Efficiency of Ensemble and Exemplar Coding for Facial Identity

Ryan Ng

Supervisors: Romina Palermo & Markus Neumann

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.

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)

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

More likely to recognise

than

Average CompositeOf Set Faces(Notactually seen!)

Actual IndividualMember (This wasseen)

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

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)

Hypotheses Set duration (Experiment 1)

Set Size

Se

nsit

ivit

y

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

ExemplarsMorphs

Set Duration

Se

nsit

ivit

y

Experiment 1 – Set Duration

+

Set Durations (ms)

50 | 100 | 200 | 400 | 800 | 1600 | 3200 | 6400

Matching Exemplar

Non-MatchingExemplar

MatchingMorph

Non-MatchingMorph

Match Non-Match

Exemplar(Individual)

Morph

Probe Face

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

50 100 200 400 800 1600 3200 64000

0.2

0.4

0.6

0.8

1

1.2

1.4

ExemplarsMorphs

Set Duration (ms)

Diff

ere

nce

Sco

res

Results

Pairwise comparisons between sensitivity differences, at each duration

* t(23) = 4.83, p < .001

*t(23) = 10.54, p < .001

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

Experiment 2 – Set Size

8

Match Non-Match

Exemplar(Individual)

Morph

Non-MatchingExemplar

Probe Face

MatchingMorph

Non-MatchingMorph

Matching Exemplar

Set Sizes

2 | 4 | 6 | 8

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

Results

2 4 6 80

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

ExemplarsMorphs

Set Size (Numerosity)

Z (

Ma

tch

– M

ism

atc

h)

*t(23) = 10.54, p < .001

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)

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

Thank you

Thank you for your time!

I would also like to thank my supervisors Romina and Markus for being a great help!

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

Results

50 100 200 400 800 1600 3200 64000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Morphs (M)

Morphs (MM)

Exemplar (M)

Exemplar (MM)

Set Duration (ms)

% o

f "P

res

en

t" R

es

po

ns

es

Significant Interaction of probe type * match type * duration, F(7, 161) = 10.51, p < .001, η2partial = .314.

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

2 4 6 80

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Morphs (M)

Morphs (MM)

Exemplars (M)

Exemplars (MM)

Set Size (Numerosity)

% o

f "P

res

en

t" R

es

po

ns

es