No abstract

There have been many studies on improving intellectual concentration. Concentration on intellectual work tends to decrease over time, and this needs to be prevented to improve intellectual concentration. In conventional office work, for example, a 10-minute break was taken every hour, but in this case, concentration drops by the next break. Therefore, there is a possibility of suppressing the decline in intellectual concentration by interspersing short breaks of a few seconds to a few tens of seconds in a shorter cycle than this. In this study, we named this break "micro-refresh" and aimed to show its effect on improving intellectual concentration by experiments, and then to study the environmental control method to present it appropriately in the actual working environment. The "micro-refresh" in this study differs from the conventional "microbreak" in that it effectively encourages office workers to refresh themselves in a short period of time. In other words, this research aims to actively encourage office workers to refresh themselves through some kind of action, such as controlling the indoor environment.Therefore, as a basis for this study, it was firstly confirmed that the effect of micro-refresh can be measured quantitatively. Short breaks of a few seconds to tens of seconds were forcibly given to the office workers during the cognitive task, and the difference in intellectual concentration was confirmed using objective indicators. In addition, the difference in subjective perception of fatigue and workload was also confirmed by several questionnaires.In this experiment, a comparison problem developed by Ueda et al1). was used as a cognitive task. As a simulated micro-refresh, a system, in which the screen changes to gray when an arbitrary time elapses and the answer to the problem being solved at that time is completed, was implemented. The interval between the screen changing was set to 7 minutes and 30 seconds, and the time until the changed screen returned to normal was set to 20 seconds. Participants performed a 25-minute cognitive task with and without the simulated micro-refresh. Their intellectual concentration was measured as an index, “CTR (Concentration Time Ratio)”, which expresses concentration time ratio among total working time and was calculated from response time data of the cognitive task. In addition, this experiment measured participants’ fatigue and workload through several questionnaires; (1) Progress questionnaire asking about subjective level of concentration and fatigue (2) NASA-TLX asking about workload (3) Subjective symptom screening capturing changes in fatigue status over time.At this point, the experiment is not yet completed, but it will be completed by the time the full paper is submitted. From the data obtained, it will be shown that the response time data, CTR, and participants’ fatigue and workload are able to be measured and it will be discussed that whether simulated micro-break prevent CTR from decreasing or reduce participants’ fatigue and workload.1) Kimi Ueda, Hiroshi Shimoda, Hirotake Ishii, Fumiaki Obayashi, Kazuhiro Taniguchi: Development of a New Cognitive Task to Measure Intellectual Concentration Affected by Room Environment, The Fifth International Conference on Human-Environment System, 2016.

IntroductionThe purpose of this study is to examine the worker’s motivation effect on intellectual concentration by an experiment and a cognitive process simulation. By utilizing the Adaptive Control of Thought-Rational (ACT-R) concept, it helps to understand the cognitive models of the different motivational condition. ACT-R is one of the prevalent cognitive architectures that mainly differ the cognitive process into production modules and declarative modules.Several studies have been investigating the human cognitive performance in respect of others factors such as working environments. Inspirited by previous studies, this study has explored the human motivational factors that might influence intellectual concentration. Additionally, exploring the differences in workers’ individual problem-solving strategy is one of the alluring factors in this study given the differences in motivational conditions.MethodsFive participants were recruited for the experiment the range of their ages between 18-22 years old as a pilot study. The simple summation mathematical task was given during the experiments and shows two numbers on the left side and right side of the monitor screen. They were asked to answer the problem by pressing the numerical keyboard and the answering time was automatically recorded on the experiment program. The eye gaze movements were recorded and analyzed during the experiments. In this study, the participants performed the task under two different motivational conditions. The first condition was set to create the urge to finish the task immediately which generates high motivation to finish the task as fast as they can. The other condition was set in a neutral condition which the participants could perform at their own pace without a rush. From this experiment, approximately 3000 answering time data and 900.000 frames of the eye gaze movement were gathered and analyzed.ResultsThe different motivational conditions influenced the user’s task performance, showing a statistical difference in the user’s answering time in a high motivational and neutral conditions (p < 0.01). The distribution of the answering time follows the log-normal distribution shapes shown by both conditions.The participants performed different strategies in the cognitive task. Following the ACT-R modules, in general, the identified eye gaze patterns are divided into 2 patterns. The first pattern follows the production rules: (1)find-left-number, (2)attend-left-number, (3)encode-left-number, (4)retrieve-left-number, (5)find-right-number, (6)attend-right-number, (7)encode-right-number, (8)waiting-for-start-back, (9)attend-start-back, summation, and (10)keyboard-click. On the other hand, the second pattern follows the production rules: (1)find-left-number, (2)attend-left-number, (3)encode-left-number, (4)retrieve-left-number, (5)find-right-number, (6)encode-right-number, (7)summation, keyboard-click, (8)waiting-for-start-back, and (9)attend-start-back.In the different motivational conditions, the pattern’s appearance is altered. For participants 1, 3, and 5 the second patterns were dominant during the high motivational condition. Some irregular patterns arouse in the last 5 minutes of the task. This condition might due to the degradation of the user’s cognitive performance when the user feels tired and it might be the time duration that affected their cognitive performance. The results of this study might enlighten future research on cognitive performance by proposing a new method of eye gaze pattern identification on ACT-R cognitive models.

No abstract

Micro-breaks are very short time breaks such as several tens of seconds during intellectual work and they are expected to be effective to recover intellectual concentration. A controlled laboratory experiment was conducted to examine the effect of the micro-breaks focusing on individual characteristics. Thirty-one participants were recruited in this study performing cognitive comparison tasks for 25 minutes on a tablet PC under two conditions, which are micro-break intervention condition and no micro-break intervention condition. Quantitative answering times were analyzed to find the individual's performance characteristic concerning the micro-breaks intervention. Five patterns grouping of performance variations were determined utilizing the moving average and locally-weighted scatterplot smoother data. The result shows that 42% of the total participants resulted that micro-breaks condition outperformed the condition without micro-breaks during all the task periods incorporated in the first pattern group. The second pattern group does not show the eminence of micro-breaks towards 10% of total participants. In the third pattern group, the superiority of the micro-breaks appears after a certain time and not from the beginning of the task shown by 26% of participants' data. The degradation of the micro-break effectiveness is shown in a fourth pattern group for 19% of the sample group. In the last pattern group, both the superiority and deterioration of the micro-break are found in 3% of the data. The results demonstrate each individual's characteristic in responding to the micro-breaks intervention during cognitive task presented in the five patterns group. The majority of the participants benefited from the micro-breaks indicated by more stable and faster performance compared to the no micro-breaks condition.