Affective ratings of sound stimuli

Redondo, Jaime; Fraga, Isabel; Padrón, Isabel; Piñeiro, Antonio

doi:10.3758/brm.40.3.784

Cited by 46 publications

(50 citation statements)

References 17 publications

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“…3 fits the typical boomerang shape found by Bradley and Lang in both the first (1999b) and the second (2007a) editions of the IADS, as well as by Redondo et al (2008) and Fernández-Abascal et al (2008) in the Spanish standardizations of the first edition of the IADS database. As is demonstrated by inspection of Fig.…”

Section: Resultsmentioning

confidence: 62%

“…We then explore sex differences in these ratings. Finally, we present data on cross-cultural differences between the EP adaptation of the IADS and the American (Bradley & Lang, 2007a) and Spanish (Fernández-Abascal et al, 2008;Redondo et al, 2008) standardizations.…”

Section: Resultsmentioning

confidence: 99%

“…These sounds can be related to humans (e.g., baby, erotic couple, boy laugh, giggling, female cough, man wheeze, or child abuse), animals (e.g., cat, puppy, growl, dog, seagull, or robin), objects (e.g., music box, typewriter, Polaroid camera, or doorbell), musical instruments (e.g., bugle, harp, guitar, or bagpipes), means of transport (e.g., jet, helicopter, train, plane crash, or bike wreck), or scenarios (e.g., tropical rainforest, country night, restaurant, or brook). Of note, the IADS database has already been adapted to Spanish populations (Fernández-Abascal et al, 2008;Redondo, Fraga, Padrón, & Piñeiro, 2008), and beyond this threedimensional assessment, a categorical assessment of the IADS sounds within five discrete categories (happiness, sadness, fear, disgust, and anger) is also available with a sample of American English participants (see Stevenson & James, 2008).…”

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confidence: 99%

“…Since IADS data-set norms are already available for other languages, we additionally explored whether cross-cultural differences in sounds' affective ratings would be observed when comparing the EP adaptation presented in this article with the American (Bradley & Lang, 2007a) and Spanish (Fernández-Abascal et al, 2008;Redondo et al, 2008) IADS standardizations, as was done for the recent adaptation of ANEW to EP (Soares et al, 2012). As Mesquita and Walker (2003) pointed out, emotions are biological as well as sociocultural phenomena.…”

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confidence: 99%

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Affective auditory stimuli: Adaptation of the International Affective Digitized Sounds (IADS-2) for European Portuguese

et al. 2013

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In this study, we present the normative values of the adaptation of the International Affective Digitized Sounds (IADS-2;Bradley & Lang, 2007a) for European Portuguese (EP). The IADS-2 is a standardized database of 167 naturally occurring sounds that is widely used in the study of emotions. The sounds were rated by 300 college students who were native speakers of EP, in the three affective dimensions of valence, arousal, and dominance, by using the Self-Assessment Manikin (SAM). The aims of this adaptation were threefold: (1)to provide researchers with standardized and normatively rated affective sounds to be used with an EP population; (2)to investigate sex and cultural differences in the ratings of affective dimensions of auditory stimuli between EP and the American

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Section: Resultsmentioning

confidence: 62%

Section: Resultsmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Affective auditory stimuli: Adaptation of the International Affective Digitized Sounds (IADS-2) for European Portuguese

et al. 2013

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“…Various psychological and psychophysiological experiments have revealed that these stimuli sets have great potential in the investigation of sad, fear, and disgust emotion [43,50]. Additionally, Mikels et al (Mikels et al, 2005) and Redondo et al (Redondo et al, 2008) provided a more complete characterisation of the categorical structure of the IAPS and IADS stimuli set, with the objective of identifying images and sounds that elicit one discrete emotion more than other emotions. The IAPS pictures 1 [disgust: valence-mean ± SD = 2.43 ± 1.51, arousal mean ± SD = 5.90 ± 2.25; fear: valence mean ± SD = 3.80 ± 1.89, arousal mean ± SD = 5.85 ± 2.12; sadness: valence-mean ± SD = 2.74 ± 1.57, arousal mean ± SD = 5.00 ± 2.08] and IADS sound 2 [disgust: valence mean ± SD = 4.00 ± 1.72, arousal mean ± SD = 5.82 ± 1.93; fear: valence mean ± SD = 4.00 ± 1.72, arousal mean ± SD = 5.82 ± 1.93; sadness: valence Emotional state classification in PD 11 mean ± SD = 3.28 ± 1.65, arousal mean ± SD = 6.61 ± 1.89] were selected and combined together according to their arousal and valence values provided in the databases.…”

Section: Stimulus Materialsmentioning

confidence: 99%

Optimal set of EEG features for emotional state classification and trajectory visualization in Parkinson's disease

Yuvaraj

Murugappan

Ibrahim

et al. 2014

International Journal of Psychophysiology

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In addition to classic motor signs and symptoms, individuals with Parkinson's disease (PD) are characterized by emotional deficits. Ongoing brain activity can be recorded as electroencephalograph (EEG) to discover the links between emotional states and brain activity.This study utilized machine-learning algorithms to categorize emotional states in PD patients compared with healthy controls (HC) using EEG. Twenty non-demented PD patients and 20 healthy age-, gender-, and education level-matched controls viewed happiness, sadness, fear, anger, surprise, and disgust emotional stimuli while fourteen-channel EEG was being recorded.Multi-modal stimulus (combination of audio and visual) was used to evoke the emotions. To classify the EEG-based emotional states and visualize the changes of emotional states over time, this paper compares four kinds of EEG features for emotional state classification and proposes an approach to track the trajectory of emotion changes with manifold learning. From the experimental results using our EEG data set, we found that (a) bispectrum feature is superior to other three kinds of features, namely power spectrum, wavelet packet and nonlinear dynamical analysis; (b) higher frequency bands (alpha, beta and gamma) play a more important role in emotion activities than lower frequency bands (delta and theta) in both the groups and; (c) the trajectory of emotion changes can be visualized by reducing subject-independent features with manifold learning. This provides a promising way of implementing visualization of patient's emotional state in real time and leads to a practical system for noninvasive assessment of the emotional impairments associated with neurological disorders.

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Affect Display Recognition Through Tactile and Visual Stimuli in a Social Robot

Marques-Villarroya¹,

Gamboa-Montero²,

Jumela-Yedra³

et al. 2022

Lecture Notes in Computer Science

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This thesis is distributed under license "Creative Commons Atributtion -NonCommercial -Non Derivatives".A toda mi familia.En primer lugar tengo que agradecer por todo el trabajo a mis directores, Fernando y José Carlos, y a mi tutor, Miguel Ángel, por toda su ayuda. Sin duda alguna, de no ser por ellos, todo este trabajo no podría haber sido posible. A Fernando quiero agradecerle su cercanía, sus ideas y todo ese dinamismo tremendamente contagioso que ha permitido que este trabajo tuviera nuevas opciones cuando parecía que habíamos tocado hueso. Quiero agradecerle las charlas, los consejos y los paseos juntos divagando sobre cualquier tema que se nos viniese a la cabeza. A José Carlos quiero darle las gracias por todo su apoyo, serenidad y buen juicio, por estar ahí siempre que lo he necesitado, y, sobre todo, por estar ahí cuando no sabía que le necesitaba. Quiero agradecerle tanto por su paciencia conmigo y su sensatez, como por los buenos momentos y las risas juntos. A Miguel Ángel tengo que agradecerle toda su vasta experiencia, por los consejos en momentos críticos del trabajo y por su guía y buen criterio. Él ha sido el que me ha brindado esta enorme oportunidad y, sobre todo, quiero agradecerle por la estabilidad y la seguridad que siempre me ha dado. Por último, aunque no forman parte 'oficial' de este trabajo, tengo que dar las gracias a María y a Álvaro también, por todo su apoyo. Siempre han estado ahí cuando he necesitado una mano o simplemente charlar, y también son una parte importante de este trabajo.I would like to switch back to English in just this paragraph to thank the Intelligent Robots and Systems Group (IRSg) for all the help they gave me during my stay in Lisbon. It was an amazing experience for me I will always remember it. I want to thank Pedro Lima for the opportunity to spend my stay there, and, above all, I would like to thank Meysam Basiri for his support and mentorship. Muito obrigado! Ahora quiero hablar de mi familia. Sin ellos, definitivamente nada de lo que he realizado hasta ahora habría sido posible. Siempre han estado entre bambalinas para levantarme cuando me he caído y para abrazarme cuando más lo he necesitado. Quiero agradecer a mis padres, a Juanjo y a Begoña, por todo el amor, el apoyo y la guía que han volcado sobre mí durante todo este tiempo. Siempre me han dado lo mejor de sí mismos y no tengo palabras para describir todo lo que les agradezco lo que han hecho y siguen haciendo por mí. A Guille, mi hermano, quiero darle las gracias también por todo su cariño, su ayuda, las risas que siempre compartimos y, sin duda alguna, por aguantarme todo este tiempo. Quiero darles las gracias a mis abuelos, a Mariano, a Julia, a Antonio y a Mercedes, por no darme otra cosa que no fuese su amor, por todo el orgullo que siempre me han demostrado y por apoyarme incondicionalmente en cada decisión que he tomado hasta ahora. No me quiero olvidar de mis tíos y de mis primos, haciendo una mención especial a mi prima Paloma, por todo el cariño que siempre me han mostrado. Por último, qui...

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Affective ratings of sound stimuli

Cited by 46 publications

References 17 publications

Affective auditory stimuli: Adaptation of the International Affective Digitized Sounds (IADS-2) for European Portuguese

Affective auditory stimuli: Adaptation of the International Affective Digitized Sounds (IADS-2) for European Portuguese

Optimal set of EEG features for emotional state classification and trajectory visualization in Parkinson's disease

Affect Display Recognition Through Tactile and Visual Stimuli in a Social Robot

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