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Individual EEG measures of attention, memory, and motivation predict population level TV viewership and Twitter engagement.

ABSTRACT: Television (TV) programming attracts ever-growing audiences and dominates the cultural zeitgeist. Viewership and social media engagement have become standard indices of programming success. However, accurately predicting individual episode success or future show performance using traditional metrics remains a challenge. Here we examine whether TV viewership and Twitter activity can be predicted using electroencephalography (EEG) measures, which are less affected by reporting biases and which are commonly associated with different cognitive processes. 331 participants watched an hour-long episode from one of nine prime-time shows (~36 participants per episode). Three frequency-based measures were extracted: fronto-central alpha/beta asymmetry (indexing approach motivation), fronto-central alpha/theta power (indexing attention), and fronto-central theta/gamma power (indexing memory processing). All three EEG measures and the composite EEG score significantly correlated across episode segments with the two behavioral measures of TV viewership and Twitter volume. EEG measures explained more variance than either of the behavioral metrics and mediated the relationship between the two. Attentional focus was integral for both audience retention and Twitter activity, while emotional motivation was specifically linked with social engagement and program segments with high TV viewership. These findings highlight the viability of using EEG measures to predict success of TV programming and identify cognitive processes that contribute to audience engagement with television shows.

SUBMITTER: Shestyuk AY

PROVIDER: S-EPMC6438528 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Individual EEG measures of attention, memory, and motivation predict population level TV viewership and Twitter engagement.

Shestyuk Avgusta Y AY Kasinathan Karthik K Karapoondinott Viswajith V Knight Robert T RT Gurumoorthy Ram R

PloS one 20190328 3

Television (TV) programming attracts ever-growing audiences and dominates the cultural zeitgeist. Viewership and social media engagement have become standard indices of programming success. However, accurately predicting individual episode success or future show performance using traditional metrics remains a challenge. Here we examine whether TV viewership and Twitter activity can be predicted using electroencephalography (EEG) measures, which are less affected by reporting biases and which are ...[more]

PMID: 30921406

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Project description:IntroductionPatients with schizophrenia typically exhibit deficits in working memory (WM) associated with abnormalities in brain activity. Alterations in the encoding, maintenance and retrieval phases of sequential WM tasks are well established. However, due to the heterogeneity of symptoms and complexity of its neurophysiological underpinnings, differential diagnosis remains a challenge. We conducted an electroencephalographic (EEG) study during a visual WM task in fifteen schizophrenia patients and fifteen healthy controls. We hypothesized that EEG abnormalities during the task could be identified, and patients successfully classified by an interpretable machine learning algorithm.MethodsWe tested a custom dense attention network (DAN) machine learning model to discriminate patients from control subjects and compared its performance with simpler and more commonly used machine learning models. Additionally, we analyzed behavioral performance, event-related EEG potentials, and time-frequency representations of the evoked responses to further characterize abnormalities in patients during WM.ResultsThe DAN model was significantly accurate in discriminating patients from healthy controls, ACC = 0.69, SD = 0.05. There were no significant differences between groups, conditions, or their interaction in behavioral performance or event-related potentials. However, patients showed significantly lower alpha suppression in the task preparation, memory encoding, maintenance, and retrieval phases F(1,28) = 5.93, p = 0.022, η2 = 0.149. Further analysis revealed that the two highest peaks in the attention value vector of the DAN model overlapped in time with the preparation and memory retrieval phases, as well as with two of the four significant time-frequency ROIs.DiscussionThese results highlight the potential utility of interpretable machine learning algorithms as an aid in diagnosis of schizophrenia and other psychiatric disorders presenting oscillatory abnormalities.

Dataset Information

Individual EEG measures of attention, memory, and motivation predict population level TV viewership and Twitter engagement.

Publications

Individual EEG measures of attention, memory, and motivation predict population level TV viewership and Twitter engagement.

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