The role of contextual materials in object recognition
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ABSTRACT:
SUBMITTER: Lauer T
PROVIDER: S-EPMC8578445 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Visual object recognition is of fundamental importance in our everyday interaction with the environment. Recent models of visual perception emphasize the role of top-down predictions facilitating object recognition via initial guesses that limit the number of object representations that need to be considered. Several results suggest that this rapid and efficient object processing relies on the early extraction and processing of low spatial frequencies (LSF). The present study aimed to investigate the SF content of visual object representations and its modulation by contextual and affective values of the perceived object during a picture-name verification task. Stimuli consisted of pictures of objects equalized in SF content and categorized as having low or high affective and contextual values. To access the SF content of stored visual representations of objects, SFs of each image were then randomly sampled on a trial-by-trial basis. Results reveal that intermediate SFs between 14 and 24 cycles per object (2.3-4 cycles per degree) are correlated with fast and accurate identification for all categories of objects. Moreover, there was a significant interaction between affective and contextual values over the SFs correlating with fast recognition. These results suggest that affective and contextual values of a visual object modulate the SF content of its internal representation, thus highlighting the flexibility of the visual recognition system.
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Project description:Predictions from perceptual load theory (Lavie, 1995, 2005) regarding object recognition across the same or different viewpoints were tested. Results showed that high perceptual load reduces distracter recognition levels despite always presenting distracter objects from the same view. They also showed that the levels of distracter recognition were unaffected by a change in the distracter object view under conditions of low perceptual load. These results were found both with repetition priming measures of distracter recognition and with performance on a surprise recognition memory test. The results support load theory proposals that distracter recognition critically depends on the level of perceptual load. The implications for the role of attention in object recognition theories are discussed.
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Project description:Objects that are semantically related to the visual scene context are typically better recognized than unrelated objects. While context effects on object recognition are well studied, the question which particular visual information of an object's surroundings modulates its semantic processing is still unresolved. Typically, one would expect contextual influences to arise from high-level, semantic components of a scene but what if even low-level features could modulate object processing? Here, we generated seemingly meaningless textures of real-world scenes, which preserved similar summary statistics but discarded spatial layout information. In Experiment 1, participants categorized such textures better than colour controls that lacked higher-order scene statistics while original scenes resulted in the highest performance. In Experiment 2, participants recognized briefly presented consistent objects on scenes significantly better than inconsistent objects, whereas on textures, consistent objects were recognized only slightly more accurately. In Experiment 3, we recorded event-related potentials and observed a pronounced mid-central negativity in the N300/N400 time windows for inconsistent relative to consistent objects on scenes. Critically, inconsistent objects on textures also triggered N300/N400 effects with a comparable time course, though less pronounced. Our results suggest that a scene's low-level features contribute to the effective processing of objects in complex real-world environments.
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Project description:Core object recognition, the ability to rapidly recognize objects despite variations in their appearance, is largely solved through the feedforward processing of visual information. Deep neural networks are shown to achieve human-level performance in these tasks, and explain the primate brain representation. On the other hand, object recognition under more challenging conditions (i.e. beyond the core recognition problem) is less characterized. One such example is object recognition under occlusion. It is unclear to what extent feedforward and recurrent processes contribute in object recognition under occlusion. Furthermore, we do not know whether the conventional deep neural networks, such as AlexNet, which were shown to be successful in solving core object recognition, can perform similarly well in problems that go beyond the core recognition. Here, we characterize neural dynamics of object recognition under occlusion, using magnetoencephalography (MEG), while participants were presented with images of objects with various levels of occlusion. We provide evidence from multivariate analysis of MEG data, behavioral data, and computational modelling, demonstrating an essential role for recurrent processes in object recognition under occlusion. Furthermore, the computational model with local recurrent connections, used here, suggests a mechanistic explanation of how the human brain might be solving this problem.
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Project description:Myotonic dystrophy type I (DM1) patients demonstrate visuospatial dysfunction and impaired performance in tasks requiring recognition or memory of figures and objects. In DM1, CUG expansion RNAs inactivate the Muscleblind-like (MBNL) proteins. We show that constitutive Mbnl2 inactivation in Mbnl2ΔE2/ΔE2 mice, selectively impairs object recognition memory in the novel object recognition test. When exploring the context of a novel arena in which the objects are later encountered, the Mbnl2ΔE2/ΔE2 dorsal hippocampus responds with a lack of enrichment for learning and memory related pathways, mounting instead transcriptome alterations predicted to impair growth and neuron viability. In Mbnl2ΔE2/ΔE2 mice, saturation effects may prevent deployment of a functionally relevant transcriptome response during novel context exploration. Post-novel context exploration alterations in genes implicated in tauopathy and dementia are observed in the Mbnl2ΔE2/ΔE2 dorsal hippocampus. Thus MBNL2 inactivation in DM1 patients may alter novel context processing in the dorsal hippocampus and impair object recognition memory.
2023-05-22 | GSE226191 | GEO
Project description:There is substantial evidence for individual differences in personality and cognitive abilities, but we lack clear intuitions about individual differences in visual abilities. Previous work on this topic has typically compared performance with only 2 categories, each measured with only 1 task. This approach is insufficient for demonstration of domain-general effects. Most previous work has used familiar object categories, for which experience may vary between participants and categories, thereby reducing correlations that would stem from a common factor. In Study 1, we adopted a latent variable approach to test for the first time whether there is a domain-general object recognition ability, o. We assessed whether shared variance between latent factors representing performance for each of 5 novel object categories could be accounted for by a single higher-order factor. On average, 89% of the variance of lower-order factors denoting performance on novel object categories could be accounted for by a higher-order factor, providing strong evidence for o. Moreover, o also accounted for a moderate proportion of variance in tests of familiar object recognition. In Study 2, we assessed whether the strong association across categories in object recognition is due to third-variable influences. We find that o has weak to moderate associations with a host of cognitive, perceptual, and personality constructs and that a clear majority of the variance in and covariance between performance on different categories is independent of fluid intelligence. This work provides the first demonstration of a reliable, specific, and domain-general object recognition ability, and suggest a rich framework for future work in this area. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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Project description:We report a spatial transcriptomics dataset of mouse brain tissue generated with the 10x Genomics Visium platform to identify gene expression profiles of spatial object recognition training.
2022-11-01 | GSE201610 | GEO
Project description:Finding and recognizing objects is a fundamental task of vision. Objects can be defined by several "cues" (color, luminance, texture, etc.), and humans can integrate sensory cues to improve detection and recognition [1-3]. Cortical mechanisms fuse information from multiple cues [4], and shape-selective neural mechanisms can display cue invariance by responding to a given shape independent of the visual cue defining it [5-8]. Selective attention, in contrast, improves recognition by isolating a subset of the visual information [9]. Humans can select single features (red or vertical) within a perceptual dimension (color or orientation), giving faster and more accurate responses to items having the attended feature [10, 11]. Attention elevates neural responses and sharpens neural tuning to the attended feature, as shown by studies in psychophysics and modeling [11, 12], imaging [13-16], and single-cell and neural population recordings [17, 18]. Besides single features, attention can select whole objects [19-21]. Objects are among the suggested "units" of attention because attention to a single feature of an object causes the selection of all of its features [19-21]. Here, we pit integration against attentional selection in object recognition. We find, first, that humans can integrate information near optimally from several perceptual dimensions (color, texture, luminance) to improve recognition. They cannot, however, isolate a single dimension even when the other dimensions provide task-irrelevant, potentially conflicting information. For object recognition, it appears that there is mandatory integration of information from multiple dimensions of visual experience. The advantage afforded by this integration, however, comes at the expense of attentional selection.
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Project description:Context plays an important role in visual recognition. Recent studies have shown that visual recognition networks can be fooled by placing objects in inconsistent contexts (e.g. a cow in the ocean). To understand and model the role of contextual information in visual recognition, we systematically and quantitatively investigated ten critical properties of where, when, and how context modulates recognition including amount of context, context and object resolution, geometrical structure of context, context congruence, time required to incorporate contextual information, and temporal dynamics of contextual modulation. The tasks involve recognizing a target object surrounded with context in a natural image. As an essential benchmark, we first describe a series of psychophysics experiments, where we alter one aspect of context at a time, and quantify human recognition accuracy. To computationally assess performance on the same tasks, we propose a biologically inspired context aware object recognition model consisting of a two-stream architecture. The model processes visual information at the fovea and periphery in parallel, dynamically incorporates both object and contextual information, and sequentially reasons about the class label for the target object. Across a wide range of behavioral tasks, the model approximates human level performance without retraining for each task, captures the dependence of context enhancement on image properties, and provides initial steps towards integrating scene and object information for visual recognition.
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