Context-Based Facilitation in Visual Word Recognition: Evidence for Visual and Lexical But Not Pre-Lexical Contributions

Susanne Eisenhauer; Christian J. Fiebach; Benjamin Gagl

doi:10.1523/ENEURO.0321-18.2019

Abstract

Word familiarity and predictive context facilitate visual word processing, leading to faster recognition times and reduced neuronal responses. Previously, models with and without top-down connections, including lexical-semantic, pre-lexical (e.g., orthographic/phonological), and visual processing levels were successful in accounting for these facilitation effects. Here we systematically assessed context-based facilitation with a repetition priming task and explicitly dissociated pre-lexical and lexical processing levels using a pseudoword (PW) familiarization procedure. Experiment 1 investigated the temporal dynamics of neuronal facilitation effects with magnetoencephalography (MEG; N = 38 human participants), while experiment 2 assessed behavioral facilitation effects (N = 24 human participants). Across all stimulus conditions, MEG demonstrated context-based facilitation across multiple time windows starting at 100 ms, in occipital brain areas. This finding indicates context-based facilitation at an early visual processing level. In both experiments, we furthermore found an interaction of context and lexical familiarity, such that stimuli with associated meaning showed the strongest context-dependent facilitation in brain activation and behavior. Using MEG, this facilitation effect could be localized to the left anterior temporal lobe at around 400 ms, indicating within-level (i.e., exclusively lexical-semantic) facilitation but no top-down effects on earlier processing stages. Increased pre-lexical familiarity (in PWs familiarized utilizing training) did not enhance or reduce context effects significantly. We conclude that context-based facilitation is achieved within visual and lexical processing levels. Finally, by testing alternative hypotheses derived from mechanistic accounts of repetition suppression, we suggest that the facilitatory context effects found here are implemented using a predictive coding mechanism.

Significance Statement

The goal of reading is to derive meaning from script. This highly automatized process benefits from facilitation depending on word familiarity and text context. Facilitation might occur exclusively within each level of word processing (i.e., visual, pre-lexical, and/or lexical-semantic) but could alternatively also propagate in a top-down manner from higher to lower levels. To test the relevance of these two alternative accounts at each processing level, we combined a pseudoword (PW) learning approach controlling for letter string familiarity with repetition priming. We found enhanced context-based facilitation at the lexical-semantic but not pre-lexical processing stage, and no evidence of top-down facilitation from lexical-semantic to earlier word recognition processes. We also identified predictive coding as the most likely mechanism underlying within-level context-based facilitation.

Introduction

Efficient reading relies on automatized visual word recognition (Rayner, 1998), which in turn involves visual-perceptual, pre-lexical orthographic and phonological, and subsequent lexical-semantic processing levels (Coltheart et al., 2001; Carreiras et al., 2014). Efficiency in reading depends mainly on our familiarity with the units of language (Zoccolotti et al., 2009; Gagl et al., 2015) and on facilitation that arises from the predictive nature of linguistic contexts during natural reading (Kliegl et al., 2006). Contextual facilitation results in reduced brain activation, most prominently of the N400, a component of the event-related brain potential (ERP) peaking ∼400 ms after word onset. The N400 reduction has typically been interpreted as reflecting facilitated processing at the lexical-semantic level of linguistic representation (for review, see Lau et al., 2008; Kutas and Federmeier, 2011). In line with this assumption, computational models like the strictly bottom-up sequential model of Laszlo and Armstrong (2014) successfully account for context-dependent N400 reduction effects by allowing neuronal fatigue within processing levels. Alternatively, however, it has also been proposed that contextual information (e.g., at the lexical-semantic level) can facilitate earlier stages of word recognition in a recurrent, top-down manner (for review, see Carreiras et al., 2014). Figure 1A visualizes these competing accounts of context effects on word recognition. Thus, the current model architectures disagree on the implementation of context-based facilitation as either within a processing level or top-down from higher processing levels.

Figure 1.

Model assumptions and predictions. A, A simplified architecture of visual word recognition (adapted from Carreiras et al., 2014) including expectations of “when” (cf. Barber and Kutas, 2007) and “where” in the brain the respective processes are implemented. Gray lines symbolize potential implementations of context-based facilitation either as within-level mechanism assumed in strictly bottom-up accounts (Laszlo and Armstrong, 2014) or, additionally, as a recursive top-down influence on hierarchically lower processing levels. B, Schematic representation of expected neuronal repetition suppression (left and central panels) and behavioral priming effects (right) with likely modulations by pre-lexical and lexical-semantic familiarity. We expect a reduction of neuronal activation and response times between primes and identical targets. At the lowest, i.e., visual processing level, we expect no familiarity modulation since all letter strings were a priori visually similar (left panel). For PWs that were familiarized through repeated exposure but without learning a new meaning, we expect selectively stronger neural repetition suppression in a “pre-lexical time window” around 150 and 300 ms (central left panel). Finding this interaction additionally at earlier time points would be evidence for a facilitatory top-down influence of pre-lexical familiarity onto earlier visual processing stages. Note that as a manipulation check, we also expect that familiar PWs should elicit increased activation already at the prime at the expected regions (Gagl et al., 2016; Laszlo and Federmeier, 2014). At the level of lexical-semantic processing, we expect stronger repetition suppression for words compared to meaningless PWs (central right panel) at around 400 ms and, in case of top-down modulation, also in previous time windows. During prime processing, activation should be highest for words reflecting lexical-semantic processing (Rabovsky et al., 2012). In behavior, we expect stronger priming effects for words and familiar PWs compared to novel PWs, which would indicate that both pre-lexical and lexical-semantic familiarity increase context-based facilitation. C, Schematic visualization of expectations for fatigue, sharpening, and predictive coding mechanisms, shown for the N400 time window (i.e., lexical-semantic processing) and the contrast of words versus novel PWs. The left panel shows an activation pattern reflecting a fatigue mechanism. This account assumes that the more activation is elicited on the prime (i.e., more semantic processing), the more neurons are “exhausted”, resulting in a stronger reduction for words versus PWs. Sharpening (second panel from left) expects a reduction of irrelevant (i.e., noisy) representations, thereby amplifying the signal. Consequently, neuronal repetition suppression should be weaker for words reflecting a focus of processing resources on informative word representations and, thus, should reduce activations selectively for PWs (Kok et al., 2012). Predictive coding (two right panels) assumes a suppression of expected signals from the input. Thus, one would expect a suppression of the predictable signal rather than of the noise (Blank and Davis, 2016). For words, the additional lexical information can be used to predict the future target, resulting in stronger repetition suppression compared to PWs. The similar patterns for fatigue and predictive coding can be differentiated based on the probability with which identical prime-target pairs occur (Grotheer and Kovács, 2014), which we implemented in experiment 2. For predictive coding, one would expect stronger repetition suppression with higher repetition probability, whereas the fatigue account makes no differential predictions depending on repetition probability.

Computationally, Laszlo and Armstrong (2014) implemented context-based facilitation by a fatigue mechanism, assuming that recently active neurons are less likely to fire again (Grill-Spector et al., 2006). However, findings from semantic priming (Lau et al., 2013b) indicate an alternative mechanism, i.e., predictive coding, which assumes the suppression of perceptual signals that are consistent with context-based internally generated expectations about upcoming input (Friston, 2005). According to this model, one processes only the residual, i.e., unpredicted part of the input, which accounts for increased N400 activation when contextual expectations are violated. A third alternative would be sharpening (Grill-Spector et al., 2006), which assumes a reduction of neuronal firing only when neurons code the input suboptimally, thereby increasing the reliability of dissociating between inputs (Blank and Davis, 2016; Richter et al., 2018). To date, we are not aware of a direct comparison of the possible mechanisms of context-based facilitation in visual word processing.

Separation of processing levels in visual word recognition research remains challenging. Experimental priming paradigms ensure a high degree of control over context factors, but require the matching of large numbers of psycho-linguistic parameters, which is difficult (Sassenhagen and Alday, 2016). Alternative regression-based accounts include these parameters as covariates, which can be realized in more natural contexts (i.e., sentence reading; Dambacher et al., 2006) but often demands large datasets (Dufau et al., 2015). We here propose that some of these problems can be ameliorated by using learning paradigms to increase familiarity and to associate information at different levels of linguistic representation with previously unfamiliar items [e.g., pseudowords (PWs): pronounceable non-words] in a controlled fashion (Taylor et al., 2011).

Using this strategy, we here dissociate between facilitation at pre-lexical and lexical levels of word processing. First, we matched pre-lexical characteristics (phonological length and orthographic familiarity) across words and PWs, ensuring comparable levels of pre-lexical processing difficulty between conditions (cf. Yarkoni et al., 2008). Second, we used a learning paradigm to increase pre-lexical familiarity of a subset of PWs (cf. Glezer et al., 2015) and measured highly time-resolved brain activation using magnetoencephalography (MEG; experiment 1). In a second, behavioral, experiment, we also included PWs to which meaning was associated. To manipulate context-based facilitation, we used repetition priming (for a discussion of priming as context manipulation, see DeLong et al., 2014). Figure 1B shows, in detail, the expected electrophysiological and behavioral responses reflecting context-based facilitation at visual, pre-lexical, and lexical-semantic processing levels. For example, we expect an interaction of lexical-semantic familiarity (presence vs absence of lexical-semantic information) and context (prime/without context vs target/with context) reflected by a stronger activation decrease (repetition suppression) for words in contrast to meaningless PWs (as shown by Almeida and Poeppel, 2013). If restricted to the N400 time window, this pattern would indicate that facilitation is implemented exclusively at the lexical-semantic level, whereas earlier effects would suggest top-down facilitation from lexical-semantic to earlier processing stages. Source localization of these effects will help to specify these conclusions further. Finally, we aim at clarifying the mechanistic implementation of context-based facilitation, by comparing predictions from predictive coding, sharpening, and fatigue (Fig. 1C).

Experiment 1: MEG

In the first experiment, we investigated pre-lexical versus lexical-semantic contributions to context-based facilitation in visual word recognition at a neuronal level, using MEG. Pre-lexical properties (orthographic Levenshtein distance/OLD20; Yarkoni et al., 2008) were matched between words and both PW groups (i.e., familiarized vs novel), so that a priori, comparable levels of pre-lexical familiarity should lead to similar levels of pre-lexical activation across all three stimulus groups (as expected from, e.g., implementation of the MROM model: Grainger and Jacobs, 1996). However, the familiarization training increases the pre-lexical familiarity with the trained PWs. We expected effects of pre-lexical familiarity, i.e., increased activation of event-related fields (ERFs) for familiar in contrast to novel PWs (Laszlo and Federmeier, 2014; Gagl et al., 2016), and lexical familiarity, i.e., increased activation for words in contrast to novel PWs (cf. Rabovsky et al., 2012), irrespective of context. As an effect on context, we expected stronger neuronal repetition suppression for familiarized PWs compared to novel PWs and words on the pre-lexical level (Fig. 1B, central left panel) and stronger repetition suppression for words in contrast to the two PW groups at the lexical level (Fig. 1B, central right panel). If we find this pattern, expected for pre-lexical processing, from 150 to 300 ms at, e.g., left fusiform regions, one can assume within-level context-based facilitation. One could come to a similar conclusion for lexical processing when the interaction pattern described above is found within the N400 time window at, e.g., left anterior temporal regions. Finding a similar pattern at earlier time windows would indicate top-down context-based facilitation.

Materials and Methods

Participants

A total of 38 healthy native speakers of German (26 females, mean age 23.0 ± 2.8 years, range: 18–29 years) recruited from university campuses participated in familiarization procedures and MEG recordings and were included in the final sensor level analyses. All participants were right-handed as assessed by the Edinburgh Handedness Inventory (Oldfield, 1971), had normal or corrected-to-normal vision, and normal reading abilities as assessed with an adult version of the Salzburg Reading Screening (unpublished adult version of Mayringer and Wimmer, 2003). A total of 19 further participants were excluded at different stages of the experimental procedure, due to the following reasons: Low reading skills (i.e., reading test score below 16th percentile; N = 5), insufficient performance during PW familiarization (i.e., accuracy for to-be-familiarized PWs <50% in the final learning session; N = 2), self-reported developmental speech disorder (N = 1), technical artifacts during the MEG measurement (N = 4), insufficient number of trials after artifact rejection (i.e., participants with <15 repetition trials in at least one condition, outliers defined as >1.5 times below the lower quartile range of the number of valid trials across all participants and familiarity conditions; N = 2), contraindication to MEG measurement (N = 1, participant with retainer which might cause artifacts in MEG data), or drop out by choice of participants (N = 4, participants did not finalize the experimental procedure). All participants gave written informed consent according to procedures approved by the local ethics committees (University Clinic of Goethe University Frankfurt, application N° 107/15; and Department of Psychology, Goethe University Frankfurt, application N° 2015-229) and received 10 € per hour or course credit as compensation.

Stimuli and presentation procedure

Words and PWs consisted of five letters, with the first letter in uppercase following convention for German nouns. PWs were generated by the Wuggy software (Keuleers and Brysbaert, 2010), conserving the phonological (i.e., sub-syllabic) structure of the input words; all PWs were pronounceable. Estimates of word frequency and orthographic Levenshtein distance 20 (OLD20; Yarkoni et al., 2008) were based on the SUBTLEX-DE database (Brysbaert et al., 2011). A complete list of stimuli including estimated variables is available at https://osf.io/fc69p/.

Sixty German nouns (logarithmic word frequency: mean ± SE = 2.14 ± 0.12, range: 0.00–4.03) and 120 pronounceable PWs were presented twice during MEG acquisition. In addition, 80 catch trials were presented (see section “Repetition priming” below). PWs were divided into two sets of 60 items, such that both PW lists and the set of words were matched on orthographic similarity (OLD20; group means: ± 1 SD: 1.825 ± 0.013; 1.717 ± 0.026; 1.743 ± 0.027) and number of syllables (1.883 ± 0.063; 1.95 ± 0.028; 1.933 ± 0.032; for stimulus characteristics, see Table 1). Despite the high similarity of the word characteristics between groups, these characteristics were included in all post hoc linear mixed models (LMMs) to account for potential confounds from the parameters (for details, see analysis section). Participants were familiarized with 60 PWs before the actual repetition priming task was conducted in the MEG (see section “PW familiarization” below for details). The second group of PWs was never seen by the participants before the MEG experiment. In addition, four further lists of 120 PWs each were generated as fillers for the familiarization procedure (one list per session).

View this table:

Table 1.

OLD20, number of syllables and word frequency for conditions of Experiment 1 (words, familiar and novel PW) and Experiment 2 (words and three PW sets)

Stimuli were presented using Experiment Builder software (SR-Research Ltd.). Words and PWs were presented in black bold Courier New font (14 pt.) in front of a white background. In the behavioral sessions, stimuli were presented on an LCD monitor with a refresh rate of 60 Hz, while during the MEG session, stimuli were projected with a refresh rate of 60 Hz onto a translucent screen.

PW familiarization

Participants visited the lab on the two days before the MEG experiment, and during each visit completed two familiarization sessions of ∼20 min in length. The two previous days were chosen to take advantage of sleep consolidation effects (James et al., 2017). Each familiarization session started with reading aloud the PWs from a printed list. Reading errors were documented (mean across all sessions: 0.7%). Subsequently, participants performed a computer-based old/new recognition task in which the to-be-familiarized PWs were presented two times per session, randomly intermingled with a new set of 120 filler PWs for every session (total of 480 filler PWs across all four sessions). For every PW, participants had to indicate by button press as fast and accurately as possible, if it was familiar to them or not. PWs were preceded by two black vertical bars displayed above and below the center of the screen where participants were asked to fixate (500 ms; Fig. 2A), and presentation was terminated with the button press.

Figure 2.

Experimental procedures. A, For the PW familiarization procedure of experiment 1, in each learning session, 60 PWs were presented until response, intermingled by novel filler PWs, in an old/new recognition task; 500 ms before stimulus onset, two vertical bars indicated the center of the screen where participants were asked to fixate. The intertrial interval was 2000 ms. B, During MEG recording, participants performed a repetition priming task. Each trial consisted of a sequence of two letter strings (prime and target) presented for 800 ms each, separated by an interval of 800 ms during which a string of five hash marks was presented. Letter strings could be words, familiarized PWs, or novel PWs (120 trials each regarding the prime); 75% of trials were repetition trials, i.e., prime and target were identical (left). The remaining 25% were non-repetition trials in which two different letter strings were presented (middle). In this case, prime and target could be from the same condition or from two different conditions, with all combinations of conditions appearing equally often. Participants were instructed to silently read presented letter strings and respond only to rare catch trials (right). Before onset of the prime, two black vertical bars presented for 800–1000 ms indicated the center of the screen where participants were asked to fixate. After presentation of the target, two gray vertical bars were presented for 1000 ms, indicating a blinking period of 1500 ms starting from onset of the bars. Before the onset of the next trial, a blank screen was presented for the remainder of the blinking period. C, In experiment 2, a paired-association task was used for familiarization of PWs with and without semantics. PWs were presented for 800 ms, followed by the presentation of an object image until button press (maximally 1500 ms). During the intertrial interval of 1000 ms, two vertical bars indicated the center of the screen where participants were asked to fixate. In the semantic condition, there was a reliable association between object and PW. In the familiarization only condition, in contrast, PWs and objects were randomly paired so that each pair occurred only once. D, In the subsequent naming task, each PW from the familiarization conditions with and without semantic associations was presented once. Participants named the object they associated with each PW, or responded “next” in case they did not associate a meaning with a PW. Before each PW presentation, two vertical bars framing the center of the screen were presented until button press by the experimenter. E, The repetition priming task involved in each trial a sequence of two letter strings presented for 800 ms each, separated by an interval of 800 ms during which five hash marks were displayed. The hash mark string was also presented for 800 ms before the onset of the first letter string. Letter strings could be words, familiarized PWs with and familiarized PWs without semantics, or novel PWs (180 trials each regarding the prime). Repetition probability was varied across blocks between 25%, 50%, and 75%. Participants were instructed to silently read presented letter strings and respond to the target whether they had an explicit semantic association with it, or not. During the intertrial interval of 800–1200 ms, two vertical bars indicated the center of the screen where participants were asked to fixate.

LMM analyses with session (centered and z-transformed) as fixed effect and participant and item as random effects on the intercept were performed with the lme4 package (Bates et al., 2015) in R, version 3.4.1, 2017-06-30 (R Development Core Team, 2008). All effects with t > 2, reflecting that the effect differs from zero by more than two SEs, were considered significant (note that p values cannot be computed in a reasonable way in the LMM approach; Kliegl et al., 2011). Note that for one participant, data of sessions 3 and 4 could not be saved due to technical issues. Old/new response sensitivity indices d’ (Green and Swets, 1966) significantly increased across familiarization sessions from 1.15 in session 1 to 2.96 in session 4 (estimate = 0.66, SE = 0.039, t = 17.17; pairwise tests between subsequent sessions: all ts > 5; Fig. 3A; for details, see Extended Data Fig. 3-1). This demonstrated that participants improved across sessions in distinguishing between familiar and novel (i.e., filler) PWs. Participants reached a high performance in the final session, with accuracies ranging between 70.0% and 99.2% for familiar and between 59.2% and 99.2% for filler PWs (Fig. 3B). Based on the strong improvement in sensitivity and the high performance in the final session, we conclude that pre-lexical familiarization of the trained PWs was successful.

Figure 3.

Behavioral results of familiarization procedures. A, B, Old/new recognition task (experiment 1). C, D, Paired-association task (experiment 2). E, F, Naming task (experiment 2). Left, Sensitivity indices d’ across all sessions. Right, Accuracy in the final session. Colored dots and lines represent individual participants. Statistical results can be found in Extended Data Figure 3-1.

Extended Data Figure 3-1

Results from the LMM analyses of d’ sensitivity indices from the PW familiarization sessions of both experiments 1 and 2, including pairwise comparisons from session to session. Download Figure 3-1, DOCX file.

Repetition priming

The repetition priming task during MEG recording was conducted on day 3 and included words, familiarized PWs, and novel PWs. At the start of each trial, participants had to fixate between two vertical black bars presented above and below the center of the screen (analogous to the familiarization procedure; cf. Fig. 2B). Stimulus presentation was initiated after an eye-fixation to the cued region was detected by an MEG compatible eye-tracker (Eyelink CL 1000, SR Research Ltd.), and comprised the successive presentation of two letter strings (prime and target) for 800 ms each, separated by an interval of 800 ms during which a string of five hash marks was shown. Both letter strings had to be read silently; the task served only to maintain attention and required a button press to catch trials, i.e., the word “Taste” (English: button) in either the first, second, or both positions. The silent reading task was chosen to avoid contaminating the neuronal response to words with motor responses; catch trials were excluded from analysis. The explicit fixation control before stimulus presentation assured that eyes were open and directed toward the position where the stimulus was presented. Response hands were counterbalanced across participants and responses were recorded using a fiber optic response pad (LUMItouch; Photon Control Inc.); 100 ms after target offset, gray vertical bars were presented for 100 ms, indicating that participants were allowed to blink for a period of 1000 ms. Stimuli were presented at a viewing distance of 51 cm yielding horizontal visual angles of ∼0.3° per letter.

The 60 letter strings per condition (words, familiar, and novel PWs) were each presented in two trials, once during each half of the experiment. As a consequence, we presented 120 trials per stimulus condition adding up to 360 trials; 75% (i.e., 270) of these trials were repetition trials, allowing the investigation of familiarity effects in a highly predictive context. The remaining 25% (i.e., 90) trials were non-repetition trials, in which each possible combination of words, familiarized, and novel PWs appeared equally often, i.e., 10 times. Also, we presented 80 catch trials resulting in a total of 440 trials. The repetition priming task lasted ∼40 min, divided into three blocks separated by breaks of ∼5 min.

MEG data acquisition

MEG datasets were acquired in accordance with guidelines for MEG recordings (Gross et al., 2013), using a 275 sensor whole-head system (Omega 2005; VSM MedTech Ltd.). Six sensors (MLF66, MLP31, MRF22, MRF24, MRO21, and MZC02) were disabled due to technical issues so that 269 sensors remained for data acquisition. Data were recorded at a sampling frequency of 1200 Hz using a synthetic third-order gradiometer configuration. Online filtering was performed with fourth-order Butterworth filters with 300-Hz low pass and 0.1-Hz high pass. Head positions of the participants relative to the gradiometer array were recorded continuously by three localization coils placed at the nasion and above both ear canal entrances using ear-plugs. Additionally, two electrodes placed centrally on each clavicula recorded an electrocardiogram (ECG), while two pairs of electrodes placed distal to the outer canthi of both eyes, and above and below the right eye, respectively, recorded an electrooculogram (EOG). The impedance of each electrode was below 5 kΩ for EOG electrodes and below 20 kΩ for ECG electrodes, measured with an electrode impedance meter (Astro-Med GmbH).

Structural magnetic resonance (MR) image acquisition

Structural MR images were acquired for 34 participants with a 1.5 T Siemens magnetom Allegra scanner (Siemens Medical Systems) using a standard T1 sequence (3D MPRAGE, 176 slices, 1 × 1 × 1 mm voxel size). To enable co-registration of MR images with MEG data, vitamin E capsules were placed at the positions of two of the MEG head localization coils (i.e., above both ear canal entrances using ear-plugs); the nasion could be identified anatomically in structural MR images. Fiducial coordinates were identified in SPM12 (http://www.fil.ion.ucl.ac.uk/spm/software/spm12/).

MEG sensor level analyses

MEG data were analyzed with FieldTrip (version 2011 11-21 for preprocessing and version 2013 01-06 for all remaining sensor level analyses; http://fieldtrip.fcdonders.nl; Oostenveld et al., 2011) under MATLAB (version 2012b, The MathWorks Inc.), except for Figure 4A,B, which was realized with MNE-Python (https://martinos.org/mne/stable/index.html; Gramfort et al., 2013, 2014). Parallel computations were performed using GNU parallel (Tange, 2011). Catch trials and any other trials during which participants made a button press were excluded from analysis. MEG data were segmented into epochs of 2600 ms in length, lasting from –160 to 2440 ms with respect to the onset of the prime.

Figure 4.

Repetition suppression: Interaction between trials without (prime) versus with (target) context (C) and repeated versus non-repeated context (i.e., repetition congruency; R). A detailed overview of all clusters obtained with common or separate baselines can be found in Extended Data Figure 4-1. ERF time courses of (A) repetition and (B) non-repetition trials for all sensors and familiarity conditions. Colored lines represent individual sensors and within-plot topographical map color-codes scalp position of each sensor. Topographies represent activation at 400 and 2000 ms, which allows a comparison of activation after 400 ms of the onset of prime and target, respectively. C, Topographical map represents F values of significant sensors averaged across the significant time window. Non-significant sensors are set to zero. Surface plot represents source locations of the effect in signal-to-noise ratio (SNR) thresholded at 50%. D, ERF time course averaged across significant sensors (of left hemisphere only, shown in topography in C). The significant time window is marked by a yellow shaded black box. Red lines correspond to prime and blue lines to target; solid lines correspond to repetition (Rep) and dashed lines to non-repetition (Non-Rep) trials (averaged across all familiarity conditions). E, Boxplots represent activation averaged across sensors and time points within the left hemisphere cluster, for repetition trials (left) and non-repetition trials (right). Colored dots and lines represent individual participants. Asterisks indicate significant results (t > 2) from post hoc LMMs.

Extended Data Figure 4-1

Overview of clusters demonstrating a prime/target by repetition congruency (repetition vs non-repetition) interaction obtained with separate or common baselines for prime and target. Download Figure 4-1, DOCX file.

Individually for each participant, trials were selected for analysis in which the head position fell within a range of 5 mm (across all blocks) relative to the majority of other trials. Trials contaminated with sensor jump and muscle artifacts were rejected automatically, using the FieldTrip routine for automatic artifact detection. For jump artifact detection, a 9^th order median filter was applied to the data, while for muscle artifact detection, an 8th order Butterworth IIR filter (110–140 Hz) was applied. The filtered data were z-transformed and averaged across sensors. Trials were rejected if for any time point the z value exceeded a threshold of z = 20 for jump artifacts and z = 6 for muscle artifacts, following standards established for the local measurement characteristics. Trials contaminated with eye blink, eye movement, or heartbeat artifacts were cleaned using independent component analysis (ICA; Makeig et al., 1996). Components whose time courses correlated with EOG and ECG electrodes were rejected, using as threshold a correlation coefficient of r > 0.1, which sufficiently removed artifacts according to visual inspection. After these procedures, an average of 51.2 repetition trials (range: 26–79) per condition could be retained. Non-repetition trials were averaged across conditions for analysis, with on average 52.6 trials per participant available (range: 29–80).

Before computation of ERFs, a 20-Hz low-pass filter was applied to data epochs to increase the signal-to-noise ratio (cf. Lau et al., 2013a). In addition, to ensure that the low filter did not mask more transient components, we performed the main analyses after filtering at 40-Hz low pass. Original epochs were split into separate epochs for prime and target stimulus, ranging from –110 to 800 ms with respect to each stimulus onset. Epochs were baseline corrected by subtracting the average activation between –110 and –10 ms from each time point. For each sensor, we identified the participants for which the recorded magnetic field averaged across repetition trials and time lay outside the range of the mean across all participants ±3.29 SDs; i.e., detecting extreme outliers outside the 99.9% confidence interval. The signal of these noisy sensors (33 sensors in total; one to nine sensors in ten participants), per participant, was approximated by trial-wise interpolation from activation in neighboring sensors. ERFs were then calculated for each subject and condition (repeated words, repeated familiar PWs, and repeated novel PWs, as well as non-repetition trials combined across all familiarity conditions), separately for prime and target, by averaging the epochs across all trials. ERFs were compared between conditions using cluster-based permutation tests (Maris and Oostenveld, 2007) for dependent samples, corrected for multiple comparisons across time points (–110 to 800 ms) and sensors at cluster level. To compute interaction statistics, we used the permANOVA functions by Helbling (2015; https://github.com/sashel/permANOVA/). Clusters were defined as spatially and temporally adjacent samples with F values exceeding an uncorrected α-level of 0.001 (cf. Eklund et al., 2016). The cluster-level statistic was calculated using the standard approach, i.e., taking the sum of F values within a cluster (Maris and Oostenveld, 2007). Empirical cluster-level statistics were compared to the distribution of cluster-level statistics obtained from Monte Carlo simulations with 5000 permutations, in which condition labels were randomly exchanged within each subject. Original cluster-level statistics larger than the 95th percentile of the distribution of cluster-level statistics obtained in the permutation procedure were considered to be significant. Note, we use the terms “significant time window/sensors” for convenience. However, we are aware that the temporal and spatial extents of clusters obtained with the permutation procedure are subject to variations based on the signal-to-noise ratio, number of trials, and the selected cluster threshold. As a consequence, we will not interpret the exact values precisely and rather focus on the condition differences within the obtained clusters.

First, as a general check of our experimental manipulation, we assessed context effects by computing a 2 × 2 interaction between the experimental factors repetition congruency (repetition vs non-repetition trials, reflecting whether context-based processing was indeed possible; referred to as R) and prime/target effect (reflecting the absence vs presence of a preceding context; referred to as C). As the low number of non-repetition trials did not allow separate analyses of these effects for the different conditions, data were pooled across familiarity conditions (words, familiar, and novel PWs). Within each familiarity condition, the number of repetition trials was randomly stratified to match the number of non-repetition trials. In the second analysis, we examined in repetition trials how familiarity (words/lexical familiarity vs familiar PWs/pre-lexical familiarity only vs novel PWs) and context (prime vs target) interact. This analysis served to examine the effects of different familiarity types on the neuronal repetition effect.

To determine the nature of significant interaction and main effects, we performed post hoc LMM analyses for pairwise differences between relevant conditions. All post hoc tests were based on participant-specific and condition-specific ERF values averaged across sensors and time points from the respective significant cluster, and included participant and item as random effects on the intercept. Since not all trials entered the analyses due to exclusion of artefactual trials, which might have affected the matching across letter string conditions, OLD20 and number of syllables, both z-transformed and centered, were entered as additional fixed effects.

To rule out the possibility that our baseline correction approach, i.e., using separate baselines for ERFs elicited by prime and target stimulus in a trial, has created artificial effects due to the presentation of hash marks only before the target, we performed the analyses of repetition congruency by prime/target and familiarity by prime/target a second time, using the period before the prime as a common baseline for correction of ERFs to both stimuli. Of in total 24 significant clusters from the analyses after separate baselining, 17 were also found significant in the analysis after common baselining. Therefore, in the results and discussion sections, we will focus on those clusters replicated with the common baseline approach. Clusters with durations < 30 ms were not interpreted (cf. Dikker and Pylkkänen, 2013, for a similar approach), which led to the rejection of three clusters of ∼20 ms. A comparison of significant clusters from both analyses can be found in Extended Data Figures 4-1, 5-1.

As a further sanity check of the separate baselines approach, we additionally report a peak-to-peak analysis for the repetition by familiarity interactions as well as main effects. For this analysis, the positive (in case of right sensors) and negative (in case of left sensors) peaks of the ERFs were identified per participant, condition, and sensor (restricted to the time window ±150 ms around the peak latency of grand average ERFs and the interval between 0 and 500 ms). In case of central sensors close to the midline (sensors MZC01, MZC03, MZC04, MZF01, MZF02, MZF03, MZO01, MZO02, MZO03, and MZP01), we separately decided whether to select the positive or negative peak, depending on which of the two peaks was absolutely higher in the across-participant ERFs. We decided against taking this approach in the majority of sensors because the ERFs typically declined during later time windows, in many cases reaching a value higher than the actual peak in absolute terms. Therefore, selecting the positive peak in the case of right sensors, and the negative peak in the case of left sensors, was the best compromise between automatic peak determination and avoidance of misplacing the actual peak value with a value that falls within the time range of decline of the ERF. We then subtracted the preceding peak value of respective other polarity (between stimulus onset and detected peak) from the already defined peak value. Statistical analyses were then performed on the absolute peak difference, using the cluster-based permutation procedure as described above, defining clusters solely based on spatial adjacency between sensors due to the lack of the temporal dimension. Given its independence from the pre-stimulus baseline, hash mark strings presented before the target cannot influence this analysis. However, a limitation of this analysis is that it cannot detect significant differences occurring at time ranges before and after the peak. Therefore, the results of this analysis did not influence whether a specific cluster was interpreted or not.

MEG source localization

Source localization was performed for those 34 participants of whom we could obtain anatomic MR images, using FieldTrip, version 2016 10-24. We created individual source grids for each participant by transforming the anatomic MR images to a standard (i.e., MNI space; Collins et al., 1994) T1 template from the SPM8 toolbox (http://www.fil.ion.ucl.ac.uk/spm). A regular 3D dipole grid (10-mm resolution) based on the T1 template was then warped with the inverse of the resulting transformation matrix. This procedure resulted in individual dipole grids for each participant, in which each grid point was located at the same brain area across participants. For each grid point and participant, lead fields were computed with a single shell forward model of the inner surface of the skull (Nolte, 2003). Before source localization, ERFs were down-sampled to 300 Hz to minimize computing costs. Source locations were computed for significant contrasts of interest from the ERF statistics as proposed by Gross et al. (2013). The procedure followed Manahova et al. (2018; the original code is available at https://data.donders.ru.nl/collections/di/dccn/DSC_3018012.15_439?0; see also Dijkstra et al., 2018; Mostert et al., 2018). Source localization was performed on ERFs by estimating two-dimensional dipole moments at each grid and time point using linearly constrained minimum variance (LCMV) spatial filters (van Veen et al., 1997). For the main prime/target effect, source localization was performed on the ERF difference between prime and target stimulus, averaged across all knowledge conditions in repetition trials. For main effects of familiarity, source localization was performed on the respective conditions separately and subtracted afterward (e.g., familiar – novel PWs averaged across prime and target). Interactions between repetition congruency and prime/target effect were resolved by performing source localization on the difference ERFs between stratified repetition and non-repetition trials, separately for prime and target, and then subtracting source activations of the prime from those of the target. Analogous, for interactions between prime/target and familiarity effects source localization was performed on the difference ERFs between prime and target, separately for each letter string condition, and then subtracted. The data covariance was estimated over the time interval of the respective significant contrast and regularized using shrinkage (Blankertz et al., 2011) with a regularization parameter of 0.01.

Two-dimensional dipole moments were reduced to a scalar value by taking the norm of the vector. This value reflects the contribution of a particular source location to sensor level activation not only in magnitude but also in dipole orientation. The latter is crucial for also detecting differences caused by different neuronal populations within the same source location. The norm of the vector is a positive value and subject to a positive bias due to noise. To counter this bias, we employed a permutation procedure (1000 permutations). For analyses on separate conditions, the sign of half of the trials was randomly flipped. For analyses on condition differences, condition labels were randomly exchanged across trials. The square of the dipole’s norm averaged across all permutations was taken as noise estimate. This noise estimate was subtracted from the square of the true data, and the data were divided by the noise estimate. Negative values were set to zero, and the square root was taken. Finally, the signal of each source location was normalized by its variance to counter the depth bias. For visualization, source locations thresholded at 50% of the maximum source activation were plotted on cortical surfaces using the nilearn package (Huntenburg et al., 2017) in Python. Brain regions were identified from the MNI coordinates of source maxima using the Harvard-Oxford cortical structural atlas (Desikan et al., 2006).

Analysis code and data accessibility

The raw data, stimulus lists, and analysis code of both experiments described in the article are freely available online at https://osf.io/fc69p/.

Results

During the MEG measurement, participants correctly identified 94% of catch trials, indicating that they were attending to the presented letter strings.

Repetition suppression phenomenon

As manipulation check of context effects, we investigated the interaction between prime/target and repetition congruency (repetition vs non-repetition trials) effects, combined over all familiarity conditions. Repetition trials (Fig. 4A) but not non-repetition trials (Fig. 4B) showed repetition suppression, i.e., reduced activity at the target stimulus (around 2000 ms into the trial or 400 ms after onset of the target word). This interaction was significant at bilateral frontal sensors in the N400 time window (280–550 ms post-stimulus onset). Source localization revealed most prominently the left superior temporal gyrus (peak activation, extending into anterior temporal cortex albeit with weaker activation), as well as left occipital pole, left inferior occipital cortex, and the junction of left middle temporal, angular, and supramarginal gyri (Table 2; Fig. 4C,D). The interaction reflected a significant decrease from prime to target in repetition trials (post hoc LMM: estimate = 2.22e ⁻ ¹⁴, SE = 0.19e ⁻ ¹⁴, t = 11.61; Fig. 4E, left) and a significant but descriptively weaker increase in non-repetition trials (post hoc LMM: estimate = –0.58e ⁻ ¹⁴, SE = 0.20e ⁻ ¹⁴, t = 2.94; Fig. 4E, right; see also Table 3). This replication of established repetition suppression effects (Deacon et al., 2004; Summerfield et al., 2011) is an important prerequisite for our main analyses investigating the effect of different familiarity conditions on repetition suppression. Sources of the effect were also compatible with previous localizations of the N400 within superior temporal gyrus (Helenius et al., 1998; Vartiainen et al., 2009), and anterior temporal cortex (Lau et al., 2013a, 2016; Lau and Nguyen, 2015).

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Table 2.

MNI coordinates of source maxima for clusters presented in Figures 4, 5 and Extended Data Figure 5-2

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Table 3.

Results from post hoc LMM analyses on ERF values (in 10⁻¹⁴ Tesla) from sensor and time point of the strongest effect for the prime/target × repetition congruency (repetition vs non-repetition) interaction cluster represented in Figure 4

Extended Data Figure 5-2

Source locations for (A) the familiarity cluster F1 contrasting familiar PWs > words and (B) the interaction cluster CxF contrasting prime versus target by words versus familiar PWs. Download Figure 5-2, TIF file.

Prime/target effects

We here refer to context effects reflected in repetition suppression from prime to target in repetition trials only. Such effects were found in multiple time-windows, spanning time ranges from 100 to 690 ms after stimulus onset (Fig. 5A–D). All effects were localized to bilateral occipital cortices (see example of the earliest cluster C1 in Fig. 5A and source coordinates in Table 2). See also Figure 5D for the sensor topographies of clusters C2-6. The majority of these clusters were not modulated by familiarity; only the frontal cluster C3 was qualified by a significant interaction with familiarity, indicated by the overlap in time and space with the interaction cluster (see section “Pre-lexical and lexical familiarity effects on the repetition suppression phenomenon” below).

Figure 5.

Main effects of context (prime vs target; A–D), pre-lexical familiarity (E–G), and the influence of letter string familiarity on context effects (H, I; repetition trials only). A detailed overview of all clusters obtained with common baseline, separate baselines, and peak-to-peak analysis can be found in Extended Data Figure 5-1. A, Topographical map represents F values of sensors showing a main effect of context (prime vs target) averaged across the significant time window. Non-significant sensors are set to zero. The surface plot represents the source location of the effect in signal-to-noise ratio (SNR) thresholded at 50%. B, ERF time course averaged across the left occipital sensors (C1 which is marked by a black box; and C6 with similar sensor topography). The significant time windows are shaded in yellow. Solid red lines correspond to prime and dark blue dashed lines to target. C, Boxplots (from C1) represent activation averaged across sensors, time points, and familiarity conditions. Colored dots and lines represent individual participants. Asterisks indicate significant results (t > 2) from post hoc LMMs; n.s. = not significant. D, Additional topographical maps showing the main effects of context, which had similar effect patterns and source locations as C1. Significant time windows are indicated. Topographical map and source location (familiar > novel PWs; see Extended Data Fig. 5-2A for familiar PWs > words; E), ERF time course (F), and boxplot (G) showing a main effect of pre-lexical familiarity (i.e., differences between familiar PWs and words/novel PWs averaged across prime and target). See Extended Data Figure 5-3 for a main effect of lexical familiarity. Topographical map and source location (prime vs target by words vs novel PWs; see Extended Data Fig. 5-2B for prime vs target by words vs familiar PWs; H), ERF time course (left hemisphere only, shown in topography in H; I), and boxplots (J) showing an interaction between context and lexical familiarity. Blue lines correspond to words, green lines to familiar and gray lines to novel PWs; solid lines correspond to prime and dashed lines to identical target. Note the different scales across contrasts. For results obtained with a low-pass filter of 40 Hz instead of 20 Hz presented here, see Extended Data Figure 5-4. Respective cluster topographies of the peak-to-peak analysis are shown in Extended Data Figure 5-5.

Extended Data Figure 5-1

Overview of clusters form the analysis investigating the prime/target × familiarity interaction obtained with separate or common baselines for prime and target, as well as peak-to-peak analysis. Download Figure 5-1, DOCX file.

Extended Data Figure 5-3

Main effect of lexical familiarity. Topographical map (A), ERF time course (B), and boxplot (C), averaged across prime and target. Download Figure 5-3, TIF file.

Extended Data Figure 5-4

Same results as in Figure 5 depicted for a low-pass filter of 40 instead of 20 Hz. Download Figure 5-4, TIF file.

Extended Data Figure 5-5

Significant clusters obtained in the peak-to-peak analysis for (A) the interaction of context (prime vs target) by lexical familiarity, (B) main effects of pre-lexical familiarity, (C) main effects of lexical familiarity, and (D) main effect of context (prime vs target). Topographical maps represent F values of significant sensors; note the different scales. Non-significant sensors are set to zero. Peak latencies averaged across significant sensors and all conditions are depicted above the topographical maps. Download Figure 5-5, TIF file.

Familiarity effects

To reiterate, due to our stimulus selection procedure, words should have comparable levels of pre-lexical (orthographic/phonological) familiarity but higher lexical familiarity (i.e., lexical-semantic information associated with words) than novel PWs. In contrast, as a result of the familiarization training, familiarized PWs should have higher levels of familiarity than novel PWs specifically at the level of pre-lexical processing. Thus, we had assumed that effects of pre-lexical familiarity should be reflected in ERF differences between familiar PWs and both novel PWs and words, while effects of lexical familiarity should be reflected in ERF differences between words and both novel and familiar PWs (cf. Fig. 1B for visualization of these hypotheses). Differences between the familiarity conditions, averaged across prime and target (i.e., representing main effects of familiarity), occurred at two topographic clusters: At left posterior sensors, localized to the left angular/supramarginal gyrus (Fig. 5E; Extended Data Fig. 5-2A; Table 2), familiar PWs elicited a more negative ERF amplitude between 290 and 380 ms than both words and novel PWs (Fig. 5F,G; Table 4; Extended Data Table 4-1). From the more negative ERF response specific to familiar PWs, we can conclude that our familiarization procedure was successful in modulating the neurophysiological processing of these PWs. The second cluster, again, occurred at frontal sensors and was qualified by a significant interaction with context (see “Pre-lexical and lexical familiarity effects on the repetition suppression phenomenon” below). The main effect in this cluster is therefore only visualized in Extended Data Figure 5-3 (see also Table 4 and Extended Data Table 4-1) and not discussed further.

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Table 4.

Results from post hoc LMM analyses on ERF values (in 10⁻¹⁴ Tesla) from sensor and time point of the strongest effect for pre-lexical (F1) and lexical (F2) familiarity clusters represented in Figure 5E–G and Extended Data Figure 5-4, respectively

Extended Data Table 4-1

Results from post hoc LMM analyses on ERF values (in 10⁻¹⁴ Tesla) from sensor and time point of the strongest effect, separately for prime and target, for pre-lexical (F1) and lexical (F2) familiarity clusters represented in Figure 5D–I and Extended Data Figure 5-4, respectively. Download Table 4-1, DOCX file.

Pre-lexical and lexical familiarity effects on the repetition suppression phenomenon

We had expected that context effects reflected in repetition suppression for target relative to the identical prime be differentially influenced by pre-lexical versus lexical familiarity (cf. Fig. 1B for visualization of these hypotheses). To assess the influence of familiarity on context effects, we examined the familiarity (words vs familiar vs novel PWs) by prime versus target interaction in repetition trials (as only these included a valid predictive context for the target). We found a significant interaction between 300 and 480 ms at bilateral frontal sensors (Fig. 5H,I; see also Table 5 for a post hoc statistic controlling for OLD20 and number of syllables). Post hoc LMMs revealed that lexical but not pre-lexical familiarity reliably modulated the repetition effect: While during prime presentation the negative-going ERF amplitude was largest for words (words vs novel PWs: estimate = 2.18e⁻¹⁴, SE = 0.47e⁻¹⁴, t = 4.67; words vs familiar PWs: estimate = 3.11e⁻¹⁴, SE = 0.41e⁻¹⁴, t = 7.56; no difference between familiar and novel PWs: estimate = –0.71e⁻¹⁴, SE = 0.40e⁻¹⁴, t = 1.76), it was smallest for words during target presentation (words vs novel PWs: estimate = –1.20e⁻¹⁴, SE = 0.37e⁻¹⁴, t = 3.28; words vs familiar PWs: estimate = –0.66e⁻¹⁴, SE = 0.37e⁻¹⁴, t = 1.81; familiar vs novel PWs: estimate = –0.69e⁻¹⁴, SE = 0.33e⁻¹⁴, t = 2.06; see also Fig. 5J and Extended Data Table 5-1). Repetition suppression, thus, was stronger for words than for PWs. This effect was localized to the left anterior temporal cortex (Fig. 5H; Extended Data Fig. 5-2B; Table 2).

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Table 5.

Results from post hoc LMM analyses on ERF values (in 10⁻¹⁴ Tesla) from sensor and time point of the strongest effect for the prime/target × lexical familiarity interaction cluster represented in Figure 5H–J

Extended Data Table 5-1

Results from post hoc LMM analyses on ERF values (in 10⁻¹⁴ Tesla) from sensor and time point of the strongest effect, separately for prime and target, for the prime/target × lexical familiarity interaction cluster represented in Figure 5H–J. Download Table 5-1, DOCX file.

Control analyses

To assess potential influences from low-pass filtering, we performed an additional control analyses with a 40-Hz low-pass filter. Results did not differ qualitatively from the results described in the previous sections obtained with a 20-Hz low-pass filter (Extended Data Fig. 5-4).

We also evaluated the robustness of effects against different choices of baselines by interpreting only those clusters that were significant when prime and target activation were both corrected with the baseline before the prime instead of separate baselines. In addition, we performed a peak-to-peak analysis (cf. Materials and Methods for further details). Significant results from the peak-to-peak analysis strongly support the interaction between prime/target and familiarity effects at left frontal sensors, the main effects of familiarity at left posterior and left frontal sensors, as well as main effects of prime versus target at bilateral frontal sensors, resembling the effects of clusters CxF, F1, F2, and C3 in Figure 5 and Extended Data Figure 5-4 (Extended Data Figs. 5-1, 5-5, including also further clusters from the peak-to-peak analysis). Due to the high similarity between standard baseline corrected ERF analysis and peak-to-peak analysis, we conclude that the presented results can be reproduced with a different analysis strategy and therefore are not artificially introduced by the specific choice of baseline correction.

Discussion Experiment 1

The main finding from experiment 1 (MEG study) was that only lexical familiarity interacted with context (here implemented as contrast prime vs identical target) to facilitate visual word recognition at the N400 window only. Please note that in the following, for the sake of brevity, we will subsume the processing of words and PWs under the term visual word recognition, as we assume similar pre-lexical processing for words and novel PWs reflecting the orthographic familiarity (OLD20) match. The finding of stronger repetition suppression for words was consistent with previous studies (Fiebach et al., 2005; Almeida and Poeppel, 2013; but see Deacon et al., 2004; Laszlo and Federmeier, 2007; Laszlo et al., 2012) and identified sources of the effect were compatible with previous localizations of the N400 within the anterior temporal cortex (Lau et al., 2013a, 2016; Lau and Nguyen, 2015). In contrast, we could not identify a pre-lexical modulation (i.e., an increased reduction of activation for familiarized PWs) at any time window. However, we observed a more negative-going amplitude for familiarized PW in contrast to novel PW and words, which is indicative of a pre-lexical familiarity influence irrespective of context. This indicates that our explicit manipulation of pre-lexical familiarity was successful. Also, we found that the pre-lexical familiarity effect was present at the end of the expected time window (Barber and Kutas, 2007) and localized to the left angular/supramarginal gyrus, indicating phonological processing (Taylor et al., 2013; Carreiras et al., 2014). We also found strong context effects without modulation of pre-lexical and lexical familiarity. Earliest the effect was present around 100 ms in the occipital cortex. The first presentation elicited a much stronger N100 response compared to the second presentation in repetition trials. In sum, we found an interaction of lexical familiarity and context in the N400 time window but only a main effect of pre-lexical processing.

The interaction of lexical familiarity and context at the N400 indicated within-level context-based facilitation at the lexical level. We related this interaction to lexical level processing since words, i.e., meaningful stimuli, differentiated from PWs, i.e., meaningless stimuli. This is in line with previous work (i.e., priming or sentence paradigms: Rugg, 1985; Simos et al., 1997; Helenius et al., 1998; Halgren et al., 2002; Lau et al., 2009, 2013a; Vartiainen et al., 2009) associating this time window and brain locations with lexical-semantic processing. Within-level facilitation was indicated by the finding that the interaction was selective for the N400 time window without indications of interactions at previous time windows. Finally, regarding the mechanistic implementation, the interaction pattern was in line with the expectation of fatigue and predictive coding (Fig. 1C). This is as sharpening proposes a suppression of the noise in the neuronal signal. Therefore, the difference between words and PWs should be easier to detect at the target (i.e., stronger difference; Kok et al., 2012), which was not the case.

Nevertheless, due to the nature of the task in experiment 1, we could not investigate whether familiarity-based and context-based brain activation differences translate to behavior. Also, we cannot rule out one further potentially confounding influence, i.e., that words and PWs differ not only in the availability of lexical-semantic knowledge but also qualitatively with respect to their word status; i.e., although the familiarity of some PWs was temporarily enhanced by the training procedure, the expertise with actual words may be qualitatively different. Moreover, the new experiment included a repetition probability manipulation (i.e., the likelihood of prime and target being the same letter string) that would allow differentiating between fatigue and predictive coding, since only the latter would predict a systematic influence of repetition probability across trials. Finally, in experiment 1, we were surprised that no evidence for an influence of pre-lexical familiarity on context-based facilitation was found. To replicate the MEG pattern in behavior and systematically examine the role of word status and implement a repetition probability manipulation, we ran a second, behavioral, repetition priming experiment.

Experiment 2

In experiment 2, we implemented the explicit investigation of word status by adding a third group of PWs. With a paired association task, we associated semantic content to these non-words. Similar as in experiment 1, we also included familiar PWs without meaning. Note, PWs with and without semantic associations were visually/perceptually familiarized to the same degree. Therefore, the two groups of PWs only varied regarding their associated semantic meaning. Including this additional lexical familiarity condition allowed us to examine potentially different roles of word status and the presence of semantic associations.

We measured behavioral response times in a repetition priming paradigm. Participants had to indicate whether or not a letter string had a semantic association. A yes response would be valid for words and familiarized PWs with semantic associations, but not for novel and only perceptually familiarized PWs.

As stated in the discussion of experiment 1, we also implemented a repetition probability manipulation (i.e., the likelihood of prime and target being the same letter string). Repetition probabilities varied across three blocks, to investigate whether the priming effect (i.e., faster responses for repeated vs non-repeated targets) increases when the local task context allows predicting that the prime is highly likely identical to the target. In previous studies with different visual stimuli higher repetition probability enhanced priming effects, mainly supporting predictive coding (Summerfield et al., 2008, 2011; Grotheer and Kovács, 2014; Olkkonen et al., 2017).