Movies and narratives as naturalistic stimuli in neuroimaging

Jä̈askëlainen, Iiro P.; Sams, Mikko; Glerean, Enrico; Ahveninen, Jyrki

doi:10.1016/j.neuroimage.2020.117445

Cited by 115 publications

(72 citation statements)

References 242 publications

(181 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For this reason, it has been an established tradition to isolate and orthogonalize musical features or acoustic properties to study their effects in music psychology and cognitive neuroscience of music. However, now that computational models can translate naturalistic stimuli into relevant features (i.e., linearizing functions), recent human neuroimaging studies have shown that it is possible to analyze complex interactions among natural features while taking advantage of the salience of naturalistic stimuli to evoke intense emotions and provide ecologically valid contexts (Goldberg et al, 2014;Sonkusare et al, 2019;Jääskeläinen et al, 2021).…”

Section: Validity Of the Proposed Frameworkmentioning

confidence: 99%

An Analytical Framework of Tonal and Rhythmic Hierarchy in Natural Music Using the Multivariate Temporal Response Function

et al. 2021

View full text Add to dashboard Cite

Even without formal training, humans experience a wide range of emotions in response to changes in musical features, such as tonality and rhythm, during music listening. While many studies have investigated how isolated elements of tonal and rhythmic properties are processed in the human brain, it remains unclear whether these findings with such controlled stimuli are generalizable to complex stimuli in the real world. In the current study, we present an analytical framework of a linearized encoding analysis based on a set of music information retrieval features to investigate the rapid cortical encoding of tonal and rhythmic hierarchies in natural music. We applied this framework to a public domain EEG dataset (OpenMIIR) to deconvolve overlapping EEG responses to various musical features in continuous music. In particular, the proposed framework investigated the EEG encoding of the following features: tonal stability, key clarity, beat, and meter. This analysis revealed a differential spatiotemporal neural encoding of beat and meter, but not of tonal stability and key clarity. The results demonstrate that this framework can uncover associations of ongoing brain activity with relevant musical features, which could be further extended to other relevant measures such as time-resolved emotional responses in future studies.

show abstract

Section: Validity Of the Proposed Frameworkmentioning

confidence: 99%

An Analytical Framework of Tonal and Rhythmic Hierarchy in Natural Music Using the Multivariate Temporal Response Function

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The Core subsystem, which is comprised of anterior medial prefrontal cortex (aMPFC) and PCC, is associated with self‐referential processing (Kim, 2012) and autobiographical memory (Andrews‐Hanna, Saxe, & Yarkoni, 2014). Previous studies using naturalistic stimuli paradigm have demonstrated the pivotal role of DMN in movie watching and social cognition (Brandman et al, 2021; Jääskeläinen, Sams, Glerean, & Ahveninen, 2021; Redcay & Moraczewski, 2020). However, the three subnetworks of DMN may play different roles in differentiating user's favorite videos from uninteresting ones due to their functional heterogeneity mentioned above.…”

Section: Introductionmentioning

confidence: 98%

Individualized video recommendation modulates functional connectivity between large scale networks

Zhou

Wang

et al. 2021

Human Brain Mapping

View full text Add to dashboard Cite

With the emergence of AI‐powered recommender systems and their extensive use in the video streaming service, questions and concerns also arise. Why can recommended video content continuously capture users' attention? What is the impact of long‐term exposure to personalized video content on one's behaviors and brain functions? To address these questions, we designed an fMRI experiment presenting participants with personally recommended videos and generally recommended ones. To examine how large‐scale networks were modulated by personalized video content, graph theory analysis was applied to investigate the interaction between seven networks, including the ventral and dorsal attention networks (VAN, DAN), frontal–parietal network (FPN), salience network (SN), and three subnetworks of default mode network (dorsal medial prefrontal (dMPFC), Core, and medial temporal lobe (MTL)). Our results showed that viewing nonpersonalized video content mainly enhanced the connectivity in the DAN‐FPN‐Core pathway, whereas viewing personalized ones increased not only the connectivity in this pathway but also the DAN‐VAN‐dMPFC pathway. In addition, both personalized and nonpersonalized short videos decreased the couplings between SN and VAN as well as between two DMN subsystems, Core and MTL. Collectively, these findings uncovered distinct patterns of network interactions in response to short videos and provided insights into potential neural mechanisms by which human behaviors are biased by personally recommended content.

show abstract

“…Especially, since listening to music is usually an emotional experience. Further research on emotions, perspective taking and neural ISC is provided by Jääskeläinen et al (2020a , b) . Classification of emotions across participants can be improved by adding inter-subject correlations to the feature space as shown by Chan et al (2020) and Haxby et al (2011) and thus may be an interesting elaboration of the current research.…”

Section: Discussionmentioning

confidence: 99%

A Sound Prediction: EEG-Based Neural Synchrony Predicts Online Music Streams

Leeuwis

Pistone

Flick³

et al. 2021

Front. Psychol.

View full text Add to dashboard Cite

Neuroforecasting predicts population-wide choices based on neural data of individuals and can be used, for example, in neuromarketing to estimate campaign successes. To deliver true value, the brain activity metrics should deliver predictive value above and beyond traditional stated preferences. Evidence from movie trailer research has proposed neural synchrony, which compares the similarity of brain responses across participants and has shown to be a promising tool in neuroforecasting for movie popularity. The music industry might also benefit from these increasingly accurate success predictors, but only one study has been forecasting music popularity, using functional magnetic resonance imaging measures. Current research validates the strength of neural synchrony as a predictive measure for popularity of music, making use of electroencephalogram to capture moment-to-moment neural similarity between respondents while they listen to music. Neural synchrony is demonstrated to be a significant predictor for public appreciation on Spotify 3 weeks and 10 months after the release of the albums, especially when combined with the release of a single. On an individual level, other brain measures were shown to relate to individual subjective likeability ratings, including Frontal Alpha Asymmetry and engagement when combined with the factors artist and single release. Our results show the predictive value of brain activity measures outperforms stated preferences. Especially, neural synchrony carries high predictive value for the popularity on Spotify, providing the music industry with an essential asset for efficient decision making and investments, in addition to other practical implications that include neuromarketing and advertising industries.

show abstract

Movies and narratives as naturalistic stimuli in neuroimaging

Cited by 115 publications

References 242 publications

An Analytical Framework of Tonal and Rhythmic Hierarchy in Natural Music Using the Multivariate Temporal Response Function

An Analytical Framework of Tonal and Rhythmic Hierarchy in Natural Music Using the Multivariate Temporal Response Function

Individualized video recommendation modulates functional connectivity between large scale networks

A Sound Prediction: EEG-Based Neural Synchrony Predicts Online Music Streams

Contact Info

Product

Resources

About