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Hansen and hansen proposed the anger superiority effect: threatening faces pop out of crowds, perhaps as a result of a preattentive, parallel search for signals of direct threat.

Pinkham (2010) demonstrated that we are faster when we have to spot an angry face than the happy one (below).

enter image description here

Can we really establish that we have an anger superiority effect?

Angry faces and non-angry faces differ for too many features...

How can we attribute this result (angry faces faster detected) to anger-related features, when so many other features (not necessarily related to anger) are different?

references

Hansen & Hansen, 1988

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    $\begingroup$ What do you mean with 'really establish'? The data show you that's the case. Apparently they are indeed sufficiently different from the distractors to be picked out more easily. What exactly do you question about the study or its results? What is your question? $\endgroup$ – AliceD Jan 5 '18 at 10:01
  • $\begingroup$ I wrote that in my opinion, if you look at the pictures, angry faces are too different, i mean the angry-face expressions appear to me like a caricature, they are exaggerated... My question, i'm trying to reformulate, is: is this evidence reliable? $\endgroup$ – Fil Jan 5 '18 at 15:15
  • $\begingroup$ @AliceD Is more clear now? I mean if you look at a visual search experiment, target and distractors differ for just one feature. That kind of experiment is efficient because i can say that if the target 'pop out' it's due to the uncommon feature... In this case i don't think it's a good evidence to conclude that we have an anger superiority effect... am i clear? $\endgroup$ – Fil Jan 5 '18 at 15:22
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    $\begingroup$ No. I mean, how strong is the evidence that states: that angry faces pop out of the visual scene when subjects have seen faces of different people with different face reading characteristics... how can we attribute this result (angry faces faster detected) to anger-related features, when so many other features (not necessarily related to anger) are different? $\endgroup$ – Fil Jan 5 '18 at 18:05
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    $\begingroup$ There's definitely a newer paper that suggests we might be cued for something else: ncbi.nlm.nih.gov/pubmed/21744984 OR maybe more than one ncbi.nlm.nih.gov/pubmed/18410189 But I'm not sure these completely invalidate prior findings. Let me read them :-) In the mean time, it's probably a good idea if you edited in your question your last couple of comments. $\endgroup$ – Fizz Jan 5 '18 at 23:19
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I'm not sure there's totally indisputable evidence on this, but I found a newer (2017) paper by Bayet et al. with infant subjects, but with fear rather than anger as the emotion tested (vs. happy), and (more importantly) using a different methodology (and IMHO better) because instead of different faces (in a crowd), they used the same face but mixed with noise, e.g. for fear [but for illustrative purposes only, see blow why]

enter image description here

They actually tried to determine which facial features enabled easier detection, eyes or mouth by mixing noise preferentially in that region (the resutling images were validated with SSIM, so there were four series of stimuli prestented (whith varying level of noise in each series).

Each of the face stimuli were presented in differents tests to the infant subjects; each test consisted of a noise-mixed face vs pure noise (two images on screen) and a camera was used to record the infant's reactions. The film was then analyzed to determine several measures of reaction, which were in turn used to calculate measures of face detection.

Two different measures of face detection were ultimately derived: A simple one based on percentages of total looking time [PTLT] and a more complex psychometric measure, the methodology of which is pretty involved (and thus perhaps a weak point of this paper):

A multivariate measure of face detection was derived by classifying trials as ‘face is on the left’ or ‘face is on the right’. The rationale for this metric is similar to the idea of ‘double psychophysics’ [48]: if one can reliably guess on which side of the screen the face was presented by looking at the infant’s behaviour, then it can be inferred that the infant is discriminating between the presence of a face or noise. We implemented this idea computationally using MVPA [42] to locate the side of presentation of the face (left or right) on each trial based on (i) PTLT to the left, (ii) number of looks to the left, (iii) number of looks to the right, (iv) duration of first look to the left, (v) duration of first look to the right, (vi) median duration of looks to the left, (vii) median duration of looks to the right, and (viii) direction of first look (left or right). Durations were log-transformed [56]. Continuous measures were z-scored within-subject. Measures were chosen a priori given the visual preference of infants for faces [57]. PTLT to the right is equal to 100% minus PTLT to the left, and thus did not need to be included. Trials from all participants were pooled to maximize the number of training examples, and a logistic regression algorithm (a common classifier for MVPA) was repeatedly trained on all trials except one and tested on the trial that was left-out (leave-one-out cross-validation). Forward sequential feature selection was implemented inside each crossvalidation loop (see electronic supplementary material, table S2 for results on the full dataset). This procedure led to locating the face side for each trial in a way that reflects generalization. We used logistic regression because it provides log-odds, a direct, criterion-free, continuous measure of evidence for each response (‘face is on the left’ versus ‘face is on the right’)—as opposed to accuracy, a binary measure dependent on a decision criterion. Raw evidence (log-odds for the right versus left side) was pooled to derive correct evidence (log-odds for the correct versus incorrect side) as a multivariate measure of face versus noise discrimination.

The overall results of their analysis look like this (one row for each detection measure):

enter image description here

Their interpretation of results:

Psychometric curves of the PTLT to the face side revealed a significantly lower threshold for the Fearful eye+ face condition (44.41 ± 1.98% face signal; [...]; figure 3a) than for the Happy eye- face condition (difference: 5.20 +/- 2.62% face signal, 95% CI [0.001 0.103]), but not the other conditions ([...] figure 3a), across all age groups. [...] A similar result was found when applying psychometric curve modelling to the correct multivariate face versus noise discrimination evidence; the face detection threshold for the Fearful eye+ condition (44.07 ± 2.14% face signal; [...] figure 3c) was significantly lower than the detection threshold for the Happy eye- condition (increase in threshold: 7.90 ± 2.52% face signal, 95% CI [0.030 0.128]) but not the other conditions (Wald confidence intervals, alpha = 5%; [...]; figure 3c)

Similar models were used to estimate the difference in threshold between the Fearful eye- condition and other conditions, or between the Happy eye+ condition and other conditions (electronic supplementary material, tables S5–S8). Results are summarized in figure 4. Overall, psychometric curve modelling of infant looking data revealed face detection thresholds at about 44% signal, with an increase of about 5% signal in threshold for Happy eye- condition compared to the Fearful eye+ condition, and intermediate thresholds for Happy eye+ and Fearful eye- conditions depending on whether PTLT alone (figure 4a) or correct multivariate discrimination evidence (figure 4b) was used as a measure of face versus noise detection.

To clarify whether these differences in detection thresholds reflected a main effect of facial expression, a main effect of eye visibility, or an interaction between the two, and to test for an effect of age, we conducted further analyses focused on the linear portion of the psychometric curves corresponding to trials around the fitted detection thresholds (40–50% signal). [...; figure 3b,c,e,f I'm omitting all the chi-square stats]

Results were mixed when considering PTLTs for the face side alone, as the effect of face emotion was restricted to 3.5-month-olds on this measure. However, correct discrimination evidence, a more comprehensive multivariate measure inclusive of PTLTs and other aspects of looking behaviour (e.g. first look) revealed a detection advantage for fearful faces compared to happy faces across all age groups (i.e. it did not significantly interact with age).

enter image description here

Of course this doesn't answer the question whether the effect holds in a crowd (or any non-laboratory setting, or if applies to anger etc.) The authors themselves say:

Future research should determine whether the readiness to detect fearful faces (compared to happy faces) in infancy generalizes to naturalistic settings beyond the laboratory. Comparing detection of fearful versus angry versus sad faces will also clarify whether the effect applies just to threat-relevant stimuli (anger and fear) or more generally to negative novel expressions (fear, anger, and sadness).

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  • $\begingroup$ Thank you, i think that the fact they use the same face "morphing" is a better evidence for a possible preference of attention on emotional stimuli $\endgroup$ – Fil Jan 10 '18 at 9:30

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