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Abstract The skin conductance (SC) and eye tracking data are two potential arousal-related psychophysiological signals that can serve as the interoceptive unconditioned response to aversive stimuli (e.g. electric shocks). The current research investigates the sensitivity of these signals in detecting mild electric shock by decoding the hidden arousal and interoceptive awareness (IA) states. While well-established frameworks exist to decode the arousal state from the SC signal, there is a lack of a systematic approach that decodes the IA state from pupillometry and eye gaze measurements. We extract the physiological-based features from eye tracking data to recover the IA-related neural activity. Employing a Bayesian filtering framework, we decode the IA state in fear conditioning and extinction experiments where mild electric shock is used. We independently decode the underlying arousal state using binary and marked point process (MPP) observations derived from concurrently collected SC data. Eight of 11 subjects present a significantly (P-value <0.001) higher IA state in trials that were always accompanied by electric shock (CS+US+) compared to trials that were never accompanied by electric shock (CS−). According to the decoded SC-based arousal state, only five (binary observation) and four (MPP observation) subjects present a significantly higher arousal state in CS+US+ trials than CS− trials. In conclusion, the decoded hidden brain state from eye tracking data better agrees with the presented mild stimuli. Tracking IA state from eye tracking data can lead to the development of contactless monitors for neuropsychiatric and neurodegenerative disorders.
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Unveiling productivity: The interplay of cognitive arousal and expressive typing in remote work
Cognitive Arousal, frequently elicited by environmental stressors that exceed personal coping resources, manifests in measurable physiological markers, notably in galvanic skin responses. This effect is prominent in cognitive tasks such as composition, where fluctuations in these biomarkers correlate with individual expressiveness. It is crucial to understand the nexus between cognitive arousal and expressiveness. However, there has not been a concrete study that investigates this inter-relation concurrently. Addressing this, we introduce an innovative methodology for simultaneous monitoring of these elements. Our strategy employs Bayesian analysis in a multi-state filtering format to dissect psychomotor performance (captured through typing speed), galvanic skin response or skin conductance (SC), and heart rate variability (HRV). This integrative analysis facilitates the quantification of expressive behavior and arousal states. At the core, we deploy a state-space model connecting one latent psychological arousal condition to neural activities impacting sweating (inferred through SC responses) and another latent state to expressive behavior during typing. These states are concurrently evaluated with model parameters using an expectation-maximization algorithms approach. Assessments using both computer-simulated data and experimental data substantiate the validity of our approach. Outcomes display distinguishable latent state patterns in expressive typing and arousal across different computer software used in office management, offering profound implications for Human-Computer Interaction (HCI) and productivity analysis. This research marks a significant advancement in decoding human productivity dynamics, with extensive repercussions for optimizing performance in telecommuting scenarios.
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- PAR ID:
- 10584613
- Editor(s):
- Battaglia, Simone
- Publisher / Repository:
- PLOS ONE
- Date Published:
- Journal Name:
- PLOS ONE
- Volume:
- 19
- Issue:
- 5
- ISSN:
- 1932-6203
- Page Range / eLocation ID:
- e0300786
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1371/journal.pone.0300786
