Dataset Information

Implicit emotion regulation in adolescent girls: An exploratory investigation of Hidden Markov Modeling and its neural correlates.

ABSTRACT: Numerous data demonstrate that distracting emotional stimuli cause behavioral slowing (i.e. emotional conflict) and that behavior dynamically adapts to such distractors. However, the cognitive and neural mechanisms that mediate these behavioral findings are poorly understood. Several theoretical models have been developed that attempt to explain these phenomena, but these models have not been directly tested on human behavior nor compared. A potential tool to overcome this limitation is Hidden Markov Modeling (HMM), which is a computational approach to modeling indirectly observed systems. Here, we administered an emotional Stroop task to a sample of healthy adolescent girls (N = 24) during fMRI and used HMM to implement theoretical behavioral models. We then compared the model fits and tested for neural representations of the hidden states of the most supported model. We found that a modified variant of the model posited by Mathews et al. (1998) was most concordant with observed behavior and that brain activity was related to the model-based hidden states. Particularly, while the valences of the stimuli themselves were encoded primarily in the ventral visual cortex, the model-based detection of threatening targets was associated with increased activity in the bilateral anterior insula, while task effort (i.e. adaptation) was associated with reduction in the activity of these areas. These findings suggest that emotional target detection and adaptation are accomplished partly through increases and decreases, respectively, in the perceived immediate relevance of threatening cues and also demonstrate the efficacy of using HMM to apply theoretical models to human behavior.

SUBMITTER: Steele JS

PROVIDER: S-EPMC5830311 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Implicit emotion regulation in adolescent girls: An exploratory investigation of Hidden Markov Modeling and its neural correlates.

Steele James S JS Bush Keith K Stowe Zachary N ZN James George A GA Smitherman Sonet S Kilts Clint D CD Cisler Josh J

PloS one 20180228 2

Numerous data demonstrate that distracting emotional stimuli cause behavioral slowing (i.e. emotional conflict) and that behavior dynamically adapts to such distractors. However, the cognitive and neural mechanisms that mediate these behavioral findings are poorly understood. Several theoretical models have been developed that attempt to explain these phenomena, but these models have not been directly tested on human behavior nor compared. A potential tool to overcome this limitation is Hidden M ...[more]

PMID: 29489856

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Project description:Immersive software tools are virtual environments designed to give their users an augmented view of real-world data and ways of manipulating that data. As virtual environments, every action users make while interacting with these tools can be carefully logged, as can the state of the software and the information it presents to the user, giving these actions context. This data provides a high-resolution lens through which dynamic cognitive and behavioral processes can be viewed. In this report, we describe new methods for the analysis and interpretation of such data, utilizing a novel implementation of the Beta Process Hidden Markov Model (BP-HMM) for analysis of software activity logs. We further report the results of a preliminary study designed to establish the validity of our modeling approach. A group of 20 participants were asked to play a simple computer game, instrumented to log every interaction with the interface. Participants had no previous experience with the game's functionality or rules, so the activity logs collected during their naïve interactions capture patterns of exploratory behavior and skill acquisition as they attempted to learn the rules of the game. Pre- and post-task questionnaires probed for self-reported styles of problem solving, as well as task engagement, difficulty, and workload. We jointly modeled the activity log sequences collected from all participants using the BP-HMM approach, identifying a global library of activity patterns representative of the collective behavior of all the participants. Analyses show systematic relationships between both pre- and post-task questionnaires, self-reported approaches to analytic problem solving, and metrics extracted from the BP-HMM decomposition. Overall, we find that this novel approach to decomposing unstructured behavioral data within software environments provides a sensible means for understanding how users learn to integrate software functionality for strategic task pursuit.

Project description:BackgroundDietary diversity, a qualitative measure of dietary intake, which reflects the variety of foods consumed has been recommended to assuage nutritional problems related to insufficient micronutrients, and food insecurity. To better understand the underlying factors for poor birth outcomes in Ghana, we assessed factors associated with dietary diversity among rural and urban pregnant adolescents in the Ashanti Region of Ghana.MethodsAs part of a larger longitudinal cohort of 416 pregnant adolescents, the FAO minimum dietary diversity for women index was used to determine the dietary diversity score (DDS) of the participants from a previous days' 24-hour dietary recall data. The household hunger scale (HHS) and lived poverty index (LPI) were used to determine hunger and socioeconomic status. Eating behavior and socio-demographic data were gathered using interviewer-administered questionnaires.ResultsThe mean age of the participants was 17.5 (±1.4) years with an MDD-W of 4.4 and 56% recording inadequate MDD score. More rural (63.6%) than urban dwellers (50.6%) had inadequate DDS (p = 0.008). Among all the multiple variables tests of associations on dietary diversity, only hunger status (p = 0.028) and both food aversion and poverty status (p = 0.003) had a significant effect on the adolescents' dietary diversity. Rural dwelling adolescents (AOR = 1.7, p = 0.035, 95% CI = 1.0-2.6) recorded higher odds for inadequate DD compared with the urban respondents. Pregnant adolescents with severe hunger had higher odds (Unadjusted OR = 1.9, p = 0.053, 95% CI 1.1-3.8) for inadequate dietary diversity compared with those with no hunger.ConclusionsInadequate DD is common among pregnant adolescents in this study and is associated with rural living, food insecurity, poverty, and food craving. Livelihood support for pregnant teenagers and nutrition education are recommended interventions to improve dietary quality and limit the consequences of poor dietary diversity.

Dataset Information

Implicit emotion regulation in adolescent girls: An exploratory investigation of Hidden Markov Modeling and its neural correlates.

Publications

Implicit emotion regulation in adolescent girls: An exploratory investigation of Hidden Markov Modeling and its neural correlates.

Similar Datasets

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