User's Cognitive Load Research Articles

Qualitative criteria in the assessment of security measures for critical infrastructure protection - a new approach

This study aims at developing a web service with Japanese Linked Data and evaluating the service. Recently, Japanese government has set open data as a new strategy in information technology field and focuses on 'Linked Open Data (LOD)' as a means to publish. However, the number of dataset as Japanese Linked Data is small, and the effect by introducing Japanese Linked Data has not been shown yet. Therefore, we created Japanese Linked Data focused on geographical or positional data, and implemented a mobility service to reduce user's cognitive load. Moreover, we conducted verification experiment for using the service and compared with conventional services. As a result, the possibilities of Linked Data to respond to various queries easily and to apply for information services by crossing some domains were explored.

Design of human-computer interaction interface considering user friendliness

Analysis and modelling for interactions on self-service terminal interface have been made in this paper based on distributed cognition theory, which is to confirm the relationship between interactions and information presentation in human-computer interaction and propose interface interaction design method of self-service terminal based on user cognitive ability. With adoption of this method, designers need to make research on users and identify the user group that needs to be taken care of firstly, and then make analysis of cognitive ability, set up user cognitive load model, describe the interactive behaviour of users, confirm basic interaction frame, and then establish interaction design matrix with universal usability design model and propose interactive design program. We take hotel self-service terminal as example, adopt this method to make design program and then verify the effectiveness of the proposed design method through making comparisons with design program formed by traditional method. This interactive design method can help designers develop self-service terminal interface that is suit-able for people to understand and use, decrease the cognitive load of users and meet the diversified demands of self-service terminal users for cognition.

Designing Multi-Dimensional User Interaction for a Virtual Museum System

The focus of this study is to improve the display platform for the State of Terengganu Virtual Museum System (muziummaya.terengganu.gov.my). The existing virtual museum can be displayed via desktop computing system where information is displayed on-line using features for searching, browsing, and manipulating artifacts and galleries. However, presentation of information on mobile device screens cannot be viewed effectively and is difficult to navigate due to the user's cognitive load. Therefore, this study will establish effective interaction design, taking into account the multi-dimensional context for the design of a user interface via a mobile device in the hopes that users who are students, curators, researchers, and the public can access the virtual museum system via desktop as well as from their mobile devices. This study was conducted in three stages i.e. user requirements analysis, interface design, and usability evaluation. The results of this research is the design of a multi-dimensional user interaction context that meets the display requirements and navigation information for mobile devices.

Adaptive Visualization of Complex Networks with FocalPoint

Maintaining situational awareness of a dynamic global computer network that consists of ten to hundreds of thousands of computers is a complex task for cyber administrators and operators looking to understand, plan and conduct operations in real time. Currently, cyber specialists must manually navigate complex networks by continuous cycles of overviews, drilldowns and manually mapping network incidents to mission impact. This is inefficient as manually maneuvering of network data is laborious, induces cognitive overload, and is prone to errors caused by distractive information resulting in important information and impacts not being seen. We are investigating “FocalPoint” an adaptive level of detail (LOD) recommender system tailored for hierarchical network information structures. FocalPoint reasons about contextual information associated with the network, user task, and user cognitive load to tune the presentation of network visualization displays to improve user performance in perception, comprehension and projection of current situational awareness. Our system is applied to two complex information constructs important to dynamic cyber network operations: network maps and attack graphs. The key innovations include: (a) context-aware automatic tailoring of complex network views, (b) multi-resolution hierarchical graph aggregation, (c) incorporation of new computational models for adaptive-decision making on user tasks, cost/benefit utility and human situation awareness, and (d) user interaction techniques to integrate recommendations into the network viewing system. Our aim is to have a direct impact on planning and operations management for complex networks by; overcoming information overload, preventing tunnel vision, reducing cognitive load, and increasing time available to focus on optimum level of details of the global network space and missions.

Implementing mobility service with Japanese Linked Data

This study aims at developing a web service with Japanese Linked Data and evaluating the service. Recently, Japanese government has set open data as a new strategy in information technology field and focuses on 'Linked Open Data (LOD)' as a means to publish. However, the number of dataset as Japanese Linked Data is small, and the effect by introducing Japanese Linked Data has not been shown yet. Therefore, we created Japanese Linked Data focused on geographical or positional data, and implemented a mobility service to reduce user's cognitive load. Moreover, we conducted verification experiment for using the service and compared with conventional services. As a result, the possibilities of Linked Data to respond to various queries easily and to apply for information services by crossing some domains were explored.

Fuzzy Navigation Engine: Mitigating the Cognitive Demands of Semi-Natural Locomotion

Many interfaces exist for locomotion in virtual reality, although they are rarely considered fully natural. Past research has found that using such interfaces places cognitive demands on the user, with unnatural actions and concurrent tasks competing for finite cognitive resources. Notably, using semi-natural interfaces leads to poor performance on concurrent tasks requiring spatial working memory. This paper presents an adaptive system designed to track a user's concurrent cognitive task load and adjust interface parameters accordingly, varying the extent to which movement is fully natural. A fuzzy inference system is described and the results of an initial validation study are presented. Users of this adaptive interface demonstrated better performance than users of a baseline interface on several movement metrics, indicating that the adaptive interface helped users manage the demands of concurrent spatial tasks in a virtual environment. However, participants experienced some unexpected difficulties when faced with a concurrent verbal task.

Classifying Cognitive Load and Driving Situation with Machine Learning

 Abstract—This paper classifies a driver's cognitive state in real driving situations to improve the in-vehicle information service that judges a user's cognitive load and driving situation. We measure the driver's eye movement and collect driving sensor data such as braking, acceleration, and steering angles that are used to classify the driver's state. A set of data about the driver's degree of cognitive load, regarded as a training set, is obtained from steering operation and task cognition. Given such information, we use a machine-learning method to classify the driver's cognitive load. We achieved reasonable accuracy in certain driving situations in which the driver moves abnormally for an appropriate service supporting safe driving. surrounding situation, the range of eye movement may increase because the driver's cognitive load has increased. This means that a high cognitive load and a change of eye movement are related. Eye movement is used in the field of physiological psychology for clarifying control (1). It is directly related to perception and can be considered an indication of mental load. Driving a car requires prediction of the surrounding environment and is influenced by the situation. Therefore, a user's cognitive load can be classified using these features. To do this, we measure the driver's eye movement and gather driving data such as accelerator use, braking, and steering. This paper takes a machine-learning approach to the above cognitive state identification problem in a realistic car-driving task. We set up cognitive loads according to the steering-entropy method (2) and a definition of the task cognition situation. This paper is organized as follows. Section II presents related works. Section III defines cognitive load. Section IV describes our classification model for cognitive load. Section V presents a performance evaluation. The final section provides conclusions.

Using Task-Induced Pupil Diameter and Blink Rate to Infer Cognitive Load

Minimizing user cognitive load is suggested as an integral part of human-centered design, where a more intuitive, easy to learn, and adaptive interface is desired. In this context, it is difficult to develop optimal strategies to improve the design without first knowing how user cognitive load fluctuates during interaction. In this study, we investigate how cognitive load measurement is affected by different task types from the perspective of the load theory of attention, using pupil diameter and blink measures. We induced five levels of cognitive load during low and high perceptual load tasks and found that although pupil diameter showed significant effects on cognitive load when the perceptual load was low, neither blink rate nor pupil diameter showed significant effects on cognitive load when the perceptual load was high. The results indicate that pupil diameter can index cognitive load only in the situation of low perceptual load and are the first to provide empirical support for the cognitive control aspect of the load theory of attention, in the context of cognitive load measurement. Meanwhile, blink is a better indicator of perceptual load than cognitive load. This study also implies that perceptual load should be considered in cognitive load measurement using pupil diameter and blink measures. Automatic detection of the type and level of load in this manner helps pave the way for better reasoning about user internal processes for human-centered interface design.

Why integrate InfoVis and SciVis?: An example from systems biology.

The more-or-less artificial barrier between information visualization and scientific visualization hinders knowledge discovery. Having an integrated view of many aspects of the target data, including a seamlessly interwoven visual display of structural abstract data and 3D spatial information, could lead to new discoveries, insights, and scientific questions. Such a view also could reduce the user's cognitive load--that is, reduce the effort the user expends when comparing views.

Automatic cognitive load evaluation using writing features: An exploratory study

Evaluating cognitive load is a key step in designing adaptive multimedia learning environments. However, there is still a lack of evaluation methods which can not only unobtrusively collect user data without supplement equipment but also objectively, quantitatively and in real time evaluate user cognitive load based on the data. This paper presents a new approach to evaluating cognitive load by combining writing features from free text and machine learning techniques. Specifically, changes in writing features are first investigated across three levels of cognitive load and the results offer some first insights for the potential of writing features to indicate cognitive load changes; further, a single feature is examined to detect which features are most predictive of cognitive load changes, and back-propagation neural networks, along with two feature selection methods, are trained to classify three cognitive load levels with 76.27% accuracy. These results show that writing features are useful for evaluating cognitive load when suitable classifiers are adopted. Relevance to industryThis study provides evidence that cognitive load can affect handwriting features, and also develops an automatic classification method of discriminating different levels of cognitive load on handwriting features using machine learning technologies. The findings can extend the use of handwritten devices and provide a new method for usability testing.

Multimodal behavior and interaction as indicators of cognitive load

High cognitive load arises from complex time and safety-critical tasks, for example, mapping out flight paths, monitoring traffic, or even managing nuclear reactors, causing stress, errors, and lowered performance. Over the last five years, our research has focused on using the multimodal interaction paradigm to detect fluctuations in cognitive load in user behavior during system interaction. Cognitive load variations have been found to impact interactive behavior: by monitoring variations in specific modal input features executed in tasks of varying complexity, we gain an understanding of the communicative changes that occur when cognitive load is high. So far, we have identified specific changes in: speech, namely acoustic, prosodic, and linguistic changes; interactive gesture; and digital pen input, both interactive and freeform. As ground-truth measurements, galvanic skin response, subjective, and performance ratings have been used to verify task complexity. The data suggest that it is feasible to use features extracted from behavioral changes in multiple modal inputs as indices of cognitive load. The speech-based indicators of load, based on data collected from user studies in a variety of domains, have shown considerable promise. Scenarios include single-user and team-based tasks; think-aloud and interactive speech; and single-word, reading, and conversational speech, among others. Pen-based cognitive load indices have also been tested with some success, specifically with pen-gesture, handwriting, and freeform pen input, including diagraming. After examining some of the properties of these measurements, we present a multimodal fusion model, which is illustrated with quantitative examples from a case study. The feasibility of employing user input and behavior patterns as indices of cognitive load is supported by experimental evidence. Moreover, symptomatic cues of cognitive load derived from user behavior such as acoustic speech signals, transcribed text, digital pen trajectories of handwriting, and shapes pen, can be supported by well-established theoretical frameworks, including O'Donnell and Eggemeier's workload measurement [1986] Sweller's Cognitive Load Theory [Chandler and Sweller 1991], and Baddeley's model of modal working memory [1992] as well as McKinstry et al.'s [2008] and Rosenbaum's [2005] action dynamics work. The benefit of using this approach to determine the user's cognitive load in real time is that the data can be collected implicitly that is, during day-to-day use of intelligent interactive systems, thus overcomes problems of intrusiveness and increases applicability in real-world environments, while adapting information selection and presentation in a dynamic computer interface with reference to load.

Semantic Based Graph Mining Technology for Automatic Service Semantic Link Network Navigation

To reduce the user's cognitive load and make sense of complex interlinked service data for realizing advanced service application, an approach to Service Semantic Link Network (S-SLN) navigation was proposed. S emantic based g raph m ining t echnology was employed as an effective solution for automatic service navigation . As one of the most important navigation tools, related service nodes recommendation was mapped as a basic graph mining problem. The closely related service nodes linked by different relations to form a sub-graph of S-SLN, which service nodes and links with different type and weight represent different service scenarios and relationship to the maximum extent such that sub-graph of S-SLN can improve or speed up user’s navigation decisions by providing the most probable related Web service and links which are most likely to be of interest. The experimental results and analysis shows that the proposed approach is efficient, feasible and practical.

User keyword preference: the Nwords and Rwords experiments

Everyday, millions of people use some form of text-based interface to search inefficiently for information. This reflects a lack of penetration of key developments in Human Computer Interaction (HCI) designed to expedite document retrieval. In the context of document search, the value of textual language is self-evident for searching natural language documents. However, there has been little human factors research into general user preferences such as the number of words preferred to describe or search for a document. Such heuristics can help to balance processing overheads of tasks like clustering against user cognitive load in the realisation of, overall, more efficient document retrieval process. We report the results from two internet delivered experiments designed to obtain this heuristic information.

MULTIMODAL HUMAN-MACHINE INTERFACE AND USER COGNITIVE LOAD MEASUREMENT

Multimodal user interface (MMUI) is an emerging technology that aims at providing a more intuitive and natural way for people to operate and control a computer or a machine. MMUI allows users to control a computer using various input modalities, including speech, touch, gestures and hand-writing. It has potential to minimise user's cognitive load when performing complex tasks. In this paper we present our work in building an MMUI research platform for intelligent transport system applications, and our attempt to evaluate a user's cognitive load based on analysis of his or her multimodal behaviours and physiological measurement.

Authoring game-based adaptive units of learning with IMS Learning Design and <e-Adventure>

Electronic games and simulations (eGames) are a valuable support for adaptive learning. This adaptation can be based on different inputs, such as the user's performance, behaviour or cognitive load. Both adaptation and eGames can be modelled with IMS Learning Design (IMS LD) or integrated from an external resource. In this article we show the relation between IMS LD and the Project when it comes to authoring adaptive Units of Learning (UoLs) integrated with eGames. We first describe the challenges of this objective and the several different solutions on authoring and integration. We also describe the content-centred authoring approach in , and the need for a communication service with IMS LD that makes a bidirectional influence on the user's adaptive learning experience. At the end, we describe a practical example that illustrates how an adaptive IMS LD UoL with an integrated eGame is developed.

사용자 행동 패턴 선호도 학습을 위한 퍼지 귀납 학습 시스템

본 논문은 스마트 홈과 같이 다양한 센서 및 제어 네트워크가 밀집되어 있는 유비쿼터스 환경 하에서 복잡한 인터페이스의 사용에 대한 사용자의 인지 부담(cognitive load)을 줄이고, 개인화된(personalized) 서비스를 자율적으로 제공하기 위한 새로운 사용자 행동 패턴 선호도 학습기법을 제안하였다. 이를 위해 지식 발견(knowledge discovery)을 위한 평생 학습(life-long learning)의 관점에서 퍼지귀납(fuzzy inductive) 학습 방법론을 제안하며, 이것은 수치 데이터로부터 입력 공간에 대한 효율적인 퍼지 분할(fuzzy partition)을 얻어내고 일관성 있는(consistent) 퍼지 상관 롤(fuzzy association rule)을 얻어내도록 한다. Smart home is one of the ubiquitous environment platforms with various complex sensor-and-control network. In this paper, a now learning methodology for learning user's behavior preference pattern is proposed in the sense of reductive user's cognitive load to access complex interfaces and providing personalized services. We propose a fuzzy inductive learning methodology based on life-long learning paradigm for knowledge discovery, which tries to construct efficient fuzzy partition for each input space and to extract fuzzy association rules from the numerical data pattern.

Balancing user satisfaction and cognitive load in coverage-optimised retrieval

Coverage-optimised retrieval (CORE) is a new case-based reasoning (CBR) approach to product recommendation which ensures that for any case that is acceptable to the user, one of the recommended cases is at least as good in an objective sense and so also likely to be acceptable. Similarity to the users query, the standard criterion on which recommendations are based in CBR, is only one of several preference criteria according to which a given case may be considered at least as good as another in CORE. We present a detailed analysis of retrieval in CORE and the trade-off between user satisfaction and cognitive load in the approach.