Temporal dynamics of anxiety-related attentional bias: is affective context a missing piece of the puzzle?

Previous research has demonstrated that anxious individuals attend to negative emotional information at the expense of other information. This is commonly referred to as attentional bias. The field has historically conceived of this process as relatively static; however, research by [Zvielli, A., Bernstein, A., & Koster, E. H. W. (2014). Dynamics of attentional bias to threat in anxious adults: Bias towards and/or away? PLoS ONE, 9(8), e104025; Zvielli, A., Bernstein, A., & Koster, E. H. W. (2015). Temporal dynamics of attentional bias. Clinical Psychological Science, 3(5), 772-788.], and others, challenges this assumption by demonstrating considerable temporal variability in attentional bias amongst anxious individuals. Still, the mechanisms driving these temporal dynamics are less well known. Using a modified dot-probe task, the present study examined the impact of two relevant contextual variables- affective valence and trial repetition. Affective context was instantiated by the presentation of negative versus neutral pictures before each trial, while repetition context was achieved via the presentation of the valenced pictures in either a blocked- or mixed-trial design. Results indicate that individuals with higher trait-anxiety levels were significantly more influenced by blocked presentations of negative affective information, leading to greater temporal fluctuations in attentional bias. Furthermore, our findings provide additional evidence that attentional bias is best conceptualised as dynamic and variable, and that an individual's affective experience is one factor that regulates attentional bias dynamics. Implications relating to theoretical and methodological factors are discussed.

Does animacy affect visual statistical learning? Revisiting the effects of selective attention and animacy on visual statistical learning.

Animates receive preferential attentional processing over inanimates because, from an evolutionary perspective, animates are important to human survival. We investigated whether animacy affects visual statistical learning-the detection and extraction of regularities in visual information from our rich, dynamic, and complex environment. Participants completed a selective-attention task, in which regularities were embedded in two visual streams, an attended and an unattended visual stream. The attended visual stream always consisted of line-drawings of non-objects, while the unattended visual stream consisted of line-drawings of either animates or inanimates. Participants then completed a triplet-discrimination task, which assessed their ability to extract regularities from the attended and unattended visual streams. We also assessed participants' awareness of regularities in the visual statistical learning task, and asked if any learning strategies were used. We were specifically interested in whether the animacy status of line-drawings in the unattended visual stream would affect visual statistical learning. There were four key findings. First, selective attention modulates visual statistical learning, with greater visual statistical learning for attended than for unattended information. Second, animacy does not affect visual statistical learning, with no differences found in visual statistical learning performance between the animate and inanimate condition. Third, awareness of regularities was associated with visual statistical learning of attended information. Fourth, participants used strategies (e.g., naming or labelling stimuli) during the visual statistical learning task. Further research is required to understand whether visual statistical learning is one of the adaptive functions that evolved from ancestral environments.

A study into the mental health of PhD students in Australia: investigating the determinants of depression, anxiety, and suicidality.

PhD students are particularly vulnerable to experiencing poor mental health. Mental health concerns that arise during their studies can not only affect their study progress but also have long-lasting impacts on their mental health after their studies. Extensive research has been conducted on the mental health of university students, but few studies have focused on PhD students and even fewer on PhD students in Australia. The present study investigated demographic, occupational, psychological, social, and relational determinants of mental health symptoms (depression, anxiety, and suicidality) in PhD students in Australia. PhD students enrolled in Australian universities were invited to complete an online survey. Results from regression analyses identified key determinants of mental health symptoms in our sample of PhD students (N = 302). In particular, higher levels of imposter thoughts, perfectionism discrepancy, and loneliness were strong predictors of depression, anxiety, and suicidality. These findings contribute to our understanding of the mental health of PhD students in Australia. Importantly, these findings inform areas of focus where potential strategies can be implemented to better protect the mental health of this population. For example, strategies that mitigate loneliness or foster effective, collaborative student-supervisor relationships.

Age differences in visual statistical learning: Investigating the effects of selective attention and stimulus category.

A cognitive function that is of interest when investigating age-related changes is statistical learning-the ability to detect and extract regularities in sensory information from our rich, dynamic, and complex environment. A previous study has suggested that there were age differences in visual statistical learning, with older adults demonstrating visual statistical learning of attended and unattended information (due to the "hyper-binding effect"). In the present study, we were interested in investigating whether there are age differences in visual statistical learning and whether stimulus category influenced visual statistical learning of unattended information in older adults. We tested two stimulus categories: highly familiar line drawings and abstract shapes. Participants completed a selective-attention task, in which regularities were embedded into both the attended and unattended visual streams. Then, participants completed a triplet-discrimination task, which assessed their ability to extract regularities from the attended and unattended visual streams. We also implemented a 4-point confidence-rating scale in the triplet-discrimination task as an assessment of participants' awareness of these regularities. There were four key findings. First, selective attention modulates visual statistical learning, with greater visual statistical learning for attended information than for unattended information. Second, there were age differences in visual statistical learning, but these differences were only observed for visual statistical learning of attended information. Third, stimulus category did not affect visual statistical learning of unattended information in older adults. Fourth, visual statistical learning occurs with awareness of statistical regularities. Further research is warranted to investigate the age-related mechanisms underlying visual statistical learning. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Are animates special? Exploring the effects of selective attention and animacy on visual statistical learning.

Our visual system is built to extract regularities in how objects in our visual environment appear in relation to each other across time and space ("visual statistical learning"). Existing research indicates that visual statistical learning is modulated by selective attention. Our attentional system prioritises information that enables adaptive behaviour; for example, animates are prioritised over inanimates (the "animacy advantage"). The present study examined the effects of selective attention and animacy on visual statistical learning in young adults (N = 284). We tested visual statistical learning of attended and unattended information across four animacy conditions: (1) living things that can self-initiate movement (animals); (2) living things that cannot self-initiate movement (fruits and vegetables); (3) non-living things that can generate movement (vehicles); and (4) non-living things that cannot generate movement (tools and kitchen utensils). We implemented a 4-point confidence rating scale as an assessment of participants' awareness of the regularities in the visual statistical learning task. There were four key findings. First, selective attention plays a critical role by modulating visual statistical learning. Second, animacy does not play a special role in visual statistical learning. Third, visual statistical learning of attended information cannot be exclusively accounted for by unconscious knowledge. Fourth, performance on the visual statistical learning task is associated with the proportion of stimuli that were named or labelled. Our findings support the notion that visual statistical learning is a powerful mechanism by which our visual system resolves an abundance of sensory input over time.

Keeping an eye on visual search patterns in visuospatial neglect: A systematic review.

Patients with visuospatial neglect exhibit a failure to detect, respond, or orient towards information located in the side of space opposite to their brain lesion. To extend our understanding of the underlying cognitive processes involved in neglect, some studies have used eye movement measurements to complement behavioural data. We provide a qualitative synthesis of studies that have used eye-tracking in patients with neglect, with a focus on highlighting the utility of examining eye movements and reporting what eye-tracking has revealed about visual search patterns in these patients. This systematic review includes twenty studies that met the eligibility criteria. We extracted information pertaining to patient characteristics (e.g., age, type of stroke, time since stroke), neglect test(s) used, type of stimuli (e.g., static, dynamic), eye-tracker specifications (e.g., temporal and spatial resolution), and eye movement measurements (e.g., saccade amplitude, fixation duration). Five key themes were identified. First, eye-tracking is a useful tool to complement pen-and-paper neglect tests. Second, the lateral asymmetrical bias in eye movement patterns observed during active exploration also occurred while at rest. Third, the lateral asymmetrical bias was evident not only in the horizontal plane but also in the vertical plane. Fourth, eye movement patterns were modulated by stimulus- and task-related factors (e.g., visual salience, local perceptual features, image content, stimulus duration, presence of distractors). Fifth, measuring eye movements in patients with neglect is useful for determining and understanding other cognitive impairments, such as spatial working memory. To develop a fuller, and a more accurate, picture of neglect, future research would benefit from eye movement measurements.

Risk and safety perception on urban and rural roads: Effects of environmental features, driver age and risk sensitivity.

OBJECTIVE: The ability to detect changing visual information is a vital component of safe driving. In addition to detecting changing visual information, drivers must also interpret its relevance to safety. Environmental changes considered to have high safety relevance will likely demand greater attention and more timely responses than those considered to have lower safety relevance. The aim of this study was to explore factors that are likely to influence perceptions of risk and safety regarding changing visual information in the driving environment. Factors explored were the environment in which the change occurs (i.e., urban vs. rural), the type of object that changes, and the driver's age, experience, and risk sensitivity. METHODS: Sixty-three licensed drivers aged 18-70 years completed a hazard rating task, which required them to rate the perceived hazardousness of changing specific elements within urban and rural driving environments. Three attributes of potential hazards were systematically manipulated: the environment (urban, rural); the type of object changed (road sign, car, motorcycle, pedestrian, traffic light, animal, tree); and its inherent safety risk (low risk, high risk). Inherent safety risk was manipulated by either varying the object's placement, on/near or away from the road, or altering an infrastructure element that would require a change to driver behavior. Participants also completed two driving-related risk perception tasks, rating their relative crash risk and perceived risk of aberrant driving behaviors. RESULTS: Driver age was not significantly associated with hazard ratings, but individual differences in perceived risk of aberrant driving behaviors predicted hazard ratings, suggesting that general driving-related risk sensitivity plays a strong role in safety perception. In both urban and rural scenes, there were significant associations between hazard ratings and inherent safety risk, with low-risk changes perceived as consistently less hazardous than high-risk impact changes; however, the effect was larger for urban environments. There were also effects of object type, with certain objects rated as consistently more safety relevant. In urban scenes, changes involving pedestrians were rated significantly more hazardous than all other objects, and in rural scenes, changes involving animals were rated as significantly more hazardous. Notably, hazard ratings were found to be higher in urban compared with rural driving environments, even when changes were matched between environments. CONCLUSION: This study demonstrates that drivers perceive rural roads as less risky than urban roads, even when similar scenarios occur in both environments. Age did not affect hazard ratings. Instead, the findings suggest that the assessment of risk posed by hazards is influenced more by individual differences in risk sensitivity. This highlights the need for driver education to account for appraisal of hazards' risk and relevance, in addition to hazard detection, when considering factors that promote road safety.