Electrophysiological indexes of option characteristic processing

Lin, Yongling; Duan, Lian; Xu, Pengfei; Xin-Ying, LI; Gu, Ruolei; Luo, Yuejia

doi:10.1111/psyp.13403

Cited by 9 publications

(3 citation statements)

References 75 publications

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“…The employment of EEG signals in decision-making processes under uncertainty has largely concentrated on eventrelated potential (ERP) and spectral features at the sensor level [50][51][52][53]. In our study, the sensor level results reveal greater neural responses in multiple brain regions during the second half trials compared to the first half, and similarly, during not-asked trials as opposed to asked trials (Figure 5).…”

Section: Representing Uncertainties At the Sensor Levelmentioning

confidence: 62%

The Neural Correlates of Ambiguity and Risk in Human Decision-Making under an Active Inference Framework

Zhang,

Tian,

Liu

et al. 2023

Preprint

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Active inference integrates perception, decision-making, and learning into a united theoretical framework, providing an efficient way to trade off exploration and utilization by minimizing (expected) free energy. In this study, we asked how the brain represents values, uncertainty, and resolves the uncertainty under the active inference framework in the exploration-exploitation trade-off. 25 participants performed a contextual two-step two-armed bandit task, with electroencephalogram (EEG) recordings. By comparing the fitting results from the active inference and reinforcement learning model, we show that active inference can better capture the exploration instinct of humans, which helps resolve the uncertainty of the environment. The EEG sensor-level results show that the activity in the frontal, central, and parietal regions is associated with uncertainty, while activity in the frontal and central brain regions is associated with risk. The EEG source-level results indicate that the expected free energy is encoded in the lateral occipital cortex and the uncertainty in the middle temporal pole. Our study dissociates the expected free energy and the uncertainty in active inference theory and their neural correlates, suggesting the reliability of active inference in characterizing cognitive processes of human decisions. It provides behavioral and neural evidence of active inference in decision processes and insights into the neural mechanism of human decision under different kinds of uncertainty.

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Section: Representing Uncertainties At the Sensor Levelmentioning

confidence: 62%

The Neural Correlates of Ambiguity and Risk in Human Decision-Making under an Active Inference Framework

Zhang,

Tian,

Liu

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…However, the draw outcome of each trial would not be revealed until the participants had completed all the required tasks for the experiment to avoid the potential influence of the prior outcome on subsequent decision-making ( Zhang et al. , 2013 ; Lin et al. , 2019 ).…”

Section: Methodsmentioning

confidence: 99%

The hierarchical sensitivity to social misalignment during decision-making under uncertainty

Lin

Luan

et al. 2021

Social Cognitive and Affective Neuroscience

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Social misalignment occurs when a person’s attitudes and opinions deviate from those of others. We investigated how individuals react to social misalignment in risky (outcome probabilities are known) or ambiguous (outcome probabilities are unknown) decision contexts. During each trial, participants played a forced-choice gamble, and they observed the decisions of four other players after they made a tentative decision, followed by an opportunity to keep or change their initial decision. Behavioral and event-related potential data were collected. Behaviorally, the stronger the participants’ initial preference, the less likely they were to switch their decisions; whereas the more their decisions were misaligned with the majority, the more likely they were to switch. Electrophysiological results showed a hierarchical processing pattern of social misalignment. Misalignment was first detected binarily (i.e., match/mismatch) at an early stage, as indexed by the N1 component. During the second stage, participants became sensitive to low levels of misalignment, which were indexed by the feedback-related negativity. The degree of social misalignment was processed in greater detail, as indexed by the P3 component. Moreover, such hierarchical neural sensitivity is generalizable across different decision contexts (i.e., risky and ambiguous). These findings demonstrate a fine-grained neural sensitivity to social misalignment during decision-making under uncertainty.

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“…All the ROIs were selected based on whole-brain activation elicited by the framing effect (Table 1). For the positive effect of frame, the left amygdala (-24, -4, -10) was selected; for the negative effect of frame, the right dACC (14,20,32), left AI cortex (-40, 10, 2), and right AI cortex (36,18,10) were selected. All ROIs were extracted as 5 mm-radius spheres.…”

Section: Imaging Data Analysismentioning

confidence: 99%

Dysfunction of the motivational brain

Xu¹

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Adaptive behaviors are fundamental to an individuals' survival and development. However, making optimal decisions to approach or avoid particular goals are generally difficult tasks. These decision-making processes are directed by motivational systems, which rely on the adequate functioning of multiple brain systems responsible for integrated bottom-up sensory input and topdown control processing of affective and cognitive information. While approach and avoidance comprise dual components of motivational systems, dysfunctions of either one or both components have been proposed as core impairments underpinning distinct maladaptive goal-directed and psychopathological behaviors in different psychiatric disorders such as mood disorder, anxiety disorders and schizophrenia.To investigate the neurocognitive deficits of motivational system underlying the maladaptive goal-directed behaviors in psychiatry disorders, we examine in the current thesis whether abnormally low approach motivation in schizophrenia could be underpinning the 'negative' amotivational symptoms, and whether abnormally high avoidant motivation in anxiety that could underpin defensive behavior. Chapter 2 and Chapter 3 examine the modulations of topdown/bottom-up systems on approach behaviors in schizophrenia. We first systematically integrate previous neuroimaging findings on reward processing in schizophrenia based on a coordinate-based meta-analysis (Chapter 2), which identified a set of brain regions that is consistently dysfunctional across different decision-making phases in schizophrenia. Next, we examine the dopaminergic brain system in schizophrenia in relation to social amotivation by using resting state functional magnetic resonance imaging (Chapter 3). Chapter 3 reports on a negative association between intrinsic mesocorticolimbic connectivity and social amotivation in schizophrenia. Converging results from these experiments suggest that hypo-active dopaminergic brain areas and their weakened connections with brain areas related to top-down control may contribute to amotivation and maladaptive approach behaviors in schizophrenia.Chapter 4 and Chapter 5 examine the modulations of top-down/bottom-up systems on avoidance behaviors in anxiety. By manipulating emotional context of risk decision making, we examined the neural basis of risk preference in trait anxiety in the third experimental chapter II (Chapter 4). Results showed that risk aversive behaviors in anxious individuals can be modulated by an emotional frame/context, which was found to be underpinned by a hyperactive amygdala centric 'emotional' system and a hypoactive analytic system in the dorsal anterior cingulate cortex.Chapter 5 reports on further investigation of what drives the risk avoidant behavior reported in Chapter 4. By experimentally dissociating subjective aversion to risk from aversion to loss, it was shown that it is loss aversion rather than risk aversion dominantly contributes to the irrational/emotional behaviors in anxiety. This was found to be accompanied by an in...

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Electrophysiological indexes of option characteristic processing

Cited by 9 publications

References 75 publications

The Neural Correlates of Ambiguity and Risk in Human Decision-Making under an Active Inference Framework

The Neural Correlates of Ambiguity and Risk in Human Decision-Making under an Active Inference Framework

The hierarchical sensitivity to social misalignment during decision-making under uncertainty

Dysfunction of the motivational brain

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