Cortical sensitivity to visual features in natural scenes.
Ontology highlight
ABSTRACT:
SUBMITTER: Felsen G
PROVIDER: S-EPMC1233414 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Natural visual scenes are rich in information, and any neural system analysing them must piece together the many messages from large arrays of diverse feature detectors. It is known how threshold detection of compound visual stimuli (sinusoidal gratings) is determined by their components' thresholds. We investigate whether similar combination rules apply to the perception of the complex and suprathreshold visual elements in naturalistic visual images. Observers gave magnitude estimations (ratings) of the perceived differences between pairs of images made from photographs of natural scenes. Images in some pairs differed along one stimulus dimension such as object colour, location, size or blur. But, for other image pairs, there were composite differences along two dimensions (e.g. both colour and object-location might change). We examined whether the ratings for such composite pairs could be predicted from the two ratings for the respective pairs in which only one stimulus dimension had changed. We found a pooling relationship similar to that proposed for simple stimuli: Minkowski summation with exponent 2.84 yielded the best predictive power (r=0.96), an exponent similar to that generally reported for compound grating detection. This suggests that theories based on detecting simple stimuli can encompass visual processing of complex, suprathreshold stimuli.
| S-EPMC2495046 | biostudies-literature
Project description:The nucleus basalis of the basal forebrain is an essential component of the neuromodulatory system controlling the behavioral state of an animal and it is thought to be important in regulating arousal and attention. However, the effect of nucleus basalis activation on sensory processing remains poorly understood. Using polytrode recording in rat visual cortex, we found that nucleus basalis stimulation caused prominent decorrelation between neurons and marked improvement in the reliability of neuronal responses to natural scenes. The decorrelation depended on local activation of cortical muscarinic acetylcholine receptors, whereas the increased reliability involved distributed neural circuits, as evidenced by nucleus basalis-induced changes in thalamic responses. Further analysis showed that the decorrelation and increased reliability improved cortical representation of natural stimuli in a complementary manner. Thus, the basal forebrain neuromodulatory circuit, which is known to be activated during aroused and attentive states, acts through both local and distributed mechanisms to improve sensory coding.
| S-EPMC3576925 | biostudies-literature
Project description:Figure-ground organization and border-ownership assignment are essential for understanding natural scenes. It has been shown that many neurons in the macaque visual cortex signal border-ownership in displays of simple geometric shapes such as squares, but how well these neurons resolve border-ownership in natural scenes is not known. We studied area V2 neurons in behaving macaques with static images of complex natural scenes. We found that about half of the neurons were border-ownership selective for contours in natural scenes, and this selectivity originated from the image context. The border-ownership signals emerged within 70 ms after stimulus onset, only ?30 ms after response onset. A substantial fraction of neurons were highly consistent across scenes. Thus, the cortical mechanisms of figure-ground organization are fast and efficient even in images of complex natural scenes. Understanding how the brain performs this task so fast remains a challenge.
| S-EPMC5197405 | biostudies-literature
Project description:Biologically inspired deep convolutional neural networks (CNNs), trained for computer vision tasks, have been found to predict cortical responses with remarkable accuracy. However, the internal operations of these models remain poorly understood, and the factors that account for their success are unknown. Here we develop a set of techniques for using CNNs to gain insights into the computational mechanisms underlying cortical responses. We focused on responses in the occipital place area (OPA), a scene-selective region of dorsal occipitoparietal cortex. In a previous study, we showed that fMRI activation patterns in the OPA contain information about the navigational affordances of scenes; that is, information about where one can and cannot move within the immediate environment. We hypothesized that this affordance information could be extracted using a set of purely feedforward computations. To test this idea, we examined a deep CNN with a feedforward architecture that had been previously trained for scene classification. We found that responses in the CNN to scene images were highly predictive of fMRI responses in the OPA. Moreover the CNN accounted for the portion of OPA variance relating to the navigational affordances of scenes. The CNN could thus serve as an image-computable candidate model of affordance-related responses in the OPA. We then ran a series of in silico experiments on this model to gain insights into its internal operations. These analyses showed that the computation of affordance-related features relied heavily on visual information at high-spatial frequencies and cardinal orientations, both of which have previously been identified as low-level stimulus preferences of scene-selective visual cortex. These computations also exhibited a strong preference for information in the lower visual field, which is consistent with known retinotopic biases in the OPA. Visualizations of feature selectivity within the CNN suggested that affordance-based responses encoded features that define the layout of the spatial environment, such as boundary-defining junctions and large extended surfaces. Together, these results map the sensory functions of the OPA onto a fully quantitative model that provides insights into its visual computations. More broadly, they advance integrative techniques for understanding visual cortex across multiple level of analysis: from the identification of cortical sensory functions to the modeling of their underlying algorithms.
| S-EPMC5933806 | biostudies-literature
Project description:PurposeIn performing search tasks, the visual system encodes information across the visual field at a resolution inversely related to eccentricity and deploys saccades to place visually interesting targets upon the fovea, where resolution is highest. The serial process of fixation, punctuated by saccadic eye movements, continues until the desired target has been located. Loss of central vision restricts the ability to resolve the high spatial information of a target, interfering with this visual search process. We investigate oculomotor adaptations to central visual field loss with gaze-contingent artificial scotomas.MethodsSpatial distortions were placed at random locations in 25° square natural scenes. Gaze-contingent artificial central scotomas were updated at the screen rate (75 Hz) based on a 250 Hz eye tracker. Eight subjects searched the natural scene for the spatial distortion and indicated its location using a mouse-controlled cursor.ResultsAs the central scotoma size increased, the mean search time increased [F(3,28) = 5.27, p = 0.05], and the spatial distribution of gaze points during fixation increased significantly along the x [F(3,28) = 6.33, p = 0.002] and y [F(3,28) = 3.32, p = 0.034] axes. Oculomotor patterns of fixation duration, saccade size, and saccade duration did not change significantly, regardless of scotoma size.ConclusionsThere is limited automatic adaptation of the oculomotor system after simulated central vision loss.
| S-EPMC3432450 | biostudies-literature
Project description:Previous research has shown that viewing images of nature scenes can have a beneficial effect on memory, attention, and mood. In this study, we aimed to determine whether the preference of natural versus man-made scenes is driven by bottom-up processing of the low-level visual features of nature. We used participants' ratings of perceived naturalness as well as esthetic preference for 307 images with varied natural and urban content. We then quantified 10 low-level image features for each image (a combination of spatial and color properties). These features were used to predict esthetic preference in the images, as well as to decompose perceived naturalness to its predictable (modeled by the low-level visual features) and non-modeled aspects. Interactions of these separate aspects of naturalness with the time it took to make a preference judgment showed that naturalness based on low-level features related more to preference when the judgment was faster (bottom-up). On the other hand, perceived naturalness that was not modeled by low-level features was related more to preference when the judgment was slower. A quadratic discriminant classification analysis showed how relevant each aspect of naturalness (modeled and non-modeled) was to predicting preference ratings, as well as the image features on their own. Finally, we compared the effect of color-related and structure-related modeled naturalness, and the remaining unmodeled naturalness in predicting esthetic preference. In summary, bottom-up (color and spatial) properties of natural images captured by our features and the non-modeled naturalness are important to esthetic judgments of natural and man-made scenes, with each predicting unique variance.
| S-EPMC4407505 | biostudies-literature
Project description:Natural scenes are inherently structured, with meaningful objects appearing in predictable locations. Human vision is tuned to this structure: When scene structure is purposefully jumbled, perception is strongly impaired. Here, we tested how such perceptual effects are reflected in neural sensitivity to scene structure. During separate fMRI and EEG experiments, participants passively viewed scenes whose spatial structure (i.e., the position of scene parts) and categorical structure (i.e., the content of scene parts) could be intact or jumbled. Using multivariate decoding, we show that spatial (but not categorical) scene structure profoundly impacts on cortical processing: Scene-selective responses in occipital and parahippocampal cortices (fMRI) and after 255 ms (EEG) accurately differentiated between spatially intact and jumbled scenes. Importantly, this differentiation was more pronounced for upright than for inverted scenes, indicating genuine sensitivity to spatial structure rather than sensitivity to low-level attributes. Our findings suggest that visual scene analysis is tightly linked to the spatial structure of our natural environments. This link between cortical processing and scene structure may be crucial for rapidly parsing naturalistic visual inputs.
| S-EPMC7267931 | biostudies-literature
Project description:Previous research has shown that interacting with natural environments vs. more urban or built environments can have salubrious psychological effects, such as improvements in attention and memory. Even viewing pictures of nature vs. pictures of built environments can produce similar effects. A major question is: What is it about natural environments that produces these benefits? Problematically, there are many differing qualities between natural and urban environments, making it difficult to narrow down the dimensions of nature that may lead to these benefits. In this study, we set out to uncover visual features that related to individuals' perceptions of naturalness in images. We quantified naturalness in two ways: first, implicitly using a multidimensional scaling analysis and second, explicitly with direct naturalness ratings. Features that seemed most related to perceptions of naturalness were related to the density of contrast changes in the scene, the density of straight lines in the scene, the average color saturation in the scene and the average hue diversity in the scene. We then trained a machine-learning algorithm to predict whether a scene was perceived as being natural or not based on these low-level visual features and we could do so with 81% accuracy. As such we were able to reliably predict subjective perceptions of naturalness with objective low-level visual features. Our results can be used in future studies to determine if these features, which are related to naturalness, may also lead to the benefits attained from interacting with nature.
| S-EPMC4273965 | biostudies-literature
Project description: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 approximately 10-ms resolution is a crucial property of the neural code entering cortex.
| S-EPMC2602720 | biostudies-literature
Project description:Accurate estimation of neuronal receptive fields is essential for understanding sensory processing in the early visual system. Yet a full characterization of receptive fields is still incomplete, especially with regard to natural visual stimuli and in complete populations of cortical neurons. While previous work has incorporated known structural properties of the early visual system, such as lateral connectivity, or imposing simple-cell-like receptive field structure, no study has exploited the fact that nearby V1 neurons share common feed-forward input from thalamus and other upstream cortical neurons. We introduce a new method for estimating receptive fields simultaneously for a population of V1 neurons, using a model-based analysis incorporating knowledge of the feed-forward visual hierarchy. We assume that a population of V1 neurons shares a common pool of thalamic inputs, and consists of two layers of simple and complex-like V1 neurons. When fit to recordings of a local population of mouse layer 2/3 V1 neurons, our model offers an accurate description of their response to natural images and significant improvement of prediction power over the current state-of-the-art methods. We show that the responses of a large local population of V1 neurons with locally diverse receptive fields can be described with surprisingly limited number of thalamic inputs, consistent with recent experimental findings. Our structural model not only offers an improved functional characterization of V1 neurons, but also provides a framework for studying the relationship between connectivity and function in visual cortical areas.
| S-EPMC4922657 | biostudies-literature