells, the sensitivity was markedly higher for natural images than for random stimuli. Such elevated sensitivity leads to increased detectability of the visual features and thus an improved cortical representation of natural scenes. Interestingly, this effect is due not to the spatial power spectra of natural images, but to their phase regularities. These results point to a distinct visual-coding strategy that is mediated by contextual modulation of cortical responses tuned to the spatial-phase structure of natural scenes.
Cortical Sensitivity to Visual Features in Natural Scenes  [PDF]
Gidon Felsen,Jon Touryan,Feng Han,Yang Dan
PLOS Biology , 2005, DOI: 10.1371/journal.pbio.0030342
Abstract: A central hypothesis concerning sensory processing is that the neuronal circuits are specifically adapted to represent natural stimuli efficiently. Here we show a novel effect in cortical coding of natural images. Using spike-triggered average or spike-triggered covariance analyses, we first identified the visual features selectively represented by each cortical neuron from its responses to natural images. We then measured the neuronal sensitivity to these features when they were present in either natural images or random stimuli. We found that in the responses of complex cells, but not of simple cells, the sensitivity was markedly higher for natural images than for random stimuli. Such elevated sensitivity leads to increased detectability of the visual features and thus an improved cortical representation of natural scenes. Interestingly, this effect is due not to the spatial power spectra of natural images, but to their phase regularities. These results point to a distinct visual-coding strategy that is mediated by contextual modulation of cortical responses tuned to the spatial-phase structure of natural scenes.
On a common circle: natural scenes and Gestalt rules  [PDF]
M. Sigman,G. A. Cecchi,C. D. Gilbert,M. O. Magnasco
Quantitative Biology , 2001, DOI: 10.1073/pnas.98.4.1935
Abstract: To understand how the human visual system analyzes images, it is essential to know the structure of the visual environment. In particular, natural images display consistent statistical properties that distinguish them from random luminance distributions. We have studied the geometric regularities of oriented elements (edges or line segments) present in an ensemble of visual scenes, asking how much information the presence of a segment in a particular location of the visual scene carries about the presence of a second segment at different relative positions and orientations. We observed strong long-range correlations in the distribution of oriented segments that extend over the whole visual field. We further show that a very simple geometric rule, cocircularity, predicts the arrangement of segments in natural scenes, and that different geometrical arrangements show relevant differences in their scaling properties. Our results show similarities to geometric features of previous physiological and psychophysical studies. We discuss the implications of these findings for theories of early vision.
Timing Precision in Population Coding of Natural Scenes in the Early Visual System  [PDF]
Ga?lle Desbordes,Jianzhong Jin,Chong Weng,Nicholas A. Lesica,Garrett B. Stanley,Jose-Manuel Alonso
PLOS Biology , 2012, DOI: 10.1371/journal.pbio.0060324
Abstract: The timing of spiking activity across neurons is a fundamental aspect of the neural population code. Individual neurons in the retina, thalamus, and cortex can have very precise and repeatable responses but exhibit degraded temporal precision in response to suboptimal stimuli. To investigate the functional implications for neural populations in natural conditions, we recorded in vivo the simultaneous responses, to movies of natural scenes, of multiple thalamic neurons likely converging to a common neuronal target in primary visual cortex. We show that the response of individual neurons is less precise at lower contrast, but that spike timing precision across neurons is relatively insensitive to global changes in visual contrast. Overall, spike timing precision within and across cells is on the order of 10 ms. Since closely timed spikes are more efficient in inducing a spike in downstream cortical neurons, and since fine temporal precision is necessary to represent the more slowly varying natural environment, we argue that preserving relative spike timing at a ~10-ms resolution is a crucial property of the neural code entering cortex.
Timing Precision in Population Coding of Natural Scenes in the Early Visual System  [PDF]
Ga?lle Desbordes ,Jianzhong Jin,Chong Weng,Nicholas A Lesica,Garrett B Stanley,Jose-Manuel Alonso
PLOS Biology , 2008, DOI: 10.1371/journal.pbio.0060324
Abstract: The timing of spiking activity across neurons is a fundamental aspect of the neural population code. Individual neurons in the retina, thalamus, and cortex can have very precise and repeatable responses but exhibit degraded temporal precision in response to suboptimal stimuli. To investigate the functional implications for neural populations in natural conditions, we recorded in vivo the simultaneous responses, to movies of natural scenes, of multiple thalamic neurons likely converging to a common neuronal target in primary visual cortex. We show that the response of individual neurons is less precise at lower contrast, but that spike timing precision across neurons is relatively insensitive to global changes in visual contrast. Overall, spike timing precision within and across cells is on the order of 10 ms. Since closely timed spikes are more efficient in inducing a spike in downstream cortical neurons, and since fine temporal precision is necessary to represent the more slowly varying natural environment, we argue that preserving relative spike timing at a ~10-ms resolution is a crucial property of the neural code entering cortex.
A Hierarchical Probabilistic Model for Rapid Object Categorization in Natural Scenes  [PDF]
Xiaofu He,Zhiyong Yang,Joe Z. Tsien
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0020002
Abstract: Humans can categorize objects in complex natural scenes within 100–150 ms. This amazing ability of rapid categorization has motivated many computational models. Most of these models require extensive training to obtain a decision boundary in a very high dimensional (e.g., ~6,000 in a leading model) feature space and often categorize objects in natural scenes by categorizing the context that co-occurs with objects when objects do not occupy large portions of the scenes. It is thus unclear how humans achieve rapid scene categorization.
Boosting k-NN for categorization of natural scenes  [PDF]
Paolo Piro,Richard Nock,Frank Nielsen,Michel Barlaud
Computer Science , 2010,
Abstract: The k-nearest neighbors (k-NN) classification rule has proven extremely successful in countless many computer vision applications. For example, image categorization often relies on uniform voting among the nearest prototypes in the space of descriptors. In spite of its good properties, the classic k-NN rule suffers from high variance when dealing with sparse prototype datasets in high dimensions. A few techniques have been proposed to improve k-NN classification, which rely on either deforming the nearest neighborhood relationship or modifying the input space. In this paper, we propose a novel boosting algorithm, called UNN (Universal Nearest Neighbors), which induces leveraged k-NN, thus generalizing the classic k-NN rule. We redefine the voting rule as a strong classifier that linearly combines predictions from the k closest prototypes. Weak classifiers are learned by UNN so as to minimize a surrogate risk. A major feature of UNN is the ability to learn which prototypes are the most relevant for a given class, thus allowing one for effective data reduction. Experimental results on the synthetic two-class dataset of Ripley show that such a filtering strategy is able to reject "noisy" prototypes. We carried out image categorization experiments on a database containing eight classes of natural scenes. We show that our method outperforms significantly the classic k-NN classification, while enabling significant reduction of the computational cost by means of data filtering.
Key visual features for rapid categorization of animals in natural scenes  [PDF]
Arnaud Delorme,Ghislaine Richard,Michele Fabre-Thorpe
Frontiers in Psychology , 2010, DOI: 10.3389/fpsyg.2010.00021
Abstract: In speeded categorization tasks, decisions could be based on diagnostic target features or they may need the activation of complete representations of the object. Depending on task requirements, the priming of feature detectors through top–down expectation might lower the threshold of selective units or speed up the rate of information accumulation. In the present paper, 40 subjects performed a rapid go/no-go animal/non-animal categorization task with 400 briefly flashed natural scenes to study how performance depends on physical scene characteristics, target configuration, and the presence or absence of diagnostic animal features. Performance was evaluated both in terms of accuracy and speed and d′ curves were plotted as a function of reaction time (RT). Such d′ curves give an estimation of the processing dynamics for studied features and characteristics over the entire subject population. Global image characteristics such as color and brightness do not critically influence categorization speed, although they slightly influence accuracy. Global critical factors include the presence of a canonical animal posture and animal/background size ratio suggesting the role of coarse global form. Performance was best for both accuracy and speed, when the animal was in a typical posture and when it occupied about 20–30% of the image. The presence of diagnostic animal features was another critical factor. Performance was significantly impaired both in accuracy (drop 3.3–7.5%) and speed (median RT increase 7–16 ms) when diagnostic animal parts (eyes, mouth, and limbs) were missing. Such animal features were shown to influence performance very early when only 15–25% of the response had been produced. In agreement with other experimental and modeling studies, our results support fast diagnostic recognition of animals based on key intermediate features and priming based on the subject’s expertise.
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