scholarly journals Boosting Search Performance Using Query Variations

2019 ◽  
Vol 37 (4) ◽  
pp. 1-25 ◽  
Author(s):  
Rodger Benham ◽  
Joel Mackenzie ◽  
Alistair Moffat ◽  
J. Shane Culpepper
Keyword(s):  
Search Performance

The Influences of Goal and Laterality of a Prepared Hand Response on a Co-Occurring Visual Search

2015 ◽  
Vol 74 (1) ◽  
pp. 55-60 ◽  
Author(s):  
Alexandre Coutté ◽  
Gérard Olivier ◽  
Sylvane Faure
Keyword(s):  
Visual Search ◽  
Visual Target ◽  
Motor Planning ◽  
Search Performance ◽  
Response Preparation ◽  
Manual Response ◽  
Response Hand ◽  
Computer Screen ◽  
Left Hand ◽  
Manual Interaction

Computer use generally requires manual interaction with human-computer interfaces. In this experiment, we studied the influence of manual response preparation on co-occurring shifts of attention to information on a computer screen. The participants were to carry out a visual search task on a computer screen while simultaneously preparing to reach for either a proximal or distal switch on a horizontal device, with either their right or left hand. The response properties were not predictive of the target’s spatial position. The results mainly showed that the preparation of a manual response influenced visual search: (1) The visual target whose location was congruent with the goal of the prepared response was found faster; (2) the visual target whose location was congruent with the laterality of the response hand was found faster; (3) these effects have a cumulative influence on visual search performance; (4) the magnitude of the influence of the response goal on visual search is marginally negatively correlated with the rapidity of response execution. These results are discussed in the general framework of structural coupling between perception and motor planning.

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

scholarly journals When more is more: redundant modifiers can facilitate visual search

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Gwendolyn Rehrig ◽  
Reese A. Cullimore ◽  
John M. Henderson ◽  
Fernanda Ferreira
Keyword(s):  
Visual Search ◽  
Real World ◽  
Search Space ◽  
Relevant Information ◽  
Target Object ◽  
Search Performance ◽  
Redundant Information ◽  
Additional Information ◽  
Target Template ◽  
Template Specificity

Abstract According to the Gricean Maxim of Quantity, speakers provide the amount of information listeners require to correctly interpret an utterance, and no more (Grice in Logic and conversation, 1975). However, speakers do tend to violate the Maxim of Quantity often, especially when the redundant information improves reference precision (Degen et al. in Psychol Rev 127(4):591–621, 2020). Redundant (non-contrastive) information may facilitate real-world search if it narrows the spatial scope under consideration, or improves target template specificity. The current study investigated whether non-contrastive modifiers that improve reference precision facilitate visual search in real-world scenes. In two visual search experiments, we compared search performance when perceptually relevant, but non-contrastive modifiers were included in the search instruction. Participants (NExp. 1 = 48, NExp. 2 = 48) searched for a unique target object following a search instruction that contained either no modifier, a location modifier (Experiment 1: on the top left, Experiment 2: on the shelf), or a color modifier (the black lamp). In Experiment 1 only, the target was located faster when the verbal instruction included either modifier, and there was an overall benefit of color modifiers in a combined analysis for scenes and conditions common to both experiments. The results suggest that violations of the Maxim of Quantity can facilitate search when the violations include task-relevant information that either augments the target template or constrains the search space, and when at least one modifier provides a highly reliable cue. Consistent with Degen et al. (2020), we conclude that listeners benefit from non-contrastive information that improves reference precision, and engage in rational reference comprehension. Significance statement This study investigated whether providing more information than someone needs to find an object in a photograph helps them to find that object more easily, even though it means they need to interpret a more complicated sentence. Before searching a scene, participants were either given information about where the object would be located in the scene, what color the object was, or were only told what object to search for. The results showed that providing additional information helped participants locate an object in an image more easily only when at least one piece of information communicated what part of the scene the object was in, which suggests that more information can be beneficial as long as that information is specific and helps the recipient achieve a goal. We conclude that people will pay attention to redundant information when it supports their task. In practice, our results suggest that instructions in other contexts (e.g., real-world navigation, using a smartphone app, prescription instructions, etc.) can benefit from the inclusion of what appears to be redundant information.

scholarly journals Effects of Visual Complexity Levels and Information Decluttering Methods for Cockpit Displays on Human Search Performance

2019 ◽  
Vol 63 (1) ◽  
pp. 96-100
Author(s):  
Sung Ho Kim ◽  
Ji Hwan Lee ◽  
Donggun Park ◽  
Yushin Lee ◽  
Myung Hwan Yun
Keyword(s):  
Situation Awareness ◽  
Human Error ◽  
Visual Complexity ◽  
Search Time ◽  
Human Perception ◽  
Search Performance ◽  
Design Variables ◽  
Cockpit Displays ◽  
Main Effect ◽  
Speed And Accuracy

Clutter problem of modern cockpit displays can occur frequently due to a large amount of information. So, decluttering less important information is required to minimize search time to find target information and prevent human error in interpreting display information. This study is to compare human search performance by visual complexity levels and decluttering methods of cockpit displays. Visual complexity of cockpit displays was designed to be three levels (High, medium, and low) by combining four design variables (number of stimuli, number of colors, number of icons, and variance of divisions) affecting visual complexity. A threat scoring equation was developed to determine what information to be decluttered and four decluttering methods (removal, dimming, dotting, and small sizing) were used to figure out how to declutter the information effectively. Human search performance was measured through search time of visual search task in terms of speed and number of hits of signal detection task in terms of accuracy. The main effect of visual complexity levels and the interaction effect were not significant in both search time and number of hits. Meanwhile, the main effect of decluttering methods was significant in search time. Especially, dotting was the most effective decluttering method in terms of speed and accuracy of human perception performance. The results of this study can be applied to information processing of cockpit displays and then contribute to improve pilot situation awareness.

Semantic Search on Unstructured Data

2010 ◽  
Vol 6 (2) ◽  
pp. 17-35 ◽  
Author(s):  
Alex Kohn ◽  
François Bry ◽  
Alexander Manta
Keyword(s):  
Retrieval System ◽  
Pharmaceutical Research ◽  
Information Retrieval System ◽  
Semantic Search ◽  
Unstructured Data ◽  
Search Performance ◽  
Adaptive Search ◽  
Retrieval Performance ◽  
Enterprise Search ◽  
Existing Data

Studies agree that searchers are often not satisfied with the performance of current enterprise search engines. As a consequence, more scientists worldwide are actively investigating new avenues for searching to improve retrieval performance. This paper contributes to YASA (Your Adaptive Search Agent), a fully implemented and thoroughly evaluated ontology-based information retrieval system for the enterprise. A salient particularity of YASA is that large parts of the ontology are automatically filled with facts by recycling and transforming existing data. YASA offers context-based personalization, faceted navigation, as well as semantic search capabilities. YASA has been deployed and evaluated in the pharmaceutical research department of Roche, Penzberg, and results show that already semantically simple ontologies suffice to considerably improve search performance.

Isoresponse time regions for the evaluation of visual search performance in ergonomic interface models

Ergonomics ◽  
1992 ◽  
Vol 35 (3) ◽  
pp. 243-252 ◽  
Author(s):  
DOHYUNG KEE ◽  
EUI S. JUNG ◽  
MIN K. CHUNG
Keyword(s):  
Visual Search ◽  
Search Performance ◽  
Interface Models
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