In search of the emotional face: Anger versus happiness superiority in visual search.

Savage, Ruth A.; Lipp, Ottmar V.; Craig, Belinda M.; Becker, Stefanie I.; Horstmann, Gernot

doi:10.1037/a0031970

Cited by 81 publications

(84 citation statements)

References 26 publications

(111 reference statements)

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“…Dickins & Lipp, 2013;Lipp et al, 2009), an ASE was predicted in homogenous backgrounds. Apparatus and Materials.…”

Section: Discussionmentioning

confidence: 99%

Different faces in the crowd: A happiness superiority effect for schematic faces in heterogeneous backgrounds.

Craig¹,

Becker²,

Lipp³

2014

Emotion

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Recently, D.V. Becker, Anderson, Mortensen, Neufeld, and Neel (2011) proposed recommendations to avoid methodological confounds in visual search studies using emotional photographic faces. These confounds were argued to cause the frequently observed Anger Superiority Effect (ASE), the faster detection of angry than happy expressions, and conceal a true Happiness Superiority Effect (HSE). In Experiment 1, we applied these recommendations (for the first time) to visual search among schematic faces that previously had consistently yielded a robust ASE. Contrary to the prevailing literature, but consistent with D.V. Becker et al. (2011), we observed a HSE with schematic faces. The HSE with schematic faces was replicated in Experiments 2 and 3 using a similar method in discrimination tasks rather than fixed target searches. Experiment 4 isolated background heterogeneity as the key determinant leading to the HSE.

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“…Dickins & Lipp, 2013;Lipp et al, 2009), an ASE was predicted in homogenous backgrounds. Apparatus and Materials.…”

Section: Discussionmentioning

confidence: 99%

Different faces in the crowd: A happiness superiority effect for schematic faces in heterogeneous backgrounds.

Craig¹,

Becker²,

Lipp³

2014

Emotion

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“…This recommendation has been applied in studies investigating visual search for targets defined by emotional expression. In these studies, participants search through a set of neutral faces for either happy or angry targets (Becker, Anderson, Mortensen, Neufeld, & Neel, ; Frischen, Eastwood, & Smilek, ; Savage, Lipp, Craig, Becker, & Horstmann, ). In the context of search for own‐ and other‐age faces, participants may search through a constant set of middle‐aged faces for young adult and older adult targets.…”

Section: Discussionmentioning

confidence: 99%

The relationship between visual search and categorization of own‐ and other‐age faces

Craig

Lipp

2018

British J of Psychology

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Young adult participants are faster to detect young adult faces in crowds of infant and child faces than vice versa. These findings have been interpreted as evidence for more efficient attentional capture by own-age than other-age faces, but could alternatively reflect faster rejection of other-age than own-age distractors, consistent with the previously reported other-age categorization advantage: faster categorization of other-age than own-age faces. Participants searched for own-age faces in other-age backgrounds or vice versa. Extending the finding to different other-age groups, young adult participants were faster to detect young adult faces in both early adolescent (Experiment 1) and older adult backgrounds (Experiment 2). To investigate whether the own-age detection advantage could be explained by faster categorization and rejection of other-age background faces, participants in experiments 3 and 4 also completed an age categorization task. Relatively faster categorization of other-age faces was related to relatively faster search through other-age backgrounds on target absent trials but not target present trials. These results confirm that other-age faces are more quickly categorized and searched through and that categorization and search processes are related; however, this correlational approach could not confirm or reject the contribution of background face processing to the own-age detection advantage.

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“…In the original KDEF database some happy faces have their teeth visible and others not. As teeth visibility artificially attracts viewer’s attention [85], they will be covered by a grey Gaussian-type filter which optimizes gaze patterns towards more informative emotional areas of the face [86] (see Fig. 3).…”

Section: Methodsmentioning

confidence: 99%

Attentional bias modification in depression through gaze contingencies and regulatory control using a new eye-tracking intervention paradigm: study protocol for a placebo-controlled trial

et al. 2016

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BackgroundAttentional biases, namely difficulties both to disengage attention from negative information and to maintain it on positive information, play an important role in the onset and maintenance of the disorder. Recently, researchers have developed specific attentional bias modification (ABM) techniques aimed to modify these maladaptive attentional patterns. However, the application of current ABM procedures has yielded, so far, scarce results in depression due, in part, to some methodological shortcomings.The aim of our protocol is the application of a new ABM technique, based on eye-tracker technology, designed to objectively train the specific attentional components involved in depression and, eventually, to reduce depressive symptoms.MethodsBased on sample size calculations, 32 dysphoric (BDI ≥13) participants will be allocated to either an active attentional bias training group or a yoked-control group. Attentional training will be individually administered on two sessions in two consecutive days at the lab. In the training task series of pairs of faces (i.e. neutral vs. sad; neutral vs. happy; happy vs. sad) will be displayed. Participants in the training group will be asked to localize as quickly as possible the most positive face of the pair (e.g., the neutral face in neutral vs. sad trials) and maintain their gaze on it for 750 ms or 1500 ms, in two different blocks, to advance to the next trial. Participants’ maintenance of gaze will be measured by an eye-tracking apparatus. Participants in the yoked-control group will be exposed to the same stimuli and the same average amount of time than the experimental participants but without any instruction to maintain their gaze or any feedback on their performance. Pre and post training measures will be obtained to assess cognitive and emotional changes after the training.DiscussionThe findings from this research will provide a proof-of-principle of the efficacy of eye-tracking paradigms to modify attentional biases and, consequently, to improve depressed mood. If the findings are positive, this new training approach may result in the improvement of cognitive bias modification procedures in depression.Trial registrationThis trial was retrospectively registered on July 28, 2016 with the ClinicalTrials.gov NCT02847793 registration number and the title ‘Attentional Bias Modification Through Eye-tracker Methodology (ABMET)’.Electronic supplementary materialThe online version of this article (doi:10.1186/s12888-016-1150-9) contains supplementary material, which is available to authorized users.

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In search of the emotional face: Anger versus happiness superiority in visual search.

Cited by 81 publications

References 26 publications

Different faces in the crowd: A happiness superiority effect for schematic faces in heterogeneous backgrounds.

Different faces in the crowd: A happiness superiority effect for schematic faces in heterogeneous backgrounds.

The relationship between visual search and categorization of own‐ and other‐age faces

Attentional bias modification in depression through gaze contingencies and regulatory control using a new eye-tracking intervention paradigm: study protocol for a placebo-controlled trial

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