Visual one-shot learning as an ‘anticamouflage device’: a novel morphingparadigm

Tetsuo Ishikawa & Ken Mogi

Cognitive Neurodynamics
ISSN 1871-4080
Cogn Neurodyn
DOI 10.1007/s11571-011-9171-z

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DOI 10.1007/s11571-011-9171-z

RESEARCH ARTICLE

Visual one-shot learning as an ‘anti-camouflage device’: a novel
morphing paradigm
Tetsuo Ishikawa • Ken Mogi

Received: 10 March 2011 / Revised: 13 August 2011 / Accepted: 19 August 2011
Ó Springer Science+Business Media B.V. 2011

Abstract Once people perceive what is in the hidden
figure such as Dallenbach’s cow and Dalmatian, they seldom seem to come back to the previous state when they
were ignorant of the answer. This special type of learning
process can be accomplished in a short time, with the effectof learning lasting for a long time (visual one-shot learning). Although it is an intriguing cognitive phenomenon,
the lack of the control of difficulty of stimuli presented has
been a problem in research. Here we propose a novel
paradigm to create new hidden figures systematically byusing a morphing technique. Through gradual changes
from a blurred and binarized two-tone image to a blurredgrayscale image of the original photograph including
objects in a natural scene, spontaneous one-shot learning
can occur at a certain stage of morphing when a sufficientamount of information is restored to the degraded image. A
negative correlation between confidence levels and reaction times is observed, giving support to the fluency theoryof one-shot learning. The correlation between confidence
ratings and correct recognition rates indicates that participants had an accurate introspective ability (metacognition).
The learning effect could be tested later by verifying
whether or not the target object was recognized quicker inthe second exposure. The present method opens a way for aT. Ishikawa (&)
Department of Computational Intelligence and Systems Science,
Tokyo Institute of Technology, G3-46, 4259 Nagatsuta-cho,
Midori-ku, Yokohama 226-8502, Japane-mail: tetsuoishikaha@gmail.com
T. Ishikawa  K. Mogi
Sony Computer Science Laboratories, Inc., Takanawa Muse
Bldg, 3-14-13 Shinagawa-ku, Tokyo 141-0022, Japansystematic production of ‘‘good’’ hidden figures, which canbe used to demystify the nature of visual one-shot learning.
Keywords Visual one-shot learning  Hidden figure 
Fluency theory  Metacognition  Morphing

Introduction
Studying various modes of visual perception provides asalient tool for clarifying certain aspects of awareness andconsciousness. Considerable cognitive efforts are needed toperceive surroundings in dim light by using scotopic
vision, as color information is useless and spatial resolution
is much lower than usual. To segregate the figure from its
ground is difficult in these impoverished contexts. Mammals, birds and also insects need to judge shapes of objects
by, for example, perceiving illusory contour (Nieder 2002).
The ability to perceive illusory contour in partial occlusion
or in dimly lit situation such as under the moonlight isbiologically adaptive in natural surroundings, as it isadvantageous to be able to detect species which mimic
their environmental patterns (e.g., felid’s camouflage patterns, Allen et al. 2011) as quickly as possible to flee frompredators or to target prey. A visual system with suchability to uncover concealed objects is called an ‘‘anticamouflage device’’ (Ramachandran 1987). Originally theterm ‘‘anti-camouflage device’’ referred only to illusory
contour perception. The concept is also applicable to thesituation of seeing hidden figures such as the grayscale
picture of a cow (Dallenbach 1951) and the two-tone imageof a Dalmatian dog (Gregory 1970). When people have an
initial look at these hidden figures, they can see nothing but
black and white meaningless patterns. But once they realize what is in the figure, a rapid perceptual learning occurs

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and is completed in a very short time. The dramatic transition from an unconscious impasse to a conscious epiphany
is thought to be a special type of learning called visual oneshot learning (Mogi et al. 2005; Mogi and Tamori 2006,
2007; Giovannelli et al. 2010), or ‘‘Aha!’’ experience (Gickand Lockhart 1995). During such an experience of insight(Bowden et al. 2005), it is thought that synaptic connectivities are changed rapidly to form a new association (Hebb1949). Therefore, one-shot learning associates meaninglesspatterns to meaningful understanding to gain a new concept
unknown before the learning. It is differentiated from mere
memory formation: encoding, retaining, and retrieval ofwell-known objects or things. This learning effect is long
lasting. It is also called the Eureka effect (Ahissar andHochstein 1997). Two-tone degraded images of human face
are named Mooney faces (Mooney 1957). In the insightful
moment when subjects perceive Mooney faces, neuralsynchronization spreads all over the brain, which lasts forabout 100 ms (Rodriguez et al. 1999). Such synchronizationis thought to be related to some of the Gestalt rules andfeature binding (Singer 2009) or a mechanism for transientfunctional neurocognitive connectivity (Werner 2009). In
general, when ‘‘Mooney’’ objects (i.e., bi-level quantizedimages of various objects) and their original grayscale
photographs are presented alternately, activities of inferior
temporal and parietal regions are enhanced (Dolan et al.
1997). Activities in the early retinotopic cortex and foveal
confluence are modulated by top-down interpretation as
well as the ventral visual stream and the lateral occipitalcomplex (Hsieh et al. 2010). Repetitive transcranial magnetic stimulations (rTMS) over the parietal cortex during
presentation of the undegraded images disrupt the identification of the degraded counterparts 30 min later (Giovannelli et al. 2010). The activation of left amygdala predicts
memory performance 1 week later in a similar paradigm(Ludmer et al. 2011), suggesting the importance of emotional aspects of one-shot learning.
The abrupt realization of the hidden figure provides arobust experimental tool to investigate certain aspects ofconscious visual perception in its systematic and temporalrichness. Historically, several psychological tests using the
aforementioned types of hidden figures have been developed to study Gestalt perception such as completion or
closure: Street Gestalt completion test (Street 1931), a new
closure test (Mooney and Ferguson 1951), Gestalt completion task and Snowy pictures task (Ekstrom et al. 1976),
and Waterloo Gestalt closure task (Bowers et al. 1990). In
many cases, however, there is a lack of specific description
about how to create these figures. In the case of Waterloo
Gestalt closure task, there is a short statement that artists
drew a series of stimuli based on their experience. The
famous hidden figures of cow (Dallenbach 1951) andDalmatian (Gregory 1970) are actually photographs: the123

former was a collection of Leo Potishman and the latter
was taken by the photographer, R. C. James. The conditions for shooting these photos and other details are notknown. A simple quantisation of photographic images
almost always results in either too obvious or difficult toperceive images and do not induce insight (Mogi and Tamori 2006, 2007). It is still unknown how to make ‘‘good’’
hidden figures systematically without manual retouch
based on heuristics. Repetitive usage of the same hidden
figure is essentially invalid, as each hidden figure can be
only effective to a naı̈ve subject. Thus, there is a shortageof a battery of controlled stimuli to be used in an experiment. Note that the stimuli used in almost all of the preceding imaging studies were too difficult for subjects torecognize by themselves. An often used practice, instead of
waiting for realization on their own, is to present pairs of
the problem (the degraded two-tone picture) and the solution (the original grayscale or color picture) alternately toprovide an answer directly and immediately (‘‘alternate
presentation paradigm’’, Dolan et al. 1997; Hsieh et al.
2010; Giovannelli et al. 2010; Ludmer et al. 2011). However, learning processes with cognitive effort to try to solvea problem without seeing the hint or answer are impaired in
the alternate presentation paradigm. These studies therefore
focused not on spontaneous (unsupervised) learning but on
forced (supervised) learning. An induced insight is different from a spontaneous one (Ludmer et al. 2011). The
unaided perception of ‘‘good’’ hidden figures like Dallenbach’s cow or Gregory’s Dalmatian is concerned with thelatter, where learning process takes some time, from a few
seconds to a few minutes or more. In some cases therealization takes place after quite a long time, e.g., hours todays. Practically finite experimental time makes it difficult
to adopt the free exploration paradigm without time limit.
Here we present a novel procedure which enables production in quantity of hidden figures to clarify the behavioral
characteristics of unsupervised visual one-shot learning. By
morphing ‘‘Mooney’’ objects with the original grayscale
images, figures of varied perceptual difficulties were produced. As a ‘‘happy medium’’ of previous paradigms, i.e.,
the alternate presentation (the answer is presented immediately) and the static presentation (presentation of a hidden
figure as still image for a prolonged time), the techniquepresented here sets out to morph a hidden figure and its
solution with intermediate images between them. The morphed sequence of images facilitate the subject’s quasispontaneous one-shot learning in a short time.
We expected that the percentage of perceived responses
indicated by button press before the end of the movie
would be high enough in the present morphing method.
The correct rate was predicted to be considerably larger
than that of in the conventional static paradigm. Features ofindividual stimuli (reaction time, morphing levels

Author's personal copy
Cogn Neurodynnecessary for perceiving objects, and correct rates) andtheir interrelationship were investigated. When the correct
rates are on a comparable level, reaction time is usable as
an index of the degree of difficulty. The spectrum ofreaction time for individual movies is supposed to bebroader, if the multifariousness of the visual world isreflected in the stimuli.
The fluency theory (Oppenheimer 2008) has been
applied to insight (Topolinski and Reber 2010). It predicts
that when a certain cognitive process is executed fluently,
the confidence and belief of truth about the process become
high, independently of the objective truth. The fluency
theory applied to visual one-shot learning would predictthat the reaction time as an objective index of fluency must
be correlated with confidence levels in a negative manner.
We also investigated the effect of repetitive presentationsof the same stimuli to confirm the accomplishment ofvisual one-shot learning. Once one-shot learning has
occurred, the learning effect is found to be long-lasting.
The comparison between the first and the second exposureswould confirm the basic ‘‘once and for all’’ nature of ‘‘oneshot’’ learning. If the one-shot learning has an adaptivefunction, reaction times should be shorter in the second
time compared to the first. The confidence levels in thesecond exposure should be larger than that for the firsttime. Finally, we examined the relationship that bridges theobjective and subjective aspects of one-shot learning. A
positive correlation between the objective correct rates andthe subjective confidence levels would suggest that the
subjects could judge their internal states accurately byintrospection, suggesting proper metacognitive abilities.
Through the variation of morphing and temporal transition parameters, we constructed an external means tocontrol the perception of figures in the conscious domain.
Morphing provides a means of dynamically probing into
the cognitive processes of one-shot learning, as opposed tothe typical static approaches of the conventional hidden
figure research. The analysis of results shed light on theinteraction of the search and memory recall processes
involved.

Methods
Subjects
Eight healthy adult volunteers (4 males, aged 25–31 years;
mean age 28.8 years) took part in the experiment. Participants were all right-handed, and had normal or correctedto-normal vision. They were all native Japanese speakers.
Instructions by the experimenter and verbal reports byparticipants were provided in Japanese.

Materials
Thirty-two grayscale photographs (300 9 300 pixels) ofcommonly familiar objects were blurred with Gaussian filter(radius = 3 pixels) and binarized to make ambiguous twotone images. Movies were made by the computer software
MorphX 2.9.5 (Norrkross Software) to display the wholemorphing sequence in 1% morphing transitions, from thedegraded image (e.g., Fig. 1 leftmost column) to the blurredgrayscale original (e.g., Fig. 1 rightmost column). The
frame rate was 5 fps (1% morphing every 200 ms). The
movie sequence consisted of 101 frames, with a total
duration of 20.2 s. The subjects sat in a comfortable position
at a viewing distance of 60 cm from the computer display(Apple 13-inch MacBook). Stimuli (10° 9 10°) were presented against a black background. We confirmed throughenquiries after the sessions that all subjects saw all stimuli
for the first time at the first exposure in this experiment.
The objects were selected from the list of ‘‘A Standardized Set of 260 pictures’’ (Snodgrass and Vanderwart1980) which contained some familiar categories: insects,
musical instruments, vegetables, fruits, animals, vehicles,
carpenter’s tools, etc.
Procedure
Subjects self-pacedly started watching movies by clicking
the mouse to extinguish a fixation cross and tried to perceive what was in the movie. They were instructed to stop
the movie by clicking the mouse button and to shut theireyes when they recognized the object in it or when themovie was finished without recognition. The subjects wereasked to close their eyes at the moment of realization, inorder to prevent the inspection of the freeze-frame which
remained on screen until the experimenter recorded theframe number of the movie and refreshed the display forthe next trial. The subjects then verbally reported the nameof the object and their ‘‘sureness’’ in a 11-point (0–10)
scale. With the experimenter’s verbal cue (‘‘ready’’), they
opened their eyes and proceeded to the next trial (Fig. 2).
The experiment was conducted twice after the practicesession (two trials): In the initial exposure, thirty movies
were played in a pseudorandom order. After a few minutes
break, the same set of stimuli were presented in the sameorder in the second exposure. The interval for the presentation of the same stimulus was more than 15 min.
Statistical analysis
When no response occurs before the end of the movie,‘‘reaction time’’ cannot be defined. In statistics, such a timeout trial is called a censored observation. To deal with thecensored data properly, a survival (time to event) analysis

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2-tone (0% Grayscale)

(25%)

(50%)

(75%)

Grayscale (100%)

HardestMedian RT = 19.9 secs, Morphing level: 99.5%, Correct rate = 37.5%

Moderate

Median RT = 6.8 secs, Morphing level: 34.0%, Correct rate = 62.5%

EasiestMedian RT = 1.3 secs, Morphing level: 6.5%,
Fig. 1 Typical frame examples extracted from the morphing movies.
Top row: the hardest to perceive movie (‘‘Alligator’’), middle row:moderate difficulty movie (‘‘Cherry’’), and bottom row: the easiest toperceive movie (‘‘Bicycle’’). The median reaction time, medianmorphing level and mean correct rate of these movies are set down
underneath each figures. Difficulties of movies are assessed by the
median reaction time, or the median morphing level. Time required

Fixation
Movie presentation
Aha! Button press
Verbal report:
Sureness (0-10 rating)
Object name

Correct rate = 100%for a half of participants to perceive is usable as index of difficulty
unless more than a half of them time out (which was not the case forall stimuli in the present study). Therefore, the hardest movie meant
the longest/highest RT/morphing level. The easiest one would be thestimulus with the shortest/lowest RT/morphing level. Moderate
stimulus had an intermediate difficulty, i.e., the 15th RT/morphinglevel among all 30 stimuli

analysis, or two-way analysis of variance (ANOVA) wasapplied to the correctness of the answer (correct rate) andconfidence data. The significance level was set to 0.05 forall statistical tests. Multiple comparisons are corrected by
the Bonferroni method.

Results

Go to the next trial...

Fig. 2 The time course of a single trial is depicted in illustrative
manner. Until the moment the subject presses the button, a morphingmovie is played in an ascending fashion, from 0% of grayscale (twotone) frame to 100% of grayscale one. The replay speed is 5 frames
per second. All movies are constructed from 101 frames, with amaximum duration of 20.2 s. After the key press or at the end of themovie, the participant were asked to report verbally the degree ofconfidence and what was perceived. For the movie in this figure, thecorrect answer was ‘‘Chicken’’with the Kaplan–Meier method (Bland and Altman 1998),
which has been applied to the analysis of an another domain
of insight, i.e., matchstick problem (Chi and Snyder 2011),
was conducted. A logrank test was used to compare the timeto event curve. Regardless of whether participants responded or not in each trial, the answer for the movie (objectname) and confidence rating were available. Hence the twotailed paired/one-sample t-test, Pearson’s correlation

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The earlier frames of morphing movies were degraded andambiguous so that the subjects found it hard to perceive
what was in the frame. However, as the movie frames
gradually got close to the original grayscale picture in themovie sequence, they could perceive something (either
correctly or incorrectly) in the frame. Key press responses
before reaching the end of morphing movies were observedin 92.0 ± 5.5% (mean ± SD) of trials in the first presentation. Significantly more reactions (95.4% ± 5.5% of trials) occurred in the second presentation (t(7) = 2.65,
P = 0.03). Participants answered faster in the second
exposure than the first exposure (first quantile: 15.8%,
median: 35.5%, the third quantile: 69.3% for the first timeand first quantile: 9.0%, median: 18.0%, the third quantile:
42.3% for the second time (logrank test: v2(1) = 20.5,
P = 5.9e–6). Verbal reports of the object name were
judged by the experimenter to be either correct (correct

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Reaction Time [sec]
0

5

10

15

B

20

BicycleCar
Airplane
Brush
Cat
Apple
Swing
Baby carriage
Butterfly
ChickenCaterpillar
Asparagus
BarrelBoot
CherryAxe
Clothespin
Camel
Ant
Bird
Bear
ViolinBee
BananaCloud
BalloonTurtleBread
BeetleAlligator

BicycleCar
Airplane
Brush
Cat
Apple
Swing
Baby carriage
Butterfly
ChickenCaterpillar
Asparagus
BarrelBoot
CherryAxe
Clothespin
Camel
Ant
Bird
Bear
ViolinBee
BananaCloud
BalloonTurtleBread
BeetleAlligator

Stimulus

Stimulus

A

0

20

40

60

80

0

100

2

Morphing Level [%]

C

D

6

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Reaction Time [sec]
0

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20

0

2

4

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10

BicycleCar
Airplane
Brush
Cat
Apple
Swing
Baby carriage
Butterfly
Chicken
Asparagus
Caterpillar
BarrelBoot
CherryAxe
Clothespin
Camel
Ant
Bird
Bear
ViolinBee
BananaCloud
BalloonTurtleBread
BeetleAlligator

Sureness Score

Stimulus

4

Sureness Rating

0.0

0.2

0.4

0.6

0.8

1.0

Correct Rate

0

20

40

60

80

100

Morphing Level [%]Fig. 3 a The reaction times (upper abscissa) and morphing levels(lower abscissa) of individual movies in the first exposure areindicated in a box-whisker plot. The vertical bold line in a box
indicates the median (i.e., the second quartile, Q2). The left and rightsides of the box show the first quartile (Q1) and the third quartile(Q3),
respectively. The left and right end of the whisker correspond to theminimum and maximum of the data in 1.5 times of interquartile range(IQR = Q3 - Q1) from Q1 or Q3, respectively. Blank circles show
the data point outside the range of [Q1 - 1.5 IQR, Q3 ? 1.5 IQR].

The movies are sorted by the ascending order of the median of
response time from the top to the bottom of the graph. b The surenessscores of individual movies are shown in a box-whisker diagram. The
alignment sequence of stimuli is the same as a. c The correct rate ofanswer object name is plotted for each stimulus in a barplot. The
error bars indicate standard error of the mean. d The correlation
between median morphing levels and mean sureness scores was
significant (Pearson’s correlation coefficient r = -0.67, P = 4.9e–05)

answer, CA) or incorrect (incorrect answer, IA, including a
non-perceived trial). Taking into account of whether theanswer was correct or not, 72.5 ± 9.7% of the movies werecorrectly perceived in the first exposure and significantly
more 81.7 ± 6.2% of the movies were recognized aright inthe second exposure (t(7) = 3.67, P = 0.008).
To elucidate the features in a variety of movies, thefollowing analysis was conducted on individual stimuli

using the data of the first exposure. Distributions of themorphing levels defined as percentages of containing
grayscale picture (or RTs) at the time when participants
perceived the hidden object were calculated for each movie
and sorted by median RTs (Fig. 3a). In many cases, thedistribution was not symmetric and had a fat tail in theright side. The sureness score (Fig. 3b) and the correct rate
(Fig. 3c) were plotted in the same order as in Fig. 3a. The

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0.2

0.4
0

20

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80

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0.8

100

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100

Morphing Level [%]Reaction Time [sec]

D

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0

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1st exposure
2nd exposure

0.2

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2nd exposure

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Proportion of subjects yet to recognize

15

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520

Morphing Level [%]Reaction Time [sec]015

1st exposure
2nd exposure

Morphing Level [%]

C

10

1.0

0

0.6

0.6

0.8

1st exposure
2nd exposure

Reaction Time [sec]

B

0.4

20

0.2

15

Proportion of subjects yet to recognize

10

1.0

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0.0

Proportion of subjects yet to recognize

0

0.0

Reaction Time [sec]

A

0

20

40

60

80

100

Morphing Level [%]Fig. 4 The time to event curves using Kaplan–Meier (KM) method
are compared on exposure time. There are four conditions because of
the combination of correctness (IA or CA) for each exposure time (1stor 2nd exposure). a IA–IA condition: when answers were incorrect in
the first and the second exposure, the KM-curves are not significantly
different (logrank test: v2(1) = 0.7, P = 0.399). b IA–CA condition:
when a correct perception occurred not for the first time but for thesecond time, a logrank test did not reveal significant difference

between the first and the second exposure (v2(1) = 0.3, P = 0.563).
c CA–IA condition: when the answer changed to incorrect in thesecond exposure although the answer was correct in the first exposure,the time to event curves did not differ from each other (v2(1) = 0.1,
P = 0.741). d CA–CA condition: only in this condition, or when theanswer in the second presentation was the same as the first correctone, the KM-curves of the second exposure dipped faster than that of
the first one (v2(1) = 35.2, P = 2.94e–09)

longer the subjects took to perceive, the less were confidence levels reported by them. (Fig. 3d). There was asignificant negative correlation between median RTs (ormorphing levels) and the mean sureness score (r = -0.67,
P = 4.9e-05). The mean confidence ratings were correlated
positively with mean correct rates (r = 0.56, P = 0.0012).
Median RTs (or morphing levels), however, did not correlate with mean correct rates (r = -0.36, P = 0.051).
To compare the naı̈ve observation (before learning
occurred) with the second time one (when a learning mightor might not have already occurred), the performance for aparticular image was classified into four categories: IA–IA,
IA–CA, CA–IA, and CA–CA conditions, based on thecorrectness of the subject’s answer in the first and thesecond exposures. The probabilities of stimuli categorized
for each conditions were IA–IA: 15.8 ± 4.9%, IA–CA:
11.7 ± 8.3%, CA–IA: 2.5 ± 3.2% and CA–CA: 70.0 ±12.0% of stimuli (mean ± SD). Time to event (IA or CA)
curve analysis was conducted for the four conditions(Fig. 4). No significant difference was found between the
curves of the first presentation and the second one in the
IA–IA, IA–CA and CA–IA condition (logrank test : allPs [0.05, Fig. 4a to c). On the other hand, only when thecondition was the CA–CA one, the reaction time (ormorphing level) was smaller in the second exposure than in
the first exposure (P \ 0.001, Fig. 4d).

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0.8
2

0.6
0.4

0

1

Correct Rate

**

0.2

−1

Sureness 2 − Sureness 1

3

1.0

**

1st exposure
2nd exposure

−2

IA−CA

CA−IA

CA−CA

Fig. 5 Difference of sureness ratings between the first presentationand the second one. In the IA–CA condition (two-tailed one-sample
t test: t(27) = 3.55, P = 0.0014) and the CA–CA condition(t(167) = 3.16, P = 0.0019), the differences were significantly
different from zero. In other words, sureness ratings were larger inthe second exposure than in the first exposure. In the IA–IA condition(t(37) = 1.08, P = 0.287) and CA–IA condition (t(5) = 0.10,
P = 0.921), a t-test did not reveal significant difference of surenessscores between the exposure time. Error bars show the standard errorof the mean. Asterisks show the significance of P values adjusted in
multiple comparison: ** P \ 0.01

Differences of sureness scores (i.e., DS = Sureness2 - Sureness 1) indexed the change of confidence in thesecond exposure from the first time. DS was plotted for
four experimental conditions (Fig. 5). If there is no changeof sureness ratings, DS must be zero. To test the null
hypothesis, one-sample t-tests were carried out. Significant
differences from zero were yielded in the IA–CA and CA–
CA conditions (Ps \ 0.01). For the rest, i.e., in the IA–IA
and CA–IA conditions, the confidence levels were statistically unchanged (Ps [ 0.05).
There are two parameters which characterize the performance: subjective confidence and objective accuracy.
The relationship between them is described in a line plot
(Fig. 6). The sureness data were summed up into six levels,
because of the numbers of lower sureness scores were too
small. To clarify the effect of sureness and the number of
exposures on the correct rate, a two-way ANOVA was
conducted. It showed the significant main effect of sureness(P \ 0.001), whereas the effect of exposure (P [ 0.05) andinteraction (P [ 0.05) were not significant. A correlation
analysis was performed to examine the linear relationshipbetween sureness and correct rate. Correct rate was positively correlated with sureness in both of the exposure
times: Pearson’s correlation coefficient r = 0.93 (P =0.007) for the first exposure and r = 0.96 (P = 0.003) for
the second exposure.

0.0

IA−IA

0−1

2−3

4−5

6−7

8−9

10

Sureness Score
Fig. 6 Correct rate as a function of sureness scores and number of
exposures is shown in a line graph. The error bars represent thestandard error of the mean. Two-way ANOVA (Sureness 9 Exposure) revealed that the main effect of sureness was significant (F(5,468) = 20.88, P = 7.47e–19). The effect of exposure (F(1,468) = 0.398, P = 0.097) and the interaction (F(5, 468) = 0.32,
P = 0.051) were not significant. Correct rate and sureness rating is
positively correlated in both the first exposure (Pearson’s correlation
coefficient r = 0.93, P = 0.007) and the second exposure (r = 0.96,
P = 0.003)

Discussion
The participants ‘‘perceived’’ some figures (irrespective ofcorrectness) and responded in 92.0 ± 5.5% of stimuli inthe first exposure before the end of the movie. Considering
only the CA cases, 72.5 ± 9.7% of stimuli were perceived
correctly for up to 20.2 s of presentation in the first trial. It
is significantly larger than that of the previous alternate
presentation paradigm: for example, the spontaneous recognition rate was 27 ± 3% for up to 10 s in experiment 2of Ludmer et al. (2011). Moreover, in the conventional
static presentation paradigm, where participants kept on
searching a stationary hidden figure (Dallenbach 1951;
Gregory 1970; Mogi et al. 2005; Mogi and Tamori 2006,2007), the spontaneous recognition rate was relatively low.
For instance, the correct rates of the famous hidden figures
were 6.2% for ‘‘Cow’’ and 12.0% for ‘‘Dalmatian’’ after
300 s in a series of mega-lab experiments with the numberof subjects N = 113 (Mogi, Sekine and Tamori, unpublished data). In comparison, more than 70% of the stimuli
were perceived correctly within a few tens of seconds inour study. Hence, it is suggested that the present study
should provide a suitable method to investigate spontaneous one-shot learning rather than induced insight.

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In the first exposures, the morphing levels necessary forcognition and the ratings for sureness for individual stimuli
were negatively correlated. It is possible that the decline ofconfidence level as RTs (or morphing levels) became larger
due to the decrease in the correct rate. However, this was
not the case, because the RTs (or morphing levels) andcorrect rate were not significantly correlated in the firstpresentation. Therefore, the results support the fluency
hypothesis of insight (Topolinski and Reber 2010), which
predicts that a more fluent processing (i.e., the shorter RT)
induces a more confident feeling in the subjects.
The morphing levels, or response time lengths can bethought of as indicators of the degree of difficulty forrecognition of the objects. A higher sureness score would
indicate a lower degree of difficulty, and hence a higher
fluency. Individual stimuli used in the present study indicated a wide spectrum of RTs or morphing levels (Fig. 3).
In turn, the battery of stimuli used reflected a wide range inthe degree of difficulty. Neither too difficult nor too easy
problems, but problems with a right degree of difficulty,
are proposed to be one of the prerequisites for insight(Hebb 1949). A robust set of hidden figures with appropriate difficulty levels, (i.e. ‘‘good’’ hidden figures) isneeded to demystify the nature of one-shot learning,
because a hidden figure can be used only once per a subject
in an experiment. By applying the present method, newhidden figures with various difficulty levels can be made ina systematic manner.
The reaction time (or morphing level) of recognition at
the second trials was smaller (or lower) than the first trial
only in the CA–CA cases, suggesting that one-shot learning
leads to an appropriate prior knowledge to be used in thenext trial when participants perceived correctly in the naı̈ve
state. Once learning is accomplished in the right direction,
it should be adaptive to save time in grasping the situation
instantaneously using the ‘‘anti-camouflage’’ device todetect hidden objects without additional exploration.
Familiarity of objects’ names were ensured by using thestandardized stimuli set (Snodgrass and Vanderwart 1980).
A complete list of all objects used in the experiment isprovided in Fig. 3. Note that the initial letter of most words
was A, B or C, apart from a few exception. In Japanese,
however, names of objects started with varied phonemes.
Therefore there was no chance that a certain type of phonological priming effect occurred accidentally.
The same stimuli order used in the first and second
exposure might have led to the possibility of contextual
effect or trial-to-trial dependencies. If such an effectoccurred generally, time to event curves would have beendifferent between the first and second exposures. However,this was not always the case. The change of time to event
curve was only observed in the CA–CA condition.
Although this analysis alone does not necessarily signify

123that there was no trial-to-trial dependencies of any kind, it
does suggest that contextual effects, if any, had a limited
impact on the main results.
The average rating of sureness in the second trial was
higher than that in the first one in the IA–CA and the CA–
CA conditions. For the former case, it is possible that theparticipants were aware of the incorrectness of theiranswers at the first exposure, reflected in the change to thecorrect answer at the second trial. For the latter case, it ispossible that they became more confident because of therepetition of subjective feeling of certainty, or fluency.
There was a positive correlation between the rating ofsureness and the correct rate. Thus, the subjective evaluations of confidence were consistent with the objective
performance, providing a measure for metacognition. The
scene consistency between the perceived figure and groundthat ensures the accuracy of object recognition (Davenportand Potter 2004) may subserve the appropriate
metacognition.
Humans can learn to recognize difficult Mooney faces
after a brief (about 5 s) exposure of unambiguous counterparts. Neurons in the inferior temporal cortex of primates exhibit an enhancement reflecting the neuralsubstrate of the rapid perceptual learning (Tovee et al.
1996). A patient with anterograde amnesia (Korsakoff’s
syndrome) possibly caused by a medial temporal lobe
damage showed no evidence of perceptual learning (Ramachandran 1995). Although his response time for therecognition of hidden figures was relatively normal, there
was no reduction in latency when the same figures were
exposed more than once. These evidences suggest that theinferior temporal visual cortex is involved in visual oneshot learning.
In sum, we proposed a novel paradigm to create newhidden figures with a broad spectrum of difficulty levels ina systematic manner by using a morphing technique.
Through gradual changes from a blurred and binarized
two-tone image to the blurred grayscale image of the original photograph including objects in a natural scene,
spontaneous one-shot learning could occur at a certain
stage of morphing when a sufficient amount of informationis restored to the degraded image. A negative correlation
between confidence levels and the reaction time or morphing level necessary for perceiving objects is consistent
with the fluency theory of insight (Topolinski and Reber
2010) in the domain of visual one-shot learning. A strong
correlation between the confidence ratings and the correct
recognition rate indicates that the subjects had accurate
metacognition. The comparison between the first and thesecond exposures confirms the basic ‘‘once and for all’’nature of ‘‘one-shot’’ learning. The learning effect could betested later by checking whether the target object is recognized quicker in the second exposure. The results

Author's personal copy
Cogn Neurodynreported here suggest a potential relationship between theunderpinning mechanism of one-shot learning and theneural substrate of short-term (Rutishauser et al. 2010) or
long-term (Ludmer et al. 2011) memory formation andretrieval. Thus, we note that memory components of oneshot learning is the result of the learning and not the otherway around. Further sets of research are needed to clarify
the reason why and how one-shot learning leads to long
lasting memories based on just a single trial. The present
method paves a new path for the production of ‘‘good’’
hidden figures in large quantities, which can be used toeventually demystify the nature of visual one-shot learning.
Acknowledgments We thank Tomomitsu Herai, Eiichi Hoshino,
Shinichi Nozawa, Ayako Onzo, Kei Omata, Takayasu Sekine, Tamami Sudo, Tsugumi Takano, Fumiko Tanabe, Fumihiko Taya, Mayumi Toshima, and Toru Yanagawa, for their helpful discussion. We
also thank three anonymous reviewers for their insights.

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