Predicting and interpreting identification errors in military vehicle training using multidimensional scaling

Bohil, Corey J.; Higgins, Nicholas A.; Keebler, Joseph R.

doi:10.1080/00140139.2014.899631

Cited by 4 publications

(4 citation statements)

References 17 publications

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“…Because the research was exploratory and there were no groups to compare (and thus no effect size to expect), power analyses were not conducted. Instead, sample sizes were based on previous research using similar methods (i.e., MDS analysis on stimulus similarity ratings) that inspired the current research (e.g., Markman & Makin, , included 24 participants in a classification study using methods comparable with the current study; see also Bohil, Higgins, & Keebler, ). The sample size used in the similarity rating task (for analysis with MDS) was above the minimum found to be reliable for metric recovery in Monte Carlo simulations testing 2D, 3D, and 4D MDS models (Rodgers, ).…”

Section: Methodsmentioning

confidence: 99%

“…The feature ratings were regressed onto the x, y coordinates of the two-dimensional MDS space for each stimulus set. A similar approach has been used in previous studies to link feature ratings to MDS space dimensions, thus providing additional clues as to interpretation of psychological dimensions underlying similarity ratings (Bohil et al, 2014;Kruskal & Wish, 1978;Markman & Makin, 1998).…”

Section: Regression Of Feature Ratings Onto Mdsmentioning

confidence: 99%

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Perceptual representation of spam and phishing emails

Patel

Sarno

Lewis

et al. 2019

Applied Cognitive Psychology

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SummaryUnderstanding how computer users allocate attention to features of potentially dangerous emails could help mitigate costly errors. Which features are salient? How stable is attention allocation across variation in email features? We attempted to measure the mental salience of several email features common in spam and/or phishing emails. We created two email sets: one in which messages contained company logos and urgent actionable links and one without these features. Participants rated pairwise similarity of emails within each set. Multidimensional scaling (MDS) analysis was conducted to quantify psychological similarity between emails. A separate group rated the same emails for presence of five other features: important downloadable content, collecting personal information, account deletion or suspension, advertisement, and large images with clickable content. Regressing feature ratings onto the MDS coordinates revealed that similarity judgments were influenced mostly by advertisement/large images and collecting personal information, regardless of presence or absence of company logos and urgent actionable links.

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Section: Methodsmentioning

confidence: 99%

Section: Regression Of Feature Ratings Onto Mdsmentioning

confidence: 99%

Perceptual representation of spam and phishing emails

Patel

Sarno

Lewis

et al. 2019

Applied Cognitive Psychology

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“…Vehicle characteristics with little predictive value for the vehicle's identity such as barrel length and tank treads were not given to participants as a defining feature. These features are often over-attended to and can lead to confusion between vehicles and is a cause of friendly fire incidents in the military (Bohil, Higgins & Keebler, 2014). Thus, critical cues as described in Keebler, Jentsch, & Hudson (2011), such as smoke grenade position, number of driver viewports, and camera position were used to help the participants to more effectively identify tanks by drawing their attention away from confusable features.…”

Section: Methodsmentioning

confidence: 99%

Combat Identification in Mixed Gender Teams

Baker¹,

Keebler²,

Rarick³

et al. 2017

Proceedings of the Human Factors and Ergonomics Society Annual

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This study examined the effects of team gender composition on combat identification ability. We investigated the relationship between gender composition and team success in decision making during combat identification scenarios. Participants were shown images and videos of various armored vehicles in both daytime and nighttime environments in order to elicit team decisions on how to identify each vehicle. Measures were taken to determine an individual’s potential role in the team and success of identification when presented with tanks at different angles. Results showed that male-male teams scored significantly higher on combat identification than male-female teams, and males outperformed females on spatial visualization. In addition, spatial visualization ability was a strong predictor of combat identification success. These findings lay a foundation for optimizing team performance in combat identification tasks when teams with different genders are utilized, and this study further supports future research into mixed-gender teams in combat roles.

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“…Another recent study has demonstrated that individuals who are not trained tend to classify/identify objects based on unacceptable cues (Bohil, Higgins, & Keebler, 2014). Specifically, novices in the study classified vehicles on such features as the vehicles' antennae or whether the vehicle had distinguishable sections -features that are not reliable (i.e., accidental properties), and should not be used in actual identification performance due to the fact that they change across different viewing angles.…”

Section: Novice Differencesmentioning

confidence: 99%

Review of Combat Identification Training

Keebler¹,

Smith

Patzer

et al. 2015

Proceedings of the Human Factors and Ergonomics Society Annual

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Combat identification (CID) has been studied throughout the 20th and into the 21st century, with a renewed interest in the topic in the past few decades. CID research has demonstrated that an emerging set of technologies could potentially mitigate some of the negative battlefield outcomes of failures in CID, including high rates of fratricide due to friendly fire. This paper discusses major CID research and provides an update on previous CID research by the authors. We review training technologies, effective measurement tools in this research, and important individual differences to consider for others researching training outcomes in relation to learning to differentiate between highly similar combat vehicles.

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Predicting and interpreting identification errors in military vehicle training using multidimensional scaling

Cited by 4 publications

References 17 publications

Perceptual representation of spam and phishing emails

Perceptual representation of spam and phishing emails

Combat Identification in Mixed Gender Teams

Review of Combat Identification Training

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