A comparison of post-saccadic oscillations in European-Born and China-Born British University Undergraduates

Previous research has revealed that people from different genetic, racial, biological, and/or cultural backgrounds may display fundamental differences in eye-tracking behavior. These differences may have a cognitive origin or they may be at a lower level within the neurophysiology of the oculomotor network, or they may be related to environment factors. In this paper we investigated one of the physiological aspects of eye movements known as post-saccadic oscillations and we show that this type of eye movement is very different between two different populations. We compared the post-saccadic oscillations recorded by a video-based eye tracker between two groups of participants: European-born and Chinese-born British students. We recorded eye movements from a group of 42 Caucasians defined as White British or White Europeans and 52 Chinese-born participants all with ages ranging from 18 to 36 during a prosaccade task. The post-saccadic oscillations were extracted from the gaze data which was compared between the two groups in terms of their first overshoot and undershoot. The results revealed that the shape of the post-saccadic oscillations varied significantly between the two groups which may indicate a difference in a multitude of genetic, cultural, physiologic, anatomical or environmental factors. We further show that the differences in the post-saccadic oscillations could influence the oculomotor characteristics such as saccade duration. We conclude that genetic, racial, biological, and/or cultural differences can affect the morphology of the eye movement data recorded and should be considered when studying eye movements and oculomotor fixation and saccadic behaviors

Optimal approaches to the quality control checking of product labels

Quality control checkers at fresh produce packaging facilities occasionally fail to detect incorrect information presented on labels. Despite being infrequent, such errors have significant financial and environmental repercussions. To understand why label-checking errors occur, observations and interviews were undertaken at a large packaging facility and followed up with a laboratory-based label-checking task. The observations highlighted the dynamic, complex environment in which label-checking took place, whilst the interviews revealed that operatives had not received formal training in label-checking. On the laboratory-based task, overall error detection accuracy was high but considerable individual differences were found between professional label-checkers. Response times were shorter when participants failed to detect label errors, suggesting incomplete checking or ineffective checking strategies. Furthermore, eye movement recordings indicated that checkers who adopted a systematic approach to checking were more successful in detecting errors. The extent to which a label checker adopted a systematic approach was not found to correlate with the number of years of experience that they had accrued in label-checking. To minimize the chances of label errors going undetected, explicit instruction and training, personnel selection and/or the use of software to guide performance towards a more systematic approach is recommended

Digital Traces of behaviour within addiction: Response to Griffiths (2017)

Griffiths’ (2017) response to the recent commentary piece by Ryding and Kaye (2017) on “Internet Addiction: A conceptual minefield” provided a useful critique and extension of some key issues. We take this opportunity to further build upon on one of these issues to provide some further insight into how the field of “internet addiction” (IA) or technological addictions more generally, may benefit from capitalising on behavioural data. As such, this response extends Griffiths’ (2007) points surrounding the efficacy of behavioural data previously used in studies on problematic gambling, to consider its merit for future research on IA or associated topics such as Internet Gaming Disorder (IGD) or “Smartphone addiction”. Within this, we highlight the challenges associated with utilising behavioural data but provide some practical solutions which may support researchers and practitioners in this field. These recent developments could, in turn, advance our understanding and potentially validate such concepts by establishing behavioural correlates, conditions and contexts. Indeed, corroborating behavioural metrics alongside self-report measures presents a key opportunity if scholars and practitioners are to move the field forward