Honeybee (Apis mellifera) vision can discriminate between and recognise images of human faces
1 Institut fur Zoologie III (Neurobiologie), Johannes Gutenberg
Universität, Mainz, 55099, Germany,
2 Clinical Vision Sciences, La Trobe University, Victoria 3086,
Australia
3 School of Biological Sciences, Queen Mary, University of London, London,
E1 4NS, UK
* Author for correspondence at present address: Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge, CB2 3EA, UK (e-mail: a.dyer{at}latrobe.edu.au)
Accepted 13 October 2005
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Summary |
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Key words: visual processing, face recognition, honeybee, brain
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Introduction |
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The evidence for a special region of the human brain for processing faces
has recently been challenged, however, by data showing that the fusiform gyrus
in subjects who have a particular field of expertise, for example bird
watchers or car experts, also shows increased activity when these subjects
view stimuli from their specific class of expertise
(Gauthier et al., 2000;
Tarr and Gauthier, 2000
). In
addition, a fMRI study of subjects with autism shows that face processing can
be reliably facilitated by regions of the brain other than the fusiform gyrus
(Pierce et al., 2001
).
Other animals, including invertebrates, are able to recognise conspecifics
using facial cues (Kendrick et al.,
2001; Tibbetts,
2002
; Tibbetts and Dale,
2004
). For example, individual paper wasps (Polistes
fuscatus) are able to recognise the facial features of other individual
wasps to help maintain a strong social order within a hive
(Tibbetts, 2002
). However,
even in humans it is currently not clear whether face recognition requires
specialised, species-specific neuronal circuitry, or if face recognition might
be a learned expertise, as a result of extensive experience with a certain
class of visual stimuli (Tarr and Cheng,
2003
). In the present study, the honeybee (Apis
mellifera) was used as an animal model that has not been exposed to
evolutionary pressure for recognising human faces but does have impressive
pattern recognition and cognitive abilities that might facilitate the task
(Gould, 1985
;
Lehrer, 1997
;
Chittka et al., 2003
;
Stach et al., 2004
;
Dyer and Chittka, 2004
;
Zhang and Srinivasan, 2004
).
For example, there is evidence that bees may solve visual tasks using either
configural retinotopic-template type matching strategies
(Wehner, 1981
; Gould,
1985
,
1986
;
Srinivasan, 1994
;
Giger and Srinivasan, 1995
)
and/or a set of features extracted from stimuli
(Srinivasan, 1994
;
Giger and Srinivasan, 1995
;
Efler and Ronacher, 2000
;
Stach et al., 2004
). If bees
can recognise human faces, then this is evidence that face recognition
requires neither a specialised neuronal circuitry nor a fundamentally advanced
nervous system.
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Materials and methods |
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Stimuli were 6x8 cm achromatic photographs presented on a vertical,
circular plastic screen of 50 cm diameter that could be rotated to prevent
position learning (Fig. 1A).
Two target and two distractor stimuli were mounted on freely rotating hangers
with a landing platform (Fig.
1B). The experiment allowed the bees to fly towards a stimulus and
choose the visual angle required to solve the task
(Lehrer, 1993;
Giger and Srinivasan, 1995
;
Horridge, 1996
;
Efler and Ronacher, 2000
). A
target stimulus contained a reward of a 10 µl drop of 25% sucrose solution
and a distractor stimulus contained a punishment of a 10 µl drop of 0.12%
quinine hemisulphate to promote motivation to perform difficult visual
discrimination tasks (Chittka et al.,
2003
).
|
The task was then changed by presenting distractor face stimuli that were
similar to the target stimuli (Fig.
1C, column ii). The bees were trained with differential
conditioning (Giurfa et al.,
1999; Dyer and Chittka,
2004
), and when acquisition reached 80% (10 training bouts) each
bee was tested in a nonrewarded trial that terminated when the test bee first
lost interest in making choices and temporarily abandoned the test apparatus.
Distractor stimuli were then removed from the screen, and a drop of sucrose
solution was placed on the target stimuli. When the bee subsequently returned
it was allowed to collect sucrose from the target stimuli until satiated to
ensure motivation for additional non-rewarded trials.
Each bee was then presented with non-rewarded tests using the target stimuli and two different types of novel distractor stimuli (Fig. 1C, columns iii and iv, respectively). These two tests were done sequentially, and between these tests bees were rewarded to ensure motivation.
Bees were then given a non-rewarded test using the original training face stimuli where both of these stimuli were rotated by 180° (Fig. 1C, column v). The entire sequence of tests was completed in one day for each of five bees.
Finally, two of the bees were given a non-rewarded test with original training stimuli two days after their initial training to evaluate whether the learning of face stimuli had led to formation of a long-term memory.
Human subject subjective rankings of faces
Six human subjects (mean age ±
S.E.M., 27.8±2.2 years) with normal
corrected vision were asked to rank the three distractor face stimuli in
relation to the perceptual similarity to the target face stimulus. This was
done by simultaneously presenting the target stimulus and the three different
distractor stimuli and individually asking each subject to rank the distractor
stimuli faces in order of similarity to the target stimulus. It is known that
the adult human visual system recognises faces mainly by using a configural
strategy (Tanaka and Farah,
1993; Bartlett and Searcy,
1993
; Tanaka and Sengco,
1997
; Collishaw and Hole,
2000
). The subjective rankings were determined to investigate if
bee choices for the different distractor faces correlated with how humans
ranked the perceptual similarity of distractor stimuli.
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Results |
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Following differential conditioning, correct choices for the target face
were evaluated in a non-rewarded test (Fig.
1C, column ii). During both training and the non-rewarded tests,
the bees often hovered in front of a stimulus before making a decision to land
(Fig. 1B), and the mean
hovering distance from the stimuli was 6.4±1.1 cm
(S.E.M.). The choices for the target face
were significantly different from chance (2=94.8, d.f.=1,
P<0.001), showing that it is possible for this animal model to
learn how to discriminate between images of human faces. Correct choices for
the target face versus the novel distracters were also significantly
different from chance (
2=141.5, d.f.=1, P<0.001),
indicating that, in addition to being able to discriminate a target face from
a learned distractor, bees were able to recognise the target face from novel
distractors.
Bees tested on the original training pair of faces, but with both faces rotated by 180° (Fig. 1C, column v), performed significantly poorer (Wilcoxon Signed Rank Test; N=5, Z=2.023, P=0.043), indicating that stimulus rotation disrupts the bees' ability to process the learned images of the faces.
Two bees tested 2 days after the initial training retained the information in long-term memory. One bee scored 93.9% on the initial day of training and 79.2% 2 days later; and a second bee achieved scores of 87.4% and 75.9%, respectively. This shows that the differential conditioning to face stimuli led to the formation of a long-term memory in bees.
Human subject subjective rankings of faces
All six subjects ranked distractor faces in the same order; compared with
target, the most similar face was the distractor in
Fig. 1C column iv, the
distractor in column ii was ranked second and the least similar face was the
distractor in column iii. The ranking of the three distractor face stimuli in
relation to the perceptual similarity of the target face stimulus by human
subjects was tested for a correlation with how accurately the bees
discriminated between stimuli. Whilst the bees choices shown in
Fig. 1C indicate a trend that
bees do discriminate between faces more accurately when the faces are ranked
perceptually less similar by humans, this relationship was not significant
(rs=0.151, N=15, P=0.591).
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Discussion |
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Two possible mechanisms by which bees may process spatial information are
either by using a retinotopic-template type matching strategy or by using a
set of features extracted from the stimuli
(Efler and Ronacher, 2000).
There is evidence that in some circumstances bees use retinotopic-template
type matching that is consistent with configural processing
(Wehner, 1981
;
Gould, 1985
;
Gould, 1986
;
Srinivasan, 1994
;
Giger and Srinivasan, 1995
),
whilst in some circumstances the data are more consistent with a feature
extraction model of visual processing
(Srinivasan, 1994
;
Giger and Srinivasan, 1995
;
Efler and Ronacher, 2000
;
Stach et al., 2004
). For face
recognition tasks, adult humans mainly use a configural visual strategy
(Tanaka and Farah, 1993
;
Bartlett and Searcy, 1993
;
Tanaka and Sengco, 1997
;
Collishaw and Hole, 2000
). Bee
choices for the different faces were not significantly correlated with adult
human perceptual rankings for faces, possibly suggesting bees were not using a
configural strategy to solve the task. However, a confounding factor is that
in human processing of faces, subjects develop different visual strategies
between the ages of 6 and 10 years; at 6 years of age children appear to use a
feature extraction model of visual processing but by 10 years of age
performance is more consistent with a configural model of visual processing
(Carey and Diamond, 1977
) and
it is also known that bees do use different visual strategies depending upon
the type and level of conditioning to spatial features
(Giurfa et al., 2003
).
Furthermore, recent work using eye movement studies in humans suggests that
whilst adults use a configural mechanism to recognize faces, the learning of
faces by humans requires some level of feature extraction processing to
promote reliable recognition (Henderson et
al., 2005
). The absence of a relationship between bee choices and
adult human perceptual rankings could thus be a result of either (1) the
extent to which bees were using configural processing not being as strong as
in adult humans who have had much more extensive experience with faces or (2)
bees solving the task using a feature extraction model of visual processing.
In the former, bees may move from feature extraction to configural processing
depending upon their individual level of experience.
Stach et al. (2004) show
that bees are able to link and assemble local features of a visual pattern to
construct a representation of a stimulus, suggesting that some form of a
feature extraction model may enable bees to solve the face recognition task.
However, the large decrease in the frequency of bee correct choices when the
stimuli were rotated by 180° suggests that configural processing may have
been disrupted, since this type of stimulus manipulation has a much greater
effect on configural rather than feature extraction mechanisms in human visual
processing of face stimuli (Collishaw and
Hole, 2000
). Thus, from the data in the current experiments, it is
not possible to definitively conclude which mechanism bees use to recognise
images of human faces, which indeed may be because individual bees might use
different mechanisms depending upon the level of experience with the training
stimuli (Giurfa et al., 2003
).
Future experiments may be able to reveal the visual mechanism used by bees to
facilitate face recognition by using a wider range of face stimuli and by
considering the variety of approaches previously used to reveal feature
extraction and configural mechanisms for the human perception of faces (e.g.
Yin, 1969
;
Carey and Diamond, 1977
;
Bruce, 1988
;
Rizzo et al., 1987
;
Tanaka and Farah, 1993
;
Tanaka and Sengco, 1997
;
Collishaw and Hole, 2000
).
The honeybee brain has less than 0.01% the number of neurons of the human
brain (Zhang and Srinivasan,
2004). There has been considerable debate about the level of
cognitive resources required to recognise faces. Some evidence suggests that
highly specialised neural regions within the mammalian brain are required for
face recognition tasks (Kanwisher et al.,
1997
; Kanwisher,
2000
), whilst other studies suggest that face recognition is only
one case of visual expertise with a certain class of stimuli
(Gauthier et al., 2000
;
Tarr and Gauthier, 2000
;
Tarr and Cheng, 2003
).
Pascalis et al. (2002
) tested
the visual capabilities of 6-month-old and 9-month-old human infants and
showed that the six-month-old infants were equally good at recognising both
human and non-human primates, whilst by 9 months of age the perceptual window
narrows towards recognising individuals within the human species. However,
this neural plasticity in humans appears to be confined to face categories
that include human and primate faces but not faces from a totally different
species such as cows (Campbell et al.,
1997
). Many hymenopteran insects have impressive cognitive
abilities that are used to detect and identify different types of flowers in
order to collect nutritional rewards
(Gould, 1985
;
Lehrer, 1997
;
Chittka et al., 2003
;
Stach et al., 2004
;
Dyer and Chittka, 2004
;
Zhang and Srinivasan, 2004
).
The studies by Tibbetts (2002
)
and Tibbetts and Dale (2004
)
show that hymenopteran insects are capable of recognising the faces of
conspecifics in the context of complex social structures, but to our knowledge
this current study is the first report that invertebrates have sufficient
neural flexibility to learn how to discriminate between and recognise faces of
other species.
In normal human subjects, the processing by the visual and neural system
for faces is very fast (Campbell et al.,
1997; Pascalis et al.,
2002
) and can deal with a large number of different faces
(Standing et al., 1970
;
Bruce, 1988
). Whilst an
insect's brain is unlikely to be able to reach these levels of performance,
our results show that recognition of human faces can be achieved by a honeybee
brain following differential conditioning to this class of visual stimuli.
This suggests that face recognition is a task that can be solved, at least to
a certain level, by a general neural system that has a reasonable degree of
plasticity. The finding that bees can reliably recognise faces may seem
surprising in the context that there are human subjects who suffer from
prosopagnosia and are unable to recognise the faces of familiar persons,
despite having reasonably normal visual processing
(Rizzo et al., 1987
;
De Renzi and di Pellegrino,
1998
; Duchaine, 2004). However, there is evidence that subjects
with prosopagnosia may covertly recognise individual faces and that the
inability to be able to report recognition is due to limitations on the
activation of associated memory for a face
(Tranel and Damasio, 1985
),
even though the visual system has captured sufficient information to allow for
a recognition (Tranel and Damasio,
1985
; Rizzo et al.,
1987
). The results in this current study show that even bees are
capable of recognising human faces and thus supports the view that the human
brain may not need to have a visual area specific for the recognition of faces
(Gauthier et al., 2000
;
Tarr and Gauthier, 2000
;
Tarr and Cheng, 2003
).
However, the result cannot fully exclude the possibility that the human brain
does have a specific region for the processing of faces since there is also
evidence from one subject for whom face processing remains normal despite
agnosia (a recognition deficit) for non-face objects
(Moscovitch et al., 1997
).
Further experiments with bees that tackle fundamental questions that have been
investigated in humans for face recognition tasks
(Yin, 1969
;
Carey and Diamond, 1977
;
Bruce, 1988
;
Rizzo et al., 1987
;
Tanaka and Farah, 1993
;
Tanaka and Sengco, 1997
;
Collishaw and Hole, 2000
) may
reveal the extent to which a relatively simple brain can solve these tasks and
may thus help define a baseline for the minimum cognitive resources required
to facilitate face recognition.
Conclusion
This study shows that it is possible for honeybees to both learn to
discriminate between similar human faces and to subsequently recognise a
target face when it is presented in conjunction with novel distractor faces.
The findings indicate that it is possible for the visual and neural system of
an animal to learn to reliably recognise a human face, even though the animal
has no evolutionary history for the task.
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Acknowledgments |
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