The role of working memory on graphical information processing

This research extends previous graphics research by examining how individual differences in working memory (WM) capacity and changes in graphic design influence graphical information processing. An experiment compared decision accuracy of two graphic decision aids and an unaided group for a task at two levels of complexity. There were no accuracy differences for the low complexity task. At high levels of task complexity, accuracy depended upon WM capacity and how the graphic aid influenced perception. Eye movement data show information processing differences also are contingent upon graphic design features and WM capacity. We postulate that graphs reduce cognitive overhead by shifting some of the cognitive burden to our visual perception system. More efficient graphical perceptual will improve decision performance only if our cognitive resources are capacity constrained and those cognitive resources are used elsewhere in the problem solving process.     

The role of working memory on graphical information processing

This research extends previous graphics research by examining how individual differences in working memory (WM) capacity and changes in graphic design influence graphical information processing. An experiment compared decision accuracy of two graphic decision aids and an unaided group for a task at two levels of complexity. There were no accuracy differences for the low complexity task. At high levels of task complexity, accuracy depended upon WM capacity and how the graphic aid influenced perception. Eye movement data show information processing differences also are contingent upon graphic design features and WM capacity. We postulate that graphs reduce cognitive overhead by shifting some of the cognitive burden to our visual perception system. More efficient graphical perceptual will improve decision performance only if our cognitive resources are capacity constrained and those cognitive resources are used elsewhere in the problem solving process.     

When does an easy task become hard? A systematic review of human task-evoked pupillary dynamics versus cognitive efforts

The amount of working memory recourses available (or required) to process a cognitive task (easy or hard) represents human cognitive effort. Working memory resources (visual or auditory) and cognitive efforts are interconnected with visual or auditory pathways. In this review, various facets of pupillary dynamics literature are compared in order to determine an optimal method of cognitive effort assessment. Some key categorical areas of interest are identified including the presented stimulus, observed response, comparisons and different methods of analysis. In details review, a set of predetermined evaluation criteria were used and a decision matrix is developed to outline the best practice in papillary dynamics. Based on the summery table in the form of the decision matrix, a quantitative model with artificial neural network (ANN) is selected for a best practice of cognitive effort estimation. The mental multiplication task is found an effective stimulus (cognitive task) to evoke the pupillary response for various level of task difficulty. In most cases, aural and visual are considered as two presentation modes, two sensory inputs, and two mental resources and greatly imparts in cognitive workload. Through this review, it is also explored that, linking together by transfer and error functions, a combination of an ANN and a multinomial processing tree can used in cognitive effort analysis. This research direction can further explored to estimate the relationship between cognitive task and cognitive effort, to facilitate in technology development for neurological disorders, such as narcolepsy, on the neural pathways involved in cognitive processing.

     

The factors that affect the performance of open source software development – the perspective of social capital and expertise integration

Drawing on social capital theory, we develop a theoretical model aiming to explore how open source software (OSS) project effectiveness (in terms of team size, team effort and team's level of completion) is affected by expertise integration. This in turn is influenced by three types of social capital – relational capital, cognitive capital and structural capital. In addition, this study also examines two moderating effects – the impact of technical complexity on the relationship between cognitive capital and expertise integration, and of task interdependence on the relationship between expertise integration and task completion. Through a field survey of 160 OSS members from five Taiwanese communities, there is support for some of the proposed hypotheses. Both reciprocity and centrality affect expertise integration as expected, but the influence of commitment and cognitive capital (including expertise and tenure) on expertise integration is not significant. Finally, expertise integration affects both team size and team effort, which in turn jointly influence task completion. This research contributes to advancing theoretical understanding of the effectiveness of free OSS development as well as providing OSS practitioners with insight into how to leverage social capital for improving the performance of OSS development.     

Fuzzy measure theoretical approach to screening product innovations

Variety of decision models have been proposed in contemporary literature to tackle the problem of screening product innovations. Although linear models have gained considerable attention and recommendation, contemporary literature contains strong evidence in support of nonlinear noncompensatory models. In this paper, the authors first demonstrate how fuzzy measures, which are defined on subsets of decision attributes, and their Choquet-integral formulation, which exhibits both compensatory and noncompensatory properties, have meaningful behavioral interpretations within the context of new-product screening. Then, they show how to address the complex problem of building such measures by applying a learning algorithm that relies on methods of judgment analysis. An accompanying case study demonstrates how organizations may customize a new product decision aid and fine tune their business strategy as actual results accrue. Finally, the authors present the results of analytical studies to compare the Choquet-integral model with other noncompensatory models, such as Martino's extended scoring model and Einhorn's conjunctive model, and heuristic approaches, such as Tversky's EBA and the lexicographic method. For the new-product-decision scenario considered in the study, the Choquet-integral model provided the best fit, measured by Pearson's rank order correlation coefficient, with all of the competing models.     

Expert systems and behavioral decision research

This paper reviews a part of the literature on behavioral decision research (policy capturing, psychophysics of numerical judgments and cognitive illusions) and examines implication for knowledge elicitation in expert systems. The literature on policy capturing demonstrates that simple and compact numerical models of expert knowledge can be built, but that experts are poor in verbalizing the knowledge expressed in them. The psychophysical literature indicates that numerical encoding of expert knowledge may be difficult and biased, but that it has definitive advantages over qualitative elicitation schemes: Numerical encoding forces hard throught, encourages precision, and allows to access a substantial computational apparatus. The literature on cognitive illusions suggests that the expert knowledge one elicits may be an illusion. The review concludes by recommending to use numerical judgments and explicit models by experts where possible, and to decompose the elicitation task in order to avoid cognitive illusions.     

Modeling driver behavior in a cognitive architecture

OBJECTIVE: This paper explores the development of a rigorous computational model of driver behavior in a cognitive architecture--a computational framework with underlying psychological theories that incorporate basic properties and limitations of the human system. BACKGROUND: Computational modeling has emerged as a powerful tool for studying the complex task of driving, allowing researchers to simulate driver behavior and explore the parameters and constraints of this behavior. METHOD: An integrated driver model developed in the ACT-R (Adaptive Control of Thought-Rational) cognitive architecture is described that focuses on the component processes of control, monitoring, and decision making in a multilane highway environment. RESULTS: This model accounts for the steering profiles, lateral position profiles, and gaze distributions of human drivers during lane keeping, curve negotiation, and lane changing. CONCLUSION: The model demonstrates how cognitive architectures facilitate understanding of driver behavior in the context of general human abilities and constraints and how the driving domain benefits cognitive architectures by pushing model development toward more complex, realistic tasks. APPLICATION: The model can also serve as a core computational engine for practical applications that predict and recognize driver behavior and distraction.     

The cognitive impact of interactive design features for learning complex materials in medical education

To identify the most effective way for medical students to interact with a browser-based learning module on the symptoms and neurological underpinnings of stroke syndromes, this study manipulated the way in which subjects interacted with a graphical model of the brain and examined the impact of functional changes on learning outcomes. It was hypothesized that behavioral interactions that were behaviorally more engaging and which required deeper consideration of the model would result in heightened cognitive interaction and better learning than those whose manipulation required less deliberate behavioral and cognitive processing. One hundred forty four students were randomly assigned to four conditions whose model controls incorporated features that required different levels of behavioral and cognitive interaction: Movie (low behavioral/low cognitive, n = 40), Slider (high behavioral/low cognitive, n = 36), Click (low behavioral/high cognitive, n = 30), and Drag (high behavioral/high cognitive, n = 38). Analysis of Covariates (ANCOVA) showed that students who received the treatments associated with lower cognitive interactivity (Movie and Slider) performed better on a transfer task than those receiving the module associated with high cognitive interactivity (Click and Drag, partial eta squared = .03). In addition, the students in the high cognitive interactivity conditions spent significantly more time on the stroke locator activity than other conditions (partial eta squared = .36). The results suggest that interaction with controls that were tightly coupled with the model and whose manipulation required deliberate consideration of the model's features may have overtaxed subjects' cognitive resources. Cognitive effort that facilitated manipulation of content, though directed at the model, may have resulted in extraneous cognitive load, impeding subjects in recognizing the deeper, global relationships in the materials. Instructional designers must, therefore, keep in mind that the way in which functional affordances are integrated with the content can shape both behavioral and cognitive processing, and has significant cognitive load implications.     

Predicting different conceptualizations of system use: Acceptance in hedonic volitional context (Facebook)

This research examines the relationship between the predictors of use and the different conceptualizations of system use in a hedonic volitional setting (Facebook). Using the unified theory of acceptance and use of technology (UTAUT) model, an investigation into the three aspects of system use: the user, system and task were carried out. Results from a cross-sectional survey of 449 students show that behavioral intention has a significant influence on all aspects and dimensions of system use including cognitive absorption and deep structure use. Performance expectancy, effort expectancy and social influence are significantly related to system use. From the component model, performance expectancy is only significant with deep structure use. Hedonic performance expectancy is found to be significantly related to cognitive absorption. Results also demonstrate that predictors of usage have a significant relationship with the user aspect of system use. The variance explained in usage conceptualized as the user/task aspects is much higher than that of the system/task aspects or one-dimensional measures. Overall, conceptualizing system use using the user/task aspects offers greater explanatory power in Facebook use.     

The role of social interaction in software effort estimation: Unpacking the “magic step” between reasoning and decision-making

ContextSoftware effort estimation is a core task regarding planning, budgeting and controlling software development projects. However, providing accurate effort estimates is challenging. Estimation work is increasingly group based, and to support it, there is a need to reveal how work practices are carried out as collaborative efforts.ObjectiveThis paper examines the use of concepts in software effort estimation by analysing group work as communicative practice. The objective is to improve our understanding of how software professionals invoke different types of knowledge when talking, reasoning and reaching a decision on a software effort estimate.MethodEstimation meetings in the industry where planning poker was used as the estimation method have been video recorded and analysed by means of the interaction analysis technique, focusing on the communicative and collaborative aspects of the group work.ResultsThe user story mediates the types of resources and knowledge needed to solve the task. Concepts from the knowledge domain are used to frame the task and allow the participants to reach consensus, sufficient to take the next step in the problem-solving activity. Individual knowledge seems to be the dominating orientation when it comes to specifying the work needed for solving the tasks.ConclusionThe step from reasoning to decision-making has been called the “magic step” in software effort estimation. We argue that the magic step is found in the analysis of the social interaction in which the concepts used are anchored in the knowledge domain of software engineering and in the historical experiences of the participants and subsequently become activated. We propose that by taking a socio-cultural perspective on concepts in activities, the ways in which software professionals reach a decision can be unpacked. The paper contributes to an understanding of the role of concepts in group work and of software effort estimation as a specific work practice.     

A framework for explaining reliance on decision aids

This study presents a framework for understanding task and psychological factors affecting reliance on advice from decision aids. The framework describes how informational asymmetries in combination with rational, motivational and heuristic factors explain human reliance behavior. To test hypotheses derived from the framework, 79 participants performed an uncertain pattern learning and prediction task. They received advice from a decision aid either before or after they expressed their own prediction, and received feedback about performance. When their prediction conflicted with that of the decision aid, participants had to choose to rely on their own prediction or on that of the decision aid. We measured reliance behavior, perceived and actual reliability of self and decision aid, responsibility felt for task outcomes, understandability of one's own reasoning and of the decision aid, and attribution of errors. We found evidence that (1) reliance decisions are based on relative trust, but only when advice is presented after people have formed their own prediction; (2) when people rely as much on themselves as on the decision aid, they still perceive the decision aid to be more reliable than themselves; (3) the less people perceive the decision aid's reasoning to be cognitively available and understandable, the less people rely on the decision aid; (4) the more people feel responsible for the task outcome, the more they rely on the decision aid; (5) when feedback about performance is provided, people underestimate both one's own reliability and that of the decision aid; (6) underestimation of the reliability of the decision aid is more prevalent and more persistent than underestimation of one's own reliability; and (7) unreliability of the decision aid is less attributed to temporary and uncontrollable (but not external) causes than one's own unreliability. These seven findings are potentially applicable for the improved design of decision aids and training procedures.     

NDM-Based Cognitive Agents for Supporting Decision-Making Teams

Naturalistic decision making (NDM) focuses on how people actually make decisions in realistic settings that typically involve ill-structured problems. Taking an experimental approach, we investigate the impacts of using an NDM-based software agent (R-CAST) on the performance of human decision-making teams in a simulated C3I (Communications, Command, Control and Intelligence) environment. We examined four types of decision-making teams with mixed human and agent members playing the roles of intelligence collection and command selection. The experiment also involved two within-group control variables: task complexity and context switching frequency. The result indicates that the use of an R-CAST agent in intelligence collection allows its team member to consider the latest situational information in decision making but might increase the team member's cognitive load. It also indicates that a human member playing the role of command selection should not rely too much on the agent serving as his or her decision aid. Together, it is suggested that the roles of both humans and cognitive agents are critical for achieving the best possible performance of C3I decision-making teams: Whereas agents are superior in computation-intensive activities such as information seeking and filtering, humans are superior in projecting and reasoning about dynamic situations and more adaptable to teammates' cognitive capacities. This study has demonstrated that cognitive agents empowered with NDM models can serve as the teammates and decision aids of human decision makers. Advanced decision support systems built upon such team-aware agents could help achieve reduced cognitive load and effective human-agent collaboration.     

Context enabled decision aids to support the situation awareness and performance of risk engineers carrying out loss prevention surveys

Routine inspection by insurance companies at their clients’ facility, also known as loss prevention survey, help identify the best strategies to minimize damages when there is a high-speed wind event. More specifically, wind vulnerabilities associated with a building are evaluated using a process known as windstorm risk inspection. This routine inspection helps clients reduce the extent of damages caused by high-speed wind events including hurricane and tornado. Risk engineers make use of their subjective and analytical deduction skills to successfully carry out the inspection tasks. In this research the researchers investigated the effect of context-based visualization strategies on situation awareness and their understanding of the situation. The study examined how different types of information contribute towards the three levels of situation awareness. Following a between-subjects study design, 65 participants completed the study. Each session lasted 90–120 min. A checklist based and predictive display-based decision aids were tested and found to be effective in supporting the situation awareness requirements as well as performance of risk engineers. However, the predictive display only helped with certain tasks such as understanding the interaction among different components on the rooftop. For remaining tasks such as perceiving obvious issues like membrane tear, clogged drains and vegetation growth, checklist alone was sufficient. This study helped the understanding of the advantages and disadvantages of the decision aids tested. More specifically, these decision aids can improve the mental model of novice risk engineers. Additionally, this study provided insights that could help design training materials for infrastructure inspectors.     

Uncovering the requirements of cognitive work

OBJECTIVE: In this article, the author provides an overview of cognitive analysis methods and how they can be used to inform system analysis and design. BACKGROUND: Human factors has seen a shift toward modeling and support of cognitively intensive work (e.g., military command and control, medical planning and decision making, supervisory control of automated systems). Cognitive task analysis and cognitive work analysis methods extend traditional task analysis techniques to uncover the knowledge and thought processes that underlie performance in cognitively complex settings. METHODS: The author reviews the multidisciplinary roots of cognitive analysis and the variety of cognitive task analysis and cognitive work analysis methods that have emerged. RESULTS: Cognitive analysis methods have been used successfully to guide system design, as well as development of function allocation, team structure, and training, so as to enhance performance and reduce the potential for error. CONCLUSIONS: A comprehensive characterization of cognitive work requires two mutually informing analyses: (a) examination of domain characteristics and constraints that define cognitive requirements and challenges and (b) examination of practitioner knowledge and strategies that underlie both expert and error-vulnerable performance. A variety of specific methods can be adapted to achieve these aims within the pragmatic constraints of particular projects. APPLICATION: Cognitive analysis methods can be used effectively to anticipate cognitive performance problems and specify ways to improve individual and team cognitive performance (be it through new forms of training, user interfaces, or decision aids).     

Development and evaluation of a virtual training environment for on-line robot programming

The paper reports on the development and evaluation of a virtual reality system to support training in on-line programming of industrial robots. The system was evaluated by running training experiments with three groups of engineering students in the real, virtual and virtual augmented robot conditions. Results suggest that, the group with prior training in the virtual reality system augmented with cognitive/perceptual aids clearly outperformed the group that executed the tasks in the real robot only. The group trained in the non-augmented virtual reality system did not demonstrate the same results. It is concluded that the cognitive/perceptual aids embedded in the augmented virtual reality system had a positive effect on all task performance metrics and on the consistency of results across participants on the real robot. Virtual training environments need not be designed as close as possible to the real ones. Specifically designed augmented cognitive/perceptual aids may foster skill development that can be transferred to the real task. The suggested training environment is simple and cost effective in training of novices in an entry level task.     

A Model of Perceptual Task Effort for Bar Charts and its Role in Recognizing Intention

This paper presents a model of perceptual task effort for use in hypothesizing the message that a bar chart is intended to convey. It presents our rules, based on research by cognitive psychologists, for estimating perceptual task effort, and discusses the results of an eye tracking experiment that demonstrates the validity of our model. These rules comprise a model that captures the relative difficulty that a viewer would have performing one perceptual task versus another on a specific bar chart. The paper outlines the role of our model of relative perceptual task effort in recognizing the intended message of an information graphic. Potential applications of this work include using this message to provide (1) a more complete representation of the content of the document to be used for searching and indexing in digital libraries, and (2) alternative access to the information graphic for visually impaired users or users of low-bandwidth environments.     

Presence and task performance: an approach in the light of cognitive style

The paper highlights the relationship between each of four bi-polar dimensions of personality cognitive style, such as extraversion–introversion, sensing–intuition, thinking–feeling and judging–perceiving, and the level of sense of presence experienced. Findings indicate that individuals who are more sensitive, more feeling or more introverted experience a higher level of presence. While not reaching statistical significance, differing cognitive styles appear to impact on task performance. The apparent negative relationship discovered between sense of presence and task performance should be considered in the light of task characteristics. We discuss the implications of these findings and how they contribute to an understanding of the complex relationship that exists between presence and task performance and how this subsequently ought to influence the design of virtual environments.     

Lost in space? Cognitive fit and cognitive load in 3D virtual environments

In this paper, we explore how visual representations of information in 3D virtual environments (3DVEs) supports both individual and shared understanding, and consequently contribute to group decision making in tasks with a strong visual component. We integrate insights from cognitive fit theory and cognitive load theory in order to formulate hypotheses about how 3DVEs can contribute to individual understanding, shared understanding, and group decision making. We discuss the results of an experiment in which 192 participants, in 3-person teams, were asked to select an apartment. As proposed by cognitive fit theory, our results indicate that 3DVEs are indeed more effective in supporting individual understanding than 2D information presentations. Next, in line with cognitive load theory, the static presentation of 3D information turns out to be more effective in supporting shared understanding and group decision making than an immersive 3DVE. Our results suggest that although the 3DVE capabilities of realism, immersion and interactivity contribute to individual understanding, these capabilities combined with the interaction and negotiation processes required for reaching a shared understanding (and group decision), increases cognitive load and makes group processes inefficient. The implications of this paper for research and practice are discussed.     

A cognitivist model for decision support: COGITA project, a problem formulation assistant

The formulation of a problem may be defined as a process of acquisition and organization of knowledge related to a given situation, on which a decision maker projects some action. The assistance in the problem formulation that we may expect within decision support systems is difficult to design and to implement. This is mainly due to the frequent lack of attention to a sufficiently formalized conceptual framework which would consider the decision with a more cognition sciences oriented approach. In the first part, we will present an instrumental model for the study of decision processes as an attempt to simulate the cognitive process of knowledge acquisition and organization carried out by a decision maker facing a problematic situation. Considering its epistemological foundations, this model can be named “cognitivist model”. Within this model, the decision is defined as a cognitive construction which we call “decisional construct”. It consists of the elaboration of one or several abstract representations of the problematic situation (formulation phase), and the design of operational models (solving phase). In the second part, we will present the COGITA project, which consists of the design and realization of an environment for the development of problem formulation assistance systems. The modelization and simulation of cognitive processes call for relevant techniques originating either in artificial intelligence or in connectionism. We will show which are the main characteristics, potentials, limits and complementarity of these techniques and why their integration is fundamental and necessary to the simulation of the cognitive process associated with the formulation. COGITA is a hybrid system currently under development which tends to integrate symbolic artificial intelligence techniques and connectionist models in a cooperative hybridation the general architecture of which is presented.     

Exploring the cognitive costs and benefits of using multiple-view visualisations

Multiple-view visualisations offer several advantages, such as providing different perspectives on the data. However, there are also associated cognitive costs, including the load on working memory and the effort required for comparison. Furthermore, little perception-based research has been conducted, with few answers to questions such as what tasks multiple views are best used for. Since task performance can be limited by visual attention and working memory, this article investigates how different tasks and their respective loads on attention and working memory affect the usability of two different multiple-view visualisations, namely sequential and simultaneous views. In Study 1, the effects when attention was loaded were studied, where users performed a real-time monitoring task. The results suggest that the divided attention problem was an issue with both view types, but design issues apparent with the simultaneous view were not issues with the sequential view. In Study 2, the effects when working memory was loaded were studied, where users made comparisons of different object sets. The results support the previous work on visual- and memory-based comparisons, i.e. the simultaneous view was more useful than the sequential view. The outcomes from both studies illustrate the importance of understanding how different tasks and their impact on attention and working memory can influence the usability of multiple-view visualisations.