Content uploaded by Patrick Bennett
Author content
All content in this area was uploaded by Patrick Bennett
Content may be subject to copyright.
1
Superior identification of familiar visual patterns a year
after learning
Zahra Hussain1, Allison B. Sekuler2,3 & Patrick J. Bennett2,3
1. School of Psychology, University of Nottingham, Nottingham, England, NG72RD
2. Department of Psychology, Neuroscience & Behaviour, McMaster University,
Hamilton, ON, Canada, L8S 4K1
3. Center for Vision Research, York University, Toronto, ON Canada
These authors contributed equally to this work.
Practice improves visual performance on simple tasks in which stimuli vary along
one dimension (e.g., Fiorentini & Berardi, 1981). Such learning frequently is
stimulus-specific and enduring, and has been associated with plasticity in striate
corext (e.g., Furmanski, Schluppeck & Engel, 2004). It is unclear if similar lasting
effects occur for naturalistic patterns that vary on multiple dimensions. We
measured perceptual learning in identification tasks that used faces and textures,
stimuli that engage multiple stages in visual processing. Performance improved
significantly across two consecutive days of practice. More importantly, the effects
of practice were remarkably stable across time: improvements were maintained
approximately one year later, and the relative difficulty of identifying individual
stimuli, as well as individual differences were essentially constant across sessions.
Finally, the effects of practice were largely stimulus specific. Our results suggest
that the characteristics of perceptual learning are similar across a spectrum of
stimulus complexities.
2
Real objects comprise multiple features, and tasks like identification generally require
observers to utilize information carried by more than one stimulus dimension. As with
simple tasks, performance in visual tasks using complex stimuli improves with practice
(Kolers 1976; Sireteanu & Rettenbach, 1995; Gold, Bennett & Sekuler, 1999; Hussain,
Bennett & Sekuler, 2009a, 2009b; Lobley & Walsh, 1998; Tseng, Gobell & Sperling,
2004; Tanaka, Curran & Sheinberg, 2005), indicating that there is plasticity at later
stages in visual processing that combine the outputs of earlier areas. Do these effects of
perceptual learning in tasks using complex stimuli persist for long periods of time? In
certain cases the improvements do not last (Lobley & Walsh, 1998), or lasting
improvements are not specific to the stimuli or tasks used during training (Sireteanu &
Rettenbach, 1995; Tanaka et al, 2005), suggesting that there is greater long-term
retention of more general perceptual operations. Such learning may form the basis of
visual expertise in categorizing patterns and objects, which generalizes to novel
instances of familiar categories (Tanaka et al, 2005). However, long-lasting item-
specific effects have been reported in implicit memory for complex items such as words
and pictures (Sloman, Hayman, Ohta, Law & Tulving, 1988; Cave, 1997; deSchepper &
Triesman, 1996), and in the motor domain for a serial reaction-time task (Romano,
Howard & Howard, 2010). These long-term effects obtained across paradigms suggest
that the visual system might also show resilient and specific learning of complex input,
as suggested by an early report of content-specific retention of improvements in reading
mirror-transformed text (Kolers, 1976).
Performance in a 10-AFC face and texture identification task improves substantially
with practice (Gold et al, 1999): a noisy stimulus is presented briefly at one of several
3
contrasts, followed by a choice of ten noiseless exemplars, one of which is the correct
response (Figure 1). Improvements in identification accuracy, both with faces and
textures, are largely stimulus-specific: there is little transfer to novel items that share the
spatial characteristics of the trained set, to the trained items rotated by 180 degrees
(Hussain et al, 2009a, 2009b), and, in the case of textures, to contrast-reversed versions
of the trained items (Hussain et al, 2009a). Here, we ask whether this type of stimulus-
specificity endures for long periods. Unlike previous studies of item-specific memory of
objects and words (Sloman et al, 1998; Cave, 1997), or of basic attributes of simple
visual stimuli (Fiorentini & Berardi, 1981; Karni & Sagi, 1993; Ball & Sekuler, 1982;
Adini, Sagi & Tsodyks, 2004), the present task addresses learning of complex stimuli at
the perceptual level, both for items that are highly familiar (faces), and unfamiliar
patterns with no semantic content (textures). Long lasting, stimulus-specific learning of
face - and texture identification, if found, would suggest that the characteristics of
perceptual learning are similar across a range of stimulus complexities, and also
resemble learning in higher-level cognition.
METHOD
Nine subjects performed a face-identification task, and six subjects performed a
texture-identification task on two occasions: an initial learning and test phase (Days 1 &
2) in experiments that were conducted with a larger sample of observers, and a follow-
up test 10-18 months later. On average, subjects in the face- and texture identification
task performed the follow-up session respectively 13 months (SD = 3.4) and 15 months
(SD =1.6) after the initial test session.
The apparatus, stimuli and procedure have been described in detail previously
(Gold et al, 1999; Hussain et al, 2009a, 2009b). Briefly, faces were cropped to show
4
only internal features and were equated in terms of their global amplitude spectrum. The
textures were band-limited noise patterns. The stimuli subtended 4.8 x 4.8 degrees of
visual angle from the viewing distance of 114 cm, and were presented in low, medium,
or high levels of static two-dimensional Gaussian noise. Stimulus contrast was varied at
one of seven levels (per noise condition), using the method of constant stimuli.
On each of Days 1 and 2, observers performed 840 trials per day, with one
stimulus set within a given stimulus class (faces or textures). The follow-up session
consisted of 420 trials with the trained stimulus set, and 420 trials with a novel stimulus
set for the same stimulus class. The same and novel sets of stimuli were presented in
separate blocks of trials and the order was roughly counterbalanced across subjects. For
analyses, the 840 trials on Days 1 and 2 were divided into four bins of 210 sequential
trials (Trial bins 1-8), and the 420 trials for each of the stimulus sets during follow-up
were divided into two bins of 210 trials (Bins A and B). For each bin, the proportion of
correct responses was calculated after collapsing across all levels of stimulus contrasts
and noise.
RESULTS
Performance with faces and textures was assessed separately. For each type of stimulus,
learning during the initial session was assessed with three planned comparisons: Bin 1
vs. Bin 4, Bin 5 vs. Bin 8, and the average of Day 1 vs. the average of Day 2. Retention
of learning was assessed with eight planned comparisons: familiar stimuli in Bin A vs.
Bins 1, 4 and 8; novel stimuli in Bin A vs. Bins 1, 4, and 8; and familiar vs. novel
stimuli in Bins A and B. Hence, there were 11 comparisons for each type of stimulus.
Bins 1, 4, and 8 were chosen because they represent pre-learning baseline (Bin 1), post-
learning performance at the end of Day 1 (Bin 4), and post-learning performance at the
5
end of both days (Bin 8). For each set of 11 comparisons, Holm’s sequential Bonferroni
test (Kirk, 1995) was used to maintain a familywise Type I error rate of 0.05.
Significant p values are indicated by asterisks.
Face identification accuracy showed significant within-session improvement on Day 1,
as evidenced by a 20% increase in accuracy from Bin 1 to Bin 4 (t(8) = 9.54, p <
.00001*; Figure 2 top). There also was significant within-session improvement on Day
2, with performance improving by 10% from Bin 5 to Bin 8 (t(8) = 4.54, p < .01*).
Overall accuracy averaged across bins was 18% higher on Day 2 than on Day 1, (t(8) =
11.36, p < .00001*). Approximately one year later, accuracy in the first trial bin (Bin A)
in the same-face condition was 18% higher than in Bin 1 (t(8) = 7.883, p < .0001*), did
not differ from accuracy in Bin 4 (t(8) = 1.054, p = .322), and was 15% lower than
accuracy in Bin 8 (t(8) = 7.17, p < .0001*). Therefore, performance in the follow-up
session in the same-face condition was equivalent to that achieved at the end of the first,
but not the second, session. In the novel-face condition, accuracy in Bin A was no
different than accuracy in Bin 1 (t(8) = 1.8, p = .10), 15% lower than accuracy in Bin 4
(t(8) = 4.05, p = .0036*), and 29% lower than accuracy in Bin 8 (t(8) = 8.17, p <
.0001*). Finally, accuracy in the same-face condition was higher than accuracy in the
novel-face condition in Bin A (difference = 14%; t(8) = 3.57, p = .007*) and Bin B
(difference = 20%; t(8) = 4.81, p = .001*). These analyses confirm that response
accuracy in the follow-up session, held approximately one year after training, was
higher for faces that were seen in the original training sessions.
Texture identification accuracy (Figure 2 bottom), improved significantly on Day 1
(25% from Bin 1 to Bin 4, t(5) = 5.070, p = .003*), but the improvement on Day 2 was
6
not significant (16% from Bin 5 to Bin 8, t(5) = 2.96, p = .03). Averaged across bins,
accuracy on Day 2 was 21% higher than on Day 1 (t(5) = 8.18, p = .0004*).
Approximately one year later, accuracy in Bin A in the same-texture condition was 35%
higher than in Bin 1 (t(5) = 15.70, p < .0001*), but did not differ significantly from
accuracy in Bin 4 (t(5) = 2.68, p = .04) or Bin 8 (t(5) = 1.54, p = .18). In the novel-
texture condition, accuracy in Bin A was 18% better than in Bin 1 (t(5) = 6.41, p =
.001*), no different than in Bin 4 (t(5) = 1.87, p = .11), and 24% lower than in Bin 8
(t(5) = 4.88, p = .004*). Performance in the same-texture condition was significantly
better than in the novel-texture condition in Bin A (16% difference; t(5) = 6.44, p =
.0013*) but not in Bin B (10% difference; t(5) = 3.29, p = .02), indicating that a
significant proportion of the improvement was stimulus-specific.
We conducted several tests to compare retention measured with faces and textures. The
Holm’s sequential Bonferroni test (Kirk, 1995) was again used to maintain a familywise
error rate of 0.05. First, we examined if the amount of transfer to novel items differed
for faces and textures by comparing the difference between Bin A and Bin 1in the
novel-texture and novel-face conditions. There was a significant difference in the
amount of transfer to novel items obtained with faces (4%) and textures (18%)
(t(10.873) = 3.79, p = .003*). However, the advantage of same over novel items did not
differ between faces and textures in Bin A (faces (19%) vs. textures (11%); t(12.637) =
.55, p = .58) or in Bin B (faces (19%) vs. textures (11%), t(12.998) = 1.69, p = .11).
Thus, although there was more generalization to novel items in the texture identification
task, the same-stimulus advantage did not differ between faces and textures.
7
Despite the large variability in performance across observers, the rank order of
individuals was stable across sessions in both tasks (Figure 3). For subjects who were
tested with faces, the Spearman rank-order correlation was significant for all pairs of
test sessions (rho ! 0.96, p < .001). For subjects who were tested with textures, the
rank-order correlations were all positive, but, perhaps due to the small sample size (n =
6), only the correlation between Day 1 and the follow-up session was significant (Days
1 & 2: rho = 0.60, p = .24; Day 1 & follow-up: rho= .94, p = .015; Day 2 & follow-up:
rho = .71, p = .13).
To examine whether the effects of practice reflected improved performance for all items
or just a few items, we calculated the mean accuracy for individual faces and textures
during each test session (Figure 4). Each symbol in Figure 4a and 4b represents
response accuracy for a single face averaged across subjects. There were clear
differences in the initial difficulty in identification across individual items. However,
regardless of initial difficulty, accuracy relative to Day 1 was higher for 29 out of 30
faces on Day 2 (Figure 4a), and 27 out of 30 faces during follow-up (Figure 4b). The
accuracies for individual faces measured during different sessions were highly
correlated (all r's ! .90, p < .001), implying that learning did not change the relative
discriminability of the faces.
Figures 4c and 4d show the effects of practice on accuracy for individual textures.
Again, response accuracy was higher for every texture on Day 2 than on Day 1 (Figure
4c) and for 19 out of 20 textures during follow-up (Figure 4d). As was found with
faces, identification accuracy for each texture remained relatively stable across all
8
sessions (all r's ! .71, p < .001), which suggests that learning (or forgetting) did not
produce qualitative changes in the way the items were represented.
DISCUSSION
Perceptual learning of face- and texture identification was remarkably stable and
stimulus-specific 10-18 months after practice. Some of the long-term benefits of
learning generalized to novel stimuli, but the degree of stimulus specificity was similar
to what has been obtained in experiments using a retention interval of just one day
(Hussain et al, 2009a, 2009b). Individual differences in response accuracy were stable
across testing sessions, consistent with research showing that practice does not
eliminate, but rather crystallizes, initial performance differences for certain skilled tasks
(deSchepper & Triesman, 1996; Ackerman, 2007). Finally, despite significant variation
across stimuli in the initial level identification accuracy, practice increased performance
nearly uniformly for all items, indicating that the relative discriminability of the stimuli
was unaltered after learning.
The textures used in our experiment were unfamiliar stimuli that lacked the spatial
regularities and semantic content that exist in faces. Nevertheless, accuracy a year later
was much higher for familiar than novel textures, indicating that familiarity with the
object class prior to training was not essential for long-term retention. Analogous
learning in the auditory system has been reported recently for noise stimuli, with the
effects of previous exposure lasting up to 3 weeks (Agus, Thorpe & Pressnitzer, 2010).
The enduring stimulus specificity of learning found with faces also is notable given the
9
exposure to other faces that presumably occurred for all participants in natural contexts
during the intervening period. Evidently, exposure to other faces did not diminish the
specific effects of perceptual learning up to a year later. The primary goal of this study,
however, was not to compare the extent and duration of learning across stimulus
classes, so one cannot conclude, for example, that unfamiliar or uncommon stimuli lead
to longer lasting learning than familiar or common stimuli. Future studies could assess
this issue more explicitly by controlling factors such as stimulus similarity and initial
task difficulty directly.
Long-lasting, stimulus-specific effects of learning on simple visual discriminations have
been attributed to changes early in the visual pathway, particularly to the primary visual
area (Furmanski et al, 2005; Hua et al, 2010; Yotsumoto, Watanabe & Sasaki, 2008).
However, it is likely that performance in our tasks depends in part on a more distributed
network of neurons, including those in inferotemporal cortex (IT), which show
stimulus-selective activation by stimuli such as faces and other complex patterns
(Logothetis, Pauls & Poggio, 1995; Leopold, Bondar & Giese, 2006), and the selectivity
of which can be altered by practice (Li & DiCarlo, 2008). Other work has shown a role
for IT neurons in the acquisition of complex visual skills (Poldrack, Desmond, Glover
& Gabrieli, 1998), in the formation of visual memory (Desimone, 1996) and in long-
term visual priming (Meister et al, 2005), which suggests common neural substrates for
long-lasting perceptual learning and visual memory.
Learning even in simple conditions is thought to activate a broad network that
encompasses task context among other stimulus parameters (Yotsumoto et al, 2008;
Logothetis et al, 1995; Meister et al, 2005; Xiao et al, 2008). Therefore, as with the
10
short-term learning effects, it is plausible that long-lasting effects of practice are
accompanied by changes in a broad cortical network that includes several levels of the
visual hierarchy. Although the underlying mechanisms may differ, the long-lasting and
stimulus-specific perceptual learning reported here resembles durable effects found with
in implicit memory (Sloman et al, 1988; Cave, 1997; deSchepper & Triesman, 1996;
Romano et al, 2010), and certain types of sensory adaptation (Jones & Holding, 1975).
References
Ackerman, P. L. (2007). New developments in understanding skilled performance.
Current Directions in Psychological Sciences. 16(5), 235-239.
Adini, Y., Sagi, D., & Tsodyks, M. (2002). Context-enabled learning in the human
visual system. Nature, 415, 790-793.
Agus, T. R., Thorpe, S. J., & Pressnitzer, D. (2010) Rapid formation of robust auditory
memories: Insights from noise. Neuron, 66, 610-618.
Ball, K., & Sekuler, R. (1982). A specific and enduring improvement in visual motion
discrimination. Science, 218, 697-698.
Cave, C. B. (1997). Very long-lasting priming in picture naming. Psychological
Science, 8(4), 322-325. (1997).
deSchepper, B., & Triesman, A. (1996). Visual memory for novel shapes: Implicit
11
coding without attention. Journal of Experimental Psychology: Learning,
Memory & Cognition. 22(1), 22-47.
Desimone, R. (1996). Neural mechanisms for visual memory and their role in attention.
Proceedings of the National Academy of Sciences-USA, 93, 13494-13499.
Fiorentini, A., & Berardi, N. (1981). Learning in waveform discrimination: specificity
for orientation and spatial frequency. Vision Research, 27, 1149-1158.
Furmanski, C. S., Schluppeck, D., & Engel, S. A. (2004). Learning strengthens the
response of primary visual cortex to simple patterns. Current Biology, 14, 573-
576.
Gold, J., Bennett, P. J., & Sekuler, A. B. (1999). Signal but not noise changes in
perceptual learning. Nature, 402, 176-178.
Hua, T. M., Bao, P. L., Huang, C. B., Wang, Z. H., Xu, J. W., Zhou, Y. F., & Lu, Z. L.
(2010). Perceptual learning improves contrast sensitivity of V1 neurons in cats.
Current Biology, 20(10), 887-894.
Hussain, Z., Sekuler, A. B., & Bennett, P. J. (2009a). Contrast-reversal abolishes
Perceptual learning. Journal of Vision. 9(4), 1-8.
Hussain, Z., Sekuler, A. B., & Bennett, P. J. (2009b). Perceptual learning modifies
12
inversion effects for faces and textures, Vision Research, 49, 2273-2284.
Jones, P. D., & Holding, D. H. (1975). Extremely long-term persistence of the
McCollough Effect. Journal of Experimental Psychology: Human Perception
and Performance, 1(4), 322-327.
Karni, A., & Sagi, D. (1993). The time course of learning a visual skill. Nature, 365,
250- 252.
Kirk, R. E. (1995). Experimental design: Procedures for the behavioural sciences (3rd
Ed.). Pacific Grove, California: Brooks/Cole.
Kolers, P. (1976). Pattern-analyzing memory. Science, 191, 1280-1281.
Leopold, D. A., Bondar, I. V., & Giese, M. A. (2006). Norm-based face encoding by
single neurons in the monkey inferotemporal cortex. Nature, 442(3), 572-575.
Li, N., & DiCarlo, J. (2008). Unsupervised natural experience rapidly alters invariant
object representation in visual cortex. Science, 321, 1502-1506.
Lobley, K., & Walsh, W. (1998). Perceptual learning in visual conjunction search.
Perception, 27, 1245-1255.
Logothetis, N., Pauls, J., & Poggio, T. (1995). Shape representation in the inferior
13
temporal cortex of monkeys. Current Biology, 5, 552-563.
Meister, I. G., Weidemann, J., Foltys, H., Brand, H., Wilmes, K., Krings, T., Thron, A.,
Topper, R & Boroojerdi, B. (2005). The neural correlate of very-long-term
picture priming. European Journal of Neuroscience. 21, 1101-1106.
Poldrack, R. A., Desmond, J. E., Glover, G. H. & Gabrieli, J. D. E. (1998). The neural
basis of visual skill learning: An fMRI study of mirror reading. Cerebral Cortex,
8, 1-10.
Romano, J. C., Howard Jr. J. H., & Howard, D. V. (2010). One-year retention of
general and sequence-specific skills in a probabilistic, serial reaction time task.
Memory, 18(4), 427-441.
Sireteanu, R., & Rettenbach, R. (1995). Perceptual learning in visual search: Fast,
enduring but non-specific. Vision Research, 35, 2037-2043.
Sloman, S. A., Hayman, C. A., Ohta, N., Law, J. & Tulving, E. (1988). Forgetting in
primed fragment completion. Journal of Experimental Psychology: Learning,
Memory & Cognition. 14(2), 223-239.
Tanaka, J. W., Curran, T & Sheinberg, D. L. (2005). The training and transfer of real-
world perceptual expertise. Psychological Science, 16(2),145-151.
14
Tseng, C., Gobell, J. L. & Sperling, G. (2004). Long-lasting sensitization to a given
colour after visual search. Nature, 428, 657-660.
Xiao, L. Q., Zhang, J. Y., Wang, R., Klein, S. A., Levi, D. M., & Yu, C. (2008).
Complete transfer of perceptual learning across retinal locations enabled by
double training. Current Biology, 18(24). 1922-1926.
Yotsumoto, Y., Watanabe, T., & Sasaki, Y. (2008). Different dynamics of performance
and brain activation in the time course of perceptual learning. Neuron, 57, 827-
833.
15
Acknowledgements
We thank Donna Waxman for her assistance in running subjects. This research was funded by NSERC
and the Canada Research Chair program. The authors declare no competing interests.
Correspondence and requests for materials should be addressed to Zahra Hussain (e-mail:
zahra.hussain@nottingham.ac.uk).
Figure captions
Figure 1. Examples of the stimuli and a schematic representation of the trial sequence.
16
Figure 2. Proportion correct on Day 1, Day 2, and approximately 1 year (10-18 months)
later for 9 subjects in the face identification task (top), and 6 subjects in the texture
identification task (bottom). Each session comprised 840 trials, therefore each trial bin
represents 210 trials. Performance a year later was measured both with the same items
shown on Days 1 and 2 (filled symbols), and with novel items that subjects had not seen
before (open symbols).
Figure 3: Performance of individual subjects on the face and texture identification tasks
on Day 1, Day 2, and during the follow-up session about one year later. Each symbol
represents a subject. Different subjects performed the face- and texture identification
tasks.
Figure 4. Scatter plots showing accuracy for three sets of ten faces used in separate
experiments in the face identification task (a, b), and two sets of ten textures used in the
texture identification task. (c, d). Each point is based on the average of 2-6 subjects.
Area above the solid line indicates improvements. Dashed line indicates the least
squares fit. Face identification: each symbol shows accuracy averaged across four
(black circles), three (stars), and two (open circles) subjects. a, b) Day 1 vs. Day 2. c, d)
Day 1 vs. Follow-up. All correlations are positive and significant. Learning is retained a
year later relative to performance on Day 1 (c,d).
fixate 100ms
stimulus 200ms
thumbnails
until response
ISI 100ms
ISI 100ms
time
Fixate 100ms
Stimulus 200ms
Thumbnails
until response
ISI 100ms
ISI 100ms
time
1 yr later
0.8
0.6
0.4
0.2
Day 1 Day 2
0.6
0.4
0.2
87654321
Bin
A B
same
novel
same
novel
Faces
Textures
0.8
0.6
0.4
0.2
Face identification Texture identification
Day 1 Day 1Day 2 Day 21 yr later 1 yr later
0.2 0.4 0.6 0.8 1.0 0.2 0.4 0.6 0.8 1.0
0.2 0.4 0.6 0.8 1.00.2 0.4 0.6 0.8 1.0
0.2
0.4
0.6
0.8
1.0
0.2
0.4
0.6
0.8
1.0
0.2
0.4
0.6
0.8
1.0
0.2
0.4
0.6
0.8
1.0
r < 0.90, p < .0001 r < 0.92, p < .0001
r < 0.71, p < .0001r < 0.89, p < .0001
Day 1 accuracy Day 1 accuracy
Day 1 accuracyDay 1 accuracy
Day 2 accuracy
Accuracy 1 yr later
Day 2 accuracy
Accuracy 1 yr later
a b
c d
Faces Faces
Textures Textures
