Intuitiveness of geospatial uncertainty visualizations: a user study on point symbols

In the process of map making, the attention is given to the resulting image map (to be accurate, readable, and suit the primary purpose) and its user aspects. Current cartography understands the user issues as all matters relating to user perception, map use and also user preferences. Most commercial cartographic production is strongly connected to economic circumstances. Companies are discovering user’s interests and market demands. However, is it sufficient to focus just on the user’s preferences? Recent research on user aspects at Palacký University Olomouc addresses a much wider scope of user aspects. The user’s preferences are very often distorting – the users think that the particular image map is kind, beautiful, and useful and they wants to buy it (or use it – it depends on the form of the map production). But when the same user gets the task to use practically this particular map (such as finding the shortest way), so the user concludes that initially preferred map is useless, and uses a map, that was worse evaluated according to his preferences. It is, therefore, necessary to evaluate not only the correctness of image maps and their aesthetics but also to assess the user perception and other user issues. For the accomplishment of such testing, eye-tracking technology is a useful tool. The research analysed how users read image maps, or if they prefer image maps over traditional maps. The eye tracking experiment on the comparison of the conventional and image map reading was conducted. The map readers were asked to solve few simple tasks with either conventional or image map. The readers’ choice of the map to solve the task was one of investigated aspect of user preferences. Results demonstrate that the user preferences and user needs are often quite different issues. The research outcomes show that it is crucial to implement map user testing into the cartographic production process.

Download Full-text

Multivariate mapping for experienced users: comparing extrinsic and intrinsic maps with univariate maps

Miscellanea Geographica ◽

10.2478/mgrsd-2020-0068 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Jolanta Korycka-Skorupa ◽

Izabela Gołębiowska

Keyword(s):

Eye Tracking ◽

Task Difficulty ◽

Performance Metrics ◽

Cognitive Effort ◽

Subjective Rating ◽

Tracking Data ◽

Three Solutions ◽

Visual Variables ◽

The Difference ◽

Task Types

Abstract Multivariate mapping is a technique in which multivariate data are encoded into a single map. A variety of design solutions for multivariate mapping refers to the number of phenomena mapped, the map type, and the visual variables applied. Unlike other authors who have mainly evaluated bivariate maps, in our empirical study we compared three solutions when mapping four variables: two types of multivariate maps (intrinsic and extrinsic) and a simple univariate alternative (serving as a baseline). We analysed usability performance metrics (answer time, answer accuracy, subjective rating of task difficulty) and eye-tracking data. The results suggested that experts used all the tested maps with similar results for answer time and accuracy, even when using four-variable intrinsic maps, which is considered to be a challenging solution. However, eye-tracking data provided more nuances in relation to the difference in cognitive effort evoked by the tested maps across task types.

Download Full-text

Predicting Spatial Visualization Problems’ Difficulty Level from Eye-Tracking Data

Sensors ◽

10.3390/s20071949 ◽

2020 ◽

Vol 20 (7) ◽

pp. 1949

Author(s):

Xiang Li ◽

Rabih Younes ◽

Diana Bairaktarova ◽

Qi Guo

Keyword(s):

Machine Learning ◽

Eye Tracking ◽

Eye Movement ◽

Spatial Visualization ◽

Difficulty Level ◽

Tracking Data ◽

Multiple Choice Questions ◽

Essential Information ◽

E Learning ◽

The Difference

The difficulty level of learning tasks is a concern that often needs to be considered in the teaching process. Teachers usually dynamically adjust the difficulty of exercises according to the prior knowledge and abilities of students to achieve better teaching results. In e-learning, because there is no teacher involvement, it often happens that the difficulty of the tasks is beyond the ability of the students. In attempts to solve this problem, several researchers investigated the problem-solving process by using eye-tracking data. However, although most e-learning exercises use the form of filling in blanks and choosing questions, in previous works, research focused on building cognitive models from eye-tracking data collected from flexible problem forms, which may lead to impractical results. In this paper, we build models to predict the difficulty level of spatial visualization problems from eye-tracking data collected from multiple-choice questions. We use eye-tracking and machine learning to investigate (1) the difference of eye movement among questions from different difficulty levels and (2) the possibility of predicting the difficulty level of problems from eye-tracking data. Our models resulted in an average accuracy of 87.60% on eye-tracking data of questions that the classifier has seen before and an average of 72.87% on questions that the classifier has not yet seen. The results confirmed that eye movement, especially fixation duration, contains essential information on the difficulty of the questions and it is sufficient to build machine-learning-based models to predict difficulty level.

Download Full-text

An R based interface to understand cognitive ability of different participants using fixation and saccade detection

International Journal of Research in Pharmaceutical Sciences ◽

10.26452/ijrps.v10i3.1438 ◽

2019 ◽

Vol 10 (3) ◽

pp. 2127-2131

Author(s):

Akshay S ◽

Ashika P ◽

Aswathy Ramesh

Keyword(s):

Computer Vision ◽

Eye Tracking ◽

Cognitive Ability ◽

Cognitive Abilities ◽

Fixation Duration ◽

Experimental Results ◽

Tracking Data ◽

Detailed Survey ◽

Wide Range ◽

The Difference

Eye-tracking is an emerging area of science in a wide range of computer vision-based applications. Eye-tracking mainly deals with where the person is looking at and for what duration. In this work, we propose an R based interface to visualize the eye-tracking data as fixations and saccades that depicts where the person looking at –fixations and saccades and what duration – fixation duration. Through the eye-tracking metrics that are visualized in our work, one can visualize the difference between the viewing behaviour of various participants. The differences thus depicted can later be studied in order to understand the cognitive abilities of the participants. The paper contains a detailed survey of the existing literature and the experimental results generated using the R interface.

Download Full-text

A User Study of Web Search Session Behaviour using Eye Tracking data

10.14236/ewic/hci2012.35 ◽

2012 ◽

Author(s):

Mari-Carmen Marcos ◽

David F Nettleton ◽

Diego Saez-Trumper

Keyword(s):

Eye Tracking ◽

Web Search ◽

User Study ◽

Tracking Data

Download Full-text

USER PREFERENCES IN IMAGE MAP USING

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b4-599-2016 ◽

2016 ◽

Vol XLI-B4 ◽

pp. 599-603

Author(s):

A. Vondráková ◽

V. Vozenilek

Keyword(s):

Eye Tracking ◽

Production Process ◽

User Preferences ◽

User Needs ◽

User Perception ◽

User Testing ◽

Map Reading ◽

Research Outcomes ◽

Strongly Connected ◽

Tracking Technology

In the process of map making, the attention is given to the resulting image map (to be accurate, readable, and suit the primary purpose) and its user aspects. Current cartography understands the user issues as all matters relating to user perception, map use and also user preferences. Most commercial cartographic production is strongly connected to economic circumstances. Companies are discovering user’s interests and market demands. However, is it sufficient to focus just on the user’s preferences? Recent research on user aspects at Palacký University Olomouc addresses a much wider scope of user aspects. The user’s preferences are very often distorting – the users think that the particular image map is kind, beautiful, and useful and they wants to buy it (or use it – it depends on the form of the map production). But when the same user gets the task to use practically this particular map (such as finding the shortest way), so the user concludes that initially preferred map is useless, and uses a map, that was worse evaluated according to his preferences. It is, therefore, necessary to evaluate not only the correctness of image maps and their aesthetics but also to assess the user perception and other user issues. For the accomplishment of such testing, eye-tracking technology is a useful tool. The research analysed how users read image maps, or if they prefer image maps over traditional maps. The eye tracking experiment on the comparison of the conventional and image map reading was conducted. The map readers were asked to solve few simple tasks with either conventional or image map. The readers’ choice of the map to solve the task was one of investigated aspect of user preferences. Results demonstrate that the user preferences and user needs are often quite different issues. The research outcomes show that it is crucial to implement map user testing into the cartographic production process.

Download Full-text

The Opposition of Surprisal and Semantic Similarity in the Prediction of Language Processing: Evidence from Eye-tracking Data

10.31234/osf.io/zypk9 ◽

2020 ◽

Author(s):

Kun Sun

Keyword(s):

Eye Tracking ◽

Semantic Similarity ◽

Cognitive Processing ◽

Language Processing ◽

Language Comprehension ◽

Word Processing ◽

Reading Time ◽

Computational Models ◽

Tracking Data ◽

Dynamic Approach

Expectations or predictions about upcoming content play an important role during language comprehension and processing. One important aspect of recent studies of language comprehension and processing concerns the estimation of the upcoming words in a sentence or discourse. Many studies have used eye-tracking data to explore computational and cognitive models for contextual word predictions and word processing. Eye-tracking data has previously been widely explored with a view to investigating the factors that influence word prediction. However, these studies are problematic on several levels, including the stimuli, corpora, statistical tools they applied. Although various computational models have been proposed for simulating contextual word predictions, past studies usually preferred to use a single computational model. The disadvantage of this is that it often cannot give an adequate account of cognitive processing in language comprehension. To avoid these problems, this study draws upon a massive natural and coherent discourse as stimuli in collecting the data on reading time. This study trains two state-of-art computational models (surprisal and semantic (dis)similarity from word vectors by linear discriminative learning (LDL)), measuring knowledge of both the syntagmatic and paradigmatic structure of language. We develop a `dynamic approach' to compute semantic (dis)similarity. It is the first time that these two computational models have been merged. Models are evaluated using advanced statistical methods. Meanwhile, in order to test the efficiency of our approach, one recently developed cosine method of computing semantic (dis)similarity based on word vectors data adopted is used to compare with our `dynamic' approach. The two computational and fixed-effect statistical models can be used to cross-verify the findings, thus ensuring that the result is reliable. All results support that surprisal and semantic similarity are opposed in the prediction of the reading time of words although both can make good predictions. Additionally, our `dynamic' approach performs better than the popular cosine method. The findings of this study are therefore of significance with regard to acquiring a better understanding how humans process words in a real-world context and how they make predictions in language cognition and processing.

Download Full-text