scholarly journals Real-world Statistical Regularities Guide the Deployment of Visual Attention, Even in the Absence of Semantic Scene Recognition

2010 ◽  
Vol 10 (7) ◽  
pp. 1285-1285
Author(s):  
A. Sherman ◽  
G. Alvarez
Keyword(s):  
Visual Attention ◽  
Real World ◽  
Scene Recognition ◽  
Statistical Regularities ◽  
Semantic Scene

Prior real-world experience influences non-linguistic statistical learning

2020 ◽  
Author(s):  
Stephen Charles Van Hedger ◽  
Ingrid Johnsrude ◽  
Laura Batterink
Keyword(s):  
Statistical Learning ◽  
Real World ◽  
Language Experience ◽  
Pitch Range ◽  
Test Items ◽  
Instrument Measuring ◽  
Statistical Regularities ◽  
Context Free ◽  
Specific Learning

Listeners are adept at extracting regularities from the environment, a process known as statistical learning (SL). SL has been generally assumed to be a form of “context-free” learning that occurs independently of prior knowledge, and SL experiments typically involve exposing participants to presumed novel regularities, such as repeating nonsense words. However, recent work has called this assumption into question, demonstrating that learners’ previous language experience can considerably influence SL performance. In the present experiment, we tested whether previous knowledge also shapes SL in a non-linguistic domain, using a paradigm that involves extracting regularities over tone sequences. Participants learned novel tone sequences, which consisted of pitch intervals not typically found in Western music. For one group of participants, the tone sequences used artificial, computerized instrument sounds. For the other group, the same tone sequences used familiar instrument sounds (piano or violin). Knowledge of the statistical regularities was assessed using both trained sounds (measuring specific learning) and sounds that differed in pitch range and/or instrument (measuring transfer learning). In a follow-up experiment, two additional testing sessions were administered to gauge retention of learning (one day and approximately one-week post-training). Compared to artificial instruments, training on sequences played by familiar instruments resulted in reduced correlations among test items, reflecting more idiosyncratic performance. Across all three testing sessions, learning of novel regularities presented with familiar instruments was worse compared to unfamiliar instruments, suggesting that prior exposure to music produced by familiar instruments interfered with new sequence learning. Overall, these results demonstrate that real-world experience influences SL in a non-linguistic domain, supporting the view that SL involves the continuous updating of existing representations, rather than the establishment of entirely novel ones.

scholarly journals Scaling up visual attention and visual working memory to the real world

2020 ◽  
Author(s):  
Timothy F. Brady ◽  
Viola S. Störmer ◽  
Anna Shafer-Skelton ◽  
Jamal Rodgers Williams ◽  
Angus F. Chapman ◽  
...  
Keyword(s):  
Working Memory ◽  
Visual Attention ◽  
Visual Working Memory ◽  
Real World ◽  
Scaling Up ◽  
The Real ◽  
Vision Science ◽  
Precise Understanding ◽  
Complex Settings ◽  
And Control

Both visual attention and visual working memory tend to be studied with very simple stimuli and low-level paradigms, designed to allow us to understand the representations and processes in detail, or with fully realistic stimuli that make such precise understanding difficult but are more representative of the real world. In this chapter we argue for an intermediate approach in which visual attention and visual working memory are studied by scaling up from the simplest settings to more complex settings that capture some aspects of the complexity of the real-world, while still remaining in the realm of well-controlled stimuli and well-understood tasks. We believe this approach, which we have been taking in our labs, will allow a more generalizable set of knowledge about visual attention and visual working memory while maintaining the rigor and control that is typical of vision science and psychophysics studies.

scholarly journals Where the action could be: Speakers look at graspable objects and meaningful scene regions when describing potential actions

2019 ◽  
Author(s):  
Gwendolyn L Rehrig ◽  
Candace Elise Peacock ◽  
Taylor Hayes ◽  
Fernanda Ferreira ◽  
John M. Henderson
Keyword(s):  
Visual Attention ◽  
Real World ◽  
Language Production ◽  
Native English Speakers ◽  
English Speakers ◽  
Linguistic Processing ◽  
General Meaning ◽  
New Evidence ◽  
Shed Light ◽  
Better Than

The world is visually complex, yet we can efficiently describe it by extracting the information that is most relevant to convey. How do the properties of real-world scenes help us decide where to look and what to say? Image salience has been the dominant explanation for what drives visual attention and production as we describe displays, but new evidence shows scene meaning predicts attention better than image salience. Here we investigated the relevance of one aspect of meaning, graspability (the grasping interactions objects in the scene afford), given that affordances have been implicated in both visual and linguistic processing. We quantified image salience, meaning, and graspability for real-world scenes. In three eyetracking experiments, native English speakers described possible actions that could be carried out in a scene. We hypothesized that graspability would preferentially guide attention due to its task-relevance. In two experiments using stimuli from a previous study, meaning explained visual attention better than graspability or salience did, and graspability explained attention better than salience. In a third experiment we quantified image salience, meaning, graspability, and reach-weighted graspability for scenes that depicted reachable spaces containing graspable objects. Graspability and meaning explained attention equally well in the third experiment, and both explained attention better than salience. We conclude that speakers use object graspability to allocate attention to plan descriptions when scenes depict graspable objects within reach, and otherwise rely more on general meaning. The results shed light on what aspects of meaning guide attention during scene viewing in language production tasks.

scholarly journals Visual Attention in Real‐World Conversation: Gaze Patterns Are Modulated by Communication and Group Size

2020 ◽  
Author(s):  
Thomas Maran ◽  
Marco Furtner ◽  
Simon Liegl ◽  
Theo Ravet‐Brown ◽  
Lucas Haraped ◽  
...  
Keyword(s):  
Visual Attention ◽  
Group Size ◽  
Real World ◽  
Gaze Patterns

Improving Registration of Augmented Reality by Incorporating DCNNS into Visual SLAM

2018 ◽  
Vol 32 (12) ◽  
pp. 1855022 ◽  
Author(s):  
Yongbin Chen ◽  
Hanwu He ◽  
Heen Chen ◽  
Teng Zhu
Keyword(s):  
Augmented Reality ◽  
Three Dimensional ◽  
Semantic Segmentation ◽  
Recognition Algorithm ◽  
Scene Recognition ◽  
3D Registration ◽  
Deep Convolutional Neural Networks ◽  
Severe Problem ◽  
Localization And Mapping ◽  
Semantic Scene

Augmented reality (AR) by analyzing the characteristics of the scene, the computer-generated geometric information which can be added to the real environment in the way of visual fusion, reinforces the perception of the world. Three-dimensional (3D) registration is one of the core issues of in AR. The key issue is to estimate the visual sensor’s posture in the 3D environment and figure out the objects in the scene. Recently, computer vision has made significant progress, but the registration based on natural feature points in 3D space for AR system is still a severe problem. There is the difficulty of working out the mobile camera’s posture in the 3D scene precisely due to the unstable factors, such as the image noise, changing light and the complex background pattern. Therefore, to design a stable, reliable and efficient scene recognition algorithm is still very challenging work. In this paper, we propose an algorithm which combines Visual Simultaneous Localization and Mapping (SLAM) and Deep Convolutional Neural Networks (DCNNS) to boost the performance of AR registration. Semantic segmentation is a dense prediction task which aims to predict categories for each pixel in an image when applying to AR registration, and it will be able to narrow the searching range of the feature point between the two frames thus enhancing the stability of the system. Comparative experiments in this paper show that the semantic scene information will bring a revolutionary breakthrough to the AR interaction.

scholarly journals Scaling up visual attention and visual working memory to the real world

2019 ◽  
pp. 29-69 ◽  
Author(s):  
Timothy F. Brady ◽  
Viola S. Störmer ◽  
Anna Shafer-Skelton ◽  
Jamal R. Williams ◽  
Angus F. Chapman ◽  
...  
Keyword(s):  
Working Memory ◽  
Visual Attention ◽  
Visual Working Memory ◽  
Real World ◽  
Scaling Up ◽  
The Real

The Effect of Scene Context on Visual Search in Real‐World Scenes

2017 ◽  
Author(s):  
Samia Hussein
Keyword(s):  
Visual Search ◽  
Eye Movements ◽  
Visual Attention ◽  
Real World ◽  
Target Object ◽  
Scene Context ◽  
Efficient Search ◽  
Object Attention ◽  
Study Participants

The present study examined the effect of scene context on guidance of attention during visual search in real‐world scenes. Prior research has demonstrated that when searching for an object, attention is usually guided to the region of a scene that most likely contains that target object. This study examined two possible mechanisms of attention that underlie efficient search: enhancement of attention (facilitation) and a deficiency of attention (inhibition). In this study, participants (N=20) were shown an object name and then required to search through scenes for the target while their eye movements were tracked. Scenes were divided into target‐relevant contextual regions (upper, middle, lower) and participants searched repeatedly in the same scene for different targets either in the same region or in different regions. Comparing repeated searches within the same scene across different regions, we expect to find that visual search is faster and more efficient (facilitation of attention) in regions of a scene where attention was previously deployed. At the same time, when searching across different regions, we expect searches to be slower and less efficient (inhibition of attention) because those regions were previously ignored. Results from this study help to better understand how mechanisms of visual attention operate within scene contexts during visual search. 

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