Academia.eduAcademia.edu

Outline

Title
Abstract
Introduction
Materials and Methods
Participants
Materials
Procedures
Data Processing-Subjective Liking
Data Processing-Physiological Measures
Statistical Analysis
Results
Subjective Liking
Discussion
Subjective Liking Assessment
Predicting Time-Dependent Subjective Likeability
Study Limitations
Final Comments and Future Directions
Conclusion
References

Neural Correlates of Preference: A Transmodal Validation Study

Profile image of Alvaro M DiasAlvaro M Dias

Frontiers in Human Neuroscience

https://doi.org/10.3389/FNHUM.2019.00073
visibility

…

description

13 pages

link

1 file

Abstract

Liking is one of the most important psychological processes associated with the reward system, being involved in affective processing and pleasure/displeasure encoding. Currently, there is no consensus regarding the combination of physiological indicators which best predict liking, especially when applied to dynamic stimuli such as videos. There is a lack of a standard methodology to assess likeability over time and therefore in assessing narrative and semantic aspects of the stimulus. We developed a time-dependent method to evaluate the physiological correlates of likeability for three different thematic categories, namely: adventure (AV), comedy (CM), and nature landscape (LS). Twenty-eight healthy adults with ages ranging from 18 to 35 years (average: 23.85 years) were enrolled in the study. The participants were asked to provide likeability ratings for videos as they watched them, using a response box. Three 60-s videos were presented, one for each category, in randomized order while the participant's physiological data [electroencephalogram (EEG), electrocardiogram (ECG) and eye tracking (ET)] was recorded. The comedy video (CM) presented the smallest minimum accumulated normalized rating (ANR; p = 0.013) and the LS video presented the highest maximum ANR (p = 0.039). The LS video presented the longest time for first response (p < 0.001) and the AV video presented the shortest time for maximum response (p = 0.016). The LS video had the highest mean likeability rating with 1.43 ± 2.31 points; and the CM video had the lowest with 0.57 ± 1.77. Multiple linear regression models were created to predict the likeability of each video using the following physiological indicators; AV: power in beta band at C4 and P4 (p = 0.004, adj. R 2 = 0.301); CM: alpha power in Fp2 (p = 0.001, adj. R 2 = 0.326) and LS: alpha power in P4, F8, and Fp2; beta power in C4 and P4 and pupil size, (p = 0.002, adj. R 2 = 0.489). Despite its limitations (e.g., using one 1-min video per category) our findings suggest that there is a considerable difference in the psychophysiological correlates of stimuli with different contextual properties and that the use of time-dependent methods to assess videos should be considered as best practices.

Related papers

Looking at pictures: Affective, facial, visceral, and behavioral reactions
Alfons Hamm

Psychophysiology, 1993

Colored photographic pictures that varied widely across the affective dimensions of valence (pleasant-unpleasant) and arou.sal (excited-calm) were each viewed for a 6-s period while facial electromyographic (zygomatic and corrugator muscle activity) and visceral (heart rate and skin conductance) reactions were measured. Judgments relating to pleasure, arousal, interest, and emotional state were measured, as was choice viewing time. Significant covariation was obtained between (a) facial expression and affective valence judgments and (b) skin conductance magnitude and arousal ratings. Interest ratings and viewing time were also associated with arousal. Although differences due to the subject's gender and cognitive style were obtained, affective responses were largely independent of the personality factors investigated. Response specificity, particularly facial expressiveness, supported the view that specific affects have unique patterns of reactivity. The consistency of the dimensional relationships between evaluative judgments (i.e., pleasure and arousal) and physiological response, however, emphasizes that emotion is fundamentally organized by these motivational parameters.

View PDFchevron_right
Psychophysiological Responses to Still vs. Animated Pictures With Different Levels of Emotional Valence
Jiawei Liu, Advances in Cognitive Psychology

Advances in Cognitive Psychology, 2022

With the rapid development of multimedia and Internet technologies, understanding what visual contexts are well-received among viewers is important for message designers and visual practitioners. This study investigated the effects of valence (positive vs. negative), picture type (animated vs. still), and their interaction effect on attention, motivational intensity (sympathetic arousal), memory sensitivity and judgment. Eighty-seven participants in a mixed factorial design experiment were exposed to six pictures that were either animated or still. Results indicated that animated pictures led to greater attention and motivational intensity than still ones. Negative pictures also elicited greater motivational intensity but were less remembered than positive ones. Additionally, people exhibited greater sympathetic arousal when processing negative animated pictures than other types of pictures. Implications are also discussed.

View PDFchevron_right
From the eyes and the heart: a novel eye-gaze metric that predicts video preferences of a large audience
Christoforos Christoforou, Maria Theodorou, Fofi Constantinidou

Frontiers in Psychology, 2015

Eye-tracking has been extensively used to quantify audience preferences in the context of marketing and advertising research, primarily in methodologies involving static images or stimuli (i.e., advertising, shelf testing, and website usability). However, these methodologies do not generalize to narrative-based video stimuli where a specific storyline is meant to be communicated to the audience. In this paper, a novel metric based on eye-gaze dispersion (both within and across viewings) that quantifies the impact of narrative-based video stimuli to the preferences of large audiences is presented. The metric is validated in predicting the performance of video advertisements aired during the 2014 Super Bowl final. In particular, the metric is shown to explain 70% of the variance in likeability scores of the 2014 Super Bowl ads as measured by the USA TODAY Ad-Meter. In addition, by comparing the proposed metric with Heart Rate Variability (HRV) indices, we have associated the metric with biological processes relating to attention allocation. The underlying idea behind the proposed metric suggests a shift in perspective when it comes to evaluating narrative-based video stimuli. In particular, it suggests that audience preferences on video are modulated by the level of viewers lack of attention allocation. The proposed metric can be calculated on any narrative-based video stimuli (i.e., movie, narrative content, emotional content, etc.), and thus has the potential to facilitate the use of such stimuli in several contexts: prediction of audience preferences of movies, quantitative assessment of entertainment pieces, prediction of the impact of movie trailers, identification of group, and individual differences in the study of attention-deficit disorders, and the study of desensitization to media violence.

View PDFchevron_right
Detecting Viewer Interest in Video using Facial and Heart Rate Responses
Prithwi Raj Chakraborty

Interest is a knowledge-based and anticipatory emotion, which is known to be the central and the most frequently aroused emotion in video viewing. Unlike prototypical emotions (i.e., happiness, sadness, fear, disgust, anger, and surprise), interest carries motivational and exploratory attributes that encourage a viewer to engage with the video content. Automatic analysis of evoked interest in the viewer is therefore more practical in the case of video viewing. Existing studies predominantly use other emotion types for affectrelated measurements and applications. A few studies measure interest in viewing context but with static visual stimuli. Video is a dynamic stimulus type that contains auditory and visual channels; as well, it has time-varying affects on viewers. Video thus is more suitable as stimuli in viewing context. Emotions evoked during video viewing indicate certain episodes of viewing where the viewer felt interested into the viewed content. Such information about viewer interest manifests through viewer responses, which are either visually observable or physiological signals, both of which include facial expression, eye gaze, and heart rate. Detection of viewer interest from viewer responses (in response to video stimuli) is challenging due to subjective biasing and individual preferences. The visual and physiological signals are subtle and each of them has different characteristics in response to the same video stimuli. Moreover, they do not necessarily correlate with interest-evoking events occurring in the video. Existing studies do not consider whether the feature distributions of these signals have any potential similarity, difference, or stimuli dependence. These factors are crucial for designing a domain-general detection model for viewer interest. This project develops a methodology to measure viewer interest using facial and heart rate responses of viewers. It demonstrates the feasibility of a generic model trained to detect viewer interest in sports and movie videos. The project tests the methodology with facial expression and heart rate responses from 12 and 20 subjects collected in response to 5 sports and 20 movie stimuli. The data collection setup involves a closed room with a video camera and two heart rate sensors. A number of machine learning techniques and timevarying features are utilised to conduct the experiment. The main goal of this project is investigating machine learning and statistical techniques to detect viewer interest using facial expression and heart rate signals in response to sports v and movie stimuli. Three experiments have been conducted towards designing a more general detection model for viewer interest using: (1) soccer clips; (2) soccer and tennis clips; (3) sports and movie clips. For testing the methodologies, this project detects interesting and not-interesting video segments using the viewer interest detection outcomes and validates them against subjective feedbacks. A methodology has been explored for detecting sports video highlights using facial expression and heart rate signals, where their amplitudes are relatively high. Statistical methods have been adopted to demonstrate the complementary nature as well as the temporal asynchrony of facial expression and heart rate signals with the video highlight events. Subsequent experiments demonstrate the significant statistical differences between the distributions of facial expression and heart rate features obtained in response to soccer and tennis clips. Similar statistical differences are demonstrated across the feature distributions obtained in response to movie clips tagged with different emotion labels. The feasibility of a sports-general classification model is presented by comparing sports-general, sportsspecific and cross-sports classification approaches over the facial expression and heart rate features extracted in response to soccer and tennis clips. The sports-specific approach achieves higher accuracy (53%) than the sports-general (41%) and cross-sports (44%) ones in classifying interesting and not-interesting segments from soccer and tennis clips. An extension of this methodology presents the feasibility of a cross-domain classification approach by testing the facial expression and heart rate features obtained in response to sports and movie clips. It has been demonstrated that a domain-specific classification approach outperforms (accuracy: 48-60%) the cross-domain approaches (accuracy: 57-67%). This presents a coherent conclusion by confirming that a domain-specific has consistent performance over a domain-general approach in classifying 'interesting' and 'not-interesting' video segments. This project also demonstrates the usability of visual eye gaze and attention features in classifying the facial expression of 'interest' from the facial videos. The performance is higher when the tests are conducted with the facial videos recorded in response to relatively longer duration clips (accuracy: 67%). This outcome suggests that the visual reaction of a viewer depends on the characteristics of the viewed content. It has also been

View PDFchevron_right
Users' response to affective film content: A narrative perspective
Riccardo Leonardi

Content-Based …, 2010

In this paper, we take a human-centred view to the definition of the affective content of films. We investigate the relationship between users physiological response and multimedia features extracted from the movies, from the perspective of narrative evolution rather than by measuring average values. We found a certain dynamic correlation between arousal, derived from measures of Galvanic Skin Resistance during film viewing, and specific multimedia features in both sound and video domains. Dynamic physiological ...

View PDFchevron_right
II Internal Mechanisms : Enjoyment , Appeal , and Physiological and Adults 334 Response 147
Kim Bissell

2014

Media entertainment refers to users&#39; enjoyment of media-induced sensations. This chapter argues that users feel entertained to the extent that media supports their adaptive tendencies to recreate and grow psychologicallY. Within the boundaries of their physiological regulation, users are believed to engage actively in cognitive, affective, and behavioral challenges imposed by media, in order to grow psychologically. However, a lack of physiological resources may limit users&#39; engagement in challenges and consequently strengthen their enjoyment of more recreational and supportive media content. Theorizing Media Entertainment Media entertainment has been characterized traditionally as an enjoyable experience for the user (Verderer, Klimmt, & Ritterfeld, 2004; Zillmann, 2003). Such a perspective views entertainment as a hedonistic user experience, typically triggered by cheerful comedy shows, light-hearted romantic stories, stimulating action movies, or relaxing media offerings ...

View PDFchevron_right
Integrated data analysis for the quantification of emotional responses during video observation
Margherita Pillan

2017 IEEE 3rd International Forum on Research and Technologies for Society and Industry (RTSI), 2017

The use of questionnaires at the end of a specific task only evaluates what is expressed by the conscious mind and, therefore, cannot give a complete representation of the individual's emotional state. By adding data from physiological measures, such as cerebral activity, skin conductance, heart rate and gaze position, more accurate information about cognitive engagement and emotional responses to a given task could be provided. This study aims to evaluate participants' emotional (arousal, valence) and cognitive (memorization effort, attention, pleasantness) responses toward two videos, through the integration of above cited measures. Our findings show that the two tested videos produce two different unconscious reactions (one video causes a significantly higher increase in heart rate and the other one requires higher memorization effort), while producing similar conscious responses (no statistically significant differences were found by analyzing questionnaires' answers). Further, eye tracking device provided a way to investigate reasons behind these differences. The results show that the integration of self-reported and biological measures with eye tracking data could effectively help to understand emotional and cognitive responses during video observation.

View PDFchevron_right
Arousal and valence in the direct scaling of emotional response to film clips
Nancy Alvarado

Motivation and Emotion, 1997

Contributions of differential attention to valence versus arousal (Feldman, 1995) in self-reported emotional response may be difficult to observe due to (1) confounding of valence and arousal in the labeling of rating scales, and (2) the assumption of an interval scale type. Ratings of ...

View PDFchevron_right
Attention and emotion: Does rating emotion alter neural responses to amusing and sad films?
Cendri Hutcherson, L. Barrett

NeuroImage, 2005

Functional neuroimaging of affective systems often includes subjective self-report of the affective response. Although self-report provides valuable information regarding participants' affective responses, prior studies have raised the concern that the attentional demands of reporting on affective experience may obscure neural activations reflecting more natural affective responses. In the present study, we used potent emotion-eliciting amusing and sad films, employed a novel method of continuous self-reported rating of emotion experience, and compared the impact of rating with passive viewing of amusing and sad films. Subjective rating of ongoing emotional responses did not decrease either self-reported experience of emotion or neural activations relative to passive viewing in any brain regions. Rating, relative to passive viewing, produced increased activity in anterior cingulate, insula, and several other areas associated with introspection of emotion. These results support the use of continuous emotion measures and emotionally engaging films to study the dynamics of emotional responding and suggest that there may be some contexts in which the attention to emotion induced by reporting emotion experience does not disrupt emotional responding either behaviorally or neurally. D

View PDFchevron_right

References (53)

  1. Baddeley, A. (2010). Working memory. Curr. Biol. 20, R136-R140. doi: 10.1016/j. cub.2009.12.014
  2. Berridge, K. C. (2004). Motivation concepts in behavioral neuroscience. Physiol. Behav. 81, 179-209. doi: 10.1016/j.physbeh.2004.02.004
  3. Berridge, K. C., and Kringelbach, M. L. (2013). Neuroscience of affect: brain mechanisms of pleasure and displeasure. Curr. Opin. Neurobiol. 23, 294-303. doi: 10.1016/j.conb.2013.01.017
  4. Berridge, K. C., and Kringelbach, M. L. (2015). Pleasure systems in the brain. Neuron 86, 646-664. doi: 10.1016/j.neuron.2015.02.018
  5. Berridge, K. C., Robinson, T. E., and Aldridge, J. W. (2009). Dissecting components of reward: 'liking', 'wanting', and learning. Curr. Opin. Pharmacol. 9, 65-73. doi: 10.1016/j.coph.2008.12.014
  6. Bradley, M. M., and Lang, P. J. (1994). Measuring emotion: The self-assessment manikin and the semantic differential. J. Behav. Ther. Exp. Psychiatry 25, 49-59. doi: 10.1016/0005-7916(94)90063-9
  7. Bradley, M. M., Miccoli, L., Escrig, M. A., and Lang, P. J. (2008). The pupil as a measure of emotional arousal and autonomic activation. Psychophysiology 45, 602-607. doi: 10.1111/j.1469-8986.2008.00654.x
  8. Carvalho, S., Leite, J., Galdo-Álvarez, S., and Gonçalves, Ó. F. (2012). The emotional movie database (EMDB): a self-report and psychophysiological study. Appl. Psychophysiol. Biofeedback 37, 279-294. doi: 10.1007/s10484-012- 9201-6
  9. Chan, Y.-C., Liao, Y.-J., Tu, C.-H., and Chen, H.-C. (2016). Neural correlates of hostile jokes: cognitive and motivational processes in humor appreciation. Front. Hum. Neurosci. 10:527. doi: 10.3389/fnhum.2016.00527
  10. Codispoti, M., Surcinelli, P., and Baldaro, B. (2008). Watching emotional movies: affective reactions and gender differences. Int. J. Psychophysiol. 69, 90-95. doi: 10.1016/j.ijpsycho.2008.03.004
  11. Couto, B., Adolfi, F., Velasquez, M., Mesow, M., Feinstein, J., Canales-Johnson, A., et al. (2015). Heart evoked potential triggers brain responses to natural affective scenes: a preliminary study. Auton. Neurosci. 193, 132-137. doi: 10.1016/j. autneu.2015.06.006
  12. D'Mello, S. K., and Kory, J. (2015). A review and meta-analysis of multimodal affect detection systems. ACM Comput. Surv. 47:A43. doi: 10.1145/2682899
  13. Davidson, R., and Tomarken, A. (1989). ''Laterality and emotion: an electrophysiological approach,'' in Handbook of Neuropsychology, eds F. Boller and A. J. Grafman (Amsterdam: Elsevier), 419-441.
  14. Delorme, A., and Makeig, S. (2004). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods 134, 9-21. doi: 10.1016/j.jneumeth.2003.10.009
  15. Fernández, C., Pascual, J. C., Soler, J., Elices, M., Portella, M. J., and Fernández- Abascal, E. (2012). Physiological responses induced by emotion-eliciting films. Appl. Psychophysiol. Biofeedback 37, 73-79. doi: 10.1007/s10484-012- 9180-7
  16. Gerber, A. J., Posner, J., Gorman, D., Colibazzi, T., Yu, S., Wang, Z., et al. (2008). An affective circumplex model of neural systems subserving valence, arousal, and cognitive overlay during the appraisal of emotional faces. Neuropsychologia 46, 2129-2139. doi: 10.1016/j.neuropsychologia.2008.02.032
  17. Golland, Y., Keissar, K., and Levit-Binnun, N. (2014). Studying the dynamics of autonomic activity during emotional experience. Psychophysiology 51, 1101-1111. doi: 10.1111/psyp.12261
  18. Gomez, P., Zimmermann, P., Guttormsen-Schär, S., and Danuser, B. (2005). Respiratory responses associated with affective processing of film stimuli. Biol. Psychol. 68, 223-235. doi: 10.1016/j.biopsycho.2004.06.003
  19. Guimarães, H. N., and Santos, R. A. (1998). A comparative analysis of preprocessing techniques of cardiac event series for the study of heart rhythm variability using simulated signals. Braz. J. Med. Biol. Res. 31, 421-430. doi: 10.1590/s0100-879x1998000300015
  20. Güzel Aydin, S., Kaya, T., and Guler, H. (2016). Wavelet-based study of valence- arousal model of emotions on EEG signals with LabVIEW. Brain Inform. 3, 109-117. doi: 10.1007/s40708-016-0031-9
  21. Han, C.-H., Lee, J.-H., Lim, J.-H., Kim, Y.-W., and Im, C.-H. (2017). Global electroencephalography synchronization as a new indicator for tracking emotional changes of a group of individuals during video watching. Front. Hum. Neurosci. 11:577. doi: 10.3389/fnhum.2017.00577
  22. Henson, R. N. A. (1998). Short-term memory for serial order: the start-end model. Cogn. Psychol. 36, 73-137. doi: 10.1006/cogp.1998.0685
  23. Holway, A. H., and Hurvich, L. (1937). Differential gustatory sensitivity to salt. Am. J. Psychol. 49, 37-48. doi: 10.2307/1416050
  24. Hort, J., Kemp, S., and Hollowood, T. (2017). Time-Dependent Measures of Perception in Sensory Evaluation. Sussex, UK: Willey Blackwell.
  25. Jager, G., Schlich, P., Tijssen, I., Yao, J., Visalli, M., de Graaf, C., et al. (2014). Temporal dominance of emotions: measuring dynamics of food-related emotions during consumption. Food Qual. Prefer. 37, 87-99. doi: 10.1016/j. foodqual.2014.04.010
  26. Just, M. A., Carpenter, P. A., and Miyake, A. (2003). Neuroindices of cognitive workload: neuroimaging, pupillometric and event-related potential studies of brain work. Theor. Issues Ergon. Sci. 4, 56-88. doi: 10.1080/1463922021 0159735
  27. Koelstra, S., Muhl, C., Soleymani, M., Lee, J.-S., Yazdani, A., Ebrahimi, T., et al. (2012). DEAP: a database for emotion analysis;using physiological signals. IEEE Trans. Affect. Comput. 3, 18-31. doi: 10.1109/t-affc.2011.15
  28. Kortelainen, J., Väyrynen, E., and Seppänen, T. (2015). High-frequency electroencephalographic activity in left temporal area is associated with pleasant emotion induced by video clips. Comput. Intell. Neurosci. 2015:762769. doi: 10.1155/2015/762769
  29. Kringelbach, M. L., and Berridge, C. K. (2010). Pleasures of the Brain. New York, NY: Oxford University Press.
  30. Kühn, S., and Gallinat, J. (2012). The neural correlates of subjective pleasantness. Neuroimage 61, 289-294. doi: 10.1016/j.neuroimage.2012.02.065
  31. Kuppens, P., Tuerlinckx, F., Russell, J. A., and Barrett, L. F. (2013). The relation between valence and arousal in subjective experience. Psychol. Bull. 139, 917-940. doi: 10.1037/a0030811
  32. Mari, J. J., and Williams, P. (1986). A validity study of a psychiatric screening questionnaire (SRQ-20) in primary care in the city of Sao Paulo. Br. J. Psychiatry 148, 23-26. doi: 10.1192/bjp.148.1.23
  33. Neale, S., and Krutnik, F. (1990). Popular Film and Television Comedy. London: Routledge.
  34. Oostenveld, R., Fries, P., Maris, E., and Schoffelen, J. M. (2011). FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Comput. Intell. Neurosci. 2011:156869. doi: 10.1155/2011/156869
  35. Perakakis, P., Joffily, M., Taylor, M., Guerra, P., and Vila, J. (2010). KARDIA: a Matlab software for the analysis of cardiac interbeat intervals. Comput. Methods Programs Biomed. 98, 83-89. doi: 10.1016/j.cmpb.2009.10.002
  36. R.-Tavakoli, H., Atyabi, A., Rantanen, A., Laukka, S. J., Nefti-Meziani, S., and Heikkilä, J. (2015). Predicting the valence of a scene from observers' eye movements. PLoS One 10:e0138198. doi: 10.1371/journal.pone.0138198
  37. Ramsøy, T. Z., Jacobsen, C., Friis-Olivarius, M., Bagdziunaite, D., and Skov, M. (2017). Predictive value of body posture and pupil dilation in assessing consumer preference and choice. J. Neurosci. Psychol. Econ. 10, 95-110. doi: 10.1037/npe0000073.supp
  38. Russell, J. A. (1980). A circumplex model of affect. J. Pers. Soc. Psychol. 39, 1161-1178. doi: 10.1037/h0077714
  39. Russell, J. A. (2003). Core affect and the psychological construction of emotion. Psychol. Rev. 110, 145-172. doi: 10.1037//0033-295x.110.1.145
  40. Sánchez-Navarro, J. P., Martínez-Selva, J. M., Torrente, G., and Román, F. (2008). Psychophysiological, behavioral and cognitive indices of the emotional response: a factor-analytic study. Span. J. Psychol. 11, 16-25. doi: 10.1017/s1138741600004078
  41. Schellberg, D., Besthorn, C., Klos, T., and Gasser, T. (1990). EEG power and coherence while male adults watch emotional video films. Int. J. Psychophysiol. 9, 279-291. doi: 10.1016/0167-8760(90)90060-q
  42. Silberstein, R. B., and Nield, G. E. (2008). Brain activity correlates of consumer brand choice shift associated with television advertising. Int. J. Advert. 27, 359-380. doi: 10.2501/s0265048708080025
  43. Smith, K. S., Berridge, K. C., and Aldridge, J. W. (2011). Disentangling pleasure from incentive salience and learning signals in brain reward circuitry. Proc. Natl. Acad. Sci. U S A 108, E255-E264. doi: 10.1073/pnas.11019 20108
  44. Steiner, J. E., Glaser, D., Hawilo, M. E., and Berridge, K. C. (2001). Comparative expression of hedonic impact: affective reactions to taste by human infants and other primates. Neurosci. Biobehav. Rev. 25, 53-74. doi: 10.1016/s0149- 7634(00)00051-8
  45. Sudre, J., Pineau, N., Loret, C., and Martin, N. (2012). Comparison of methods to monitor liking of food during consumption. Food Qual. Prefer. 24, 179-189. doi: 10.1016/j.foodqual.2011.10.013
  46. Taylor, D., and Pangborn, R. M. (1990). Temporal aspects of hedonic responses. J. Sens. Stud. 4, 241-247. doi: 10.1111/j.1745-459x.1990.tb00475.x
  47. Thomas, A., Visalli, M., Cordelle, S., and Schlich, P. (2015). Temporal drivers of liking, food quality and preference. Food Qual. Prefer. 40, 365-375. doi: 10.1016/j.foodqual.2014.03.003
  48. Uekermann, J., Daum, I., and Channon, S. (2007). Toward a cognitive and social neuroscience of humor processing. Soc. Cogn. 25, 553-572. doi: 10.1521/soco. 2007.25.4.553
  49. Vallar, G., and Papagno, C. (1986). Phonological short-term store and the nature of the recency effect: evidence from neuropsychology. Brain Cogn. 5, 428-442. doi: 10.1016/0278-2626(86)90044-8
  50. Vecchiato, G., Astolfi, L., De Vico Fallani, F., Cincotti, F., Mattia, D., Salinari, S., et al. (2010). Changes in brain activity during the observation of TV commercials by using EEG, GSR and HR measurements. Brain Topogr. 23, 165-179. doi: 10.1007/s10548-009-0127-0
  51. Vecchiato, G., Cherubino, P., Maglione, A. G., Ezquierro, M. T. H., Marinozzi, F., Bini, F., et al. (2014). How to measure cerebral correlates of emotions in marketing relevant tasks. Cogn. Comput. 6, 856-871. doi: 10.1007/s12559-014- 9304-x Vecchiato, G., Toppi, J., Astolfi, L., De Vico Fallani, F., Cincotti, F., Mattia, D., et al. (2011). Spectral EEG frontal asymmetries correlate with the experienced pleasantness of TV commercial advertisements. Med. Biol. Eng. Comput. 49, 579-583. doi: 10.1007/s11517-011-0747-x
  52. Vrticka, P., Black, J. M., and Reiss, A. L. (2013). The neural basis of humour processing. Nat. Rev. Neurosci. 14, 860-868. doi: 10.1038/nrn3566
  53. YIlmaz, B., Korkmaz, S., Arslan, D. B., Güngör, E., and AsyalI, M. H. (2014). Like/dislike analysis using EEG: determination of most discriminative channels and frequencies. Comput. Methods Programs Biomed. 113, 705-713. doi: 10.1016/j.cmpb.2013.11.010

Related papers

The Emotional Movie Database (EMDB): A Self-Report and Psychophysiological Study
Sandra Carvalho

Film clips are an important tool for evoking emotional responses in the laboratory.When compared with other emotionally potent visual stimuli (e.g., pictures), film clips seem to be more effective in eliciting emotions for longer periods of time at both the subjective and physiological levels. The main objective of the present study was to develop a new database of affective film clips without auditory content, based on a dimensional approach to emotional stimuli (valence, arousal and dominance). The study had three different phases: (1) the pre-selection and editing of 52 film clips (2) the self-report rating of these film clips by a sample of 113 participants and (3) psychophysiological assessment (skin conductance level (SCL) and the heart rate (HR)) on 32 volunteers. Film clips from different categories were selected to elicit emotional states from different quadrants of affective space. The results also showed that sustained exposure to the affective film clips resulted in a pattern of a SCL increase and HR deceleration in high arousal conditions (i.e., horror and erotic conditions). The resulting Emotional Movie Database (EMDB) can reliably be used in research requiring the presentation of non-auditory film clips with different ratings of valence, arousal and dominance.

View PDFchevron_right
Presenting and analyzing movie stimuli for psychocinematic research
Reham Khalel

Movies have an extraordinary way of capturing our perceptual, conceptual and emotional processes. As such, they offer a useful means of exploring psychological phenomenon in the laboratory. Until recently, it has been rather difficult to present animated stimuli and collect behavioral responses online. However, with advances in digital technology and commercially available software to construct, present, and analyze movies for behavioral investigations, there is a growing interest in psychocinematic research. A rather simple, yet useful procedure is described that presents movie clips and collects multiple behavioral responses during its presentation. It uses E-prime 2.0 Professional software to run the experiment and Microsoft Excel to sort and analyze the data.

View PDFchevron_right
Discrete, recurrent, and scalable patterns in human judgement underlie affective picture ratings
Shamal Lalvani

2022

Operant keypress tasks, where each action has a consequence, have been analogized to the construct of "wanting" and produce lawful relationships in humans that quantify preferences for approach and avoidance behavior. It is unknown if rating tasks without an operant framework, which can be analogized to "liking", show similar lawful relationships. We studied three independent cohorts of participants (= 501, 506, and 4,019 participants) collected by two distinct organizations, using the same 7-point Likert scale to rate negative to positive preferences for pictures from the International Affective Picture Set. Picture ratings without an operant framework produced similar value functions, limit functions, and tradeoff functions to those reported in the literature for operant keypress tasks, all with goodness of fits above 0.75. These value, limit, and trade-off functions were discrete in their mathematical formulation, recurrent across all three independent cohorts, and demonstrated scaling between individual and group curves. In all three experiments, the computation of loss aversion showed 95% confidence intervals below the value of 2, arguing against a strong overweighting of losses relative to gains, as has previously been reported for keypress tasks or games of chance with calibrated uncertainty. Graphed features from the three cohorts were similar and argue that preference assessments meet three of four criteria for lawfulness, providing a simple, short, and low-cost method for the quantitative assessment of preference without forced choice decisions, games of chance, or operant keypressing. This approach can easily be implemented on any digital device with a screen (e.g., cellphones).

View PDFchevron_right
The apparent reality of movies and emotional arousal: A study using physiological and self-report measures
Eilis Hennessy

Poetics, 2012

Historical developments of cinema technology have contributed to the apparent reality of movie-goers' experience. The current study uses both self-report and physiological measures (heart-rate, skin conductance, skin temperature) as indices of 29 participants' negative emotional arousal so as to investigate the effect of increasing a movie's perceptual realism (i.e. stereoscopic depth) on emotional experience. Data were recorded while half of the participants viewed emotional movie scenes in 3D, and half viewed them in 2D. The groups did not differ significantly in terms of their self-reported feelings of negative emotional arousal, tonic skin conductance level or skin temperature. However, the 3D group reported their experience as significantly more perceptually realistic (natural) and they also demonstrated a significantly higher heart-rate change-score than their counterparts in the 2D condition. Importantly, the current study provides evidence that these results are not due to group differences in emotional sensitivity, engagement, or the novelty of the 3D effect. Group differences in heart-rate, but not skin conductance level, suggests that increasing stereoscopic depth reduces the emotional regulation processes. Although caution is expressed about assumptions of causation, consideration is given to the idea that increased physiological arousal contributes to perceived apparent reality and vice versa.

View PDFchevron_right
Psychophysiological Response Patterns to Affective Film Stimuli
Marieke Bos

PLoS ONE, 2013

Psychophysiological research on emotion utilizes various physiological response measures to index activation of the defense system. Here we tested 1) whether acoustic startle reflex (ASR), skin conductance response (SCR) and heart rate (HR) elicited by highly arousing stimuli specifically reflect a defensive state and 2) the relation between resting heart rate variability (HRV) and affective responding. In a within-subject design, participants viewed film clips with a positive, negative and neutral content. In contrast to SCR and HR, we show that ASR differentiated between negative, neutral and positive states and can therefore be considered as a reliable index of activation of the defense system. Furthermore, resting HRV was associated with affect-modulated characteristics of ASR, but not with SCR or HR. Interestingly, individuals with low-HRV showed less differentiation in ASR between affective states. We discuss the important value of ASR in psychophysiological research on emotion and speculate on HRV as a potential biological marker for demarcating adaptive from maladaptive responding. Citation: Bos MGN, Jentgens P, Beckers T, Kindt M (2013) Psychophysiological Response Patterns to Affective Film Stimuli. PLoS ONE 8(4): e62661.

View PDFchevron_right

Cited by

Emotion Elicitation through Vibrotactile Stimulation as an Alternative for Deaf and Hard of Hearing People: An EEG Study
Tomás Ortiz

Electronics

Despite technological and accessibility advances, the performing arts and their cultural offerings remain inaccessible to many people. By using vibrotactile stimulation as an alternative channel, we explored a different way to enhance emotional processes produced while watching audiovisual media and, thus, elicit a greater emotional reaction in hearing-impaired people. We recorded the brain activity of 35 participants with normal hearing and 8 participants with severe and total hearing loss. The results showed activation in the same areas both in participants with normal hearing while watching a video, and in hearing-impaired participants while watching the same video with synchronized soft vibrotactile stimulation in both hands, based on a proprietary stimulation glove. These brain areas (bilateral middle frontal orbitofrontal, bilateral superior frontal gyrus, and left cingulum) have been reported as emotional and attentional areas. We conclude that vibrotactile stimulation can el...

View PDFchevron_right
580 California St., Suite 400
San Francisco, CA, 94104
© 2025 Academia. All rights reserved