How bad becomes good: A neurocomputational model of affect-informed choice.

People often draw on their current affective experience to inform their decisions, yet little is known about the underlying mechanisms of this process. Understanding them has important implications for many big questions in both the affective and decision sciences. Do the same neural circuits that generate affect generate value? What differentiates people who have greater contextual flexibility in their reliance on affect? Do affective choices invoke processes that are distinct from less affective choices? To investigate these questions, we developed a neurocomputational model of affect-informed choice, in which people convert subjective affect into context-sensitive decision value through a process of weighted evidence accumulation.

Recruitment of dlPFC during dietary self-regulation predicts the transience of regulatory effects.

Recent work on the cognitive regulation of dietary decision-making suggests that regulation can alter both the choices that people make in the moment and longer-lasting preferences. However, it is unclear what mechanisms lead to temporary or lingering changes. To address this question, we used fMRI during a task employing the cognitive regulation of food choice and assessed changes in food preference from baseline to post-regulation.

Alpha oscillations and event-related potentials reflect distinct dynamics of attribute construction and evidence accumulation in dietary decision making.

How does regulatory focus alter attribute value construction (AVC) and evidence accumulation (EA)? We recorded electroencephalogram during food choices while participants responded naturally or regulated their choices by attending to health attributes or decreasing attention to taste attributes. Using a drift diffusion model, we predicted the time course of neural signals associated with AVC and EA. Results suggested that event-related potentials (ERPs) correlated with the time course of model-predicted taste-attribute signals, with no modulation by regulation.

Beta oscillations following performance feedback predict subsequent recall of task-relevant information.

Reward delivery in reinforcement learning tasks elicits increased beta power in the human EEG over frontal areas of the scalp but it is unclear whether these 20-30 Hz oscillations directly facilitate reward learning. We previously proposed that frontal beta is not specific to reward processing but rather reflects the role of prefrontal cortex in maintaining and transferring task-related information to other brain areas. To test this proposal, we had subjects perform a reinforcement learning task followed by a memory recall task in which subjects were asked to recall stimuli associated either with reward feedback (Reward Recall condition) or error feedback (Error Recall condition).

Effect of Acupuncture on Pregnancy-Related Insomnia and Melatonin: A Single-Blinded, Randomized, Placebo-Controlled Trial.

Objective: The aim of the current study is to evaluate the efficacy and safety of acupuncture on sleep quality and overnight melatonin secretion, measured as urinary 6-sulfatoxymelatonin, in pregnant women.

Patients And Methods: This randomized, parallel, single-blinded (participant), controlled trial was conducted on 72 pregnant women with insomnia. Study participants were randomly assigned to either the intervention, 10 sessions of acupuncture treatment over a 3-week period, or control group by block randomization (1:1).

Altered EEG alpha and theta oscillations characterize apathy in Parkinson's disease during incentivized movement.

Apathy is a common non-motor symptom of Parkinson's disease (PD) that is difficult to quantify and poorly understood. Some studies have used incentivized motor tasks to assess apathy, as the condition is often associated with a reduction in motivated behavior. Normally event-related desynchronization, a reduction of power in specific frequency bands, is observed in the motor cortex during the peri-movement period.

Reward-based contextual learning supported by anterior cingulate cortex.

The anterior cingulate cortex (ACC) is commonly associated with cognitive control and decision making, but its specific function is highly debated. To explore a recent theory that the ACC learns the reward values of task contexts (Holroyd & McClure in Psychological Review, 122, 54-83, 2015; Holroyd & Yeung in Trends in Cognitive Sciences, 16, 122-128, 2012), we recorded the event-related brain potentials (ERPs) from participants as they played a novel gambling task. The participants were first required to select from among three games in one "virtual casino," and subsequently they were required to select from among three different games in a different virtual casino; unbeknownst to them, the payoffs for the games were higher in one casino than in the other.

Reward feedback stimuli elicit high-beta EEG oscillations in human dorsolateral prefrontal cortex.

Reward-related feedback stimuli have been observed to elicit a burst of power in the beta frequency range over frontal areas of the human scalp. Recent discussions have suggested possible neural sources for this activity but there is a paucity of empirical evidence on the question. Here we recorded EEG from participants while they navigated a virtual T-maze to find monetary rewards.

Sensitivity of frontal beta oscillations to reward valence but not probability.

Reward feedback elicits a brief increase in power in the high-beta frequency range of the human electroencephalogram (EEG) over frontal areas of the scalp, but the functional role of this oscillatory activity remains unclear. An observed sensitivity to reward expectation (HajiHosseini, Rodríguez-Fornells, and Marco-Pallarés, 2012; [2]) suggests that reward-related beta may index a reward prediction error (RPE) signal for reinforcement learning. To investigate this possibility we reanalyzed EEG data from two prior experiments that revealed RPEs in the human event-related brain potential (Holroyd and Krigolson, 2007 [12]; Holroydet al.

Beta oscillations and reward processing: Coupling oscillatory activity and hemodynamic responses.

Diverse cortical and subcortical regions are synergically engaged during reward processing. Previous studies using time-frequency decomposition of Electroencephalography (EEG) data have revealed an increase of mid-frontal beta oscillatory activity (BOA) after reward delivery, which could be a potential mechanism in the coordination of the different areas engaged during reward processing. In order to evaluate this hypothesis, twenty subjects performed a monetary gambling paradigm in two separate sessions (EEG and fMRI).

Frontal midline theta and N200 amplitude reflect complementary information about expectancy and outcome evaluation.

Feedback ERN (fERN) and frontal midline theta have both been proposed to index a dopamine-like reinforcement learning signal in anterior cingulate cortex (ACC). We investigated these proposals by comparing fERN amplitude and theta power with respect to their sensitivities to outcome valence and probability in a previously collected EEG dataset. Bayesian model comparison revealed a dissociation between the two measures, with fERN amplitude mainly sensitive to valence and theta power mainly sensitive to probability.

The role of beta-gamma oscillations in unexpected rewards processing.

Reward processing in humans is carried out by an extensive fronto-subcortical network that might be coordinated by fast oscillatory electrical activity. Previous studies have identified an increase in beta-gamma oscillatory activity after the processing of positive feedback stimuli but the functional role of this electroencephalographic (EEG) correlate remains unclear. In the present study, we used event-related brain potentials (ERPs) and trial-by-trial wavelet-based time-frequency analysis of the EEG signal to investigate the effects of expectancy and magnitude of positive and negative feedbacks associated with monetary gains and losses in a gambling task.