How do we acquire understanding of conceptual models?

In organizations, conceptual models are used for understanding the domain concepts. Such models are crucial in analysis and development of information systems. An important factor of using the conceptual models is how quickly analysts are able to learn the domain concepts as depicted in the models. Using a laboratory experiment, this research used eye tracking technique to capture the speed of acquisition of understanding conceptual models. Two sets of conceptual models were used in this study- one theory based (REA pattern) and the other non-theory based (non REA pattern). It was found that the rate of learning of the domain concepts was faster with theory based models than with non-theory based models. However, users of the non-theory based model were able to catch up with the learning of the model concepts after being repeatedly exposed to the model

Reviewing the understanding of the effects of spacing on children’s eye movements for on-screen reading

This paper endeavors to consolidate current knowledge and empirical research concerning the use of typography for children’s on-screen reading. This paper is not intended as a full literature review but attempts to raise awareness of the areas required for future investigation. This evaluation indicates a significant gap in the literature of children’s on-screen reading and proposes a need for further investigations in typographical spacing. These future studies need to objectively consider children’s eye movements and the effect of screen based text presentation on children’s comprehension

Pilots’ visual scan pattern and attention distribution during the pursuit of a dynamic target

Introduction: The current research is investigating pilots’ visual scan patterns in order to assess attention distribution during air-to-air manoeuvers. Method: A total of thirty qualified mission-ready fighter pilots participated in this research. Eye movement data were collected by a portable head-mounted eye-tracking device, combined with a jet fighter simulator. To complete the task, pilots have to search for, pursue, and lock-on a moving target whilst performing air-to-air tasks. Results: There were significant differences in pilots’ saccade duration (msec) in three operating phases including searching (M=241, SD=332), pursuing (M=311, SD=392), and lock-on (M=191, SD=226). Also, there were significant differences in pilots’ pupil sizes (pixel2) of which lock-on phase was the largest (M=27237, SD=6457), followed by pursuing (M=26232, SD=6070), then searching (M=25858, SD=6137). Furthermore, there were significant differences between expert and novice pilots on the percentage of fixation on the HUD, time spent looking outside the cockpit, and the performance of situational awareness (SA). Discussion: Experienced pilots have better SA performance and paid more attention to the HUD but focused less outside the cockpit when compared with novice pilots. Furthermore, pilots with better SA performance exhibited a smaller pupil size during the operational phase of lock-on whilst pursuing a dynamic target. Understanding pilots’ visual scan patterns and attention distribution are beneficial to the design of interface displays in the cockpit and in developing human factors training syllabi to improve safety of flight operations

Cortical Dynamics of Contextually-Cued Attentive Visual Learning and Search: Spatial and Object Evidence Accumulation

How do humans use predictive contextual information to facilitate visual search? How are consistently paired scenic objects and positions learned and used to more efficiently guide search in familiar scenes? For example, a certain combination of objects can define a context for a kitchen and trigger a more efficient search for a typical object, such as a sink, in that context. A neural model, ARTSCENE Search, is developed to illustrate the neural mechanisms of such memory-based contextual learning and guidance, and to explain challenging behavioral data on positive/negative, spatial/object, and local/distant global cueing effects during visual search. The model proposes how global scene layout at a first glance rapidly forms a hypothesis about the target location. This hypothesis is then incrementally refined by enhancing target-like objects in space as a scene is scanned with saccadic eye movements. The model clarifies the functional roles of neuroanatomical, neurophysiological, and neuroimaging data in visual search for a desired goal object. In particular, the model simulates the interactive dynamics of spatial and object contextual cueing in the cortical What and Where streams starting from early visual areas through medial temporal lobe to prefrontal cortex. After learning, model dorsolateral prefrontal cortical cells (area 46) prime possible target locations in posterior parietal cortex based on goalmodulated percepts of spatial scene gist represented in parahippocampal cortex, whereas model ventral prefrontal cortical cells (area 47/12) prime possible target object representations in inferior temporal cortex based on the history of viewed objects represented in perirhinal cortex. The model hereby predicts how the cortical What and Where streams cooperate during scene perception, learning, and memory to accumulate evidence over time to drive efficient visual search of familiar scenes.CELEST, an NSF Science of Learning Center (SBE-0354378); SyNAPSE program of Defense Advanced Research Projects Agency (HR0011-09-3-0001, HR0011-09-C-0011

Measuring cognitive load and cognition: metrics for technology-enhanced learning

This critical and reflective literature review examines international research published over the last decade to summarise the different kinds of measures that have been used to explore cognitive load and critiques the strengths and limitations of those focussed on the development of direct empirical approaches. Over the last 40 years, cognitive load theory has become established as one of the most successful and influential theoretical explanations of cognitive processing during learning. Despite this success, attempts to obtain direct objective measures of the theory's central theoretical construct – cognitive load – have proved elusive. This obstacle represents the most significant outstanding challenge for successfully embedding the theoretical and experimental work on cognitive load in empirical data from authentic learning situations. Progress to date on the theoretical and practical approaches to cognitive load are discussed along with the influences of individual differences on cognitive load in order to assess the prospects for the development and application of direct empirical measures of cognitive load especially in technology-rich contexts

Understanding the Effect of Information Presentation Order and Orientation on Information Search and Treatment Evaluation

Background. Past research finds that treatment evaluations are more negative when risks are presented after benefits. This study investigates this order effect: manipulating tabular orientation and order of risk–benefit information, and examining information search order and gaze duration via eye-tracking. Design. 108 (Study 1) and 44 (Study 2) participants viewed information about treatment risks and benefits, in either a horizontal (left-right) or vertical (above-below) orientation, with the benefits or risks presented first (left side or at top). For 4 scenarios, participants answered 6 treatment evaluation questions (1–7 scales) that were combined into overall evaluation scores. In addition, Study 2 collected eye-tracking data during the benefit–risk presentation. Results. Participants tended to read one set of information (i.e., all risks or all benefits) before transitioning to the other. Analysis of order of fixations showed this tendency was stronger in the vertical (standardized mean rank difference further from 0, M = ±.88) than horizontal orientation (M = ± 0.71). Approximately 50% of the time was spent reading benefits when benefits were shown first, but this was reduced to ~40% when risks were presented first (regression coefficient: B = −4.52, p <.001). Eye-tracking measures did not strongly predict treatment evaluations, although time percentage reading benefits positively predicted evaluation when holding other variables constant (B = 0.02, p =.023). Conclusion. These results highlight the impact of seemingly arbitrary design choices on inspection order. For instance, presenting risks where they will be seen first leads to relatively less time spent considering treatment benefits. Other research suggests these changes to inspection order can influence multi-option and multi-attribute choices, and represent an area for future research