Emotional Valence and Arousal Interact in Attentional Control

Jefferies, Lisa N.; Smilek, Daniel; Eich, Eric; Enns, James T.

doi:10.1111/j.1467-9280.2008.02082.x

Cited by 204 publications

(216 citation statements)

References 32 publications

(43 reference statements)

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“…Among the factors composing such conditions are transient affective states. Indeed, more generally, the way attention is allocated seems to be influenced by mood (Jefferies et al, 2008;Vermeulen, 2010). More specifically, in studies on nonclinical populations, mood or state affect has consistently proved to influence the way emotional information is processed (Bouhuys, 1995;Chepenik et al, 2007;Egidi and Nusbaum, 2012;Herr et al, 2012;Lee et al, 2008;Lim et al, 2012;Niedenthal et al, 2000Niedenthal et al, , 1997Schmid and Schmid Mast, 2010).…”

Section: Introductionmentioning

confidence: 99%

Joint effect of alexithymia and mood on the categorization of nonverbal emotional vocalizations

Bayot¹,

Pleyers²,

Kotsou³

et al. 2014

Psychiatry Research

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General rightsIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulationsIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: http://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. The role of stable factors, such as alexithymia (i.e., difficulties identifying and expressing feelings, externally oriented cognitive style), or temporary factors, such as affective states (mood), on emotion perception has been widely investigated in the literature. However, little is known about the separate or joint effect of the alexithymia level and affective states (positive affectivity, negative affectivity) on the recognition of nonverbal emotional vocalizations (NEV) (e.g., laughs, cries, or sighs). In this study, participants had to categorize NEV communicating 10 emotions by selecting the correct verbal emotional label. Results show that the level of alexithymia is negatively correlated to the capacity to accurately categorize negative vocalizations, and more particularly sad NEV. On the other hand, negative affectivity appeared negatively correlated with the ability to accurately categorize NEV in general, and negative vocalizations in particular. After splitting the results by the alexithymia level (high vs. low scorers), significant associations between mood and accuracy rates were found in the group of high alexithymia scorers only. These findings support the idea that alexithymic features act across sensory modalities and suggest a mood-interference effect that would be stronger in those individuals.

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Section: Introductionmentioning

confidence: 99%

Joint effect of alexithymia and mood on the categorization of nonverbal emotional vocalizations

Bayot¹,

Pleyers²,

Kotsou³

et al. 2014

Psychiatry Research

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show abstract

“…For example, following the induction of various mood states, participants in a happy mood make more frequent saccades and fixate longer on positive emotional peripheral stimuli than do control participants (Wadlinger & Isaacowitz, 2006); happy participants are more likely than sad participants to selectively process patterns at a configurational level in global-local tasks (Fredrickson, 2003;Gasper & Clore, 2002); happy participants are less likely than anxious participants to experience an attentional blink in a rapid serial visual presentation (Jefferies, Smilek, Eich, & Enns, 2008); and anxious participants are more likely to have a narrow spatial focus of attention than are control participants (Derryberry & Reed, 1998;Derryberry & Tucker, 1994). Importantly, the direction of the emotion-performance link can run the other way as well.…”

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confidence: 99%

Person perception informs understanding of cognition during visual search

Brennan

Watson

Kingstone

et al. 2011

Atten Percept Psychophys

Self Cite

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Does person perception-the impressions we form from watching others-hold clues to the mental states of people engaged in cognitive tasks? We investigated this with a two-phase method: In Phase 1, participants searched on a computer screen (Experiment 1) or in an office (Experiment 2); in Phase 2, other participants rated the searchers' video-recorded behavior. The results showed that blind raters are sensitive to individual differences in search proficiency and search strategy, as well as to environmental factors affecting search difficulty. Also, different behaviors were linked to search success in each setting: Eye movement frequency predicted successful search on a computer screen; head movement frequency predicted search success in an office. In both settings, an active search strategy and positive emotional expressions were linked to search success. These data indicate that person perception informs cognition beyond the scope of performance measures, offering the potential for new measurements of cognition that are both rich and unobtrusive.Keywords Visual search . Eye movements and visual attention . Attention "You can observe a lot by just watching." -Widely attributed to Yogi Berra A typical experiment in cognitive psychology involves the presentation of a stimulus in a controlled laboratory setting, systematic variation of the conditions under which the stimulus is presented, and measurement of the participant's response with a combination of keypresses, brief vocal responses, eye movements, and limb actions. Cognitive researchers almost never look directly at participants while they perform in an experiment, leaving it an open question whether they are missing key features of visible behavior that are relevant to the mental processes under investigation. The purpose of the present study is to ask whether researchers can enhance their understanding of cognition by adding measures of person perception to their standard toolbox of performance measurements.Our motivation is both practical and theoretical. On the practical side, most personal computers today come equipped with a built-in webcam aimed directly at the user. We can think of no reason why this resource should lie dormant without consideration of its research potential. Theoretically, many studies in social psychology over the past decade have demonstrated the surprising reliability and validity of thin-slicing, referring to the ability of persons to make rapid evaluations of the personality, disposition, and intent of others from very small samples of their behavior (Ambady, Bernieri, & Richeson, 2000;Ambady, Hallahan, & Rosenthal, 1995;Borkenau & Liebler, 1995;Borkenau, Mauer, Riemann, Spinath, & Angleitner, 2004;Carney, Colvin, & Hall, 2007;Gladwell, 2007;Naumann, Vazire, Rentfrow, & Gosling, 2009;Rule, Macrae, & Ambady, 2009;Weisbuch & Ambady, 2011). Why should cognitive researchers not also consider this potential signal?A second theoretical motivation comes from the growing interest in emotional, social, and motivational influence...

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“…Many correlations between musical features and emotional descriptors exhibit overlap. For example, slower music is typically associated with lower arousal and valence --slow music might correlate with perceptual descriptors like sad or tired (Javela et al, 2008;Jefferies et al, 2008). A good deal of further work remains in uncovering the precise amount of overlap amongst these correlates, and the impact of an initial affective state on the exhibited response to the musical feature correlates.…”

Section: Music and Emotionmentioning

confidence: 99%

Affective Calibration of Musical Feature Sets in an Emotionally Intelligent Music Composition System

Williams

Kirke

Miranda

et al. 2017

ACM Trans. Appl. Percept.

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Affectively-driven algorithmic composition (AAC) is a rapidly growing field which exploits computer-aided composition in order to generate new music with particular emotional qualities or affective intentions. An AAC system was devised in order to generate a stimulus set covering 9 discrete sectors of a 2-dimensional emotion space by means of a 16 channel feed-forward artificial neural network. This system was used to generate a stimulus set of short pieces of music, which were rendered using a sampled piano timbre and evaluated by a group of experienced listeners who ascribed a two-dimensional valence-arousal co-ordinate to each stimulus. The underlying musical feature set, initially drawn from literature, was subsequently adjusted by amplifying or attenuating the quantity of each feature in order to maximize the spread of stimuli in the valence-arousal space before a second listener evaluation was conducted. This process was repeated a third time in order to maximize the spread of valence-arousal co-ordinates ascribed to the generated stimulus set in comparison to a spread taken from an existing pre-rated database of stimuli, demonstrating that this prototype AAC system is capable of creating short sequences of music with a slight improvement on the range of emotion found in a stimulus set comprised of real-world, traditionally composed musical excerpts.

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Emotional Valence and Arousal Interact in Attentional Control

Cited by 204 publications

References 32 publications

Joint effect of alexithymia and mood on the categorization of nonverbal emotional vocalizations

Joint effect of alexithymia and mood on the categorization of nonverbal emotional vocalizations

Person perception informs understanding of cognition during visual search

Affective Calibration of Musical Feature Sets in an Emotionally Intelligent Music Composition System

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