Neural Correlates of Fixated Low- and High-level Scene Properties during Active Scene Viewing

2020 ◽  
Vol 32 (10) ◽  
pp. 2013-2023
Author(s):  
John M. Henderson ◽  
Jessica E. Goold ◽  
Wonil Choi ◽  
Taylor R. Hayes
Keyword(s):  
Real World ◽  
Visual Analysis ◽  
Scene Perception ◽  
Active Vision ◽  
Semantic Content ◽  
Retrosplenial Cortex ◽  
Edge Density ◽  
Visual Stream ◽  
Lateral Occipital Complex ◽  
High Level

During real-world scene perception, viewers actively direct their attention through a scene in a controlled sequence of eye fixations. During each fixation, local scene properties are attended, analyzed, and interpreted. What is the relationship between fixated scene properties and neural activity in the visual cortex? Participants inspected photographs of real-world scenes in an MRI scanner while their eye movements were recorded. Fixation-related fMRI was used to measure activation as a function of lower- and higher-level scene properties at fixation, operationalized as edge density and meaning maps, respectively. We found that edge density at fixation was most associated with activation in early visual areas, whereas semantic content at fixation was most associated with activation along the ventral visual stream including core object and scene-selective areas (lateral occipital complex, parahippocampal place area, occipital place area, and retrosplenial cortex). The observed activation from semantic content was not accounted for by differences in edge density. The results are consistent with active vision models in which fixation gates detailed visual analysis for fixated scene regions, and this gating influences both lower and higher levels of scene analysis.

scholarly journals Tuning in scene-preferring cortex for mid-level visual features gives rise to selectivity across multiple levels of stimulus complexity

2021 ◽  
Author(s):  
Shi Pui Donald Li ◽  
Michael F. Bonner
Keyword(s):  
Real World ◽  
Computational Models ◽  
Visual Stream ◽  
Computational Theory ◽  
Low Level ◽  
Multiple Levels ◽  
High Level ◽  
Hierarchical Encoding ◽  
Natural Statistics ◽  
High Throughput Experiments

The scene-preferring portion of the human ventral visual stream, known as the parahippocampal place area (PPA), responds to scenes and landmark objects, which tend to be large in real-world size, fixed in location, and inanimate. However, the PPA also exhibits preferences for low-level contour statistics, including rectilinearity and cardinal orientations, that are not directly predicted by theories of scene- and landmark-selectivity. It is unknown whether these divergent findings of both low- and high-level selectivity in the PPA can be explained by a unified computational theory. To address this issue, we fit hierarchical computational models of mid-level tuning to the image-evoked fMRI responses of the PPA, and we performed a series of high-throughput experiments on these models. Our findings show that hierarchical encoding models of the PPA exhibit emergent selectivity across multiple levels of complexity, giving rise to high-level preferences along dimensions of real-world size, fixedness, and naturalness/animacy as well as low-level preferences for rectilinear shapes and cardinal orientations. These results reconcile disparate theories of PPA function in a unified model of mid-level visual representation, and they demonstrate how multifaceted selectivity profiles naturally emerge from the hierarchical computations of visual cortex and the natural statistics of images.

scholarly journals Visual features influence thought content in the absence of overt semantic information

2019 ◽  
Author(s):  
Kathryn E Schertz ◽  
Omid Kardan ◽  
Marc Berman
Keyword(s):  
Cognitive Processes ◽  
Semantic Content ◽  
Visual Features ◽  
Edge Density ◽  
Low Level ◽  
Life Journey ◽  
Edge Content ◽  
High Level ◽  
Scene Identification ◽  
Selection Of

It has recently been shown that the perception of visual features of the environment can influence thought content. Both low-level (e.g., fractalness) and high-level (e.g., presence of water) visual features of the environment can influence thought content, in real-world and experimental settings where these features can make people more reflective and contemplative in their thoughts. It remains to be seen, however, if these visual features retain their influence on thoughts in the absence of overt semantic content, which could indicate a more fundamental mechanism for this effect. In this study, we removed this limitation, by creating scrambled edge versions of images, which maintain edge content from the original images but remove scene identification. Non-straight edge density is one visual feature which has been shown to influence many judgements about objects and landscapes, and has also been associated with thoughts of spirituality. We extend previous findings by showing that non-straight edges retain their influence on the selection of a “Spiritual & Life Journey” topic after scene identification removal. These results strengthen the implication of a causal role for the perception of low-level visual features on the influence of higher-order cognitive function, by demonstrating that in the absence of overt semantic content, low-level features, such as edges, influence cognitive processes.

scholarly journals Semantic Override of Low-level Features in Image Viewing – Both Initially and Overall

2008 ◽  
Vol 2 (2) ◽  
Author(s):  
Marcus Nyström ◽  
Kenneth Holmqvist
Keyword(s):  
Time Course ◽  
Movement Control ◽  
Semantic Content ◽  
Cognitive Factors ◽  
Edge Density ◽  
Low Level ◽  
Semantic Categories ◽  
Initial Fixation ◽  
High Level ◽  
Over Time

Guidance of eye-movements in image viewing is believed to be controlled by stimulus driven factors as well as viewer dependent higher level factors such as task and memory. It is currently debated what proportions these factors contribute to gaze guidance, and also how they vary over time after image onset. Overall, the unanimity regarding these issues is surprisingly low and there are results supporting both types of factors as being dominant in eye-movement control under certain conditions. We investigate how low, and high level factors influence eye guidance by manipulating contrast statistics on images from three different semantic categories and measure how this affects fixation selection. Our results show that the degree to which contrast manipulations affect fixation selection heavily depends on an image’s semantic content, and how this content is distributed over the image. Over the three image categories, we found no systematic differences between contrast and edge density at fixated location compared to control locations, neither during the initial fixation nor over the whole time course of viewing. These results suggest that cognitive factors easily can override low-level factors in fixation selection, even when the viewing task is neutral.

scholarly journals Representation of semantic information in ventral areas during encoding is associated with improved visual short-term memory

2019 ◽  
Author(s):  
Bobby Stojanoski ◽  
Stephen M. Emrich ◽  
Rhodri Cusack
Keyword(s):  
Real World ◽  
Semantic Information ◽  
Short Term Memory ◽  
Semantic Content ◽  
Neural Representation ◽  
Short Term ◽  
Visual Stream ◽  
Term Memory ◽  
Visual Short Term Memory ◽  
Visual Areas

AbstractWe rely upon visual short-term memory (VSTM) for continued access to perceptual information that is no longer available. Despite the complexity of our visual environments, the majority of research on VSTM has focused on memory for lower-level perceptual features. Using more naturalistic stimuli, it has been found that recognizable objects are remembered better than unrecognizable objects. What remains unclear, however, is how semantic information changes brain representations in order to facilitate this improvement in VSTM for real-world objects. To address this question, we used a continuous report paradigm to assess VSTM (precision and guessing rate) while participants underwent functional magnetic resonance imaging (fMRI) to measure the underlying neural representation of 96 objects from 4 animate and 4 inanimate categories. To isolate semantic content, we used a novel image generation method that parametrically warps images until they are no longer recognizable while preserving basic visual properties. We found that intact objects were remembered with greater precision and a lower guessing rate than unrecognizable objects (this also emerged when objects were grouped by category and animacy). Representational similarity analysis of the ventral visual stream found evidence of category and animacy information in anterior visual areas during encoding only, but not during maintenance. These results suggest that the effect of semantic information during encoding in ventral visual areas boosts visual short-term memory for real-world objects.

scholarly journals Layer-Edge Patterns Exploration and Presentation in Multiplex Networks: From Detail to Overview via Selections and Aggregations

Electronics ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 387 ◽  
Author(s):  
Xitao Zhang ◽  
Lingda Wu ◽  
Shaobo Yu ◽  
Kang Li
Keyword(s):  
Real World ◽  
Topological Structure ◽  
Visual Analysis ◽  
Layer Structure ◽  
Real World Data ◽  
Multiplex Networks ◽  
Level Information ◽  
Interlayer Structure ◽  
High Level ◽  
Multiple Interactions

Multiplex networks have been widely used to describe the multi-type connections of entities in the real world. However, researches for multiplex networks visualization unilaterally focus on the presentation of topological structure, lacking of specific high-level information presentation for quantitative comparison of interlayer structure. Users cannot participate in the exploration and freely choose the layers (or sub-graphs, regions, etc.) of interest for structural comparison. Contraposing the layer-edge patterns visual analysis tasks of multiplex networks, this paper puts forward a novel solution for exploration and analysis that tightly couples topological structure and high-level patterns. It mainly contains a multi-force directed model to realize the balanced layout of nodes in multi-layer topology, as well as two kinds of high-level patterns of which the visual representations are, respectively, designed by a familiar metaphor—that is, the similar pattern representation based on the area-proportional Venn diagrams and the interaction pattern representation based on the directed arrows. Furthermore, views association is implemented through underlying data sharing and multiple interactions which can be used to gain insights through the creation of selections of interest and produce high-level infographic-style overviews simultaneously. The experiments on real-world data demonstrate the support of the proposed method for layer-edge patterns analysis tasks in multiplex networks and the effectiveness for analyzing the multi-layer structure of multiplex networks.

scholarly journals The Design of an Instructional Model Based on Connectivism and Constructivism to Create Innovation in Real World Experience

2018 ◽  
Vol 11 (3) ◽  
pp. 12 ◽  
Author(s):  
Kanokrat Jirasatjanukul ◽  
Namon Jeerungsuwan
Keyword(s):  
Real World ◽  
Model Evaluation ◽  
Sampling Technique ◽  
Instructional Model ◽  
Model Design ◽  
Research Results ◽  
Purposive Sampling ◽  
Evaluation Form ◽  
Model Based ◽  
High Level

The objectives of the research were to (1) design an instructional model based on Connectivism and Constructivism to create innovation in real world experience, (2) assess the model designed–the designed instructional model. The research involved 2 stages: (1) the instructional model design and (2) the instructional model rating. The sample consisted of 7 experts, and the Purposive Sampling Technique was used. The research instruments were the instructional model and the instructional model evaluation form. The statistics used in the research were means and standard division. The research results were (1) the Instructional Model based on Connectivism and Constructivism to Create innovation in Real World Experience consisted of 3 components. These were Connectivism, Constructivism and Innovation in Real World Experience and (2) the instructional model rating was at a high level (=4.37, S.D.=0.41). The research results revealed that the Instructional Model Based on Connectivism and Constructivism to Create Innovation in Real World Experience was a model that can be used in learning, in that it promoted the creation of real world experience innovation.

scholarly journals Implementing Visual Analytics Pipelines with Simulation Data

2021 ◽  
Author(s):  
Taimur Khan ◽  
Syed Samad Shakeel ◽  
Afzal Gul ◽  
Hamza Masud ◽  
Achim Ebert
Keyword(s):  
Visual Analytics ◽  
Visual Analysis ◽  
Simulated Data ◽  
Evaluation Study ◽  
Ease Of Use ◽  
Preliminary Evaluation ◽  
Simulation Data ◽  
Visual Data Analytics ◽  
High Level ◽  
Simulation Parameters

Visual analytics has been widely studied in the past decade both in academia and industry to improve data exploration, minimize the overall cost, and improve data analysis. In this chapter, we explore the idea of visual analytics in the context of simulation data. This would then provide us with the capability to not only explore our data visually but also to apply machine learning models in order to answer high-level questions with respect to scheduling, choosing optimal simulation parameters, finding correlations, etc. More specifically, we examine state-of-the-art tools to be able to perform these above-mentioned tasks. Further, to test and validate our methodology we followed the human-centered design process to build a prototype tool called ViDAS (Visual Data Analytics of Simulated Data). Our preliminary evaluation study illustrates the intuitiveness and ease-of-use of our approach with regards to visual analysis of simulated data.

scholarly journals Informative neural representations of unseen objects during higher-order processing in human brains and deep artificial networks

2021 ◽  
Author(s):  
Ning Mei ◽  
Roberto Santana ◽  
David Soto
Keyword(s):  
Visual Processing ◽  
Visual Information ◽  
Model Simulation ◽  
Conscious Experience ◽  
Neural Representation ◽  
Visual Stream ◽  
Order Processing ◽  
High Level ◽  
Unseen Objects ◽  
Human Brains

AbstractDespite advances in the neuroscience of visual consciousness over the last decades, we still lack a framework for understanding the scope of unconscious processing and how it relates to conscious experience. Previous research observed brain signatures of unconscious contents in visual cortex, but these have not been identified in a reliable manner, with low trial numbers and signal detection theoretic constraints not allowing to decisively discard conscious perception. Critically, the extent to which unconscious content is represented in high-level processing stages along the ventral visual stream and linked prefrontal areas remains unknown. Using a within-subject, high-precision, highly-sampled fMRI approach, we show that unconscious contents, even those associated with null sensitivity, can be reliably decoded from multivoxel patterns that are highly distributed along the ventral visual pathway and also involving prefrontal substrates. Notably, the neural representation in these areas generalised across conscious and unconscious visual processing states, placing constraints on prior findings that fronto-parietal substrates support the representation of conscious contents and suggesting revisions to models of consciousness such as the neuronal global workspace. We then provide a computational model simulation of visual information processing/representation in the absence of perceptual sensitivity by using feedforward convolutional neural networks trained to perform a similar visual task to the human observers. The work provides a novel framework for pinpointing the neural representation of unconscious knowledge across different task domains.

scholarly journals Refixation Patterns of Mind-wandering during Real-world Scene Perception

2020 ◽  
Author(s):  
Han Zhang ◽  
Nicola C Anderson ◽  
Kevin Miller
Keyword(s):  
Eye Movements ◽  
Cognitive Processing ◽  
Real World ◽  
Visual Processing ◽  
Visual Information ◽  
Scene Perception ◽  
Mind Wandering ◽  
Quantification Analysis ◽  
The Difference ◽  
Thought Probes

Recent studies have shown that mind-wandering (MW) is associated with changes in eye movement parameters, but have not explored how MW affects the sequential pattern of eye movements involved in making sense of complex visual information. Eye movements naturally unfold over time and this process may reveal novel information about cognitive processing during MW. The current study used Recurrence Quantification Analysis (Anderson, Bischof, Laidlaw, Risko, & Kingstone, 2013) to describe the pattern of refixations (fixations directed to previously-inspected regions) during MW. Participants completed a real-world scene encoding task and responded to thought probes assessing intentional and unintentional MW. Both types of MW were associated with worse memory of the scenes. Importantly, RQA showed that scanpaths during unintentional MW were more repetitive than during on-task episodes, as indicated by a higher recurrence rate and more stereotypical fixation sequences. This increased repetitiveness suggests an adaptive response to processing failures through re-examining previous locations. Moreover, this increased repetitiveness contributed to fixations focusing on a smaller spatial scale of the stimuli. Finally, we were also able to validate several traditional measures: both intentional and unintentional MW were associated with fewer and longer fixations; Eye-blinking increased numerically during both types of MW but the difference was only significant for unintentional MW. Overall, the results advanced our understanding of how visual processing is affected during MW by highlighting the sequential aspect of eye movements.

scholarly journals Bibliometrics and visualization analysis of China's Service Quality Development Trend

2021 ◽  
Vol 233 ◽  
pp. 01164
Author(s):  
Lei Xu ◽  
Yuan Ni ◽  
Pengfei Han ◽  
Teng Zhang
Keyword(s):  
Social Network ◽  
Service Quality ◽  
Visual Analysis ◽  
Rapid Development ◽  
Development Trend ◽  
Network Evolution ◽  
Data Source ◽  
Application Fields ◽  
High Level ◽  
The Development Trend

From the aspect of bibliometrics and social network analysis, this paper studies the content characteristics and network evolution of service quality in China, form visual analysis charts and summarizes the research hotspots and development trends. Taking CNKI database as the data source, data mining and association analysis of sample data were carried out by means of manual judgment, keyword co-occurrence, bibliometric software BICOMB and Pajek. 872 related literatures were retrieved from 1999 to 2019, and the tendency of the number of published papers in each year was close to the inverted U type. This study expands the application fields of bibliometrics and social network software, and analyzes the development trend of service quality. The theoretical and practical research on service quality has achieved rapid development in the past 20 years. The library service quality and service quality satisfaction have always gotten a high level of attention, and the characteristics of network platforms for service quality have gradually become apparent because of the development of the Internet and computer technology.

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