Effect of Awareness on an Indicator of Cognitive Load

Bagley, Janice; Manelis, Leon

doi:10.2466/pms.1979.49.2.591

Cited by 38 publications

(19 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As in the case of fixation duration, these measures provide online measures of instantaneous load. In particular, there seems to be an inverse relationship between cognitive load and blink rate with blink rate decreasing as the cognitive workload increases (Bagley & Manelis, 1979, Brookings, Wilson, & Swain, 1996Chen, Epps, Ruiz, & Chen, 2011). Blink latency has also been found to increase with higher cognitive load (Chen et al, 2011).…”

Section: Eye Movementsmentioning

confidence: 91%

Measuring cognitive load in the presence of educational video: Towards a multimodal methodology

Kruger

Doherty

2016

AJET

View full text Add to dashboard Cite

The use of video has become well established in education, from traditional courses to blended and online courses. It has grown both in its diversity of applications as well as its content. Such educational video however is not fully accessible to all students, particularly those who require additional visual support or students studying in a foreign language. Subtitles (also known as captions) represent a unique solution to these language and accessibility barriers, however, the impact of subtitles on cognitive load in such a rich and complex multimodal environment has yet to be determined. Cognitive load is a complex construct and its measurement by means of single indirect and unidimensional methods is a severe methodological limitation. Building upon previous work from several disciplines, this paper moves to establish a multimodal methodology for the measurement of cognitive load in the presence of educational video. We show how this methodology, with refinement, can allow us to determine the effectiveness of subtitles as a learning support in educational contexts. This methodology will also make it possible to analyse the impact of other multimedia learning technology on cognitive load.

show abstract

Section: Eye Movementsmentioning

confidence: 91%

Measuring cognitive load in the presence of educational video: Towards a multimodal methodology

Kruger

Doherty

2016

AJET

View full text Add to dashboard Cite

show abstract

“…For example, found that when participants worked hard to neutralize an emotion (e.g., maintaining a neutral expression when viewing a horrific accident scene), their blink rate lowered relative to when they expressed a genuine emotion (e.g., showing fear or horror when viewing the same scene). A decrease in blink rate is a sign of cognitive load (Bageley & Manelis, 1979). In forensic settings, however, such cues are not solely exhibited by liars; truth tellers may have to think hard while answering questions in a cognitively and emotionally complex context.…”

Section: Opportunities In Lie Detectionmentioning

confidence: 99%

Pitfalls and Opportunities in Nonverbal and Verbal Lie Detection

Vrij

Granhag

Porter

2010

Psychol Sci Public Interest

374

356

View full text Add to dashboard Cite

SummaryThe question of whether discernible differences exist between liars and truth tellers has interested professional lie detectors and laypersons for centuries. In this article we discuss whether people can detect lies when observing someone's nonverbal behavior or analyzing someone's speech. An article about detecting lies by observing nonverbal and verbal cues is overdue. Scientific journals regularly publish overviews of research articles regarding nonverbal and verbal cues to deception, but they offer no explicit guidance about what lie detectors should do and should avoid doing to catch liars. We will present such guidance in the present article.The article consists of two parts. The first section focuses on pitfalls to avoid and outlines the major factors that lead to failures in catching liars. Sixteen reasons are clustered into three categories: (a) a lack of motivation to detect lies (because accepting a fabrication might sometimes be more tolerable or pleasant than understanding the truth), (b) difficulties associated with lie detection, and (c) common errors made by lie detectors. We will argue that the absence of nonverbal and verbal cues uniquely related to deceit (akin Pinocchio's growing nose), the existence of typically small differences between truth tellers and liars, and the fact that liars actively try to appear credible contribute to making lie detection a difficult task. Other factors that add to difficulty is that lies are often embedded in truths, that lie detectors often do not receive adequate feedback about their judgments and therefore cannot learn from their mistakes, and that some methods to detect lies violate conversation rules and are therefore difficult to apply in real life. The final factor to be discussed in this category is that some people are just very good liars.The common errors lie detectors make that we have identified are examining the wrong cues (in part, because professionals are taught these wrong cues); placing too great an emphasis on nonverbal cues (in part, because training encourages such emphasis); tending to too-readily interpret certain behaviors, particularly signs of nervousness, as diagnostic of deception; placing too great an emphasis on simplistic rules of thumb; and neglecting inter-and intrapersonal differences. We also discuss two final errors: that many interview strategies advocated by police manuals can impair lie detection, and that professionals tend to overestimate their ability to detect deceit.The second section of this article discusses opportunities for maximizing one's chances of detecting lies and elaborates strategies for improving one's lie-detection skills. Within this section, we first provide five recommendations for avoiding the common errors in detecting lies that we identified earlier in the article. Next, we discuss a relatively recent wave of innovative lie-detection research that goes one step further and introduces novel interview styles aimed at eliciting and enhancing verbal and nonverbal differences between liars and t...

show abstract

“…Liars also need to monitor their own behavior (Vrij & Mann, 2005) and their target's reactions (Burgoon et al, 2008), which places a high demand on the liar's cognitive processing (Carrión et al, 2010). Visible cues to cognitive effort are, for instance, fewer hand and/or arm movements (Ekman, 1997;Memon et al, 2003), less blinking (Bagley & Manelis, 1979), more gaze aversion (Ekman, 1997;Doherty-Sneddon & Phelps, 2005). Attempted behavioral control varies at an individual level and can be influenced by emotional demands and cognitive load.…”

Section: Psychological Processes Underlying Deceptionmentioning

confidence: 99%

Deception in Context: Coding Nonverbal Cues, Situational Variables and Risk of Detection

Zhang

Frumkin

Stedmon

et al. 2013

J Police Crim Psych

View full text Add to dashboard Cite

There are many situations in which deception may arise and understanding the behaviors associated with it are compounded by various contexts in which it may occur. This paper sets out a coding protocol for identifying cues to deception and reports on three studies, in which deception was studied in different contexts. The contexts involved manipulating risks (i.e., probability) of being detected and reconnaissance, both of which are related to terrorist activities. Two of the studies examined the impact of changing the risks of deception detection, whilst the third investigated increased cognitive demand of duplex deception tasks including reconnaissance and deception. In all three studies, cues to deception were analyzed in relation to observable body movements and subjective impressions given by participants.In general, the results indicate a pattern of hand movement reduction by deceivers, and suggest the notion that raising the risk of detection influences deceivers' behaviors.Participants in the higher risk condition displayed increased negative affect (found in deceivers) and tension (found in both deceivers and truth-tellers) than those in lower risk conditions.

show abstract

Effect of Awareness on an Indicator of Cognitive Load

Cited by 38 publications

References 2 publications

Measuring cognitive load in the presence of educational video: Towards a multimodal methodology

Measuring cognitive load in the presence of educational video: Towards a multimodal methodology

Pitfalls and Opportunities in Nonverbal and Verbal Lie Detection

Deception in Context: Coding Nonverbal Cues, Situational Variables and Risk of Detection

Contact Info

Product

Resources

About