Visual Attention Software: A New Tool for Understanding the “Subliminal” Experience of the Built Environment

Eye-tracking technology is a biometric tool that has found many commercial and research applications. The recent advent of affordable wearable sensors has considerably expanded the range of these possibilities to fields such as computer gaming, education, entertainment, health, neuromarketing, psychology, etc. The Visual Attention Software by 3M (3M-VAS) is an artificial intelligence application that was formulated using experimental data from eye-tracking. It can be used to predict viewer reactions to images, generating fixation point probability maps and fixation point sequence estimations, thus revealing pre-attentive processing of visual stimuli with a very high degree of accuracy. We have used 3M-VAS software in an innovative implementation to analyze images of different buildings, either in their original state or photographically manipulated, as well as various geometric patterns. The software not only reveals non-obvious fixation points, but also overall relative design coherence, a key element of Christopher Alexander’s theory of geometrical order. A more evenly distributed field of attention seen in some structures contrasts with other buildings being ignored, those showing instead unconnected points of splintered attention. Our findings are non-intuitive and surprising. We link these results to both Alexander’s theory and Neuroscience, identify potential pitfalls in the software’s use, and also suggest ways to avoid them.

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Computer-aided autism diagnosis based on visual attention models using eye tracking

Scientific Reports ◽

10.1038/s41598-021-89023-8 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Jessica S. Oliveira ◽

Felipe O. Franco ◽

Mirian C. Revers ◽

Andréia F. Silva ◽

Joana Portolese ◽

...

Keyword(s):

Visual Attention ◽

Eye Tracking ◽

Autism Spectrum ◽

Computational Method ◽

Video Frame ◽

Tracking Data ◽

Attention Model ◽

Group Case ◽

Computer Aided ◽

Fixation Points

AbstractAn advantage of using eye tracking for diagnosis is that it is non-invasive and can be performed in individuals with different functional levels and ages. Computer/aided diagnosis using eye tracking data is commonly based on eye fixation points in some regions of interest (ROI) in an image. However, besides the need for every ROI demarcation in each image or video frame used in the experiment, the diversity of visual features contained in each ROI may compromise the characterization of visual attention in each group (case or control) and consequent diagnosis accuracy. Although some approaches use eye tracking signals for aiding diagnosis, it is still a challenge to identify frames of interest when videos are used as stimuli and to select relevant characteristics extracted from the videos. This is mainly observed in applications for autism spectrum disorder (ASD) diagnosis. To address these issues, the present paper proposes: (1) a computational method, integrating concepts of Visual Attention Model, Image Processing and Artificial Intelligence techniques for learning a model for each group (case and control) using eye tracking data, and (2) a supervised classifier that, using the learned models, performs the diagnosis. Although this approach is not disorder-specific, it was tested in the context of ASD diagnosis, obtaining an average of precision, recall and specificity of 90%, 69% and 93%, respectively.

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Employing entropy measures to identify visitors in multi-occupancy environments

Journal of Ambient Intelligence and Humanized Computing ◽

10.1007/s12652-020-02824-z ◽

2020 ◽

Author(s):

Aadel Howedi ◽

Ahmad Lotfi ◽

Amir Pourabdollah

Keyword(s):

Home Environment ◽

Daily Living ◽

Wearable Sensors ◽

Permutation Entropy ◽

Care Workers ◽

Entropy Measures ◽

Novel Method ◽

High Degree ◽

Motion Detectors ◽

To Receive

AbstractHuman activity recognition (HAR) is used to support older adults to live independently in their own homes. Once activities of daily living (ADL) are recognised, gathered information will be used to identify abnormalities in comparison with the routine activities. Ambient sensors, including occupancy sensors and door entry sensors, are often used to monitor and identify different activities. Most of the current research in HAR focuses on a single-occupant environment when only one person is monitored, and their activities are categorised. The assumption that home environments are occupied by one person all the time is often not true. It is common for a resident to receive visits from family members or health care workers, representing a multi-occupancy environment. Entropy analysis is an established method for irregularity detection in many applications; however, it has been rarely applied in the context of ADL and HAR. In this paper, a novel method based on different entropy measures, including Shannon Entropy, Permutation Entropy, and Multiscale-Permutation Entropy, is employed to investigate the effectiveness of these entropy measures in identifying visitors in a home environment. This research aims to investigate whether entropy measures can be utilised to identify a visitor in a home environment, solely based on the information collected from motion detectors [e.g., passive infra-red] and door entry sensors. The entropy measures are tested and evaluated based on a dataset gathered from a real home environment. Experimental results are presented to show the effectiveness of entropy measures to identify visitors and the time of their visits without the need for employing extra wearable sensors to tag the visitors. The results obtained from the experiments show that the proposed entropy measures could be used to detect and identify a visitor in a home environment with a high degree of accuracy.

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Artificial Intelligence Application to Cognitive Radio Networks

Intelligent Network Management and Control ◽

10.1002/9781119817840.ch9 ◽

2021 ◽

pp. 217-243

Author(s):

Badr Benmammar ◽

Asma Amraoui

Keyword(s):

Artificial Intelligence ◽

Cognitive Radio ◽

Cognitive Radio Networks ◽

Radio Networks ◽

Artificial Intelligence Application ◽

Intelligence Application

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Does What We See Shape History? Examining Workload History as a Function of Performance and Ambient/Focal Visual Attention

ACM Transactions on Applied Perception ◽

10.1145/3449066 ◽

2021 ◽

Vol 18 (2) ◽

pp. 1-17

Author(s):

Shannon P. Devlin ◽

Jennifer K. Byham ◽

Sara Lu Riggs

Keyword(s):

Visual Attention ◽

Eye Tracking ◽

Performance Measure ◽

Task Demands ◽

Unexpected Finding ◽

Impact Performance ◽

History Effects ◽

And Performance ◽

Workload History ◽

Over Time

Changes in task demands can have delayed adverse impacts on performance. This phenomenon, known as the workload history effect, is especially of concern in dynamic work domains where operators manage fluctuating task demands. The existing workload history literature does not depict a consistent picture regarding how these effects manifest, prompting research to consider measures that are informative on the operator's process. One promising measure is visual attention patterns, due to its informativeness on various cognitive processes. To explore its ability to explain workload history effects, participants completed a task in an unmanned aerial vehicle command and control testbed where workload transitioned gradually and suddenly. The participants’ performance and visual attention patterns were studied over time to identify workload history effects. The eye-tracking analysis consisted of using a recently developed eye-tracking metric called coefficient K , as it indicates whether visual attention is more focal or ambient. The performance results found workload history effects, but it depended on the workload level, time elapsed, and performance measure. The eye-tracking analysis suggested performance suffered when focal attention was deployed during low workload, which was an unexpected finding. When synthesizing these results, they suggest unexpected visual attention patterns can impact performance immediately over time. Further research is needed; however, this work shows the value of including a real-time visual attention measure, such as coefficient K , as a means to understand how the operator manages varying task demands in complex work environments.

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Review of Artificial Intelligence Application in Cardiology

2020 9th Mediterranean Conference on Embedded Computing (MECO) ◽

10.1109/meco49872.2020.9134333 ◽

2020 ◽

Author(s):

Almina Seckanovic ◽

Marijana Sehovac ◽

Lemana Spahic ◽

Irma Ramic ◽

Nuraiym Mamatnazarova ◽

...

Keyword(s):

Artificial Intelligence ◽

Artificial Intelligence Application ◽

Intelligence Application

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Visual attention for solving multiple-choice science problem: An eye-tracking analysis

Computers & Education ◽

10.1016/j.compedu.2011.07.012 ◽

2012 ◽

Vol 58 (1) ◽

pp. 375-385 ◽

Cited By ~ 128

Author(s):

Meng-Jung Tsai ◽

Huei-Tse Hou ◽

Meng-Lung Lai ◽

Wan-Yi Liu ◽

Fang-Ying Yang

Keyword(s):

Visual Attention ◽

Eye Tracking ◽

Multiple Choice ◽

Science Problem

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Seeking attention: an eye tracking study of in-store merchandise displays

International Journal of Retail & Distribution Management ◽

10.1108/ijrdm-06-2013-0120 ◽

2015 ◽

Vol 43 (6) ◽

pp. 561-574 ◽

Cited By ~ 42

Author(s):

Patricia Huddleston ◽

Bridget K. Behe ◽

Stella Minahan ◽

R. Thomas Fernandez

Keyword(s):

Visual Attention ◽

Eye Tracking ◽

Purchase Intention ◽

Product Information ◽

Product Category ◽

Practical Significance ◽

Content Type ◽

First Time ◽

Tracking Technology

Purpose – The purpose of this paper is to elucidate the role that visual measures of attention to product and information and price display signage have on purchase intention. The authors assessed the effect of visual attention to the product, information or price sign on purchase intention, as measured by likelihood to buy. Design/methodology/approach – The authors used eye-tracking technology to collect data from Australian and US garden centre customers, who viewed eight plant displays in which the signs had been altered to show either price or supplemental information (16 images total). The authors compared the role of visual attention to price and information sign, and the role of visual attention to the product when either sign was present on likelihood to buy. Findings – Overall, providing product information on a sign without price elicited higher likelihood to buy than providing a sign with price. The authors found a positive relationship between visual attention to price on the display sign and likelihood to buy, but an inverse relationship between visual attention to information and likelihood to buy. Research limitations/implications – An understanding of the attention-capturing power of merchandise display elements, especially signs, has practical significance. The findings will assist retailers in creating more effective and efficient display signage content, for example, featuring the product information more prominently than the price. The study was conducted on a minimally packaged product, live plants, which may reduce the ability to generalize findings to other product types. Practical implications – The findings will assist retailers in creating more effective and efficient display signage content. The study used only one product category (plants) which may reduce the ability to generalize findings to other product types. Originality/value – The study is one of the first to use eye-tracking in a macro-level, holistic investigation of the attention-capturing value of display signage information and its relationship to likelihood to buy. Researchers, for the first time, now have the ability to empirically test the degree to which attention and decision-making are linked.

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