1. Efficiency of Ensemble and Exemplar Coding for Facial Identity
Ryan Ng
Supervisors: Romina Palermo & Markus Neumann
2. What is Ensemble Coding?
Our environment contains sets/collections of similar objects
Visual system has capacity limitations
Can’t really code each one with precision at once
Ensemble coding describes our ability to
Briefly observe sets of similar features and estimate average
information about them.
3. An Averaging Mechanism
It has been shown that observers are very accurate at Ensemble
Coding for low-level features
Determining the average size of a set of shapes (Chong & Treisman, 2005)
However, people don’t seem to remember individuals! (Ariely, 2001)
Not only average low-level features, evidence that it also occurs
for faces!
Judging the average emotion and gender from sets of faces (Haberman &
Whitney, 2007)
Efficient in abstracting facial expression
Accurately averaged emotion of 16 different faces, in only 500 ms!
(Haberman & Whitney, 2009)
4. Ensemble Coding Identity
More recently, evidence for averaging of facial identity
Exposed to 4 faces of different identities for 2 seconds (de Fockert &
Wolfenstein, 2009)
More likely to ‘falsely’ recognise morphs than actual individuals (de
Fockert & Wolfenstein, 2009; Neumann,2013)
Set of faces
Average Composite
Of Set Faces(Not
actually seen!)
Actual Individual
Member (This was
seen)
5. Averages vs Individuals
Exemplar (individual) Coding of identities
Evidence of little memory for individuals (de Fockert & Wolfenstein, 2009; de Fockert &
Gautrey, 2013)
Studies support that only a single face is coded at once (Bindemann, Burton &
Jenkins, 2005)
Averaging identity, but don’t remember individuals
Possibly counterintuitive to identification of specific individuals
So…would this always be the case?
There has been little work done on Ensemble Coding for identity
Previous study used 4 faces in a set at 2 seconds
Possible for observers to still sufficiently code individual identities
6. Current Study
So is Ensemble Coding efficient in averaging identity?
That is, estimate precise mean without requiring individuals?
Efficiency already demonstrated using facial expression? (Haberman &
Whitney, 2009)
Identity is unique, whereas expression is dynamic
Manipulated participants access to sets of exemplars (individual
face images)
Examined how Ensemble Coding was affected
Using 2 experiments, varied
Set duration (Exposure)
Set size (Number of Faces)
7. Hypotheses
Set duration (Experiment 1)
Sensitivity
Set Size
Set size (Experiment 2)
Alternative: Ensemble Coding depends on Exemplar Coding
There would then be similar patterns of increase and decrease for morphs
and exemplars
We require individual identities to form averages
Hypothesis: Ensemble Coding is efficient for identity
Averages are formed independently, like for facial expression
Sensitivity
Set Duration
Exemplars
Morphs
8. Experiment 1 – Set Duration
+
Set Durations (ms)
50 | 100 | 200 | 400 | 800 | 1600 | 3200 | 6400
Matching
Exemplar
Non-Matching
Exemplar
Matching
Morph
Non-Matching
Morph
Match Non-Match
Exemplar
(Individual)
Morph
Probe Face
9. Experiment 1 – Set Duration
2x2x8 repeated measures design
2 Match types
Matching/Non-Matching
2 Probe types
Morph/Exemplar
8 Set Durations (in milliseconds):
50, 100, 200, 400, 800, 1600, 3200 and 6400ms
Using 4 faces in every set
10. 0
0.2
0.4
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1.2
1.4
50 100 200 400 800 1600 3200 6400
DifferenceScores
Set Duration (ms)
Exemplars
Morphs
Results
Pairwise comparisons between sensitivity differences, at each duration
* t(23) = 4.83, p < .001
*t(23) = 10.54, p < .001
11. Discussion
Results suggest that Ensemble Coding depends on Exemplar
Coding of individuals
Alternative hypothesis supported
Similar patterns of increase before 3200ms
Given enough time (at least 3 seconds)
Ensemble Coding becomes reduced as people become better at
Exemplar Coding
Strongest Ensemble Coding effect from 400 to 1600ms
Seems to be optimal interval of averaging
12. Experiment 2 – Set Size
8
Match Non-Match
Exemplar
(Individual)
Morph
Non-Matching
Exemplar
Probe Face
Matching
Morph
Non-Matching
Morph
Matching
Exemplar
Set Sizes
2 | 4 | 6 | 8
13. Experiment 2 – Set Size
2x2x4 repeated measures design was used
2 Match types
Matching/Non-Matching
2 Probe types
Morph/Exemplar
4 set sizes (numerosity):
2, 4, 6 and 8
1600ms constant duration
15. Discussion
Results again suggest that Ensemble Coding depends on
Exemplar Coding individuals
Alternate hypothesis supported
As set size increases, sensitivity to both morphs and exemplars appear
to decrease together
Presented with larger groups of faces,
People are less likely to average identity (because they lack individual
information)
16. Conclusion
This study found preliminary evidence against efficiency of Ensemble
Coding
People code individual identities, then form averages
Or is individual information simply not discarded?
Optimal interval for averaging identity
Steep rise between durations of 400 to 1600ms
Given more different identities to process
Both Ensemble and Exemplar (individual) Coding become poorer
Findings suggest that Ensemble Coding for identity
Is not efficient as demonstrated for facial expression (Haberman & Whitney, 2009)
But is dependent on Exemplar (individual) Coding
17. Thank you
Thank you for your time!
I would also like to thank my supervisors Romina and Markus for
being a great help!
18. Limitations
Sample size was limited, may have affected statistical power
Non-significant morph advantage at short durations
Promising though!
Set size study was limited due to number of faces in a morph
Participants could tell when it was simply a morph
Findings have to be taken with caution
Further studies can explore if Ensemble Coding only occurs for
unique vs dynamic features, by using Sets with same vs different
identities
19. Results
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50 100 200 400 800 1600 3200 6400
%of"Present"Responses
Set Duration (ms)
Morphs (M)
Morphs (MM)
Exemplar (M)
Exemplar (MM)
Significant Interaction of probe type * match type * duration, F(7, 161) = 10.51, p < .001, η2
partial = .314.
20. Results
Significant Interaction of probe type * match type, F(1, 23) = 9.73, p = .005, probe type * set size, F(3, 69) = 8.09, p <
.001, and match type * size, F(3, 69) = 198.25, p = .005. Non-significant triple interaction, F(3, 69) = .16, p = .925
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%of"Present"Responses
Set Size (Numerosity)
Morphs (M)
Morphs (MM)
Exemplars (M)
Exemplars (MM)
