Event-Related Potentials Reveal Rapid Verification of
Predicted Visual Input
Michael Dambacher
1,2
*, Martin Rolfs
1,3
, Kristin Go¨ llner
1
, Reinhold Kliegl
1
, Arthur M. Jacobs
2
1 Department of Psychology, Universita¨t Potsdam, Potsdam, Germany, 2 Department of Education and Psychology, Freie Universita¨t Berlin, Berlin, Germany, 3 Laboratoire
Psychologie de la Perception, CNRS - Universite´ Paris Descartes, Paris, France
Abstract
Human information processing depends critically on continuous predictions about upcoming events, but the temporal
convergence of expectancy-based top-down and input-driven bottom-up streams is poorly understood. We show that,
during reading, event-related potentials differ between exposure to highly predictable and unpredictable words no later
than 90 ms after visual input. This result suggests an extremely rapid comparison of expected and incoming visual
information and gives an upper temporal bound for theories of top-down and bottom-up interactions in object recognition.
Citation: Dambacher M, Rolfs M, Go¨llner K, Kliegl R, Jacobs AM (2009) Event-Related Potentials Reveal Rapid Verification of Predicted Visual Input. PLoS ONE 4(3):
e5047. doi:10.1371/journal.pone.0005047
Editor: Mark A. Williams, Macquarie University, Australia
Received December 4, 2008; Accepted March 4, 2009; Published March 31, 2009
Copyright:
2009 Dambacher et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This research was supported by Deutsche Forschungsgemeinschaft, grants FOR868/1 and KL655/6-1. The funders had no role in study design, data
collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: michael.dambacher@uni-potsdam.de
Introduction
Perception is not the result of passive bottom-up transmission of
physical input [1]. Instead active top-down projections continu-
ously interact with earliest stages of sensory analysis. This insight
increasingly influences our understanding of cognitive efficiency
[2–5]. For instance, attention enhances neural responses to visual
stimuli in extrastriate and striate visual cortices [6], and already on
the subcortical level in the LGN [7]. In fact, studies using
functional magnetic resonance imaging (fMRI) revealed modula-
tions in cortical and subcortical areas even prior to sensory input of
expected stimuli [7–9]. We regard such anticipatory activity as
top-down predictions engaging lower-level areas involved in
sensory processing to grant fast and smooth perception of
forthcoming stimuli. Given that the quantity of feedback
connections to primary sensory areas even outnumbers pure
feedforward input [5] the interplay of top-down and bottom-up
flow appears as a major principle of perception.
Beyond fMRI-based evidence about spatial characteristics of
neural activity, temporal information contributes to the compre-
hension of bottom-up and top-down processes. Employing the
high temporal resolution of electroencephalography (EEG),
research predominantly focused the influence of attention on the
time course of visual perception [10]. For instance, spatial
attention modulates alpha band activity over occipital areas prior
to the appearance of an expected target [11,12]. After stimulus
onset amplitudes on the P1 component evolving at around 70 ms
are enhanced for stimuli appearing at attended compared to
unattended locations [13–16]. Influences of object- and feature-
based attention have typically been observed later with a post-
stimulus onset at 100 to 150 ms [17–22].
However, despite the undisputed role for top-down control,
attention cannot be equated with feedback flow per se. Gilbert
and Sigman [4] expanded the traditional concept of attention-based
top-down influences and denominated expectations and perceptual task
as further forms. Although these concepts are strongly overlapping
and can hardly be separated, the critical distinction lies in the
amount of information top-down streams carry. For example,
directing attention to a certain location presumably contains less
information than a task affording predictions about the identity of
an upcoming stimulus at that position. In particular, strong
expectations of a certain stimulus may involve a form of hypothesis
testing that compares characteristics of the incoming signal to
stored representations even prior to object identification [4]. This
idea is implemented in models integrating bottom-up and top-
down processes, such that feedforward streams transmitting
sensory information converge with feedback activity carrying
knowledge and hypotheses about stimuli. For instance, McClel-
land and Rumelhart [23,24] proposed that word identification is
driven by the interaction of linguistic and context-based
knowledge with incoming featural information. Indeed, the
amount of top-down feedback can be quantified at the level of
individual participants [25]. Grossberg [26] suggested that
stimulus-related signals are enhanced, when top-down predictions
are correct and match sensory inputs (cf., [27–30]). According to
such theories, the congruence of prediction and input facilitates
stimulus processing, potentially at early perceptual levels. An open
question is, however, at what point in time perception benefits
from the comparison of top-down and bottom-up processes, when
strong predictions are involved.
The present study used event-related potentials (ERPs) to
delineate the earliest interaction between expectations about the
identity of incoming signals and input-driven information in visual
word recognition. Sentence reading is perfectly suited to
investigate the issue. As a well-overlearned everyday activity, it
involves highly optimized object recognition processes ranging
from individual letters and sublexical units to whole words,
thereby engaging both early and higher levels of the visual system
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[31]. Critically, earliest visual cortices were found to be selectively
sensitive to trained, letter-like shapes [32]. Furthermore, during
normal reading, rapid input rates of four to five words per second
require high perceptual efficiency and encourage fast stimulus
processing. This is crucial since modulations of early sensory
processes are primarily engaged, when task demands and
perceptual load are high [10,33,34]. Finally, sentence contexts
afford strong and form-specific predictions for upcoming words.
Indeed, increased neural activity was measured on articles (i.e., a/
an) when their phonological form mismatched the initial phoneme
of a highly predictable but not yet presented noun (e.g., airplane/
kite [35]; see also [36]).
We manipulated predictability of target words in sentences to
investigate at what point in time after visual onset expectations
about upcoming stimuli are verified. To push the necessity of
efficient visual processing and to measure neural responses under
near-normal conditions, words were presented at a high rate
approximating natural reading speed [37,38]. Provided that match
and mismatch of stimulus and prediction evoke distinct neural
responses [5], an early difference between ERPs for predictable
and unpredictable words represents an upper bound for the
latency of top-down and bottom-up interactions.
Materials and Methods
Participants
Thirty-two native German readers (24 female; 29 right-handed;
mean age: 27.3, SD: 6.8), recruited at Freie Universita¨t Berlin,
received course credit for participation. They had normal or
corrected-to-normal vision and reported no history of neurological
diseases. The experiment was performed in accordance with the
ethical standards laid down in the 1964 Declaration of Helsinki. In
agreement with the ethics and safety guidelines at the Freie
Universita¨t Berlin, we obtained a verbal informed consent
statement from all individuals prior to their participation in the
study. Potential participants were informed of their right to abstain
from participation in the study or to withdraw consent to
participate at any time without reprisal.
Materials
A total of 144 sentence units formed the stimulus materials.
Each unit comprised two context sentences and one neutral
sentence. The latter was identical across conditions except for
target words setting up a two-by-two factorial design of frequency
and predictability (Figure 1A).
144 pairs of high (e.g., Schiff [ship]) and low frequency (e.g.,
Zepter [scepter]) open-class words served as targets. High frequency
words comprised lemma and word form frequencies greater than
100 and 10 occurrences per million, respectively. For low
frequency words, lemma and word form frequencies were lower
than 10 per million. Frequency norms were taken from the DWDS
data base [39]. High and low frequency words from one pair were
members of the same class (i.e., nouns, verbs, or adjectives) and,
where possible, shared the same number of letters; they differed in
one letter in 19 of the 144 cases, in two letters in 4 cases and in
three letters in 1 case. Target length varied between three and
eight letters and was matched across conditions.
Target pairs were embedded at the sixth to eighth word position
in neutral sentence frames and were always followed by at least two
more words. Two context clauses preceding the neutral sentences
triggered predictability of target words: High frequency targets were
of high predictability in context 1 and of low predictability in context
2. For low frequency targets the pattern was reversed. Predictability
norms were assessed in an independent cloze task performed by a
total of 151 voluntary participants; none of them took part in the
EEG experiment. In the cloze procedure, a context sentence was
presented together with words of the corresponding neutral sentence
up to the position prior to the target. Participants then guessed the
word that would most likely continue the sentence fragment. They
were asked to write at least one, but no more than three guesses per
sentence. Each participant was presented with only one context per
sentence unit and worked through a part of the stimuli. In total,
every sentence was rated by at least 30 subjects. Predictability was
computed as the proportion of participants correctly predicting the
target word with one of their guesses. In the 144 sentence units
entering the stimulus materials both low and high frequency words
reached cloze values of at least 0.5 in the high predictability
conditions while not exceeding 0.1 in the low predictability
conditions. Target word statistics are depicted in Table 1.
Figure 1B illustrates the distribution of predictability values in
the categories. Most low predictability targets had cloze values of
zero; in the high predictability condition the number of targets
increased with predictability. Cloze values were similarly distrib-
uted for low and high frequency words.
For the ERP study, randomized stimuli were divided into lists
such that each participant was presented with every sentence unit
only once. A Latin square design provided that each version of a
sentence unit was presented to the equal number of participants.
This resulted in 72 high and 72 low predictability trials per subject,
with 36 high and low frequency words in either category.
Procedure
Participants were seated at a distance of 60 cm from the
monitor in a dimly lit room and were asked to silently read two-
sentence stories for comprehension. A trial started with a context
sentence that was displayed in its entirety until subjects pressed a
button. Thereafter, a fixation cross, preceded and followed by a
500 ms blank interval, indicated for 1000 ms the required fixation
position at monitor center. The stimuli of the neutral sentence
together with their adjacent punctuation were then presented
word by word with a stimulus onset asynchrony (SOA) of 280 ms
(i.e., stimulus: 250 ms; blank: 30 ms). The presentation sequence
of context and neutral sentences is schematized in Figure 1C. After
the neutral sentence, either the next trial was initiated (66.67%) or
a three-alternative multiple-choice question was inserted to test
sentence comprehension (33.33%). Questions referred to the
content either of the context or the neutral sentence, but were
never related to the target word.
Participants were asked to avoid eye movements and blinks
during the interval of word-wise sentence presentation. After eight
practice trials and 72 sentence units of the main experiment, they
took a short break. Stimuli (font: Courier New; size: 18 pt) were
presented in black on a white background.
Electrophysiological recording and data processing
EEG data were recorded from 50 scalp locations corresponding
to the 10/20 international system. Impedances were kept below
10 kV. All scalp electrodes and one channel on the right mastoid
originally referenced to the left mastoid were re-referenced offline
to the average of scalp electrodes. Two horizontal and two vertical
EOG electrodes recorded bipolarly oculomotor signals and blinks.
Data continuously recorded with a sampling rate of 512 Hz were
re-sampled offline to 256 Hz. Amplifier settings cut off frequencies
below .01 and above 100 Hz. Data were bandpass filtered offline
from .1 to 30 Hz (24 dB; 50 Hz notch).
EEG data contaminated by muscular artifacts and drifts were
rejected offline via visual inspection. Independent component
analysis (Vision Analyzer, Brain Products GmbH, Germany) was
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used to remove oculomotor artifacts and blinks. Additionally, an
automatic algorithm rejected segments with an absolute amplitude
larger than 90 mV in at least one channel. The rejection procedure
resulted in the exclusion of 3.17% of all target intervals (low
frequency – low predictability: 2.78%; low frequency – high
predictability: 2.17%; high frequency – low predictability: 3.82%;
high frequency – high predictability: 3.91%). In the remaining
data, the continuous EEG signal was divided into epochs from
200 ms before to 700 ms after the target. Epochs were corrected
relative to a 200 ms pre-stimulus baseline.
Effect onset was detected on the basis of 95% confidence intervals
computed from 5000 bootstrap samples of single-average difference
curves. Sampling points were considered as significant at the 5%-
level, when upper and lower bound of the confidence band shared
algebraic signs for an interval exceeding 10 ms. ERP amplitudes
collapsed across sampling points in the epoch from 50 to 90 ms were
examined in repeated measures analyses of variance (ANOVA). The
Huynh-Feldt correction was applied to adjust degrees of freedom
(rounded down) and P-values for violations of the sphericity
assumption.
Results
Grand average ERPs for low and high predictability target
words are illustrated in Figure 2A for a sample of nine scalp
electrodes. Curves are displayed for the interval from 200 ms
before target onset up to the appearance of the target-succeeding
word at 280 ms. Inspection of the data suggested amplitude
differences at a surprisingly early latency – well before 100 ms.
Amplitudes for high compared to low predictability words were
more negative at posterior left locations and more positive at
anterior right sites.
Figure 1. Stimuli and procedure. (A) Stimulus example. High (ship) and low frequency (scepter) targets were embedded in a neutral sentence
frame. Two context sentences triggered low or high predictability of target words. (B) Distribution of predictability values. Bars illustrate the
distribution of target predictability across the stimulus material. Low predictability targets (orange) include cloze probabilities no larger than .1. High
predictability words (blue) comprise cloze values of at least .5. Lines reflect the dispersion of predictability norms within low (light orange circles) and
high frequency (light blue squares) categories. Note that the entire corpus comprises a total of 576 predictability values, since each of the 144
sentence units involves a low and a high frequency target that both serve as low and as high predictability word. (C) Presentation sequence. A
context sentence was fully displayed until participants pressed a button. After a fixation cross, the neutral sentence was presented word by word at
monitor center. Each word was displayed for 250 ms and followed by a 30 ms blank screen.
doi:10.1371/journal.pone.0005047.g001
Table 1. Descriptive statistics of target words.
LF-LP LF-HP HF-LP HF-HP
Mean SD Mean SD Mean SD Mean SD
Word form
freq.
3.76 2.08 3.76 2.08 155.58 194.63 155.58 194.63
Lemma freq. 4.87 2.68 4.87 2.68 362.19 875.30 362.19 875.30
Predictability .01 .02 .83 .13 .01 .02 .84 .13
Length 5.32 1.11 5.32 1.11 5.36 1.16 5.36 1.16
Word position 6.94 .76 6.94 .76 6.94 0.76 6.94 .76
Target word norms [mean and standard deviation (SD)] according to the 262
experimental manipulation of frequency (low: LF; high: HF) and predictability
(low: LP; high: HP). 144 target word pairs consisted of 92 noun-, 37 verb-, and 15
adjective-pairs.
doi:10.1371/journal.pone.0005047.t001
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The visual impression was corroborated in statistical analyses
examining temporal onsets and durations of the first predictability
effect (Figure 3A). From 0 to 100 ms, a total of 19 out of 50 scalp
electrodes revealed significant amplitude differences with an
average onset latency of 60 ms (SD: 4 ms) and a mean duration
of 28 ms (SD: 16 ms). The earliest effect emerged at 52 ms post-
stimulus. The topographical latency map (Figure 3B) identified
early predictability effects at right anterior and left posterior sites.
Based on these results, we conducted statistical tests on mean
amplitudes in the epoch from 50 to 90 ms after stimulus onset
(dashed lines in Figure 3A). An ANOVA with frequency (2),
predictability (2), and electrode (50) as within-subject factors
yielded a main effect of electrode [F(2,70) = 14.66; P,.001;
partial-g
2
= .321] and, critically, an interaction of predictabil-
ity6electrode [F(4,132) = 2.97; P = .019; partial-g
2
= .098]. Nei-
ther the interaction of frequency6electrode (P = .298) nor the
three-way interaction (P = .478) was significant (note that only
interactions with the factor electrode are meaningful in this
ANOVA because the average reference sets mean amplitudes
across scalp channels to zero).
In order to strengthen evidence that the observed predictability
effect was related to the experimental manipulation of targets, we
examined ERPs for the two words prior to the target. These
stimuli were identical across all conditions and were not subject to
the predictability modulation from context sentences. Hence,
amplitudes should not reveal any significant differences in the
critical interval from 50 to 90 ms. ANOVAs with frequency (2),
predictability (2), and electrode (50) as factors yielded no reliable
effects for frequency, predictability, or the interaction of
frequency6predictability (all Fs,1). Additionally, ANOVAs on
each of the two target-preceding words were performed in seven
successive epochs of 40 ms, ranging from 0 to 280 ms after
stimulus onset. None of these intervals revealed significant effects
involving the factors frequency, predictability, or the interaction of
frequency6predictability (all Ps..15). Grand average ERPs for
the target-preceding word are displayed in Figure 2B.
To scrutinize the predictability effect on the target word we
grouped the 50 scalp electrodes into nine regions according to a
grid of three sagittal (left, midline, right) and three coronal
(anterior, central, posterior) fields (see Figure 4A). ERP amplitudes
were collapsed across electrodes in corresponding regions and
submitted to an ANOVA with the factors frequency (2),
predictability (2), and region (9). The main effect of region
[F(1,51) = 13.27; P,.001; partial-g
2
= .300] and the interaction of
predictability6region [F(2,80) = 3.36; P = .028; partial-g
2
= .098]
were significant. No other factors were statistically reliable (all
Ps..15). Post-hoc two-way ANOVAs with the factors frequency
(2) and predictability (2) in each of the nine regions yielded
significant predictability effects at anterior-midline [F(1,31) = 4.47;
P = .043; partial-g
2
= .126], anterior-right [F(1,31) = 4.73; P = .037;
partial-g
2
= .132], central-right [F(1,31) = 9.43; P = .004; partial-
g
2
= .233], and posterior-left sites [F(1,31) = 10.67; P = .003; partial-
g
2
= .256; shown in Figure 4B]. The main effect of frequency and
the interaction of frequency6predictability were not reliable in
any of the nine regions (all Ps..10).
Finally, we conducted separate analyses for low and high
frequency words in the posterior-left region, which yielded the
strongest effect (Figure 4A–C). As shown in Figure 4C, we
consistently found more negative amplitudes for high than for low
predictability words within the low frequency (t(31) = 22.25;
P = .032) as well as within the high frequency condition
(t(31) = 22.54; P = .016).
Discussion
The present study examined the earliest index for the interplay
between expectancy-based top-down and stimulus-driven bottom-
up processes in sentence reading. ERPs to predictable and
Figure 2. Grand averages for a sample of nine electrodes. ERPs for low (orange) and high predictability (blue) target conditions when (A) the
target word or (B) the target-preceding word was presented. Background shading illustrates the stimulus sequence (gray: word present; white: blank
screen). Dashed lines border the interval from 50 to 90 ms.
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unpredictable words differed in an interval from 50 to 90 ms after
stimulus onset, a latency that is considerably faster than most
previous reports of interactions between top-down and bottom-up
information in visual perception. It should be noted that other
target properties cannot serve as an explanation for the effect
because low and high predictability conditions utilized the same
words in identical sentence frames; only preceding context
sentences rendered targets expected or unexpected. Words prior
to the target did not evoke differential ERPs across frequency and
predictability conditions, corroborating the view that the observed
effect resulted from the experimental manipulation of the target
word. Importantly, the predictability effect held across levels of
word frequency (Figure 4C) pointing to the reliability of the result.
Furthermore, the independence from frequency rules out visual
word familiarity as an explanation. We therefore propose that
ERP differences have emerged from a rapid match of form-specific
predictions with incoming visual patterns.
The finding contributes to the idea that active top-down
predictions play a major role in early visual processing [2–
4,12,23,24,26–30,32]. As was noted previously, the large amount
of feedback connections warrants projections to early cortical
regions (e.g., [5]). Accordingly, fMRI studies have revealed top-
down activations of primary sensory areas prior to the occurrence
of expected stimuli [7,9]. In visual word recognition, predictions
were shown to pre-activate form-specific patterns of expected
words (e.g., [35]). The present data indicate, that these predictions
are verified very rapidly with the actual incoming stimulus, i.e.,
before 90 ms after visual onset.
Notably, the predictability effect occurred substantially earlier
than in previous research. We consider two explanations why
top-down effects at comparable latencies have been rarely
reported before. First, we presume that powerful top-down
projections are required to produce measurable influences at
early latencies. In previous studies, effects potentially were
indiscoverable or absent as a consequence of insufficiently strong
feedback information. For example, effects of spatial attention
were usually found from around 70 ms on P1 amplitudes,
whereas the C1 component from 50 to 90 ms was unaffected
[13–16]. However, variable SOAs inducing temporal uncertainty
may have reduced the strength of attention towards upcoming
stimuli. By contrast, with fixed SOAs and individual differences
taken into account, attention effects on the C1 were found after
57 ms [40]. Beyond that, top-down influences vary in the
amount of information they carry [4]. Feedback signals issuing
spatial selection are presumably weaker than expectations pre-
activating form-specific representations of predicted stimuli
[35,36]. The present data indicate that word predictability
afforded top-down modulations that were strong enough to affect
earliest perceptual processes.
As a second explanation, we presume that the observation of
early top-down modulations depends on the perceptual task (see
also [4]). In particular, early processes are enforced when task
demands and perceptual load are sufficiently high [10,33,34]. In
word recognition, normal reading speed of four to five words per
second sets tight time constraints for stimulus processing.
Compared to that, ERP reading experiments typically used slow
rates of one or two words per second and potentially missed
adequate demands. Those mostly revealed predictability effects
from 200 to 500 ms on the N400 component [41–43]; only a few
authors reported earlier effects, from 120 to 190 ms [44,45].
Employing a quasi-normal reading speed, the present setup
presumably approximated temporal conditions word recognition
is optimized for and encouraged rapid integration of both top-
down and bottom-up information. This is comparable to auditory
sentence processing at normal speaking rate, where expected and
unexpected inflections on adjectives evoked differential ERPs no
later than after 50 ms [36].
Figure 3. Latencies of the first predictability effect. (A) Gray bars
illustrate onset and duration of the first significant predictability effect
on 50 scalp electrodes. In the interval from 50 to 90 ms (dashed lines),
the effect emerges on 19 channels. (B) The onset topography reveals
early predictability effects at right anterior and left posterior sites.
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These two proposals are neither exclusive nor exhaustive and,
certainly, a number of additional factors will influence the timing
of convergence between bottom-up and top-down streams in
visual processing. To examine the validity of the present
suggestions and to complete the picture of short-latency top-down
effects, further research will be necessary. The reconciliation of
these findings with feedback modulations occurring later in time
will contribute to a comprehensive understanding about the
interplay of internal brain states and information from the
environment.
Clearly, the present data point to the efficiency of stimulus
encoding in visual perception. Evidence from electro- and
magneto-encephalography revealed that bottom-up activation
spreads in the primary visual cortex at around 50 ms post-
stimulus and is rapidly transmitted to higher cortical areas.
Activity reaches a large proportion of extrastriate and frontal
regions within 70 and 80 ms, respectively [46,47]. Can these
signals be interpreted and compared to stored information before
90 ms? Converging empirical support comes from visual search.
Sigman and colleagues showed that extensive training with letter-
like shapes grants selective responsiveness in earliest visual cortices
[32]. Further, complex search patterns that were either predictive
or unpredictive with respect to target position evoked differential
magneto-encephalographic responses from 50 to 100 ms at
occipital sites. Since participants were not aware of the pattern-
target associations, this result points to fast elaboration of visual
input that rapidly contacts unconscious memory [48]. An
explanation for the high processing speed of visual input is
provided by recent theories proposing that meaningful informa-
tion is already extracted from the first 1–5% of the bottom-up
signal. Thereby, top-down processes, acting as temporal bias,
increase stimulus saliency [49,50]. Consistent with these ideas, our
data indicate that in the presence of strong predictions, the cortex
matches pre-activated representations with incoming stimuli
shortly after the visual signal is available.
This interpretation is in line with models assuming interactions
between feedforward and feedback information (e.g., [23,24,26–
30]). For instance, Di Lollo and co-workers [27] proposed that
early visual processes generate preconscious hypotheses about the
identity of an incoming stimulus. These hypotheses re-enter low
visual areas and are iteratively compared with the input. An
affirmative match enhances the signal and affords conscious
perception of a stimulus. This interactive view of feedforward
and feedback information successfully accounted for findings
from backward masking, assuming that top-down hypotheses
from a briefly presented target mismatch the visual input after a
mask has superseded the bottom-up target signature [27,51].
Further, rapid resumption of an interrupted visual search
indicates that preprocessed patterns evoke target-specific hy-
potheses which are swiftly tested against sensory information
[52,53]. The present data extend this view suggesting that top-
down hypotheses also emerge from the interpretation of semantic
contexts. Thereby, the instantaneous match with the visual input
is compatible with the idea that top-down influences dynamically
reconfigure filters in the visual system to grant optimal processing
of relevant information from incoming signals [54]. Thus, visual
perception appears as an active process that rapidly compares
internal semantic representations with task-relevant aspects of
incoming stimuli [55–57].
The observed predictability effect was strongest over posterior
electrodes. This region is situated above the left hemispheric
occipito-temporal network that is strongly linked to the so-called
visual word form area [58,59]. As these cortical structures are
gradually sensitive to the processing of word-like stimuli [31],
they reflect a plausible ground for the matching of top-down
predictions and incoming signals. Another relevant structure may
be the foveal portion of the retinotopic cortex that was shown to
receive category-specific feedback information as response to
peripherally presented objects. Hence, V1 was proposed to serve
as scratch pad for the storage and computation of task-relevant
Figure 4. Predictability effect in scalp regions. (A) Topography of mean amplitude differences (low minus high predictability) in the epoch
from 50 to 90 ms. Nine regions of scalp electrodes are delimited by black borders. (B) Mean amplitudes from seven electrodes at the posterior left
region. In the interval from 50 to 90 ms (dashed lines), amplitudes are more negative for high (blue) than for low predictability (orange) words. The
lower panel shows the difference waveform (low minus high predictability). Mid-gray and light-gray error bands depict 95% and 99% confidence
intervals, respectively, computed from 5000 bootstrap samples. Background shading illustrates the stimulus sequence (gray: word present; white:
blank screen). (C) Within-frequency class ERPs at the posterior left region. The early effect of predictability is independent from target frequency.
Background shading reflects the stimulus sequence (shaded: word present; white: blank screen).
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visual information [60] (see also [4]). Note, however, that
suggestions about underlying sources of the predictability effect
remain speculative, as no strong inferences about localization can
be drawn on the basis of the present ERP data.
In conclusion, previous research has shown that predictions
about upcoming words pre-activate representations of specific
word forms. The present results indicate that, under near-normal
reading speed, these predictions are checked in an interval from
50 to 90 ms after the visual input. Though reading is ideally
suited to examine this issue, rapid verification of expected
physical input is fundamental to many domains, including object
recognition in general [5] and movement control [61]. If
replicable across a wide range of tasks, our finding provides a
critical temporal constraint for theories of top-down and bottom-
up interactions as well as novel insights about the efficiency of
stimulus encoding.
Acknowledgments
We thank our lab members at Freie Universita¨t Berlin and Universita¨t
Potsdam for their support and Mario Braun for research assistance. Both
laboratories contributed equally to this work. We also thank Mariano
Sigman and Mark Williams for helpful and constructive comments on an
earlier version of the article.
Author Contributions
Conceived and designed the experiments: MD KG RK. Performed the
experiments: MD. Analyzed the data: MD MR. Contributed reagents/
materials/analysis tools: RK AMJ. Wrote the paper: MD MR.
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