Is detection of angry faces more efficient in humans? 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).



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 &amp; Hansen, 1988
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]



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):



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).




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).