The Interactive Account of Ventral Occipitotemporal Contributions to Reading
Introduction
Efficient processing of written words is crucial to reading, a cerebral ability humans need in daily living. For written word processing, the encephalon non but receives lesser-up visual inputs just also generates top-downwardly predictions that assist to resolve the conflicts between visual inputs and internal lexical noesis (Price and Devlin, 2011). Following the predictive coding theory (Hinton, 2007; Friston, 2010), Price and Devlin (2011) suggested the ventral occipitotemporal expanse (vOT) as a bridge that connects bottom-up visual inputs, i.e., written words, and top-down predictions, i.e., priori associations betwixt visual inputs and phonology and semantics (McCandliss et al., 2003; Devlin et al., 2006; Reinke et al., 2008; Woodhead et al., 2011). More than specifically, words and word-like stimuli would quickly activate the associated phonology and semantics, which are fed to vOT to facilitate the processing of visual inputs. Known equally the "interactive account," this theory has gained support from neuroimaging studies, most notably, the study of the left-lateralized N170 event-related potential (ERP) component (or the N1 component in some studies). N170 is the kickoff negative ERP component recorded at posterior electrodes. Information technology arises from the occipitotemporal area and it has been used to index neural activation in vOT (Rossion et al., 2003; Brem et al., 2009). Widely recognized every bit an electrophysiological marking of orthographic processing, the N170 component is stronger in response to words than to non-words (e.g., Maurer et al., 2005; Zhao et al., 2012; Eberhard-Moscicka et al., 2015).
One of import aspect of the "interactive account" is that information technology makes a distinction between top-down (strategic) and automatic (not-strategic) predictions. Strategic predictions are easily manipulated with chore demands (e.g., Maurer et al., 2006; Lupker and Pexman, 2010). For instance, Wang and Maurer (2017) examined the consequence of task-related strategic predictions in three tasks. The results revealed stronger N170 responses to symbols than to words in their delayed naming and colour detection tasks, but not in the repetition detection task. Yum et al. (2014) examined phonological regularity in a lexical decision job and a delayed naming task. They institute that phonologically regular Chinese characters elicited stronger N170 responses than irregular Chinese characters, but only in the delayed naming chore, suggesting that top-down phonological predictions depend on job demands. While these studies provided clear prove that pinnacle-downward strategic predictions attune visual word processing, empirical evidence for automatic predictions in visual word processing is nevertheless defective.
Orthographic knowledge connects meaning and pronunciation (Liu et al., 2007), and information technology represents the legal position, the combination rules, etc., of character components in Chinese (Loh et al., 2018). It is typically measured with lexical decision tasks (due east.g., Apel, 2011) where the researcher presents real or pseudo words (or visual symbols) and the participant indicates whether an item is a real discussion or not (e.g., Tong et al., 2009). Orthographic cognition varies greatly amid readers. While some people excel on the lexical decision task, others have bully difficulties in correctly identify not-words (due east.g., pseudo words; Davis et al., 2009; Yang et al., 2012; Taha and Azaizah-Seh, 2017). According to the interactive account (Price and Devlin, 2011), words and word-like stimuli would evoke predictions. Consequently, individuals who misclassify pseudo words would evidence stronger neural responses to pseudo than to real words. The reason existence that pseudo words are not in the dictionary and as a effect, the evoked predictions will non match the visual input, leading to prediction errors and stronger neural activation in vOT. In the same vein, those who correctly allocate pseudo words as not-words would produce similar neural responses to pseudo and existent words, because the pseudo words evoke no or few prediction errors. In line with these predictions, a recent written report revealed stronger N170 responses to stimuli of low orthographic regularity (eastward.g., not-words), simply simply in immature children with depression orthographic noesis (Zhao et al., 2019). The color-matching task used in Zhao et al. (2019) did not require whatsoever linguistic processing, so orthographic regularity was candy implicitly or automatically by the brain. To the best of our knowledge, this was the commencement neuroimaging study that unambiguously examined automatic predictions in visual word processing. However, this study but examined immature children whose orthographic knowledge was nonetheless fast developing. For a proper examination of automatic prediction and its neural correlates in visual word processing, we demand to examination skilled developed readers whose lexicons are relatively stable.
The present study examined automatic predictions in visual discussion processing with a grouping of skilled Chinese developed readers. The readers were grouped based on their responses to pseudo Chinese characters, with one group more likely to misclassify pseudo words equally real words. As discussed, pseudo words would evoke more prediction errors in those who are more likely to misclassify pseudo words equally existent ones, leading to stronger neural activations in vOT. Considering explicit linguistic communication tasks and long stimulus exposure time may recruit task-related predictions, a ane-dorsum color matching task with brusk stimulus exposure fourth dimension was adopted. This color matching task has been widely used to study implicit word processing (Lin et al., 2011; Shtyrov et al., 2013; Xue et al., 2019) and the word-selective N170 response is ofttimes observed. In this task, the participants report the color of ane stimulus rather than its content, so the processing of orthographic, phonological, and semantic information is implicit by nature (Lin et al., 2011; Zhao et al., 2012).
Based on the "interactive account" (Price and Devlin, 2011), we predict that pseudo words would evoke few prediction errors in participants who correctly allocate pseudo words equally not-word (high orthographic noesis) and the N170 response should be similar to real and pseudo words. Past contrast, pseudo words would evoke more prediction errors in participants who misclassify pseudo words as real ones (depression orthographic cognition), leading to stronger N170 responses to pseudo than to existent words.
Materials and Methods
Participants
L-two college students participated in this written report. They were right-handed native Chinese speakers, who reported normal or corrected-to-normal visual vigil. 5 participants were excluded from the analyses due to excessive noise in the EEG recordings. As a result, 47 participants (19 males, hateful age = xx.6 years, SD = 2.2, range = eighteen–25 years) were included in the analyses.
In the present study, all participant completed a lexical decision task (run across Zhao et al., 2019, for a detailed discussion), and the rate of misclassifying the pseudo characters was used to median-split the participants into two groups. For convenience, we volition refer to those two groups as loftier and low orthographic noesis groups, respectively. Participants in the high orthographic noesis grouping were less likely to report the pseudo characters every bit real ones. There are 23 and 24 participants in high and depression orthographic knowledge group, respectively.
Materials
According to the Modern Chinese Frequency Lexicon (1985), the word frequency is ordinarily in the 300–600 per million range. Similar to the enquiry protocols of a previous study (Zhao et al., 2019), the present study but used Chinese characters with a "left-right" layout. In the Modernistic Chinese Dictionary, more than 80% of Chinese characters have a "left-right" layout. The other xx% of the characters are in other types of layouts (e.m., "up-down" and "semi-enclosing"). The stimuli used in the present written report included both existent and pseudo characters (encounter Figure 1A, for samples). Pseudo characters were created with the radicals from the same fix of real characters, but they evidently do not exist in the lexicon. The real and pseudo characters were matched in terms of stroke numbers (ranging between 8 and 13) and structural complication. The final set of stimuli consisted of 36 real and 72 pseudo characters. We had more pseudo characters in the tests considering a pilot written report showed that 36 pseudo characters were not enough to derive a reliable per centum measure of misclassification.
Figure ane. (A) Sample real (left) and pseudo (right) characters. (B) The lexical decision task (left panel) and the 1-back colour matching task (right panel). The lexical decision task was used to group the participants, whereas the color matching task was used to examine the neural responses associated with automated predictions.
Task Procedures
Lexical Decision Job
The lexical decision task was scripted with Python iii.half-dozen and Pygame. All stimuli were presented on a 19-inch LCD monitor (HP l1908w), which had a maximum resolution of 1024 × 768 pixels and a refresh rate of threescore Hz. For improve timing precision, stimulus presentation and response registration were controlled past a Windows PC equipped with a NVIDIA GT610 graphics card. Of the 90 items used in the lexical decision task, 30 items were existent characters, and 60 items were pseudo characters. Stimuli were presented to the participant in a random order. The characters extended ane.v° × 1.five°, and they were presented in black against a greyness groundwork at the screen center. Each grapheme was presented for 300 ms, followed by an ISI of 1450, 1525, 1600, 1675, or 1750 ms (Figure 1A). The participants reported whether the presented stimulus was a real Chinese character or not, by pressing the left and right button on a mouse. Both speed and accuracy were emphasized in this task. The response buttons were counter-balanced across the participants.
One-Back Color Matching Task
The one-back color matching task was programed with Due east-Prime ii.0, but the equipment used for stimulus presentation was identical to that used in the lexical conclusion task. Pseudo and existent characters were inter-mixed and presented in a random order. Each character was presented in greenish, red, or yellowish, against a greyness background, at the screen center. The timing of trial events in the 1-back color matching task matched that in the lexical decision task. The participant was instructed to press a key whenever a stimulus was in the same color as the previous one (i.e., one-back color repetition detection; run across Figure 1B, correct console). To get enough trials for ERP averaging, each character was presented as the non-responding target for 3 times (for similar experimental manipulations, see Maurer et al., 2008; Cao and Zhang, 2011; Lin et al., 2011; Zhao et al., 2012, 2019). A subset of 12 pseudo and 6 real characters were presented three times as the responding target (on 16.67% of the trials). In total, the existent characters were presented on 108 trials (xc as fillers and 18 as responding targets) and the pseudo characters were presented on 216 trials (180 equally fillers and 36 as responding targets). The participants responded with either the left or right alphabetize finger (counter-balanced across participants).
The participants completed both tasks in a unmarried session, and the lexical conclusion task was always carried out following the ane-back color matching chore.
Electrophysiological Recording and Analysis
Data Acquisition and Pre-processing
EEG data were collected in the one-back color-matching chore only. The EEG signal was recorded from 30 Ag/AgCl electrodes secured in an elastic cap according to the extended ten–20 system. The EEG information were recorded with a BrainAmp amplifier system and the software for EEG recording was Encephalon Vision Recorder (Brain Products GmbH, Germany). The Cz electrode served every bit an online reference, but the data were offline re-referenced to the average. Horizontal EOG was recorded in a bipolar pb from 2 boosted electrodes placed on the outer canthi of the two eyes. Vertical EOG was recorded in a bipolar lead from boosted electrodes placed on the supra-orbital and infra-orbital ridges of the right center. The electrode impedance was kept below 5 kΩ. The EEG and EOG betoken were continuously recorded and amplified at a sampling rate of 1000 Hz, with a ring laissez passer from Ac 0.i to 100 Hz.
In the offline analysis, the EEG signal was outset low-laissez passer filtered at 30 Hz and automatically scanned for artifacts in EEGLab (Delorme and Makeig, 2004), an open-source EEG data analysis toolbox implemented in MATLAB. Using the default parameters of the SASICA toolbox (Chaumon et al., 2015), eye movement artifacts in the EEG point were removed with an ICA-based procedure (Delorme et al., 2012).
Effect-Related Potential Analysis
In the ERP analysis, continuous EEG data were epoched and baseline-corrected with a 200-ms pre-stimulus period. The mail service-stimulus period was 500 ms. For both pseudo and real characters, a trial was included in the analysis only if the character was non a target and no false positive response was issued. Before averaging, segments with artifacts exceeding ±80 μV (nearly 3% of all trials) were automatically rejected. For ERP averaging, on average, there were 86 real-character trials and 173 pseudo-character trials. Electrodes P7 and P8 were selected to analyze the N170 component, based on the topographic maxima of the negative field in the occipitotemporal area (see Figure 2A). These ii electrode sites were often used to measure the N170 component in previous studies as well (e.one thousand., Bentin et al., 1999; Maurer et al., 2008; Lin et al., 2011). The time windows for the P1 (51–135 ms) and the N170 (137–267 ms) components were selected using the GFP method (meet Figure 2B); the selected time windows were similar to those reported in previous studies (Maurer et al., 2005; Cao and Zhang, 2011; Zhao et al., 2019). The height amplitude of the P1 and N170 components were the mean voltages inside a 20-ms window centered around the local maximum. For statistical analysis, Cohen's d and partial eta squared (η p 2) were reported as the consequence size measures for t-tests and ANOVAs, respectively.
Figure ii. (A) The topographic maps evoked by existent and pseudo characters, 150, 170, and 190 ms post-obit stimulus presentation. (B) The fourth dimension windows for P1 and N170 was selected with the GFP method, see text for details.
Results
Behavioral Data
The Lexical Decision Job
The rate in reporting pseudo characters as real ones (imitation positives) was used to median-split the participants into high and low orthographic knowledge groups (see Figure 3A). A t-test on the false positive rates revealed a significant group difference, t(ane,45) = 11.59, p < 0.001, Cohen's d = 3.47. We too examined the charge per unit in correctly reporting real characters (hits) but plant no between-group deviation, t(ane,45) = 0.51, p = 0.61, Cohen's d = 0.37.
Figure iii. (A) The rate in reporting a stimulus a equally real character in the loftier and depression orthographic cognition groups in the lexical decision chore. (B) Same as (A) merely showing data points from individual participant. ***p < 0.001.
To examine if there was a systematic response bias, we tested the rate in reporting a stimulus every bit a real character confronting chancel level with a binomial test (Bonferroni corrected). As is clear from Effigy 3A, the rate in correctly identifying real characters was well above chance level in both groups (all p < 0.001); all the same, the charge per unit in reporting pseudo characters as existent characters was beneath risk level in the loftier orthographic knowledge group (p < 0.0001) and above chancel level in the low orthographic grouping (p < 0.001). As shown in Figure 3B, the data points from all participants also corroborate with these results, showing that the charge per unit in reporting pseudo characters as existent ones varied profoundly amongst the participants, whereas the rate in correctly reporting real words did not vary much among the participants.
The One-Back Color Matching Chore
For the color-matching task, we offset examined the target striking rates and the response times (RTs). As is clear from Table 1, the hit rate was well higher up 90%. Analysis of the RTs, with variables Stimulus (real vs. pseudo characters) and Group (high vs. low orthographic knowledge), revealed a pregnant main effect of Group, F(1,45) = 4.85, p = 0.04, η p ii = 0.09, but no other main outcome or interaction was significant, all F'south < i, all p's > 0.57, all η p ii < 0.01.
Table 1. Hit rate (%) and reaction time (ms) to targets in the color-matching chore.
Event-Related Potential Data
The EEG information were recorded for the 1-dorsum color-matching task to reveal possible automated predictions in visual word processing. In the analysis of the EEG data, nosotros focus on the early ERP components, notably P1 and N170. Figure 4A illustrates the ERP waveforms in P7 aqueduct.
Figure 4. (A) The ERP waveforms in response to real and pseudo characters in the P7 channel. (B) Bar plots comparing the N170 amplitudes for pseudo and real characters in the high and low orthographic cognition groups in P7 aqueduct. ***p < 0.001.
The P1 Component
Assay of the P1 amplitudes, with variables Stimulus (real vs. pseudo graphic symbol), Hemisphere (left vs. correct), and Group (high vs. low orthographic noesis), revealed a pregnant principal issue of Hemisphere, F(1,45) = 14.28, p < 0.001, η p 2 = 0.21; the P1 amplitude was larger in the correct (P8) than in the left (P7) hemisphere (MD = ane.xxx). No other main outcome or interaction was significant, all F'south < 1.76, all p's > 0.fourteen, all η p ii < 0.07.
The P1 superlative latencies are reported in Table 2. Analysis of the P1 latency also revealed a main effect for Hemisphere, F(1,45) = 6.82, p < 0.05, η p 2 = 0.21; the P1 latency was shorter in the left than in the right hemisphere, Doctor = 4.00, p < 0.05. No other main consequence or interaction was significant, all F's < i.71, all p's > 0.18, all η p 2 < 0.23.
Table 2. The P1 and N170 latency (ms) for real and pseudo characters.
The N170 Component
Analysis of the N170 amplitudes revealed a master effect for Hemisphere, F(one,45) = 5.39, p = 0.02, η p 2 = 0.12, and a meaning three-manner interaction, F(one,45) = four.74, p = 0.01, η p 2 = 0.13. No other main consequence or interaction was significant, all F'southward < 2.6, all p's > 0.15, all η p 2 < 0.27. Further analysis revealed that, in the high orthographic cognition group, there was no reliable deviation between existent and pseudo characters in both hemispheres, all Doctor < 0.25, all p > 0.24. In the depression orthographic noesis group, the N170 amplitude was significantly higher for pseudo than for real characters in the left hemisphere, MD = 0.59, p < 0.001 (see Effigy 4B); withal, no difference was detected in the right hemisphere, Medico = 0.fifteen, p = 0.89.
The N170 summit latencies are reported in Table 2. The assay revealed a meaning master effect for Stimulus, F(1,45) = 5.65, p = 0.02, η p two = 0.17; the N170 pinnacle latency was longer for pseudo than for real characters, Medico = 2.36, p = 0.02. No other main effect or interaction was meaning, all F's < 3.70, all p'southward > 0.13, all η p 2 < 0.02.
Discussion
The present study was set up out to examine automated (non-strategic) predictions and the underlying neural dynamics in visual give-and-take processing. The participants were grouped based on how probable they misclassified pseudo characters as real ones. EEGs were recorded when the participants viewed pseudo and real characters in a one-back color matching job. The N170 response to pseudo and existent characters was largely the same in participants who were less probable to misclassify pseudo characters as real characters. However, the N170 response to pseudo characters was stronger in participants who were more likely to misclassify pseudo characters every bit real ones. These results show that discussion-like visual stimuli would pb to prediction errors that modulate the early stages of visual word processing.
In line with previous findings (Davis et al., 2009; Yang et al., 2012; Taha and Azaizah-Seh, 2017), the present lexical decision task showed that the power in identifying pseudo characters varied greatly among individuals. According to the "interactive account" (Cost and Devlin, 2011), predictions are generated in loftier-level linguistic communication areas and automatically feed to vOT. Prediction errors would occur if the visual input and top-down predictions do not match. In the present study, pseudo characters would evoke no or few prediction errors in participants who are less likely to classify pseudo characters equally real ones. Withal, pseudo characters would evoke more than prediction errors in participants who are likely to misclassify pseudo characters as real ones. Participants in the loftier orthographic knowledge group showed no difference in N170 response to real and pseudo characters. The reason being that pseudo characters would non trigger top-downward expectations, leading to no or little prediction fault; for existent characters, the bottom-upwards visual inputs well match the tiptop-downwardly predictions and thus there was few prediction errors likewise (Price and Devlin, 2011; Zhao et al., 2019). Consequently, no N170 difference was institute between real and pseudo characters. For the depression orthographic knowledge group, all the same, word-like stimuli (real and pseudo characters) would both trigger superlative-downward predictions (Yum et al., 2014; Zhao et al., 2019). The elevation-down predictions evoked by existent words match the lesser-up visual inputs, and there would be no or few prediction errors (Price and Devlin, 2011; Zhao et al., 2019). Therefore, the pinnacle-down predictions evoked by pseudo character do non match bottom-up visual inputs and would atomic number 82 to prediction errors. As a outcome, stronger N170 responses were observed for pseudo characters.
The summit-down predictions involved in visual word processing can be strategic and vary with task need (Cost and Devlin, 2011). It has been demonstrated in a vast number of studies that the neural activation evoked by visual word forms varies with task demands (Van Berkum et al., 2005; Dambacher et al., 2009; Dikker et al., 2010; Brothers et al., 2015; Wang and Maurer, 2017; for a review, meet Nieuwland, 2019). For instance, word-similar stimuli evoke stronger neural response in phonological than in orthographic lexical determination chore (Twomey et al., 2011). Wang and Maurer (2020) examined the N170 response to visual stimuli in a category expectation job. The results showed that Korean characters evoked a stronger N170 response in native Chinese speakers when they expected a Chinese character simply saw an unlearned Korean character. However, no N170 departure was plant between Chinese characters and Korean characters, when the participants expected a Korean character just saw a Chinese graphic symbol. An elevated N170 response is frequently accompanied by improvements in behavioral performance, e.chiliad., faster identification of target letters embedded in native than in not-native words (Maurer et al., 2008) or faster response to strings of alphabetic letters than to strings of symbols (Maurer et al., 2005; Proverbio et al., 2008). And so, in these studies, the elevated N170 responses to orthographic stimuli is partly contributed to by task-related or strategic factors (Strijkers et al., 2010; Dehaene and Cohen, 2011; Wang and Maurer, 2017). In the color matching task tested in the nowadays study, however, there was no difference in behavioral performance for real and pseudo characters. It is quite unlikely that results of the present study were confounded by task-related or strategic factors. The stronger N170 response to pseudo characters in the low orthographic knowledge grouping was almost likely due to automatic predictions.
Zhao et al. (2019) showed that, as word knowledge increases in children, more top-down information is automatically activated to aid efficient processing of the orthographic characteristics of visual inputs. The almost of import contribution of the present piece of work is that we demonstrated automatic predictions in visual word processing in skilled developed readers, providing unambiguous support to the "interactive account." Even so, equally no language information was needed to consummate the color matching chore in the present written report, the extent to which strategic and automatic (non-strategic) predictions are involved in language-relevant tasks remains unclear. Neural responses related to orthographic characteristic processing is seen in the left fusiform gyrus (LFG), which overlaps vOT. The EEG technique used in the present study does not have the spatial precision needed to trace the N170 event to vOT. A high-resolution imaging technique like fMRI is needed to further locate the neural substrates underlying automatic predictions in visual word processing.
Information Availability Statement
The data used to support the findings of this study are available from the respective author upon request.
Ethics Statement
The studies involving human being participants were reviewed and approved past local ideals commission at Hangzhou Normal University. The patients/participants provided their written informed consent to participate in this study.
Author Contributions
JZ, HY, ZH, SY, and LX designed the experiments. ZH and SY collected the data. LX provided the data analysis tools. ZH, SY, LX, and JZ analyzed the data. ZH and SY drafted the manuscript. JZ, LX, HY, and YL provided the critical revisions. All authors read and approved the submitted version.
Funding
This work was supported by grants from the National Natural Science Foundation of China (NSFC) (Grant Nos. 32171063 and 31771229) and Opening Projection of Key Laboratory of Brain, Cognition and Instruction Sciences (South Prc Normal Academy), Ministry of Education.
Conflict of Interest
The authors declare that the inquiry was conducted in the absence of whatsoever commercial or financial relationships that could exist construed as a potential conflict of interest.
Publisher'south Notation
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