Literature Review Example

Does Generalised Anxiety Disorder effect an individual s ability to attend to threats during a Dynamic Hazard Perception Test?

Anxiety States Affecting Attention.Generalised Anxiety Disorder (GAD) is a condition characterised by frequent states of anxiety (Stein Sareen, 2015) and is found in roughly 5% of the UK population: approximately 3,332,500 UK citizens (Dept Of Health Social care, 2019).

States of anxiety, both induced and pre-existing, have been shown to impact many areas of human attention. A meta study conducted by Morgan (2016) found that anxiety states can detrimentally impact attention and are related to poorer task performance. These findings were extrapolated from 177 separate studies, conducted with over 22,000 participants, lending said findings credibility.

Additionally, studies have shown that anxious individuals possess an attentional bias towards perceived threats registering threats and hazards that non-anxious individuals do not register as easily (Bar-Haim et al, 2007 Bockstaele et al, 2014). Individuals diagnosed with GAD tend to possess this attentional bias from an early age, with it increasing with severity over time (Dudeney Sharpe, 2015). However it is postulated in Eysenk Cavlo s Attentional Control Theory (ACT) (2007) that this attentional bias may impact task performance through the division of central executive functions.

Bar-Haim et al (2009) found evidence to suggest that anxious individuals may even experience time slower during stressful situations than non-anxious individuals, allowing them a greater capacity to attend to their surroundings. These findings are supported by Faravelli et al s (2012) review of anxiety literature as it pertains to the HPA Axis, which concluded that HPA Axis overactivity is reported frequently in GAD cases. This HPA Axis overactivity can present itself as a slowed down perception of time, as well a myriad of other attentional enhancements such as increased vision and sharper hearing, due to a complex system of physiological changes (Wong, 2015).

Hazard Perception.One heavily studied area of attention is hazard perception. Many of these studies have shown focus on hazard perception whilst driving, likely due to the real-world application of reducing traffic collisions, as hazard perception ability has been shown to reflect incidence of crashes (McKnight McKnight, 2008). However, it should be noted that studies aiming to measure a link between hazard perception and likelihood of collision often contain the methodological shortcoming of failing to take multiple factors into account.

Scialfa et al (2012) found that novice drivers, defined by the House of Commons Transport Committee (2007) as anybody with less than 3 years driving experience, achieve poorer results in Hazard Perception Tests (HPT s) when compared to experienced drivers, defined by the same committee as having over 5 year driving experience. This suggests that one s time spent driving reflects one s hazard perception ability. However, the test used in this study was a Static Image HPT, a now rarely used test due to its poor validity in comparison to Dynamic Video HPT s (Moran Bennett, 2019). Though it should be noted that Dynamic HPT s are also not totally accurate, with temporal D-HPT s showing greater success in hazard perception ability prediction than their spatial D-HPT counterparts (Moran Bennett, 2019). This methodological lapse reduces the reliability of the findings in this study, and any studies that use this outdated HPT method. That being said, later studies using Dynamic HPT s found similar results. Gharib et al s (2020) fMRI study found that when conducting a Dynamic HPT, experienced drivers showed greater Blood Oxygen Level Dependent (BOLD) activation in the occipital lobe, cerebellar regions, supramarginal gyrus, right anterior insular cortex, and anterior cingulate cortex. This led Gharib et al to conclude that experienced drivers perceive hazards better, lending biological support to Scialfa s findings. However, this study does suffer from the shortcomings associated with fMRI, namely it s poor temporal resolution, as well as having a poorly ecologically valid sample consisting entirely of men. These criticisms notwithstanding, Gharib et al (2020) additionally concluded that emotional awareness had an impact on a driver s ability to perceive hazards. This finding may suggest that anxiety states, in their capacity to impact emotional awareness, may detrimentally impact hazard perception whilst driving.

Anxiety States Hazard Perception.With the 5% prevalence of GAD in the UK, and with roughly 74% of adults holding a valid driving licence (Dept Of Transport, 2019), it seems safe to assume many GAD sufferers would be active drivers. As such, it is important to assess the effect of anxiety states upon driving hazard perception using as wide a variety of methods and analyses as possible, so as to assess the possibility of risk to the public.

Giorgetta et al (2012) found that GAD sufferers were more likely to act risk-aversely in risky situations. The primary neurological regions that mediate risk are the amygdala and the pre-frontal cortex (FeldmanHall et al, 2019 ), the connection between which has been demonstrated to be hyperactive in the brains of GAD sufferers (Roy et al, 2013) thus supporting Giorgetta et al s findings. However, the same participants in Giorgetta et al s study showed increased reaction times when compared to non-anxious individuals due to an anxiety regarding locking in to a decision. These findings, when applied to drivers, seem to denote the diametrically opposed conclusions that GAD sufferers would be both better and worse at avoiding crashes than non-anxious individuals as risk-aversion and reaction speed are both important facets of hazard perception. Whilst one could read the mutually exclusive friction between these statements to be a criticism of the work, these findings do in fact reflect similar conflictions in the wider literature. Gharib et al s (2020) findings, Eysenck Cavlo s (2007) ACT, and Wong Titchener s (2013) work on ACT in drivers denote that anxiety states would impede attention and thusly hazard perception too. However, Bar-Haim (2007), Bockstaele (2014), and Hoffman (2016) state that GAD sufferers would have an increased attentional bias towards threats, and thus possess an innate ability to attend to hazards more readily than others. This lack of clarity regarding whether GAD would help or hinder the process of hazard perception whilst driving further exemplifies the need for more research to be conducted in this area.

Due to the aforementioned link between one s time spent driving and one s ability to attend to hazards, studies conducted within the academic purview of attention and hazard perception often require participants who have been driving for roughly similar periods of time. Moran et al (2020) is one such study. Attempting to measure how cognitive function impacts hazard perception in driving, Moran et al recruited approximately 80 participants under the condition that they must have been driving for between 1 and 5 years. Additionally, this study required all participants to speak fluent English and had normal/corrected-to-normal eyesight. These requirements attempted to account for extraneous variables that could possibly impact the validity of results by extraneously affecting hazard perception ability. However, this effort is slightly undercut by the gender-biased sample of the study, consisting approximately of 71% female identifying participants. This bias notwithstanding, the study concluded that cognitive ability does impact one s ability to perceive hazards whilst driving. With this knowledge, studies going forward should employ preliminary cognitive tests, such as the ones used by Zicat et al (2018), so as to ensure participant driving-skill homogeneity. These tests were the Visual Object and Space Perception Battery (Warrington James, 1991), the Rey-Osterrieth Complex Figure Test (Osterrieth, 1993), the Grooved Peg Board Task (Kl ve, 1963), and the Trail Making Test (Tombaugh, 2003). Additionally, other participant requirements akin to those aforementioned should be used so that participant individual differences can be controlled for.

Methods endeavouring to measure driver reaction speeds in the field of attention vary. Independent modalities, such as Eye-Tracking technology, have found some success as a data collection method (Velichkovsky et al, 2002), due to the established link between eye-tracking and attention allocation (Moacdieh Sarter, 2012). However, methods that combine multiple modalities of measurement have been devised that show even greater success in determining the exact moment of hazard perception. Savage et al (2013) used a combination of electroencephalogram (EEG) and Eye-Tracking Technology to investigate the effects of attentional deficits and cognitive distractions upon hazard perception. By measuring both eye fixations and the electroneural activity at the time of said fixations, Savage et al found that cognitive preoccupation resulted in increased frontal theta activity and decreased occipital theta activity both of which detrimentally impacted hazard perception ability. Whether or not anxiety states could cause such cognitive preoccupation remains to be seen. In any case, the high temporal resolution of both EEG and Eye-Tracking Technology creates, when used in tandem, a far more precise understanding of exactly when brain activity occurs in response to an attentional stimuli when compared to the poor temporal resolution of methods like fMRI. As such, this method represents a way to explicitly distinguish when a hazard has been perceived by a participant, and thusly more accurately recognise how an independent variable impacts that perception time.

A consolidation of the methodologies, data collection modalities, extraneous-variable-circumventing techniques within this field into one study could aid in furthering our understanding of the directional impact of anxiety upon hazard perception when driving. This highlights our research question. How does Generalised Anxiety Disorder effect an individual s ability to attend to threats during a Dynamic Hazard Perception Test?

References.Bar-Haim, Y. Kerem, A. Lamy, D. Zakay, D. (2009). When time slows down: The influence of threat on time perception in anxiety. Cognition and Emotion, 24(2), 255-263.

Bar-Haim, Y. Lamy, D. Pergamin, L. Bakermans-Kranenburg, M. J. Van Ijzendoorn, M. H. (2007). Threat-related attentional bias in anxious and nonanxious individuals: a meta-analytic study. Psychological bulletin, 133(1), 1.

Department of Health Social Care, UK. (2019). Overview - Generalised anxiety disorder in adults. Retrieved from: https://www.nhs.uk/mental-health/conditions/generalised-anxiety-disorder/overview/#:~:text=GAD%20is%20a%20common%20condition,ages%20of%2035%20to%2059.

Department of Transport, UK. (2019). National Travel Survey Personal travel statistics within Great Britain covering English residents. Retrieved from: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/906276/national-travel-survey-2019.pdf

Dudeney, J. Sharpe, L. Hunt, C. (2015). Attentional bias towards threatening stimuli in children with anxiety: A meta-analysis. Clinical psychology review, 40, 66-75.

Eysenck, M. W. Derakshan, N. Santos, R. Calvo, M. G. (2007). Anxiety and cognitive performance: attentional control theory. Emotion, 7(2), 336.

Faravelli, C. Lo Sauro, C. Lelli, L. Pietrini, F. Lazzeretti, L. Godini, L. Ricca, V. (2012). The role of life events and HPA axis in anxiety disorders: a review. Current pharmaceutical design, 18(35), 5663.

FeldmanHall, O. Glimcher, P. Baker, A. L. Phelps, E. A. (2019). The functional roles of the amygdala and prefrontal cortex in processing uncertainty. Journal of cognitive neuroscience, 31(11), 1742-1754.

Gharib, S. Zare-Sadeghi, A. Zakerian, S. A. Haidari, M. R. (2020). The neural basis of hazard perception differences between novice and experienced drivers-An fMRI study. EXCLI journal, 19, 547.

Giorgetta, C. Grecucci, A. Zuanon, S. Perini, L. Balestrieri, M. Bonini, N. Brambilla, P. (2012). Reduced risk-taking behaviour as a trait feature of anxiety. Emotion, 12(6), 1373.

Hoffman, Y. Rosenbloom, T. (2016). Driving experience moderates the effect of implicit versus explicit threat priming on hazard perception test. Accident Analysis Prevention, 92, 82-88.

House of Commons Transport Committee. (2007). Novice Drivers. Seventh Report of Session, 2006 07, Volume I. Retrieved from: https://publications.parliament.uk/pa/cm200607/cmselect/cmtran/355/355i.pdf

Kl ve, H. (1963). "Grooved pegboard." Lafayette, IN: Lafayette Instruments.

McKnight, A. J. McKnight, A. S. (2003). Young novice drivers: careless or clueless?. Accident Analysis Prevention, 35(6), 921-925.

Moacdieh, N. M. Sarter, N. B. (2012, September). Eye tracking metrics: A toolbox for assessing the effects of clutter on attention allocation. In Proceedings of the Human Factors and Ergonomics Society annual meeting (Vol. 56, No. 1, pp. 1366-1370). Sage CA: Los Angeles, CA: SAGE Publications.

Mogg, K. Mathews, A. Eysenck, M. (1992). Attentional bias to threat in clinical anxiety states. Cognition Emotion, 6(2), 149-159.

Moran, C. Bennett, J. M. Prabhakharan, P. (2019). Road user hazard perception tests: A systematic review of current methodologies. Accident Analysis Prevention, 129, 309-333.

Moran, C. Bennett, J. M. Prabhakharan, P. (2020). The relationship between cognitive function and hazard perception in younger drivers. Transportation research part F: traffic psychology and behaviour, 74, 104-119.

Moran, T. P. (2016). Anxiety and working memory capacity: A meta-analysis and narrative review. Psychological Bulletin, 142(8), 831 864.

Osterrieth, P. (1993). The complex figure copy test. Clinical Neuropsychology, 7, 3-15.

Roy, A. K. Fudge, J. L. Kelly, C. Perry, J. S. Daniele, T. Carlisi, C. Ernst, M. (2013). Intrinsic functional connectivity of amygdala-based networks in adolescent generalized anxiety disorder. Journal of the American Academy of Child Adolescent Psychiatry, 52(3), 290-299.

Savage, S. W. Potter, D. D. Tatler, B. W. (2013). Does preoccupation impair hazard perception? A simultaneous EEG and eye tracking study. Transportation research part F: traffic psychology and behaviour, 17, 52-62.

Scialfa, C. T. Borkenhagen, D. Lyon, J. Desch nes, M. Horswill, M. Wetton, M. (2012). The effects of driving experience on responses to a static hazard perception test. Accident Analysis Prevention, 45, 547-553.

Stein, M. B. Sareen, J. (2015). Generalized anxiety disorder. New England Journal of Medicine, 373(21), 2059-2068.

Tombaugh, T. N. (2003). The Test of Memory Malingering (TOMM) in forensic psychology. Journal of Forensic Neuropsychology, 2(3-4), 69-96.

Van Bockstaele, B. Verschuere, B. Tibboel, H. De Houwer, J. Crombez, G. Koster, E. H. (2014). A review of current evidence for the causal impact of attentional bias on fear and anxiety. Psychological bulletin, 140(3), 682.

Velichkovsky, B. M. Rothert, A. Kopf, M., Dornh fer, S. M. Joos, M. (2002). Towards an express-diagnostics for level of processing and hazard perception. Transportation Research Part F: Traffic Psychology and Behaviour, 5(2), 145-156.

Warrington, E. K., James, M. (1991). The visual object and space perception battery. Bury St Edmunds, Thames Valley Test Company.

William Tank, A. Lee Wong, D. (2011). Peripheral and central effects of circulating catecholamines. Comprehensive Physiology, 5(1), 1-15.

Wong, I. Y. Mahar, D. Titchener, K. (2015). Driven by distraction: investigating the effects of anxiety on driving performance using the Attentional Control Theory. Journal of Risk Research, 18(10), 1293-1306.

Zicat, E. Bennett, J. M. Chekaluk, E. Batchelor, J. (2018). Cognitive function and young drivers: The relationship between driving, attitudes, personality and cognition. Transportation research part F: traffic psychology and behaviour, 55, 341-352.