Dataset Information

Motion cues modulate responses to emotion in movies.

ABSTRACT: Film theorists and practitioners suggest that motion can be manipulated in movie scenes to elicit emotional responses in viewers. However, our understanding of the role of motion in emotion perception remains limited. On the one hand, movies continuously depict local motion- movements of objects and humans, which are crucial for generating emotional responses. Movie scenes also frequently portray global motion, mainly induced by large camera movements, global motion being yet another source of information used by the brain during natural vision. Here we used functional MRI to elucidate the contributions of local and global motion to emotion perception during movie viewing. Subjects observed long (1?min) movie segments depicting emotional or neutral content. Brain activity in areas that showed preferential responses to emotional content was strongly linked over time with frame-wide variations in global motion, and to a lesser extent with local motion information. Similarly, stronger responses to emotional content were recorded within regions of interest whose activity was attuned to global and local motion over time. Since global motion fields are experienced during self-motion, we suggest that camera movements may induce illusory self-motion cues in viewers that interact with the movie's narrative and with other emotional cues in generating affective responses.

SUBMITTER: Dayan E

PROVIDER: S-EPMC6052113 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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Motion cues modulate responses to emotion in movies.

Dayan Eran E Barliya Avi A de Gelder Beatrice B Hendler Talma T Malach Rafael R Flash Tamar T

Scientific reports 20180718 1

Film theorists and practitioners suggest that motion can be manipulated in movie scenes to elicit emotional responses in viewers. However, our understanding of the role of motion in emotion perception remains limited. On the one hand, movies continuously depict local motion- movements of objects and humans, which are crucial for generating emotional responses. Movie scenes also frequently portray global motion, mainly induced by large camera movements, global motion being yet another source of i ...[more]

PMID: 30022121

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Project description:BackgroundMany biological specimens, such as living cells and their intracellular components, often exhibit very little amplitude contrast, making it difficult for conventional bright field microscopes to distinguish them from their surroundings. To overcome this problem phase contrast techniques such as Zernike, Normarsky and dark-field microscopies have been developed to improve specimen visibility without chemically or physically altering them by the process of staining. These techniques have proven to be invaluable tools for studying living cells and furthering scientific understanding of fundamental cellular processes such as mitosis. However a drawback of these techniques is that direct quantitative phase imaging is not possible. Quantitative phase imaging is important because it enables determination of either the refractive index or optical thickness variations from the measured optical path length with sub-wavelength accuracy. Digital holography is an emergent phase contrast technique that offers an excellent approach in obtaining both qualitative and quantitative phase information from the hologram. A CCD camera is used to record a hologram onto a computer and numerical methods are subsequently applied to reconstruct the hologram to enable direct access to both phase and amplitude information. Another attractive feature of digital holography is the ability to focus on multiple focal planes from a single hologram, emulating the focusing control of a conventional microscope.MethodsA modified Mach-Zender off-axis setup in transmission is used to record and reconstruct a number of holographic amplitude and phase images of cellular and sub-cellular features.ResultsBoth cellular and sub-cellular features are imaged with sub-micron, diffraction-limited resolution. Movies of holographic amplitude and phase images of living microbes and cells are created from a series of holograms and reconstructed with numerically adjustable focus, so that the moving object can be accurately tracked with a reconstruction rate of 300ms for each hologram. The holographic movies show paramecium swimming among other microbes as well as displaying some of their intracellular processes. A time lapse movie is also shown for fibroblast cells in the process of migration.ConclusionDigital holography and movies of digital holography are seen to be useful new tools for visualization of dynamic processes in biological microscopy. Phase imaging digital holography is a promising technique in terms of the lack of coherent noise and the precision with which the optical thickness of a sample can be profiled, which can lead to images with an axial resolution of a few nanometres.

Project description:Emotion recognition is key to interpersonal communication and to human-machine interaction. Body expression may contribute to emotion recognition, but most past studies focused on a few motions, limiting accurate recognition. Moreover, emotions in most previous research were acted out, resulting in non-natural motion, which is unapplicable in reality. We present an approach for emotion recognition based on body motion in naturalistic settings, examining authentic emotions, natural movement, and a broad collection of motion parameters. A lab experiment using 24 participants manipulated participants' emotions using pretested movies into five conditions: happiness, relaxation, fear, sadness, and emotionally-neutral. Emotion was manipulated within subjects, with fillers in between and a counterbalanced order. A motion capture system measured posture and motion during standing and walking; a force plate measured center of pressure location. Traditional statistics revealed nonsignificant effects of emotions on most motion parameters; only 7 of 229 parameters demonstrate significant effects. Most significant effects are in parameters representing postural control during standing, which is consistent with past studies. Yet, the few significant effects suggest that it is impossible to recognize emotions based on a single motion parameter. We therefore developed machine learning models to classify emotions using a collection of parameters, and examined six models: k-nearest neighbors, decision tree, logistic regression, and the support vector machine with radial base function and linear and polynomial functions. The decision tree using 25 parameters provided the highest average accuracy (45.8%), more than twice the random guess for five conditions, which advances past studies demonstrating comparable accuracies, due to our naturalistic setting. This research suggests that machine learning models are valuable for emotion recognition in reality and lays the foundation for further progress in emotion recognition models, informing the development of recognition devices (e.g., depth camera), to be used in home-setting human-machine interactions.

Dataset Information

Motion cues modulate responses to emotion in movies.

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Motion cues modulate responses to emotion in movies.

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