Low alpha power (7.5-9.5 Hz) changes during positive and negative affective learning

Everhart, D. Erik; Demaree, Heath A.

doi:10.3758/cabn.3.1.39

Cited by 17 publications

(5 citation statements)

References 26 publications

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“…Focusing on the Alpha-emotions relation, past studies are not consistent. For instance, some authors reported increased Alpha power for emotional compared with neutral stimuli both in anterior and posterior regions ( Aftanas et al, 2002 , 2004 ), while others found Alpha decreasing during emotional conditions, both in anterior and posterior sites ( Everhart and Demaree, 2003 ; Sarlo et al, 2005 ; de Cesarei and Codispoti, 2011 ; Schubring and Schupp, 2019 ). Notably, some studies did not find any relation between Alpha rhythm and emotional processing ( Müller et al, 1999 ; Balconi and Lucchiari, 2006 ).…”

Section: Introductionmentioning

confidence: 99%

Comparison of Slides and Video Clips as Different Methods for Inducing Emotions: An Electroencephalographic Alpha Modulation Study

Romeo

Fusina

Semenzato

et al. 2022

Front. Hum. Neurosci.

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Films, compared with emotional static pictures, represent true-to-life dynamic stimuli that are both ecological and effective in inducing an emotional response given the involvement of multimodal stimulation (i.e., visual and auditory systems). We hypothesized that a direct comparison between the two methods would have shown greater efficacy of movies, compared to standardized slides, in eliciting emotions at both subjective and neurophysiological levels. To this end, we compared these two methods of emotional stimulation in a group of 40 young adults (20 females). Electroencephalographic (EEG) Alpha rhythm (8–12 Hz) was recorded from 64 scalp sites while participants watched (in counterbalanced order across participants) two separate blocks of 45 slides and 45 clips. Each block included three groups of 15 validated stimuli classified as Erotic, Neutral and Fear content. Greater self-perceived arousal was found after the presentation of Fear and Erotic video clips compared with the same slide categories. sLORETA analysis showed a different lateralization pattern: slides induced decreased Alpha power (greater activation) in the left secondary visual area (Brodmann Area, BA, 18) to Erotic and Fear compared with the Neutral stimuli. Instead, video clips elicited reduced Alpha in the homologous right secondary visual area (BA 18) again to both Erotic and Fear contents compared with Neutral ones. Comparison of emotional stimuli showed smaller Alpha power to Erotic than to Fear stimuli in the left precuneus/posterior cingulate cortex (BA 7/31) for the slide condition, and in the left superior parietal lobule (BA 7) for the clip condition. This result matched the parallel analysis of the overlapped Mu rhythm (corresponding to the upper Alpha band) and can be interpreted as Mu/Alpha EEG suppression elicited by greater motor action tendency to Erotic (approach motivation) compared to Fear (withdrawal motivation) stimuli. Correlation analysis found lower Alpha in the left middle temporal gyrus (BA 21) associated with greater pleasantness to Erotic slides (r38 = –0.62, p = 0.009), whereas lower Alpha in the right supramarginal/angular gyrus (BA 40/39) was associated with greater pleasantness to Neutral clips (r38 = –0.69, p = 0.012). Results point to stronger emotion elicitation of movies vs. slides, but also to a specific involvement of the two hemispheres during emotional processing of slides vs. video clips, with a shift from the left to the right associative visual areas.

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Section: Introductionmentioning

confidence: 99%

Comparison of Slides and Video Clips as Different Methods for Inducing Emotions: An Electroencephalographic Alpha Modulation Study

Romeo

Fusina

Semenzato

et al. 2022

Front. Hum. Neurosci.

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“…These include learning psychology (e.g., Martin & Levey, 1978), social psychology (e.g., Olson & Fazio, 2001;Walther, 2002), consumer science (e.g., Allen & Janiszewski, 1989;Stuart, Shimp, & Engle, 1987), emotion research (e.g., Mallan & Lipp, 2007;Niedenthal, 1990), neuroscience (Coppens et al, 2006;Everhart & Demaree, 2003), nutrition research (e.g., conditioned taste aversion learning; Bernstein & Webster, 1980), and clinical psychology (e.g., fear conditioning; Olatunji, Lohr, Sawchuk, & Westendorf, 2005; for reviews on EC, see De Houwer et al, 2001;Field, 2005; and De Houwer, in press).…”

Section: A Short History Of Evaluative Conditioning Research and Debatesmentioning

confidence: 99%

Evaluative conditioning in humans: A meta-analysis.

Hofmann

Houwer

Perugini

et al. 2010

Psychological Bulletin

816

755

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This article presents a meta-analysis of research on evaluative conditioning (EC), defined as a change in the liking of a stimulus (conditioned stimulus; CS) that results from pairing that stimulus with other positive or negative stimuli (unconditioned stimulus; US). Across a total of 214 studies included in the main sample, the mean EC effect was d ϭ .52, with a 95% confidence interval of .466 -.582. As estimated from a random-effects model, about 70% of the variance in effect sizes were attributable to true systematic variation rather than sampling error. Moderator analyses were conducted to partially explain this variation, both as a function of concrete aspects of the procedural implementation and as a function of the abstract aspects of the relation between CS and US. Among a range of other findings, EC effects were stronger for high than for low contingency awareness, for supraliminal than for subliminal US presentation, for postacquisition than for postextinction effects, and for self-report than for implicit measures. These findings are discussed with regard to the procedural boundary conditions of EC and theoretical accounts about the mental processes underlying EC.Keywords: evaluative conditioning, affective learning, attitude learning, associative learning, propositional learning One of the most influential ideas in psychology is that human behavior is, to a large extent, governed by likes and dislikes (Allport, 1935;Martin & Levey, 1978). For instance, people prefer the company of people they like and try to avoid those they do not like; people buy and consume products they like rather than those they dislike; and they vote for and support politicians and ideas that they find sympathetic rather than repelling. Furthermore, preferences influence attention, memory, and judgments and form the basis of our emotional life (Fox, 2009). Given the pervasive impact of preferences on behavior, it is vital for our discipline to understand how preferences are formed and how they can be influenced. Although some likes and dislikes may be genetically determined (Poulton & Menzies, 2002), the vast majority of our preferences are learned rather than innate (Rozin & Millman, 1987). But precisely how humans acquire their likes and dislikes continues to be the subject of vigorous debate (Rozin, 1982;De Houwer, Thomas, & Baeyens, 2001).The present article provides a meta-analysis of research on one possible manner in which likes and dislikes can be learned: evaluative conditioning (EC), which may be best defined as an effect that is attributed to a particular core procedure. Specifically, EC refers to a change in the valence of a stimulus (the effect) that is due to the pairing of that stimulus with another positive or negative stimulus (the procedure) (De Houwer, 2007a;De Houwer et al., 2001). The first stimulus is often referred to as the conditioned stimulus (CS), and the second stimulus is often referred to as the unconditioned stimulus (US). Typically, a CS becomes more positive when it has been paired with a posit...

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“…Some studies have provided evidence that narrowly defined alpha sub-bands exhibit psychologically and topographically distinct changes in task-induced activity (Gevins et al, 1997; Klimesch, 1999; Niedermeyer, 2005; Nunez et al, 2001). Likewise, several studies have suggested that alpha-low is more sensitive than alpha-high to experimental manipulations of mood (state affect; Crawford, Clarke, & Kitner-Triolo, 1996; Davidson, Marshall et al, 2000; Everhart & Demaree, 2003; Everhart, Demaree, & Wuensch, 2003; Wacker et al, 2003). Along similar lines, tonic activity in the alpha-low band seems to be more sensitive to individual differences in temperament (trait affect; Goncharova & Davidson, 1995).…”

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confidence: 99%

Identifying robust and sensitive frequency bands for interrogating neural oscillations

et al. 2010

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Recent years have seen an explosion of interest in using neural oscillations to characterize the mechanisms supporting cognition and emotion. Oftentimes, oscillatory activity is indexed by mean power density in predefined frequency bands. Some investigators use broad bands originally defined by prominent surface features of the spectrum. Others rely on narrower bands originally defined by spectral factor analysis (SFA). Presently, the robustness and sensitivity of these competing band definitions remains unclear. Here, a Monte Carlo-based SFA strategy was used to decompose the tonic (“resting” or “spontaneous”) electroencephalogram (EEG) into five bands: delta (1–5Hz), alpha-low (6–9Hz), alpha-high (10–11Hz), beta (12–19Hz), and gamma (>21Hz). This pattern was consistent across SFA methods, artifact correction/rejection procedures, scalp regions, and samples. Subsequent analyses revealed that SFA failed to deliver enhanced sensitivity; narrow alpha sub-bands proved no more sensitive than the classical broadband to individual differences in temperament or mean differences in task-induced activation. Other analyses suggested that residual ocular and muscular artifact was the dominant source of activity during quiescence in the delta and gamma bands. This was observed following threshold-based artifact rejection or independent component analysis (ICA)-based artifact correction, indicating that such procedures do not necessarily confer adequate protection. Collectively, these findings highlight the limitations of several commonly used EEG procedures and underscore the necessity of routinely performing exploratory data analyses, particularly data visualization, prior to hypothesis testing. They also suggest the potential benefits of using techniques other than SFA for interrogating high-dimensional EEG datasets in the frequency or time-frequency (event-related spectral perturbation, event-related synchronization / desynchronization) domains.

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Low alpha power (7.5-9.5 Hz) changes during positive and negative affective learning

Cited by 17 publications

References 26 publications

Comparison of Slides and Video Clips as Different Methods for Inducing Emotions: An Electroencephalographic Alpha Modulation Study

Comparison of Slides and Video Clips as Different Methods for Inducing Emotions: An Electroencephalographic Alpha Modulation Study

Evaluative conditioning in humans: A meta-analysis.

Identifying robust and sensitive frequency bands for interrogating neural oscillations

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