Multivariate pattern analysis of event-related potentials predicts the subjective relevance of everyday objects

Decoding the Narcissistic Brain

There is a substantial knowledge gap in the narcissism literature: Less than 1% of the nearly 12,000 articles on narcissism have addressed its neural basis. To help fill this gap, we asked whether the multifacetedness of narcissism could be decoded from spontaneous neural oscillations. We attempted to do so by applying a machine learning approach (multivariate pattern analysis) to the resting-state EEG data of 162 participants who also completed a comprehensive battery of narcissism scales assessing agentic, admirative, rivalrous, communal, and vulnerable forms. Consistent with the agency-communion model of narcissism, agentic and communal forms of grandiose narcissism were reflected in distinct, non-overlapping patterns of spontaneous neural oscillations. Furthermore, consistent with a narcissistic admiration and rivalry concept model of narcissism, we observed largely non-overlapping patterns of spontaneous neural oscillations for admirative and rivalrous forms of narcissism. Vulnerable narcissism was negatively associated with power across fast and slow wave frequency bands. Taken together, the results suggest that the diverse forms of narcissism can be reliably predicted from spontaneous neural oscillations. The findings contribute to the burgeoning field of personality neuroscience.

Self-reference and emotional reaction drive aesthetic judgment

Traditional philosophical inquiry, and more recently neuroscientific studies, have investigated the sources of artworks' aesthetic appeal. A substantial effort has been made to isolate the objective features contributing to aesthetic appreciation. While variables such as contrast or symmetry have been shown to robustly impact aesthetic judgment, they only account for a small portion of the intersubjective variability in aesthetic ratings. Recent multiprocess model of aesthetic appreciation could accommodate this finding by proposing that evaluative processes based on self-reference underpin the idiosyncrasy of aesthetic judgment. We tested this hypothesis in two behavioral studies, that were basically conceptual replications of our previous work, in which we took advantage of the self-reference effect on memory. We also tried to disentangle the role of self-reference and emotional reaction to artworks in guiding aesthetic judgments, by comparing an aesthetic judgment encoding condition to a self-reference condition (Study 1), and an emotional evaluation condition (Study 2). We show that artworks encoded in an aesthetic judgment condition exhibit a similar mnesic advantage compared to both the self-reference and the emotional evaluation encoding conditions. Moreover, retrospective emotional judgment correlates with both self-reference and aesthetic judgments ratings. These results suggest that a basic mechanism, appraisal of self-relevance, could ground aesthetic judgments.

The Time-Course of Food Representation in the Human Brain

Humans make decisions about food every day. The visual system provides important information that forms a basis for these food decisions. Although previous research has focused on visual object and category representations in the brain, it is still unclear how visually presented food is encoded by the brain. Here, we investigate the time-course of food representations in the brain. We used time-resolved multivariate analyses of electroencephalography (EEG) data, obtained from human participants (both sexes), to determine which food features are represented in the brain and whether focused attention is needed for this. We recorded EEG while participants engaged in two different tasks. In one task, the stimuli were task relevant, whereas in the other task, the stimuli were not task relevant. Our findings indicate that the brain can differentiate between food and nonfood items from ∼112 ms after the stimulus onset. The neural signal at later latencies contained information about food naturalness, how much the food was transformed, as well as the perceived caloric content. This information was present regardless of the task. Information about whether food is immediately ready to eat, however, was only present when the food was task relevant and presented at a slow presentation rate. Furthermore, the recorded brain activity correlated with the behavioral responses in an odd-item-out task. The fast representation of these food features, along with the finding that this information is used to guide food categorization decision-making, suggests that these features are important dimensions along which the representation of foods is organized.

EEG-based univariate and multivariate analyses reveal that multiple processes contribute to the production effect in recognition

The production effect (PE) is the finding that reading words aloud rather than silently during study leads to improved memory. We used electroencephalography (EEG) techniques to detect the contributions of recollection, familiarity, and attentional processes to the PE in recognition memory, using Chinese stimuli. During the study phase, participants encoded each list item aloud, silently, or by performing a non-unique aloud (control) task. During the test phase, they made remember/know/new recognition judgments. We recorded EEG data in both phases. The behavioral results replicated the typical pattern with English stimuli: Recognition was better in the aloud condition than in the silent (and control) condition, and this PE was due to enhanced recollection and familiarity. At study, the amplitude of the P3b ERP component was greater in the aloud than in the silent/control conditions, suggesting that reading aloud increases attention or preparatory processing during the intention phase. At test, the recollection-based LPC old/new effect was largest in the aloud condition; however, the familiarity-based FN400 old/new effect was equivalent between the aloud condition and the silent/control conditions. Only the LPC effect correlated with the behavioral effect. Moreover, multivariate pattern analysis (MVPA) showed that accurate classification of items as 'aloud' versus 'new' mainly occurred in the later period of the recognition response, consistent with the LPC old/new effect. Our findings suggest that the within-subject PE in recognition memory reflects enhanced attention and distinctiveness, rather than increased memory strength. More broadly, our findings suggest that encoding strategies such as production enhance recollection more than familiarity.

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.

Self-interest is intuitive during opportunity (in)equity: Evidence from multivariate pattern analysis of electroencephalography data

Fairness is a remarkable preference for human society, involving both outcome and opportunity equity. Most previous studies have explored whether fairness itself or self-interest is intuitive during outcome (in)equity. However, intuition during outcome (in)equity can be affected by both fairness level and actual payoff. Since opportunity (in)equity is only affected by the fairness level, we explored only intuition during fairness by measuring event-related potential responses to opportunity (in)equity. Participants played a social non-competitive two-person choice game with advantage opportunity inequity (AI), opportunity equity (OE), and disadvantage opportunity inequity (DI). The behavioral results suggested an opportunity inequity bias, with greater feelings of fairness and pleasantness during OE than during AI and DI. However, multivariate pattern analysis of the event-related potential (ERP) data suggested that AI, OE, and DI can be significantly distinguished from each other in relatively early windows overlapping with early positive negativity (EPN), and AI and DI can be significantly further distinguished during a relatively late window overlapping with late positive potential (LPP). Moreover, the conventional ERP analysis found that EPN amplitudes were more negative for AI than for OE and DI, as well as for OE than for DI, suggesting a pleasure bias for increased self-interest. LPP amplitudes were greater for DI than for AI and OE, suggesting enhanced sensitivity to DI. These results suggest that self-interest is intuitive during opportunity (in)equity.

Early EEG responses to pre-electoral survey items reflect political attitudes and predict voting behavior

Self-reports are conventionally used to measure political preferences, yet individuals may be unable or unwilling to report their political attitudes. Here, in 69 participants we compared implicit and explicit methods of political attitude assessment and focused our investigation on populist attitudes. Ahead of the 2019 European Parliament election, we recorded electroencephalography (EEG) from future voters while they completed a survey that measured levels of agreement on different political issues. An Implicit Association Test (IAT) was administered at the end of the recording session. Neural signals differed as a function of future vote for a populist or mainstream party and of whether survey items expressed populist or non-populist views. The combination of EEG responses and self-reported preferences predicted electoral choice better than traditional socio-demographic and ideological variables, while IAT scores were not a significant predictor. These findings suggest that measurements of brain activity can refine the assessment of socio-political attitudes, even when those attitudes are not based on traditional ideological divides.

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.

Research on the influence of job embeddedness on different initiative individuals

"How to improve individual initiative" has become an important subject facing the current researchers and practitioners. This study attempts to answer this question from the perspective of on-the-job embeddedness based on social cognitive theory, organization attachment theory and cognitive neural experiment，we revealed the differences in the effects of three dimensions of on-the-job embeddednesson individuals with different initiative by Event-related Potentials (ERPs) cognitive neural experiment. The experimental results showed that the effect on high-initiative individuals was in the descending order of organization fit, organization link, organization sacrifice; the effect on general-initiative individuals was in the descending order of organization link, organization fit, organization sacrifice; the effect on low-initiative individuals was in the descending order of organization sacrifice, organization link, organization fit. The ultimate goal is to put forward management strategies for different initiative individuals from the three dimensions embedded, promote their initiative level and actively participate in production activities.

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.

High-pass filtering artifacts in multivariate classification of neural time series data

BACKGROUND

Traditionally, EEG/MEG data are high-pass filtered and baseline-corrected to remove slow drifts. Minor deleterious effects of high-pass filtering in traditional time-series analysis have been well-documented, including temporal displacements. However, its effects on time-resolved multivariate pattern classification analyses (MVPA) are largely unknown.

NEW METHOD

To prevent potential displacement effects, we extend an alternative method of removing slow drift noise - robust detrending - with a procedure in which we mask out all cortical events from each trial. We refer to this method as trial-masked robust detrending.

RESULTS

In both real and simulated EEG data of a working memory experiment, we show that both high-pass filtering and standard robust detrending create artifacts that result in the displacement of multivariate patterns into activity silent periods, particularly apparent in temporal generalization analyses, and especially in combination with baseline correction. We show that trial-masked robust detrending is free from such displacements.

COMPARISON WITH EXISTING METHOD(S)

Temporal displacement may emerge even with modest filter cut-off settings such as 0.05 Hz, and even in regular robust detrending. However, trial-masked robust detrending results in artifact-free decoding without displacements. Baseline correction may unwittingly obfuscate spurious decoding effects and displace them to the rest of the trial.

CONCLUSIONS

Decoding analyses benefit from trial-masked robust detrending, without the unwanted side effects introduced by filtering or regular robust detrending. However, for sufficiently clean data sets and sufficiently strong signals, no filtering or detrending at all may work adequately. Implications for other types of data are discussed, followed by a number of recommendations.

Decoding personality trait measures from resting EEG: An exploratory report

Can personality be predicted from oscillatory patterns produced by the brain at rest? To date, relatively few studies using electroencephalography (EEG) have yielded consistent relations between personality trait measures and spectral power. Thus, new exploratory research may help develop targeted hypotheses about how neural processes associated with EEG activity may relate to personality differences. We used multivariate pattern analysis to decode personality scores (i.e., Big Five traits) from resting EEG frequency power spectra. Up to 8 minutes of EEG data was recorded per participant prior to completing an unrelated task (N = 168, M_age = 23.51, 57% female) and, in a subset of participants, after task completion (N = 96, M_age = 23.22, 52% female). In each recording, participants alternated between open and closed eyes. Linear support vector regression with 10-fold cross validation was performed using the power from 62 scalp electrodes within 1 Hz frequency bins from 1 to 30 Hz. One Big Five trait, agreeableness, could be decoded from EEG power ranging from 8 to 19 Hz, and this was consistent across all four recording periods. Neuroticism was decodable using data within the 3-6 Hz range, albeit less consistently. Posterior alpha power negatively correlated with agreeableness, whereas parietal beta power positively correlated with agreeableness. We suggest methods to draw from our results and develop targeted future hypotheses, such as linking to individual alpha frequency and incorporating self-reported emotional states. Our open dataset can be harnessed to reproduce results or investigate new research questions concerning the biological basis of personality.

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.

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.

From ERPs to MVPA Using the Amsterdam Decoding and Modeling Toolbox (ADAM)

In recent years, time-resolved multivariate pattern analysis (MVPA) has gained much popularity in the analysis of electroencephalography (EEG) and magnetoencephalography (MEG) data. However, MVPA may appear daunting to those who have been applying traditional analyses using event-related potentials (ERPs) or event-related fields (ERFs). To ease this transition, we recently developed the Amsterdam Decoding and Modeling (ADAM) toolbox in MATLAB. ADAM is an entry-level toolbox that allows a direct comparison of ERP/ERF results to MVPA results using any dataset in standard EEGLAB or Fieldtrip format. The toolbox performs and visualizes multiple-comparison corrected group decoding and forward encoding results in a variety of ways, such as classifier performance across time, temporal generalization (time-by-time) matrices of classifier performance, channel tuning functions (CTFs) and topographical maps of (forward-transformed) classifier weights. All analyses can be performed directly on raw data or can be preceded by a time-frequency decomposition of the data in which case the analyses are performed separately on different frequency bands. The figures ADAM produces are publication-ready. In the current manuscript, we provide a cookbook in which we apply a decoding analysis to a publicly available MEG/EEG dataset involving the perception of famous, non-famous and scrambled faces. The manuscript covers the steps involved in single subject analysis and shows how to perform and visualize a subsequent group-level statistical analysis. The processing pipeline covers computation and visualization of group ERPs, ERP difference waves, as well as MVPA decoding results. It ends with a comparison of the differences and similarities between EEG and MEG decoding results. The manuscript has a level of description that allows application of these analyses to any dataset in EEGLAB or Fieldtrip format.