<p>I'm not sure there's totally indisputable evidence on this, but I found <a href="https://www.ncbi.nlm.nih.gov/pubmed/28878060" rel="nofollow noreferrer">a newer (2017) paper by Bayet et al.</a> 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]</p>

<p><a href="https://i.sstatic.net/638nl.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/638nl.png" alt="enter image description here"></a></p>

<p>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 <a href="https://en.wikipedia.org/wiki/Structural_similarity#Multi-Scale_SSIM" rel="nofollow noreferrer">SSIM</a>, so there were four series of stimuli prestented (whith varying level of noise in each series).</p>

<p>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.</p>

<p>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):</p>

<blockquote>
  <p>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.</p>
</blockquote>

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

<p><a href="https://i.sstatic.net/oLWI0.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/oLWI0.png" alt="enter image description here"></a></p>

<p>Their interpretation of results:</p>

<blockquote>
  <p>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)</p>
  
  <p>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. <strong>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.</strong></p>
  
  <p>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]</p>
  
  <p>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).</p>
</blockquote>

<p><a href="https://i.sstatic.net/FOL4E.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/FOL4E.png" alt="enter image description here"></a></p>

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

<blockquote>
  <p>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).</p>
</blockquote>

CC BY-SA 3.0
2018-01-10T09:30:27.040Z
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
Fil
CC BY-SA 3.0
2018-01-10T09:19:50.320Z
2018-01-10T09:19:50.320Z
got trolled too much this week
<p>Hansen and hansen proposed the <strong>anger superiority effect:</strong> threatening faces pop out of crowds, perhaps as a result of a preattentive, parallel search for signals of direct threat.</p>

<p><a href="https://www.ncbi.nlm.nih.gov/pubmed/20141311" rel="nofollow noreferrer">Pinkham (2010)</a> demonstrated that we are faster when we have to spot an angry face than the happy one (below).</p>

<p><a href="https://i.sstatic.net/0o6tS.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/0o6tS.png" alt="enter image description here"></a></p>

<p><em>Can we really establish that we have an anger superiority effect?</em></p>

<p>Angry faces and non-angry faces differ for too many features...</p>

<p><strong>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?</strong></p>

<p><em>references</em></p>

<p><a href="https://www.ncbi.nlm.nih.gov/pubmed/3397866" rel="nofollow noreferrer">Hansen &amp; Hansen, 1988</a></p>

CC BY-SA 3.0
2018-01-05T10:01:36.270Z
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?
AliceD
CC BY-SA 3.0
2018-01-05T15:15:32.867Z
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?
Fil
CC BY-SA 3.0
2018-01-05T15:22:54.980Z
@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?
Fil
CC BY-SA 3.0
2018-01-05T17:55:38.590Z
Could you elaborate on what you mean by "too different"? Do you mean the head tilt and other features not related to anger? I'm assuming you don't mean features related to anger, like furrowed brows and exposed teeth.
Seanny123
CC BY-SA 3.0
2018-01-05T18:05:16.237Z
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?
Fil
CC BY-SA 3.0
2018-01-05T18:09:17.560Z
I don't understand what isn't clear to you... in that papers there's no statement that justify the assumption: that faces have been detected faster because of anger-related feature... @Seanny123 why they draw this conclusion? how can they exclude for example that this effect was by chance ...
Fil
CC BY-SA 3.0
2018-01-05T23:17:36.230Z
@Fil: I see, you're basically asking how de we know there isn't some other [confounder](https://en.wikipedia.org/wiki/Confounding) in the differences between those faces that substantially contributes (or even better explains) the discrimination observed.
got trolled too much this week
CC BY-SA 3.0
2018-01-05T23:19:28.870Z
There's definitely a newer paper that suggests we might be cued for something else: https://www.ncbi.nlm.nih.gov/pubmed/21744984 OR maybe more than one https://www.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.
got trolled too much this week
CC BY-SA 3.0
2018-01-06T00:12:43.097Z
Yes, this has been a major criticism of this study.
mrt
CC BY-SA 3.0
2018-01-10T07:49:47.383Z
There may be something to it, but in terms of attention attraction rather than efficiency as a newer paper on infants (but on fear not anger) suggests: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5611515/ They've used a much better design, using the same faces with different expressions: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5611515/figure/F1/
got trolled too much this week
CC BY-SA 3.0
2018-01-10T07:57:36.663Z
Although perhaps there's something about detection threshold as well: https://www.ncbi.nlm.nih.gov/pubmed/28878060
got trolled too much this week
<social-psychology><cognitive-neuroscience><emotion>
Is detection of angry faces more efficient in humans?
Fil
# Is detection of angry faces more efficient in humans?

(Asked by: username_0 on 2018-01-05)

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)](https://www.ncbi.nlm.nih.gov/pubmed/20141311) demonstrated that we are faster when we have to spot an angry face than the happy one (below).

[![enter image description here](https://i.sstatic.net/0o6tS.png)](https://i.sstatic.net/0o6tS.png)

*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](https://www.ncbi.nlm.nih.gov/pubmed/3397866)

**Tags:** social-psychology, cognitive-neuroscience, emotion

> **Comments:**
> **username_1**: 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?  
> **username_0**: 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?  
> **username_0**: @username_1 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?  
> **username_2**: Could you elaborate on what you mean by "too different"? Do you mean the head tilt and other features not related to anger? I'm assuming you don't mean features related to anger, like furrowed brows and exposed teeth.  
> **username_0**: 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?  
> **username_0**: I don't understand what isn't clear to you... in that papers there's no statement that justify the assumption: that faces have been detected faster because of anger-related feature... @username_2 why they draw this conclusion? how can they exclude for example that this effect was by chance ...  
> **username_3**: @username_0: I see, you're basically asking how de we know there isn't some other [confounder](https://en.wikipedia.org/wiki/Confounding) in the differences between those faces that substantially contributes (or even better explains) the discrimination observed.  
> **username_3**: There's definitely a newer paper that suggests we might be cued for something else: https://www.ncbi.nlm.nih.gov/pubmed/21744984 OR maybe more than one https://www.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.  
> **username_4**: Yes, this has been a major criticism of this study.  
> **username_3**: There may be something to it, but in terms of attention attraction rather than efficiency as a newer paper on infants (but on fear not anger) suggests: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5611515/ They've used a much better design, using the same faces with different expressions: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5611515/figure/F1/  
> **username_3**: Although perhaps there's something about detection threshold as well: https://www.ncbi.nlm.nih.gov/pubmed/28878060  

---

## 1 answers

### Answer by username_3 on 2018-01-10. Score: 2 | Accepted answer

I'm not sure there's totally indisputable evidence on this, but I found [a newer (2017) paper by Bayet et al.](https://www.ncbi.nlm.nih.gov/pubmed/28878060) 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](https://i.sstatic.net/638nl.png)](https://i.sstatic.net/638nl.png)

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](https://en.wikipedia.org/wiki/Structural_similarity#Multi-Scale_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](https://i.sstatic.net/oLWI0.png)](https://i.sstatic.net/oLWI0.png)

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](https://i.sstatic.net/FOL4E.png)](https://i.sstatic.net/FOL4E.png)

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).

> **Comments:**
> **username_0**: 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  

---

