Coherent natural scene structure facilitates the extraction of task-relevant object information in visual cortex

Kaiser, Daniel; Häberle, Greta; Cichy, Radoslaw Martin

doi:10.1016/j.neuroimage.2021.118365

Cited by 6 publications

(5 citation statements)

References 49 publications

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“…Using a separate match-to-sample task (supplementary materials), we found that faces were identified faster when appeared with associated scenes than unassociated scenes. These results together suggest that co-occurring associated scenes facilitated face perception (Brandman & Peelen, 2017;Davenport & Potter, 2004;Gayet & Peelen, 2019Green & Hummel, 2006;Kaiser et al, 2021;Lauer et al, 2018). Future studies are needed to better understand the relationship between auxiliary target information on attentional guidance and attentional decision.…”

Section: Discussionmentioning

confidence: 72%

Learned associations serve as target proxies during difficult but not easy visual search

Zhou¹,

Geng²

2023

Preprint

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The target template contains information in memory that is used to guide attention during visual search. The target template is typically thought of as containing features of the actual target object. However, when targets are hard to find, it is advantageous to use other information in the visual environment that is predictive of the target’s location to help guide attention. The purpose of these studies was to test if newly learned associations between face and scene category images lead observers to use scene information as a proxy for the face target. Our results found that scene information was used as a proxy for the target to guide attention but only when the target face was difficult to discriminate from the distractor face; when the faces were easy to distinguish, attention was no longer guided by the scene proxy. The results suggest that attention is flexibly guided by both target features as well as features of objects that are predictive of the target location. The degree to which each contributes to guiding attention depends on the efficiency with which that information can be used to decode the location of the target. The results contribute to the view that attentional guidance is highly flexible in its use of information to rapidly locate the target.

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Section: Discussionmentioning

confidence: 72%

Learned associations serve as target proxies during difficult but not easy visual search

Zhou¹,

Geng²

2023

Preprint

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“…In these studies, participants were asked to attend the objects or scenes by memorizing them, completing repetition detection tasks, or categorization tasks. One recent study directly compared cross-facilitation effects between objects and scenes under different task demands (Kaiser et al 2021). In this study, spatially consistent scene context facilitated object representation more than spatially inconsistent scene context when the objects were task-relevant.…”

Section: Semantic Consistency Only Facilitates Task-relevant Represen...mentioning

confidence: 96%

“…Several previous neuroimaging studies reported a cross-facilitation between scene and object processing Peelen 2017, 2019;Kaiser et al 2021), reporting that scenes enhance the cortical representation of objects (Brandman and Peelen 2017;Kaiser et al 2021), and objects facilitate the representation of scenes (Brandman and Peelen 2019). In these studies, participants were asked to attend the objects or scenes by memorizing them, completing repetition detection tasks, or categorization tasks.…”

Section: Semantic Consistency Only Facilitates Task-relevant Represen...mentioning

confidence: 99%

Semantic scene-object consistency modulates N300/400 EEG components, but does not automatically facilitate object representations

2021

Self Cite

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During natural vision, objects rarely appear in isolation, but often within a semantically related scene context. Previous studies reported that semantic consistency between objects and scenes facilitates object perception and that scene-object consistency is reflected in changes in the N300 and N400 components in EEG recordings. Here, we investigate whether these N300/400 differences are indicative of changes in the cortical representation of objects. In two experiments, we recorded EEG signals, while participants viewed semantically consistent or inconsistent objects within a scene; in Experiment 1, these objects were task-irrelevant, while in Experiment 2, they were directly relevant for behavior. In both experiments, we found reliable and comparable N300/400 differences between consistent and inconsistent scene-object combinations. To probe the quality of object representations, we performed multivariate classification analyses, in which we decoded the category of the objects contained in the scene. In Experiment 1, in which the objects were not task-relevant, object category could be decoded from ~100 ms after the object presentation, but no difference in decoding performance was found between consistent and inconsistent objects. In contrast, when the objects were task-relevant in Experiment 2, we found enhanced decoding of semantically consistent, compared with semantically inconsistent, objects. These results show that differences in N300/400 components related to scene-object consistency do not index changes in cortical object representations but rather reflect a generic marker of semantic violations. Furthermore, our findings suggest that facilitatory effects between objects and scenes are task-dependent rather than automatic.

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“…After decades of development, the research of traditional image understanding has formed a huge theoretical system to reveal various laws and phenomena in image understanding. As a new thing derived from its technology, whether the whole scene understanding of image can still be explained by the existing theories and how much to follow the existing law of image understanding are unknown issues, which need to be studied and tested again [8]. For example,1)The Bayesian topic model has been successfully applied in describing scene reasoning, etc., but in fact, the topic model originated from document retrieval, and its application to describe the image itself is a form of technical grafting.…”

Section: Literature Reviewmentioning

confidence: 99%

Computer Image Scene and Object Information Extraction based on Bayesian Network Model

Zhao

Guo²

2023

Applied Mathematics and Nonlinear Sciences

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In order to better extract scene and object information from computer image, a construction object extraction algorithm based on Bayesian network is proposed. The algorithm is trained by multi-scene aerial images to build a grain dictionary and map the grain in the actual image to the grain dictionary to obtain the scene information of the image;Then naive Bayesian networks were used to model the constraints of the relationship between architectural targets and the spatial context of scene classes, and the extraction of architectural targets was converted into a posteriori probability problem for solving Bayesian network class nodes. The experimental results show that the proposed algorithm can effectively extract architectural objects from aerial images. The experiment result shows that:In this paper, the proportion of target pixels accurately extracted by the algorithm is taken as the standard to define the standard of target pixels accurately extracted by the algorithm to reach more than 90% of the building target pixels. The average time of training an image is 2 s, which is mainly spent on the convolution operation with the filter. After the training, the average time of processing a single test image is 0.5s. It is proved that Bayesian network model can effectively extract scene and object information from computer image.

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Coherent natural scene structure facilitates the extraction of task-relevant object information in visual cortex

Cited by 6 publications

References 49 publications

Learned associations serve as target proxies during difficult but not easy visual search

Learned associations serve as target proxies during difficult but not easy visual search

Semantic scene-object consistency modulates N300/400 EEG components, but does not automatically facilitate object representations

Computer Image Scene and Object Information Extraction based on Bayesian Network Model

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