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HUMAN FACTORS, 1987,29(2),213-234 Effects of Information-Processing Demands on Physiological Response Patterns
"... The present swdy investigated the effects of increased attentional and encoding/rehearsal demands on a /lumber of physiological measures. Encoding/rehearsal demands were varied by manipulating the number of letters (i, 3, or 5) comprising a briefly presented set that the subject was instructed to en ..."
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The present swdy investigated the effects of increased attentional and encoding/rehearsal demands on a /lumber of physiological measures. Encoding/rehearsal demands were varied by manipulating the number of letters (i, 3, or 5) comprising a briefly presented set that the subject was instructed to encode, retain, and, 5 s later, compare with a single test letter. Attentional demands were varied by presenting the subject with a cue stimulus (the numeral 1,3, or 5) 5s prior to the presentation of the letter set, informing the subject of the number of letters contained therein. The physiological measures recorded were heart rate, eyeblinks, "probe-evoked " potentials sampled from the intervals preceding and following the letter set, and "task-evoked " potentials elicited by the cue, memory set, and test stimuli. Unique pat-terns of physiological activity occurred in the intervals preceding and following the memory set. In the interval preceding the memory set, where attentional demands were varied by set size, probe ERP Pi-NI amplitude il1creased with set size. In the subsequent interval, where encoding and rehearsal demands were varied, probe ERP N i-P2 amplitude decli/1ed with increasing set size. There was also evidence for interval and set-size effects on heart rate, blink rate, and task ERPs. These results have implications for multiple-resource theories of attention.
FATIGUE IN TRUCK ACCIDENTS iiiCONTENTS
, 1989
"... Based on Coroners ' verdicts, fatigue of car or truck drivers was a contributing factor in 9.1 % of fatal accidents involving trucks. Based on the presence of factors such as extended driving hours, falling asleep at the wheel, comments about tiredness, driving right of centre and night-time dr ..."
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Based on Coroners ' verdicts, fatigue of car or truck drivers was a contributing factor in 9.1 % of fatal accidents involving trucks. Based on the presence of factors such as extended driving hours, falling asleep at the wheel, comments about tiredness, driving right of centre and night-time driving, the authors estimated fatigue contributed to 19.9 % of the accidents. There were approximately equal numbers of fatigued car drivers and truck drivers. An analysis of casualty and fatal truck accidents by time of day (adjusted for exposure) showed that accident risks were highest during the night on all five Victorian highways studied. Driver fatigue is one of the possible factors underlying this pattern of elevated risk. The report section described in-vehicle fatigue counter-measures. The distinction between fatigue monitors and alerting devices was made and it was recommended that eye closure and head nodding monitors and an alerting device be tested in the next stage of this project. Key Words: Fatigue (human), accident rate, fatality,
Using Psycho-Physiological Measures to Assess Task Difficulty in Software Development
, 2014
"... Software developers make programming mistakes that cause serious bugs for their customers. Existing work to detect problematic software focuses mainly on post hoc identifica-tion of correlations between bug fixes and code. We propose a new approach to address this problem — detect when soft-ware dev ..."
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Software developers make programming mistakes that cause serious bugs for their customers. Existing work to detect problematic software focuses mainly on post hoc identifica-tion of correlations between bug fixes and code. We propose a new approach to address this problem — detect when soft-ware developers are experiencing difficulty while they work on their programming tasks, and stop them before they can introduce bugs into the code. In this paper, we investigate a novel approach to classify the difficulty of code comprehension tasks using data from psycho-physiological sensors. We present the results of a study we conducted with 15 professional programmers to see how well an eye-tracker, an electrodermal activity sen-sor, and an electroencephalography sensor could be used to predict whether developers would find a task to be difficult. We can predict nominal task difficulty (easy/difficult) for a new developer with 64.99 % precision and 64.58 % recall, and for a new task with 84.38 % precision and 69.79 % recall. We can improve the Naive Bayes classifier’s performance if we trained it on just the eye-tracking data over the entire dataset, or by using a sliding window data collection schema with a 55 second time window. Our work brings the commu-nity closer to a viable and reliable measure of task difficulty that could power the next generation of programming sup-port tools.
° PALEST INSPECTED I NOTICE
, 1996
"... This document is available to the public through the National Technical Information ..."
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This document is available to the public through the National Technical Information
