Interplay between state anxiety, heart rate variability, and cognition: An ex-Gaussian analysis of response times

Spangler, Derek P.; Cox, Katherine; Thayer, Julian F.; Brooks, Justin; Friedman, Bruce H.

doi:10.1016/j.ijpsycho.2020.08.018

Cited by 10 publications

(11 citation statements)

References 62 publications

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“…Meanwhile, the greater attentional lapses at diastole may indicate the effect of low levels of cardiovascular activation at ventricular relaxation, which echoes the association of cardiovascular activity with the level of τ in cognitive tasks in prior studies (Spangler et al, 2021;Wawrzyniak et al, 2016). The present results are also consistent with the findings that baroreceptor activation impairs automatic cognitive processes but not effortful cognitive processing (Adelhöfer et al, 2020;Larra et al, 2020).…”

Section: Effects Of Cardiac Timing and Threatening Stimuli On Respons...supporting

confidence: 90%

“…For instance, μ , indicating response speed, may be prolonged during cardiac systole (Edwards et al, 2007; Yang et al, 2017). Furthermore, τ was reported to be negatively associated with blood pressure elevation (Wawrzyniak et al, 2016) and vagal withdrawal (i.e., lower HRV levels) during cognitive tasks (Spangler et al, 2021), suggesting that increased task‐evoked cardiovascular activation is linked to lower τ .…”

Section: Introductionmentioning

confidence: 99%

“…The parameter τ, but not μ or σ, are larger among individuals with attention deficit hyperactivity disorder (ADHD) compared to non-ADHD controls, and was negatively correlated with the performance in executive functions tasks (Galloway-Long & Huang-Pollock, 2018;Karalunas et al, 2013;Unsworth, 2010). Moreover, vagally mediated heart rate variability (HRV), a correlate of prefrontal cortex (PFC) functioning, was inversely related to τ (Spangler et al, 2018(Spangler et al, , 2021(Spangler et al, , 2022. Because ex-Gaussian parameters are associated with different processing stages, cardiac timing may differentially influence those parameters.…”

Section: Introductionmentioning

confidence: 99%

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Cardiac timing and threatening stimuli influence response inhibition and ex‐Gaussian parameters of reaction time in a Go/No‐go task

et al. 2023

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Sensorimotor responses vary as a function of the cardiac cycle phase. These effects, known as cardiac cycle time effects, have been explained by the inhibition of cardiac afferent signals on information processing. However, the validity of cardiac cycle time effects is challenged by mixed findings. Factors such as current information processing and affective context may modulate cardiac cycle time effects and account for inconsistencies in the literature. The current study examines the influence of cardiac cycle time and threatening stimuli on two aspects of sensorimotor processing, response speed and inhibition. Thirty-four participants (M age = 19.35 years; 29 female) completed an auditory Go/No-go task in no face, neutral face, and fearful face conditions. Faces were presented at either cardiac diastole or systole. Participants' reaction times (RTs) during Go trials and failures in response inhibition during No-go trials were recorded. The ex-Gaussian model was fit to RT data in each condition deriving the parameters, mu (μ) and tau (τ), that indicate response speed and attentional lapses, respectively. Repeated measures ANOVA were used to analyze behavioral data. Results showed that cardiac systole prolonged μ but decreased τ, and that cardiac diastole reduced inhibition errors in the fearful face condition but not in other conditions. These findings indicate that cardiac timing differentially modulates sensory-perceptual and top-down attentional processes and cardiac timing interacts with threatening contexts to influence response inhibition. These results highlight the specificity of cardiac cycle time effects on sensorimotor processing.

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Section: Effects Of Cardiac Timing and Threatening Stimuli On Respons...supporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Cardiac timing and threatening stimuli influence response inhibition and ex‐Gaussian parameters of reaction time in a Go/No‐go task

et al. 2023

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“…Specifically, the Gaussian components of the RT data, μ and σ , are used to mirror automatic, lower-level sensory-perceptual processes, encompassing information encoding and motor preparation/execution [13] . Conversely, the exponential tail, represented by τ , is presumed to capture lapses in attention resulting from deficiencies in top-down attentional control [13] , [37] , [50] .…”

Section: Related Workmentioning

confidence: 99%

An attention-based approach for assessing the effectiveness of emotion-evoking in immersive environment

Liu,

Zhou,

2024

Heliyon

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“…Sensors are also used to measure heart rate variability (HRV). For example, recent literature describes an interplay between HRV and various psychological and physiological diseases ( 15 – 17 ).…”

Section: Introductionmentioning

confidence: 99%

Enhancing mHealth data collection applications with sensing capabilities

Karthan

Martin

Holl

et al. 2022

Front. Public Health

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Smart mobile devices such as smartphones or tablets have become an important factor for collecting data in complex health scenarios (e.g., psychological studies, medical trials), and are more and more replacing traditional pen-and-paper instruments. However, simply digitizing such instruments does not yet realize the full potential of mobile devices: most modern smartphones have a variety of di erent sensor technologies (e.g., microphone, GPS data, camera, ...) that can also provide valuable data and potentially valuable insights for the medical purpose or the researcher. In this context, a significant development e ort is required to integrate sensing capabilities into (existing) data collection applications. Developers may have to deal with platform-specific peculiarities (e.g., Android vs. iOS) or proprietary sensor data formats, resulting in unnecessary development e ort to support researchers with such digital solutions. Therefore, a cross-platform mobile data collection framework has been developed to extend existing data collection applications with sensor capabilities and address the aforementioned challenges in the process. This framework will enable researchers to collect additional information from participants and environment, increasing the amount of data collected and drawing new insights from existing data.

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Interplay between state anxiety, heart rate variability, and cognition: An ex-Gaussian analysis of response times

Cited by 10 publications

References 62 publications

Cardiac timing and threatening stimuli influence response inhibition and ex‐Gaussian parameters of reaction time in a Go/No‐go task

Cardiac timing and threatening stimuli influence response inhibition and ex‐Gaussian parameters of reaction time in a Go/No‐go task

An attention-based approach for assessing the effectiveness of emotion-evoking in immersive environment

Enhancing mHealth data collection applications with sensing capabilities

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