ORCID Profile
0000-0002-9601-3660
Current Organisation
Federation University
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Publisher: Elsevier BV
Date: 2020
DOI: 10.1016/J.JAD.2019.09.028
Abstract: Major Depressive Disorder (MDD), anxiety disorders, and high levels of anxious symptoms are associated with impaired cognitive functioning. However, little is known of how cognitive functioning is impaired in people with anxious depression. Here, we compared cognitive functioning between people with anxious depression, non-anxious depression, and healthy controls. We also tested whether anxious depression moderated the relationship between cognitive functioning and treatment outcome. 1008 adults with MDD and 336 healthy controls completed IntegNeuro: a computerized cognitive functioning test battery. Participants were then randomised to one of three antidepressants and reassessed at 8 weeks using the 17-item Hamilton Depression Rating Scale (HRSD Syndromal anxious depression was associated with better psychomotor functioning and poorer working memory, cognitive flexibility and information processing speed compared to their non-anxious counterparts. HRSD anxious depression was associated with better psychomotor functioning compared to their non-anxious counterparts. Syndromal anxious depression moderated the relationship between verbal memory and treatment outcome. In people with syndromal anxious depression, poorer baseline verbal memory predicted poorer treatment outcome. As DSM-IV criteria was used, the DSM-5 anxious distress specifier characterisation of anxious depression could not be assessed CONCLUSIONS: Syndromal anxious depression is characterised by impaired executive functions and moderates the relationship between verbal memory functioning and treatment outcome.
Publisher: JMIR Publications Inc.
Date: 08-12-2021
Abstract: ood disorders are burdensome illnesses that often go undetected and untreated. Sensor technologies within smartphones may provide an opportunity for identifying the early changes in circadian rhythm and social support/connectedness that signify the onset of a depressive or manic episode. sing smartphone sensor data, this study investigated the relationship between circadian rhythm, which was determined by GPS data, and symptoms of mental health among a clinical s le of adults diagnosed with major depressive disorder or bipolar disorder. total of 121 participants were recruited from a clinical setting to take part in a 10-week observational study. Self-report questionnaires for mental health outcomes, social support, social connectedness, and quality of life were assessed at 6 time points throughout the study period. Participants consented to passively sharing their smartphone GPS data for the duration of the study. Circadian rhythm (ie, regularity of location changes in a 24-hour rhythm) was extracted from GPS mobility patterns at baseline. lthough we found no association between circadian rhythm and mental health functioning at baseline, there was a positive association between circadian rhythm and the size of participants’ social support networks at baseline ( i r /i =0.22 i P= /i .03 i R sup /sup /i =0.049). In participants with bipolar disorder, circadian rhythm was associated with a change in anxiety from baseline a higher circadian rhythm was associated with an increase in anxiety and a lower circadian rhythm was associated with a decrease in anxiety at time point 5. ircadian rhythm, which was extracted from smartphone GPS data, was associated with social support and predicted changes in anxiety in a clinical s le of adults with mood disorders. Larger studies are required for further validations. However, smartphone sensing may have the potential to monitor early symptoms of mood disorders.
Publisher: Springer Science and Business Media LLC
Date: 23-05-2017
DOI: 10.3758/S13414-017-1337-2
Abstract: Cognitive load from secondary tasks is a source of distraction causing injuries and fatalities on the roadway. The Detection Response Task (DRT) is an international standard for assessing cognitive load on drivers' attention that can be performed as a secondary task with little to no measurable effect on the primary driving task. We investigated whether decrements in DRT performance were related to the rate of information processing, levels of response caution, or the non-decision processing of drivers. We had pairs of participants take part in the DRT while performing a simulated driving task, manipulated cognitive load via the conversation between driver and passenger, and observed associated slowing in DRT response time. Fits of the single-bound diffusion model indicated that slowing was mediated by an increase in response caution. We propose the novel hypothesis that, rather than the DRT's sensitivity to cognitive load being a direct result of a loss of information processing capacity to other tasks, it is an indirect result of a general tendency to be more cautious when making responses in more demanding situations.
Publisher: Informa UK Limited
Date: 26-03-2019
Publisher: Elsevier BV
Date: 03-2017
Publisher: Springer Science and Business Media LLC
Date: 22-08-2016
DOI: 10.3758/S13414-016-1190-8
Abstract: In a Stroop task, participants can be presented with a color name printed in color and need to classify the print color while ignoring the word. The Stroop effect is typically calculated as the difference in mean response time (RT) between congruent (e.g., the word RED printed in red) and incongruent (GREEN in red) trials. Delta plots compare not just mean performance, but the entire RT distributions of congruent and incongruent conditions. However, both mean RT and delta plots have some limitations. Arm-reaching trajectories allow a more continuous measure for assessing the time course of the Stroop effect. We compared arm movements to congruent and incongruent stimuli in a standard Stroop task and a control task that encourages processing of each and every word. The Stroop effect emerged over time in the control task, but not in the standard Stroop, suggesting words may be processed differently in the two tasks.
Publisher: JMIR Publications Inc.
Date: 04-05-2022
DOI: 10.2196/35549
Abstract: Mood disorders are burdensome illnesses that often go undetected and untreated. Sensor technologies within smartphones may provide an opportunity for identifying the early changes in circadian rhythm and social support/connectedness that signify the onset of a depressive or manic episode. Using smartphone sensor data, this study investigated the relationship between circadian rhythm, which was determined by GPS data, and symptoms of mental health among a clinical s le of adults diagnosed with major depressive disorder or bipolar disorder. A total of 121 participants were recruited from a clinical setting to take part in a 10-week observational study. Self-report questionnaires for mental health outcomes, social support, social connectedness, and quality of life were assessed at 6 time points throughout the study period. Participants consented to passively sharing their smartphone GPS data for the duration of the study. Circadian rhythm (ie, regularity of location changes in a 24-hour rhythm) was extracted from GPS mobility patterns at baseline. Although we found no association between circadian rhythm and mental health functioning at baseline, there was a positive association between circadian rhythm and the size of participants’ social support networks at baseline (r=0.22 P=.03 R2=0.049). In participants with bipolar disorder, circadian rhythm was associated with a change in anxiety from baseline a higher circadian rhythm was associated with an increase in anxiety and a lower circadian rhythm was associated with a decrease in anxiety at time point 5. Circadian rhythm, which was extracted from smartphone GPS data, was associated with social support and predicted changes in anxiety in a clinical s le of adults with mood disorders. Larger studies are required for further validations. However, smartphone sensing may have the potential to monitor early symptoms of mood disorders.
Publisher: Elsevier BV
Date: 10-2019
Publisher: Springer Science and Business Media LLC
Date: 18-05-2020
Publisher: SAGE Publications
Date: 17-09-2019
Abstract: Scientific advances across a range of disciplines hinge on the ability to make inferences about unobservable theoretical entities on the basis of empirical data patterns. Accurate inferences rely on both discovering valid, replicable data patterns and accurately interpreting those patterns in terms of their implications for theoretical constructs. The replication crisis in science has led to widespread efforts to improve the reliability of research findings, but comparatively little attention has been devoted to the validity of inferences based on those findings. Using an ex le from cognitive psychology, we demonstrate a blinded-inference paradigm for assessing the quality of theoretical inferences from data. Our results reveal substantial variability in experts’ judgments on the very same data, hinting at a possible inference crisis.
Publisher: American Physiological Society
Date: 04-2019
Abstract: Stochastic accumulator models account for response times and errors in perceptual decision making by assuming a noisy accumulation of perceptual evidence to a threshold. Previously, we explained saccade visual search decision making by macaque monkeys with a stochastic multiaccumulator model in which accumulation was driven by a gated feed-forward integration to threshold of spike trains from visually responsive neurons in frontal eye field that signal stimulus salience. This neurally constrained model quantitatively accounted for response times and errors in visual search for a target among varying numbers of distractors and replicated the dynamics of presaccadic movement neurons hypothesized to instantiate evidence accumulation. This modeling framework suggested strategic control over gate or over threshold as two potential mechanisms to accomplish speed-accuracy tradeoff (SAT). Here, we show that our gated accumulator model framework can account for visual search performance under SAT instructions observed in a milestone neurophysiological study of frontal eye field. This framework captured key elements of saccade search performance, through observed modulations of neural input, as well as flexible combinations of gate and threshold parameters necessary to explain differences in SAT strategy across monkeys. However, the trajectories of the model accumulators deviated from the dynamics of most presaccadic movement neurons. These findings demonstrate that traditional theoretical accounts of SAT are incomplete descriptions of the underlying neural adjustments that accomplish SAT, offer a novel mechanistic account of decision-making mechanisms during speed-accuracy tradeoff, and highlight questions regarding the identity of model and neural accumulators. NEW & NOTEWORTHY A gated accumulator model is used to elucidate neurocomputational mechanisms of speed-accuracy tradeoff. Whereas canonical stochastic accumulators adjust strategy only through variation of an accumulation threshold, we demonstrate that strategic adjustments are accomplished by flexible combinations of both modulation of the evidence representation and adaptation of accumulator gate and threshold. The results indicate how model-based cognitive neuroscience can translate between abstract cognitive models of performance and neural mechanisms of speed-accuracy tradeoff.
Publisher: MDPI AG
Date: 22-08-2023
DOI: 10.3390/BS13090698
Abstract: The 2019 novel coronavirus disease (COVID-19) is a global threat that can have an adverse effect on an in iduals’ physical and mental health. Here, we investigate if disordered social media use predicts user stress and depression symptoms indirectly via fear of COVID-19. A total of 359 (timepoint 1 = 171, timepoint 2 = 188) participants were recruited via social media and snowball s ling. They completed an online survey that measured disordered social media use, fear of COVID-19, perceived stress, and depression symptomatology at two cross-sectional timepoints. We found that disordered social media use predicts depression indirectly through fear of COVID-19 at both timepoints. We also found that disordered social media use predicts perceived stress indirectly through fear of COVID-19, but only at timepoint 1. Taken together with previous research, our findings indicate that disordered social media use may lead to increased fear of COVID-19, which in turn may lead to poorer psychological wellbeing outcomes. Overall, there is evidence that the impact of the COVID-19 pandemic is affecting the physical, psychological, and emotional health of in iduals worldwide. Moreover, this impact may be exacerbated by disordered use of social media.
Publisher: JMIR Publications Inc.
Date: 13-12-2022
Abstract: ental disorders are prevalent during adolescence. Among the digital phenotypes currently being developed to monitor mental health symptoms, typing behavior is one promising candidate. However, few studies have directly assessed associations between typing behavior and mental health symptom severity, and whether these relationships differs between genders. n a cross-sectional analysis of a large cohort, we tested whether various features of typing behavior derived from keystroke metadata were associated with mental health symptoms and whether these relationships differed between genders. total of 934 adolescents from the Future Proofing study undertook 2 typing tasks on their smartphones through the Future Proofing app. Common keystroke timing and frequency features were extracted across tasks. Mental health symptoms were assessed using the Patient Health Questionnaire-Adolescent version, the Children’s Anxiety Scale-Short Form, the Distress Questionnaire 5, and the Insomnia Severity Index. Bivariate correlations were used to test whether keystroke features were associated with mental health symptoms. The false discovery rates of i P /i values were adjusted to i q /i values. Machine learning models were trained and tested using independent s les (ie, 80% train 20% test) to identify whether keystroke features could be combined to predict mental health symptoms. eystroke timing features showed a weak negative association with mental health symptoms across participants. When split by gender, females showed weak negative relationships between keystroke timing features and mental health symptoms, and weak positive relationships between keystroke frequency features and mental health symptoms. The opposite relationships were found for males (except for dwell). Machine learning models using keystroke features alone did not predict mental health symptoms. ncreased mental health symptoms are weakly associated with faster typing, with important gender differences. Keystroke metadata should be collected longitudinally and combined with other digital phenotypes to enhance their clinical relevance. ustralian and New Zealand Clinical Trial Registry, ACTRN12619000855123 www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=377664& isReview=true
Publisher: American Psychological Association (APA)
Date: 10-2020
DOI: 10.1037/REV0000192
Publisher: JMIR Publications Inc.
Date: 15-05-2023
DOI: 10.2196/44986
Abstract: Mental disorders are prevalent during adolescence. Among the digital phenotypes currently being developed to monitor mental health symptoms, typing behavior is one promising candidate. However, few studies have directly assessed associations between typing behavior and mental health symptom severity, and whether these relationships differs between genders. In a cross-sectional analysis of a large cohort, we tested whether various features of typing behavior derived from keystroke metadata were associated with mental health symptoms and whether these relationships differed between genders. A total of 934 adolescents from the Future Proofing study undertook 2 typing tasks on their smartphones through the Future Proofing app. Common keystroke timing and frequency features were extracted across tasks. Mental health symptoms were assessed using the Patient Health Questionnaire-Adolescent version, the Children’s Anxiety Scale-Short Form, the Distress Questionnaire 5, and the Insomnia Severity Index. Bivariate correlations were used to test whether keystroke features were associated with mental health symptoms. The false discovery rates of P values were adjusted to q values. Machine learning models were trained and tested using independent s les (ie, 80% train 20% test) to identify whether keystroke features could be combined to predict mental health symptoms. Keystroke timing features showed a weak negative association with mental health symptoms across participants. When split by gender, females showed weak negative relationships between keystroke timing features and mental health symptoms, and weak positive relationships between keystroke frequency features and mental health symptoms. The opposite relationships were found for males (except for dwell). Machine learning models using keystroke features alone did not predict mental health symptoms. Increased mental health symptoms are weakly associated with faster typing, with important gender differences. Keystroke metadata should be collected longitudinally and combined with other digital phenotypes to enhance their clinical relevance. Australian and New Zealand Clinical Trial Registry, ACTRN12619000855123 www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=377664& isReview=true
Publisher: Springer Science and Business Media LLC
Date: 06-03-2017
DOI: 10.3758/S13423-017-1259-Y
Abstract: The lexical-decision task is among the most commonly used paradigms in psycholinguistics. In both the signal-detection theory and Diffusion Decision Model (DDM Ratcliff, Gomez, & McKoon, Psychological Review, 111, 159-182, 2004) frameworks, lexical-decisions are based on a continuous source of word-likeness evidence for both words and non-words. The Retrieving Effectively from Memory model of Lexical-Decision (REM-LD Wagenmakers et al., Cognitive Psychology, 48(3), 332-367, 2004) provides a comprehensive explanation of lexical-decision data and makes the prediction that word-likeness evidence is more variable for words than non-words and that higher frequency words are more variable than lower frequency words. To test these predictions, we analyzed five lexical-decision data sets with the DDM. For all data sets, drift-rate variability changed across word frequency and non-word conditions. For the most part, REM-LD's predictions about the ordering of evidence variability across stimuli in the lexical-decision task were confirmed.
No related grants have been discovered for Gabriel Tillman.