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

Date Published: February 28, 2018

Publisher: Public Library of Science

Author(s): James S. Steele, Keith Bush, Zachary N. Stowe, George A. James, Sonet Smitherman, Clint D. Kilts, Josh Cisler, Marcus A. Gray.

http://doi.org/10.1371/journal.pone.0192318

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.

Partial Text

Emotions are an important mechanism facilitating behavioral responses to salient and goal-related environmental cues [1], and the ability to regulate one’s emotions is an important determinant of health and well-being [2]. Emotions are multisystem phenomenon and regulation may be imposed at multiple points, including situation selection, attentional control, and response manipulation [3]. Tasks probing the automatic attentional control over salient stimuli (i.e. “implicit emotion regulation”), have been particularly successful probes for elucidating psychological and neurobiological aspects of emotion regulation [4, 5] and in characterizing abnormalities in the processes in patients with mood and anxiety disorders [6, 7]. However, while decades of research has shown that emotional stimuli alter behavioral responses, the exact mechanisms mediating the detection and regulation of conflicting or distracting emotional stimuli have not been clearly established. Delineating these mechanisms would have clear implications for understanding affective disorders characterized by deficits in emotion regulation ability. Towards this goal, we present here a novel empirical analysis of existing theoretical models of implicit emotion regulation.

The study of automatic biases in cognitive and emotional processing has a long history of using models to formulate and test hypotheses [19, 21, 29, 30]. However, these models have only been indirectly tested through the use of simulations, which test for modeled behavior that corresponds with human behavior. Here we sought to use Hidden Markov Modeling to test previously hypothesized models of implicit emotion regulation on actual human data by first comparing the models and then testing for neural correlates of the model which provided the best explanation of the data.

 

Source:

http://doi.org/10.1371/journal.pone.0192318

 

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