Positive emotions and their upregulation increase willingness to consume healthy foods.

While highly relevant for everyday life, it is unclear whether experiencing incidental positive or negative emotional states, and active emotion regulation, influence the weighting of perceived taste and health in food choices. In Experiment 1, we examined two emotion regulation strategies, reappraisal and distraction, used to decrease negative emotions. Participants were cued to experience or decrease their emotional response for either neutral or negative incidental emotion-inducing images. They subsequently rated their willingness to consume foods, which varied in their taste and health attributes. Mixed-effects model analysis showed that compared to neutral, negative emotions decreased willingness to consume, regardless of perceived taste and health, but neither emotion regulation strategy had a significant effect. Experiment 2 used images inducing incidental positive emotions in combination with three emotion regulation strategies: reappraisal, distraction, and increasing positive emotions. Experiencing positive emotions generally increased willingness to consume, with stronger effects for tasty and healthy foods. Decreasing positive emotions via reappraisal decreased willingness to consume, particularly for healthy foods. Increasing positive emotion intensity further increased willingness to consume, with stronger effects for healthy foods. The results suggest that experiencing positive emotions increases desire particularly strongly for healthy foods, which can additionally be modulated via emotion regulation. This has important implications for designing health-related interventions targeting mood improvement.

Decoding continuous variables from event-related potential (ERP) data with linear support vector regression using the Decision Decoding Toolbox (DDTBOX).

Multivariate classification analysis for event-related potential (ERP) data is a powerful tool for predicting cognitive variables. However, classification is often restricted to categorical variables and under-utilises continuous data, such as response times, response force, or subjective ratings. An alternative approach is support vector regression (SVR), which uses single-trial data to predict continuous variables of interest. In this tutorial-style paper, we demonstrate how SVR is implemented in the Decision Decoding Toolbox (DDTBOX). To illustrate in more detail how results depend on specific toolbox settings and data features, we report results from two simulation studies resembling real EEG data, and one real ERP-data set, in which we predicted continuous variables across a range of analysis parameters. Across all studies, we demonstrate that SVR is effective for analysis windows ranging from 2 to 100 ms, and relatively unaffected by temporal averaging. Prediction is still successful when only a small number of channels encode true information, and the analysis is robust to temporal jittering of the relevant information in the signal. Our results show that SVR as implemented in DDTBOX can reliably predict continuous, more nuanced variables, which may not be well-captured by classification analysis. In sum, we demonstrate that linear SVR is a powerful tool for the investigation of single-trial EEG data in relation to continuous variables, and we provide practical guidance for users.

Decoding explicit and implicit representations of health and taste attributes of foods in the human brain.

Obesity has become a significant problem word-wide and is strongly linked to poor food choices. Even in healthy individuals, taste perceptions often drive dietary decisions more strongly than healthiness. This study tested whether health and taste representations can be directly decoded from brain activity, both when explicitly considered, and when implicitly processed for decision-making. We used multivariate support vector regression for event-related potentials (as measured by the electroencephalogram) to estimate a regression model predicting ratings of tastiness and healthiness for each participant, based on their neural activity occurring in the first second of food cue processing. In Experiment 1, 37 healthy participants viewed images of various foods and explicitly rated their tastiness and healthiness. In Experiment 2, 89 healthy participants completed a similar rating task, followed by an additional experimental phase, in which they indicated their desire to consume snack foods with no explicit instruction to consider tastiness or healthiness. In Experiment 1 both attributes could be decoded, with taste information being available earlier than health. In Experiment 2, both dimensions were also decodable, and their significant decoding preceded the decoding of decisions (i.e., desire to consume the food). However, in Experiment 2, health representations were decodable earlier than taste representations. These results suggest that health information is activated in the brain during the early stages of dietary decisions, which is promising for designing obesity interventions aimed at quickly activating health awareness.

General and specific graphic health warning labels reduce willingness to consume sugar-sweetened beverages.

Sugar-sweetened beverage (SSB) consumption is associated with obesity and other severe negative health consequences. The present study examined the effectiveness of two types of health warning labels (HWLs) in modulating dietary choices for SSBs: specific HWLs, presenting health consequences associated with consuming SSBs, and general HWLs, presenting health consequences of an unhealthy diet and obesity. While electroencephalography (EEG) was recorded, 63 participants completed a computer-based task in which they were first randomly allocated to view either SBB-specific, general, or non-warning control HWLs. They then viewed images of a range of SSB products, varying on perceived healthiness and tastiness, and rated their willingness to consume (WTC) each one. Linear mixed-effect model analyses revealed that general and specific HWLs both decreased WTC SSBs perceived as tasty, compared to the control condition. For general HWLs, this effect was reduced for SSBs perceived to be healthy, suggesting that specific HWLs may be more effective at reducing SSB consumption. The EEG data showed that SSBs considered unhealthy elicited greater N1 amplitudes, and tasty SSBs elicited greater late positive potential (LPP) amplitudes, possibly reflecting attentional allocation and craving responses, respectively. However, no strong differences between HWL types were found. Taken together, the results suggest that graphic HWLs, both general and specific, have the potential to reduce SSB consumption, but they do not strongly modulate craving-related neural responses to SSBs.

Neural patterns during anticipation predict emotion regulation success for reappraisal.

The ability to exert control over emotions, termed emotion regulation (ER), is vital for everyday functioning. ER success may be influenced by processes relating to the anticipation (prior to active regulation) and implementation (during active regulation) of ER strategy use. We investigated whether brain activity patterns recorded using electroencephalography (EEG) during the first second of anticipation and implementation of two ER strategies-distraction and reappraisal-were related to regulation success. Participants viewed negative images that evoked disgust and sadness. Before each image was presented, participants were cued to either passively view the image or decrease their emotional responses. ER success scores were calculated from subsequent self-reported disgust and sadness ratings. Using multivariate support vector regression, ER success scores were predicted from spatiotemporal patterns of event-related potentials during the first second of anticipation and implementation phases of each ER strategy. For both sadness and disgust, reappraisal success could be predicted during anticipation, while distraction success could be predicted during implementation. These findings suggest that early anticipatory cognitive processes are a key determinant of reappraisal success, but may not be similarly important for distraction. This may be because reappraisal is more cognitively demanding than distraction, requiring enhanced preparation of mental resources.