Distributed patterns of event-related potentials predict subsequent ratings of abstract stimulus attributes

Decoding in the Fourth Dimension: Classification of Temporal Patterns and Their Generalization Across Locations

Neuroimaging research has increasingly used decoding techniques, in which multivariate statistical methods identify patterns in neural data that allow the classification of experimental conditions or participant groups. Typically, the features used for decoding are spatial in nature, including voxel patterns and electrode locations. However, the strength of many neurophysiological recording techniques such as electroencephalography or magnetoencephalography is in their rich temporal, rather than spatial, content. The present report introduces the time-GAL toolbox, which implements a decoding method based on time information in electrophysiological recordings. The toolbox first quantifies the decodable information contained in neural time series. This information is then used in a subsequent step, generalization across location (GAL), which characterizes the relationship between sensor locations based on their ability to cross-decode. Two datasets are used to demonstrate the usage of the toolbox, involving (1) event-related potentials in response to affective pictures and (2) steady-state visual evoked potentials in response to aversively conditioned grating stimuli. In both cases, experimental conditions were successfully decoded based on the temporal features contained in the neural time series. Spatial cross-decoding occurred in regions known to be involved in visual and affective processing. We conclude that the approach implemented in the time-GAL toolbox holds promise for analyzing neural time series from a wide range of paradigms and measurement domains providing an assumption-free method to quantifying differences in temporal patterns of neural information processing and whether these patterns are shared across sensor locations.

Neural mechanisms of expert persuasion on willingness to pay for sugar

Introduction: Sugar consumption is associated with many negative health consequences. It is, therefore, important to understand what can effectively influence individuals to consume less sugar. We recently showed that a healthy eating call by a health expert can significantly decrease the willingness to pay (WTP) for sugar-containing food. Here, we investigate which aspects of neural responses to the same healthy eating call can predict the efficacy of expert persuasion.

Methods: Forty-five healthy participants performed two blocks of a bidding task, in which they had to bid on sugar-containing, sugar-free and non-edible products, while their electroencephalography (EEG) was recorded. In between the two blocks, they listened to a healthy eating call by a nutritionist emphasizing the risks of sugar consumption.

Results: We found that after listening to the healthy eating call, participants significantly decreased their WTP for sugar-containing products. Moreover, a higher intersubject correlation of EEG (a measure of engagement) during listening to the healthy eating call resulted in a larger decrease in WTP for sugar-containing food. Whether or not a participant’s valuation of a product was highly influenced by the healthy eating call could also be predicted by spatiotemporal patterns of EEG responses to the healthy eating call, using a machine learning classification model. Finally, the healthy eating call increased the amplitude of the P300 component of the visual event-related potential in response to sugar-containing food.

Disussion: Overall, our results shed light on the neural basis of expert persuasion and demonstrate that EEG is a powerful tool to design and assess health-related advertisements before they are released to the public.

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.

Multivariate pattern analysis of electroencephalography data reveals information predictive of charitable giving

Charitable donations are an altruistic behavior whereby individuals donate money or other resources to benefit others while the recipient is normally absent from the context. Several psychological factors have been shown to influence charitable donations, including a cost-benefit analysis, the motivation to engage in altruistic behavior, and the perceived psychological benefits of donation. Recent work has identified the ventral medial prefrontal cortex (MPFC) for assigning value to options in social decision making tasks, with other regions involved in empathy and emotion contributing input to the value computation (e.g. Hare et al., 2010; Hutcherson et al., 2015; Tusche et al., 2016). Most impressively, multivariate pattern analysis (MVPA) has been applied to fMRI data to predict donation behavior on a trial-by-trial basis from ventral MPFC activity (Hare et al., 2010) while identifying the contribution of emotional processing in other regions to the value computation (e.g. Tusche et al., 2016). MVPA of EEG data may be able to provide further insight into the timing and scalp topography of neural activity related to both value computation and emotional effects on donation behavior. We examined the effect of incidental emotional states and the perceived urgency of the charitable cause on donation behavior using support vector regression on EEG data to predict donation amount on a trial by trial basis. We used positive, negative, and neutral pictures to induce incidental emotional states in participants before they made donation decisions concerning two types of charities. One category of charity was oriented toward saving people from current suffering, and the other was to prevent future suffering. Behaviorally, subjects donated more money in a negative emotional state relative to other emotional states, and more money to alleviate current over future suffering. The data-driven multivariate pattern analysis revealed that the electrophysiological activity elicited by both emotion-priming pictures and charity cues could predict the variation in donation magnitude on a trial-by-trial basis.

Predicting participants' attitudes from patterns of event-related potentials during the reading of morally relevant statements - An MVPA investigation

Morality and language are hardly separable, given that morality-related aspects such as knowledge, emotions, or experiences are connected with language on different levels. One question that arises is: How rapidly do neural processes set in when processing statements that reflect moral value containing information? In the current study, participants read sentences about morally relevant statements (e.g., 'Wars are acceptable') and expressed their (dis)agreement with the statements while their electroencephalogram (EEG) was recorded. Multivariate pattern classification (MVPA) was used during language processing to predict the individual's response. Our results show that (1) the response ('yes' vs. 'no') could be predicted from 180 ms following the decision-relevant word (here acceptable), and (2) the attitude (pro vs. contra the topic) could be predicted from 170 ms following the topic word (here wars). We suggest that the successful MVPA classification is due to different brain activity patterns evoked by differences in activated mental representations (e.g. valence, arousal, etc.) depending on whether the attitude towards the topic is positive or negative and whether it is in accordance with the presented decisive word or not.

The Decision Decoding ToolBOX (DDTBOX) - A Multivariate Pattern Analysis Toolbox for Event-Related Potentials

In recent years, neuroimaging research in cognitive neuroscience has increasingly used multivariate pattern analysis (MVPA) to investigate higher cognitive functions. Here we present DDTBOX, an open-source MVPA toolbox for electroencephalography (EEG) data. DDTBOX runs under MATLAB and is well integrated with the EEGLAB/ERPLAB and Fieldtrip toolboxes (Delorme and Makeig 2004; Lopez-Calderon and Luck 2014; Oostenveld et al. 2011). It trains support vector machines (SVMs) on patterns of event-related potential (ERP) amplitude data, following or preceding an event of interest, for classification or regression of experimental variables. These amplitude patterns can be extracted across space/electrodes (spatial decoding), time (temporal decoding), or both (spatiotemporal decoding). DDTBOX can also extract SVM feature weights, generate empirical chance distributions based on shuffled-labels decoding for group-level statistical testing, provide estimates of the prevalence of decodable information in the population, and perform a variety of corrections for multiple comparisons. It also includes plotting functions for single subject and group results. DDTBOX complements conventional analyses of ERP components, as subtle multivariate patterns can be detected that would be overlooked in standard analyses. It further allows for a more explorative search for information when no ERP component is known to be specifically linked to a cognitive process of interest. In summary, DDTBOX is an easy-to-use and open-source toolbox that allows for characterising the time-course of information related to various perceptual and cognitive processes. It can be applied to data from a large number of experimental paradigms and could therefore be a valuable tool for the neuroimaging community.

Monetary feedback modulates performance and electrophysiological indices of belief updating in reward learning

Belief updating entails the incorporation of new information about the environment into internal models of the world. Bayesian inference is the statistically optimal strategy for performing belief updating in the presence of uncertainty. An important open question is whether the use of cognitive strategies that implement Bayesian inference is dependent upon motivational state and, if so, how this is reflected in electrophysiological signatures of belief updating in the brain. Here, we recorded the EEG of participants performing a simple reward learning task with both monetary and nonmonetary instructive feedback conditions. Our aim was to distinguish the influence of the rewarding properties of feedback on belief updating from the information content of the feedback itself. A Bayesian updating model allowed us to quantify different aspects of belief updating across trials, including the size of belief updates and the uncertainty of beliefs. Faster learning rates were observed in the monetary feedback condition compared to the instructive feedback condition, while belief updates were generally larger, and belief uncertainty smaller, with monetary compared to instructive feedback. Larger amplitudes in the monetary feedback condition were found for three ERP components: the P3a, the feedback-related negativity, and the late positive potential. These findings suggest that motivational state influences inference strategies in reward learning, and this is reflected in the electrophysiological correlates of belief updating.

Dorsolateral and medial prefrontal cortex mediate the influence of incidental priming on economic decision making in obesity

Obese individuals discount future rewards to a higher degree than lean individuals, which is generally considered disadvantageous. Moreover, their decisions are altered more easily by decision-irrelevant cues. Here, we investigated neural correlates of this phenomenon using functional MRI. We tested 30 lean and 26 obese human subjects on a primed delay discounting paradigm using gustatory and visual cues of positive, neutral and negative valence to bias their intertemporal preferences. We hypothesised that activation differences in reward-related and behavioural control areas, and changes in connectivity between these areas, would reflect the effect of these cues. Here, obese subjects were more susceptible to priming with negative gustatory cues towards delayed choices as opposed to lean subjects. This was related to lower activity in the left dorsolateral prefrontal cortex during priming. Modulation of functional connectivity between the dlPFC and the ventromedial PFC by the behavioural priming effect correlated negatively with BMI. This might indicate that default goals of obese individuals were different from those of lean participants, as the dlPFC has been suggested to be involved in internal goal pursuit. The present results further our understanding of the role of the PFC in decision-making and might inform future weight-management approaches based on non-invasive brain stimulation.

The neural encoding of information prediction errors during non-instrumental information seeking

In a dynamic world, accurate beliefs about the environment are vital for survival, and individuals should therefore regularly seek out new information with which to update their beliefs. This aspect of behaviour is not well captured by standard theories of decision making, and the neural mechanisms of information seeking remain unclear. One recent theory posits that valuation of information results from representation of informative stimuli within canonical neural reward-processing circuits, even if that information lacks instrumental use. We investigated this question by recording EEG from twenty-three human participants performing a non-instrumental information-seeking task. In this task, participants could pay a monetary cost to receive advance information about the likelihood of receiving reward in a lottery at the end of each trial. Behavioural results showed that participants were willing to incur considerable monetary costs to acquire early but non-instrumental information. Analysis of the event-related potential elicited by informative cues revealed that the feedback-related negativity independently encoded both an information prediction error and a reward prediction error. These findings are consistent with the hypothesis that information seeking results from processing of information within neural reward circuits, and suggests that information may represent a distinct dimension of valuation in decision making under uncertainty.

Categorization for Faces and Tools-Two Classes of Objects Shaped by Different Experience-Differs in Processing Timing, Brain Areas Involved, and Repetition Effects

The brain mechanisms that integrate the separate features of sensory input into a meaningful percept depend upon the prior experience of interaction with the object and differ between categories of objects. Recent studies using representational similarity analysis (RSA) have characterized either the spatial patterns of brain activity for different categories of objects or described how category structure in neuronal representations emerges in time, but never simultaneously. Here we applied a novel, region-based, multivariate pattern classification approach in combination with RSA to magnetoencephalography data to extract activity associated with qualitatively distinct processing stages of visual perception. We asked participants to name what they see whilst viewing bitonal visual stimuli of two categories predominantly shaped by either value-dependent or sensorimotor experience, namely faces and tools, and meaningless images. We aimed to disambiguate the spatiotemporal patterns of brain activity between the meaningful categories and determine which differences in their processing were attributable to either perceptual categorization per se, or later-stage mentalizing-related processes. We have extracted three stages of cortical activity corresponding to low-level processing, category-specific feature binding, and supra-categorical processing. All face-specific spatiotemporal patterns were associated with bilateral activation of ventral occipito-temporal areas during the feature binding stage at 140–170 ms. The tool-specific activity was found both within the categorization stage and in a later period not thought to be associated with binding processes. The tool-specific binding-related activity was detected within a 210–220 ms window and was located to the intraparietal sulcus of the left hemisphere. Brain activity common for both meaningful categories started at 250 ms and included widely distributed assemblies within parietal, temporal, and prefrontal regions. Furthermore, we hypothesized and tested whether activity within face and tool-specific binding-related patterns would demonstrate oppositely acting effects following procedural perceptual learning. We found that activity in the ventral, face-specific network increased following the stimuli repetition. In contrast, tool processing in the dorsal network adapted by reducing its activity over the repetition period. Altogether, we have demonstrated that activity associated with visual processing of faces and tools during the categorization stage differ in processing timing, brain areas involved, and in their dynamics underlying stimuli learning.

The real deal: Willingness-to-pay and satiety expectations are greater for real foods versus their images

Laboratory studies of human dietary choice have relied on computerized two-dimensional (2D) images as stimuli, whereas in everyday life, consumers make decisions in the context of real foods that have actual caloric content and afford grasping and consumption. Surprisingly, few studies have compared whether real foods are valued more than 2D images of foods, and in the studies that have, differences in the stimuli and testing conditions could have resulted in inflated bids for the real foods. Moreover, although the caloric content of food images has been shown to influence valuation, no studies to date have investigated whether 'real food exposure effects' on valuation reflect greater sensitivity to the caloric content of real foods versus images. Here, we compared willingness-to-pay (WTP) for, and expectations about satiety after consuming, everyday snack foods that were displayed as real foods versus 2D images. Critically, our 2D images were matched closely to the real foods for size, background, illumination, and apparent distance, and trial presentation and stimulus timing were identical across conditions. We used linear mixed effects modeling to determine whether effects of display format were modulated by food preference and the caloric content of the foods. Compared to food images, observers were willing to pay 6.62% more for (Experiment 1) and believed that they would feel more satiated after consuming (Experiment 2), foods displayed as real objects. Moreover, these effects appeared to be consistent across food preference, caloric content, as well as observers' estimates of the caloric content of the foods. Together, our results confirm that consumers' perception and valuation of everyday foods is influenced by the format in which they are displayed. Our findings raise important new insights into the factors that shape dietary choice in real-world contexts and highlight potential avenues for improving public health approaches to diet and obesity.

Incidental rewarding cues influence economic decisions in people with obesity

Recent research suggests that obesity is linked to prominent alterations in learning and decision-making. This general difference may also underlie the preference for immediately consumable, highly palatable but unhealthy and high-calorie foods. Such poor food-related inter-temporal decision-making can explain weight gain; however, it is not yet clear whether this deficit can be generalized to other domains of inter-temporal decision-making, for example financial decisions. Further, little is known about the stability of decision-making behavior in obesity, especially in the presence of rewarding cues. To answer these questions, obese and lean participants (n = 52) completed two sessions of a novel priming paradigm including a computerized monetary delay discounting task. In the first session, general differences between groups in financial delay discounting were measured. In the second session, we tested the general stability of discount rates. Additionally, participants were primed by affective visual cues of different contextual categories before making financial decisions. We found that the obese group showed stronger discounting of future monetary rewards than the lean group, but groups did not differ in their general stability between sessions nor in their sensitivity toward changes in reward magnitude. In the obese group, a fast decrease of subjective value over time was directly related to a higher tendency for opportunistic eating. Obese in contrast to lean people were primed by the affective cues, showing a sex-specific pattern of priming direction. Our findings demonstrate that environments rich of cues, aiming at inducing unhealthy consumer decisions, can be highly detrimental for obese people. It also underscores that obesity is not merely a medical condition but has a strong cognitive component, meaning that current dietary and medical treatment strategies may fall too short.

The use of neurodiagnostic technologies in the 21st century neuroscientific revolution

Neuroscience is fascinating, mysterious, and truly medicine's "final frontier" but deciphering its marvels has historically been inhibited by its sheer complexity. The recent escalation of global neuroscientific endeavors and vast financial backing from governments, foundations, and industries, however are changing this perspective. The sequencing of the human genome, development of innovative tools for mapping neuronal connectivities, and enhanced resolution capabilities of imaging techniques have made landmark contributions toward advancing neurotechnologies. Nations all around the world have initiated and launched brain mapping projects on such a profound and financially immense scale that research in 2015 and beyond are highly anticipated to revolutionize medicine and our interaction with the technological world. Although neurodiagnostic technology is not the vanguard of research interest in the scientific community, it will certainly ride the coattails of these new neuroscientific endeavors. And, in turn, these advancements will greatly impact how we diagnose, treat, and care for our patients in the future. Therefore, the purpose of this article is not only to introduce current neuroscientific enterprises, but to also explore some of the most interesting and instrumental findings using neurodiagnostic technology over the past year.