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Research Articles: Behavioral/Cognitive

Predictive Neural Computations SupportSpoken Word Recognition: Evidence from MEGand Competitor Priming

https://doi.org/10.1523/JNEUROSCI.1685-20.2021

Cite as: J. Neurosci 2021; 10.1523/JNEUROSCI.1685-20.2021

Received: 24 June 2020Revised: 22 May 2021Accepted: 25 May 2021

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Title (13 words) 1

Predictive Neural Computations Support Spoken Word Recognition: Evidence from 2

MEG and Competitor Priming 3

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Abbreviated title (48 characters) 5

Predictive Neural Computations for Spoken Words 6

7

Authors and affiliation 8

Yingcan Carol Wang1, Ediz Sohoglu2, Rebecca A. Gilbert1, Richard N. Henson1, 9

Matthew H. Davis1 10

1 MRC Cognition and Brain Sciences Unit, University of Cambridge, 15 Chaucer 11

Road, Cambridge, CB2 7EF, UK 12

2 School of Psychology, University of Sussex, Brighton, BN1 9RH, UK 13

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Number of figures: 8 15

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Word count 17

Abstract: 247 18

Introduction: 644 19

Discussion: 1542 20

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Corresponding authors and emails 22

Matthew H. Davis: matt.davis@mrc-cbu.cam.ac.uk 23

Yingcan Carol Wang: carol.wang@mrc-cbu.cam.ac.uk 24

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Conflict of interest statement 26

The authors declare no competing financial interests. 27

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Acknowledgments 29

The research was supported by the UK Medical Research Council (SUAG/044 & 30

SUAG/046 G101400) and by a China Scholarship Council award to Yingcan Carol 31

Wang. We are grateful to Clare Cook, Ece Kocagoncu and Tina Emery for their 32

assistance with data collection, and also to Olaf Hauk for his advice on MEG data 33

analysis. 34

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Abstract 37

Human listeners achieve quick and effortless speech comprehension through 38

computations of conditional probability using Bayes rule. However, the neural 39

implementation of Bayesian perceptual inference remains unclear. Competitive-40

selection accounts (e.g. TRACE) propose that word recognition is achieved through 41

direct inhibitory connections between units representing candidate words that share 42

segments (e.g. hygiene and hijack share /haidʒ/). Manipulations that increase lexical 43

uncertainty should increase neural responses associated with word recognition when 44

words cannot be uniquely identified. In contrast, predictive-selection accounts (e.g. 45

Predictive-Coding) proposes that spoken word recognition involves comparing heard 46

and predicted speech sounds and using prediction error to update lexical 47

representations. Increased lexical uncertainty in words like hygiene and hijack will 48

increase prediction error and hence neural activity only at later time points when 49

different segments are predicted. We collected MEG data from male and female 50

listeners to test these two Bayesian mechanisms and used a competitor priming 51

manipulation to change the prior probability of specific words. Lexical decision 52

responses showed delayed recognition of target words (hygiene) following 53

presentation of a neighbouring prime word (hijack) several minutes earlier. However, 54

this effect was not observed with pseudoword primes (higent) or targets (hijure). 55

Crucially, MEG responses in the STG showed greater neural responses for word-56

primed words after the point at which they were uniquely identified (after /haidʒ/ in 57

hygiene) but not before while similar changes were again absent for pseudowords. 58

These findings are consistent with accounts of spoken word recognition in which 59

neural computations of prediction error play a central role. 60

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Significance Statement 62

Effective speech perception is critical to daily life and involves computations that 63

combine speech signals with prior knowledge of spoken words; that is, Bayesian 64

perceptual inference. This study specifies the neural mechanisms that support 65

spoken word recognition by testing two distinct implementations of Bayes perceptual 66

inference. Most established theories propose direct competition between lexical units 67

such that inhibition of irrelevant candidates leads to selection of critical words. Our 68

results instead support predictive-selection theories (e.g. Predictive-Coding): by 69

comparing heard and predicted speech sounds, neural computations of prediction 70

error can help listeners continuously update lexical probabilities, allowing for more 71

rapid word identification. 72

73

Introduction 74

In daily conversation, listeners identify ~200 words/minute (Tauroza & Allison, 1990) 75

from a vocabulary of ~40,000 words (Brysbaert et al., 2016). This means that they 76

must recognise 3-4 words/second and constantly select from sets of transiently 77

ambiguous words (e.g. hijack and hygiene both begin with /haidʒ/). Although it is 78

recognised that humans achieve word recognition by combining current speech input 79

with its prior probability using Bayes theorem (Norris & McQueen, 2008; Davis & 80

Scharenborg, 2016), the underlying neural implementation of Bayesian perceptual 81

inference remains unclear (Aitchison & Lengeyl, 2017). 82

Here, we test two computational accounts of spoken word recognition that 83

both implement Bayes rules. In competitive-selection accounts (e.g. TRACE, 84

McClelland & Elman, 1986, Figure 1A), word recognition is achieved through within-85

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layer lateral inhibition between neural units representing similar words. By this view, 86

hijack and hygiene compete for identification such that an increase in probability for 87

one word inhibits units representing other similar-sounding words. Conversely, 88

predictive-selection accounts (e.g. Predictive-Coding, Davis & Sohoglu, 2020) 89

suggest that word recognition is achieved through computations of prediction error 90

(Figure 1D). On hearing transiently ambiguous speech like /haidʒ/, higher-level units 91

representing matching words make contrasting predictions (/æk/ for hijack, /i:n/ for 92

hygiene). Prediction error (the difference between sounds predicted and actually 93

heard) provides a signal to update word probabilities such that the correct word can 94

be selected. 95

In this study, we used the competitor priming effect (Monsell & Hirsh, 1998; 96

Marsolek, 2008), which is directly explicable in Bayesian terms, i.e. the recognition of 97

a word (hygiene) is delayed if the prior probability of a competitor word (hijack) has 98

been increased due to an earlier exposure. This delay could be due to increased 99

lateral inhibition (competitive-selection) or greater prediction error (predictive-100

selection). Thus, similar behavioural effects of competitor priming are predicted by 101

two distinct neural computations (Spratling, 2008). To distinguish them, it is critical to 102

investigate neural data that reveals the direction, timing and level of processing at 103

which competitor priming modulates neural responses. Existing neural data remains 104

equivocal with some evidence consistent with competitive-selection (Bozic et al., 105

2010; Okada & Hickok, 2006), predictive-selection (Gagnepain et al, 2012), or both 106

mechanisms (Brodbeck et al., 2018; Donhauser et al., 2019). We followed these 107

studies in correlating two computational measures with neural activity: lexical entropy 108

(competitive-selection) and segment prediction error (or phoneme surprisal, for 109

predictive-selection). 110

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Here, we used MEG to record the location and timing of neural responses 111

during spoken words recognition in a competitor priming experiment. Pseudowords 112

(e.g. hijure) were included in our analysis to serve as a negative control for 113

competitor priming, since existing research found that pseudowords neither produce 114

nor show this effect (Monsell & Hirsh, 1998). We compared items with the same 115

initial segments (words hygiene, hijack, pseudowords hijure, higent share /haidʒ/) 116

and measured neural and behavioural effects concurrently to link these two effects 117

for single trials. 118

While lexical entropy and prediction error are correlated for natural speech, 119

this competitor priming manipulation allows us to make differential predictions as 120

illustrated in Figure 1. Specifically: (1) before the deviation point (DP, the point at 121

which similar-sounding words diverge), competitor priming increases lexical entropy 122

and hence neural responses (Figure 1B&C Pre-DP). Such responses might be 123

observed in inferior frontal regions (Zhuang et al., 2011) and posterior temporal 124

regions (Prabhakaran et al., 2006). However, prediction error will be reduced for pre-125

DP segments, since heard segments are shared and hence more strongly predicted 126

(Figure 1E&F Pre-DP). This should be reflected in the superior temporal gyrus (STG, 127

Sohoglu & Davis, 2016). (2) After the DP, predictive-selection but not competitive-128

selection accounts propose that pseudowords evoke greater signals in the left-STG, 129

since they evoke maximal prediction errors (Figure 1E&F Pseudoword, Post-DP). (3) 130

Furthermore, in predictive-selection theories, competitor priming is associated with 131

an increased STG response to post-DP segments due to enhanced prediction error 132

caused by mismatch between primed words (predictions) and heard speech (Figure 133

1E&F Word, Post-DP). 134

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136

Materials and Methods 137

Participants 138

Twenty-four (17 female, 7 male) right-handed, native English speakers were tested 139

after giving informed consent under a process approved by the Cambridge 140

Psychology Research Ethics Committee. This sample size was selected based on 141

previous studies measuring similar neural effects with the same MEG system 142

(Gagnepain et al. 2012; Sohoglu & Davis, 2016; Sohoglu et al. 2012, etc.). All 143

participants were aged 18-40 years and had no history of neurological disorder or 144

hearing impairment based on self-report. Two participants’ MEG data were excluded 145

from subsequent analyses respectively due to technical problems and excessive 146

head movement, resulting in 22 participants in total. All recruited participants 147

received monetary compensation. 148

149

Experimental Design 150

To distinguish competitive- and predictive-selection accounts, we manipulated 151

participants’ word recognition process by presenting partially mismatched auditory 152

stimuli prior to targets. Behavioural responses and MEG signals were acquired 153

simultaneously. Prime and target stimuli pairs form a repeated measures design with 154

two factors (lexicality and prime type). The lexicality factor has 2 levels: word and 155

pseudoword, while the prime type factor contains 3 levels: unprimed, primed by 156

same lexical status, primed by different lexical status. Hence the study is a factorial 2 157

x 3 design with 6 conditions: unprimed word (hijack), word-primed word (hijack-158

hygiene), pseudoword-primed word (basef-basis), unprimed pseudoword (letto), 159

pseudoword-primed pseudoword (letto-lettan), word-primed pseudoword (boycott-160

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boymid). Prime-target pairs were formed only by stimuli sharing the same initial 161

segments. Items in the two unprimed conditions served as prime items in other 162

conditions and they were compared with target items (Figure 2A). 163

The experiment used a lexical decision task (Figure 2B) implemented in 164

MATLAB through Psychtoolbox-3 (Kleiner et al. 2007), during which participants 165

heard a series of words and pseudowords while making lexicality judgments to each 166

stimulus by pressing buttons using their left index and middle fingers only, with the 167

index finger pressing one button indicating word and the middle finger pressing the 168

other button indicating pseudoword. 344 trials of unique spoken items were 169

presented every ~3 seconds in two blocks of 172 trials, each block lasting 170

approximately 9 minutes. Each prime-target pair was separated by 20 to 76 trials of 171

items that do not start with the same speech sounds, resulting in a relatively long 172

delay of ~1-4 minutes between presentations of phonologically-related items. This 173

delay was chosen based on Monsell and Hirsh (1998), who suggest that it prevents 174

strategic priming effects (Norris et al. 2002). Stimuli from each of the quadruplets 175

were Latin-square counterbalanced across participants, i.e. stimulus quadruplets that 176

appeared in one condition for one participant were allocated to another condition for 177

another participant. The stimulus sequences were pseudo-randomised using Mix 178

software (van Casteren & Davis, 2006), so that the same type of lexical status 179

(word/pseudoword) did not appear successively on more than 4 trials. 180

181

Stimuli 182

The stimuli consisted of 160 sets of four English words and pseudowords, with 183

durations ranging from 372 to 991 ms (M = 643, SD = 106). Each set contained 2 184

words (e.g. letter, lettuce) and 2 phonotactically-legal pseudowords (e.g. letto, lettan) 185

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that share the same initial segments (e.g. /let/) but diverge immediately afterwards. 186

We used polysyllabic word pairs (Msyllable = 2.16, SDsyllable =0.36) instead of 187

monosyllabic ones in our experiments so as to identify a set of optimal lexical 188

competitors that are similar to their prime yet dissimilar from all other items. All words 189

were selected from the CELEX database (Baayen et al., 1993). Their frequencies 190

were taken from SUBTLEX UK corpus (Van Heuven et al., 2014) and restricted to 191

items under 5.5 based on log frequency per million word (Zipf scale, Van Heuven et 192

al., 2014). In order to ensure that any priming effect was caused purely by 193

phonological but not semantic similarity, we also checked that all prime and target 194

word pairs have a semantic distance of above 0.7 on a scale from 0 to 1 based on 195

the Snaut database of semantic similarity scores (Mandera et al., 2017), such that 196

morphological relatives (e.g. darkly/darkness) were excluded. 197

All spoken stimuli were recorded onto a Marantz PMD670 digital recorder by a 198

male native speaker of southern British English in a sound-isolated booth at a 199

sampling rate of 44.1 kHz. Special care was taken to ensure that shared segments 200

of stimuli were pronounced identically (any residual acoustic differences were 201

subsequently eliminated using audio morphing as described below). 202

The point when items within each quadruplet begin to acoustically differ from 203

each other is the deviation point (hereafter DP, see Figure 3A). Pre-DP length 204

ranged from 150 to 672 ms (M = 353, SD = 96), while post-DP length ranged from 42 205

to 626 ms (M = 290, SD = 111, see Figure 3B). Epochs of MEG data were time-206

locked to the DP. Using phonetic transcriptions (phonDISC) in CELEX, the location 207

of the DP was decided based on the phoneme segment at which items within each 208

quadruplet set diverge (Mseg=3.53, SDseg=0.92). To determine when in the speech 209

files corresponds to the onset of the first post-DP segment, we aligned phonetic 210

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transcriptions to corresponding speech files using the WebMAUS forced alignment 211

service (Kisler et al., 2017; Schiel, 1999). In order to ensure that the pre-DP portion 212

of the waveform was acoustically identical, we cross-spliced the pre-DP segments of 213

the 4 stimuli within each quadruplet and conducted audio morphing to combine the 214

syllables using STRAIGHT (Kawahara, 2006) implemented in MATLAB. This method 215

decomposes speech signals into source information and spectral information, and 216

permits high quality speech re-synthesis based on modified versions of these 217

representations. This enables flexible averaging and interpolation of parameter 218

values that can generate acoustically intermediate speech tokens (see Rogers & 219

Davis, 2017, for example). In the present study, this method enabled us to present 220

speech tokens with entirely ambiguous pre-DP segments, and combine these with 221

post-DP segments without introducing audible discontinuities or other degradation in 222

the speech tokens. This way, phonological co-articulation in natural speech was 223

reduced to the lowest level possible at the DP, hence any cross-stimuli divergence 224

evoked in neural responses can only be caused by post-DP deviation. 225

226

Post-test Gating Study 227

As encouraged by a reviewer, we conducted a post-test perceptual experiment using 228

a gating task in order to confirm that the cross-splicing and morphing of our stimuli 229

worked as expected. This experiment used a gating task implemented in JavaScript 230

through JSpsych (de Leeuw, 2015). During the experiment, auditory segments of all 231

160 pairs of words used in the MEG study were played. Twenty British English 232

speakers were recruited through Prolific Academic online with monetary 233

compensation. The sample size was selected based on a similar gating study 234

conducted by Davis et al. (2002). Participants were evenly divided into two groups, 235

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one group were presented with 160 stimuli words with different pre-DP segments 236

(e.g. hygiene), while the other group were presented with the other paired 160 stimuli 237

(e.g. hijack). Therefore, participants only ever heard one of the two items in each pair. 238

Stimuli segments of each word item consist of the pre-DP segment and, depending 239

on the stimuli length, also longer segments that are 75ms, 150ms, 225ms and 240

300ms post DP. The segments of each word were presented in a gating manner, 241

with the shortest segment played the first and the full item played at the end. After 242

hearing each segment (e.g. /haidʒ/), participants were also presented with the 243

writing of the word (e.g. hygiene) that contained the segment and the other paired 244

word that shared the same pre-DP segment (e.g. hijack) on the screen. We asked 245

the participants to choose which item the auditory segment matches and indicate 246

their confidence from a rating scale of 1 to 6, with 1 representing being very 247

confident that the item is the one on the left and 6 representing being very confident 248

that the item is the one on the right, while 3 and 4 representing guessing the possible 249

item. In order to avoid potential practice effect, we also added 40 filler stimuli that are 250

identifiable on initial presentation. 251

Given our goal of assessing whether there is any information to distinguish the 252

words prior to the divergence point, we needed to adopt an analysis approach that 253

could confirm the null hypothesis that no difference exists between perception of the 254

shared first syllable of word pairs like hijack and hygiene. We therefore analysed the 255

results using Bayesian methods which permit this inference. Participants’ response 256

accuracy was analysed using mixed-effect logistic regression and confidence rating 257

scores were analysed using mixed-effect linear regression using the brms package 258

(Bürkner, 2017) implemented in R. Response scores were computed in a way such 259

that correct and most confident responses were scored 1, while incorrect and most 260

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confident responses were scored 6 and so on. Participants and items were included 261

as random factors of the models and there was no fixed factor since we are only 262

interested in the intercepts, whose estimates indicate the logit transformed 263

proportion of correctness in the logistic model and the mean rating in the linear 264

model respectively. We chose weakly informative priors for each model and 265

conducted Bayes Factor analyses through the Savage-Dickey density ratio method 266

(Wagenmakers et al., 2010). Model estimate, standard error, lower and upper 267

boundary of 95% credible interval (CI) are also reported. 268

When checking our data, we found that 16 pairs of word items were not 269

morphed correctly, hence the spectral information of the pre-DP segments of these 270

word pairs were not exactly the same and some of them diverged acoustically before 271

the DP due to coarticulation. Therefore, we excluded these items from analyses of 272

the gating data and confirmed that excluding these items did not modify the 273

interpretation or significance of the MEG or behavioral results reported in the paper. 274

As shown in Figure 3C, we found that when gating segments ended at the DP, 275

Bayes factor provides strong evidence in favour of the null hypothesis, chance-level 276

accuracy (i.e. proportion of correct responses is 0.5), β = 0.04, SE = 0.08, lCI = -0.11, 277

uCI = 0.20, BF01 = 23.04. This indicates that participants could not predict the full 278

stimuli based on hearing the pre-DP segments. On the other hand, the Bayes factor 279

at later alignment points is close to 0, providing extremely strong evidence for the 280

alternative hypothesis that the proportion of correct responses is higher than 0.5 281

(75ms post-DP: β = 3.41, SE = 0.22, lCI = 2.99, uCI = 3.85, BF01 < 0.01; 150ms 282

post-DP: β = 6.26, SE = 0.56, lCI = 5.24, uCI = 7.41, BF01 < 0.01; 225ms post-DP: β 283

= 7.39, SE = 1.02, lCI = 5.65, uCI = 9.72, BF01 < 0.01; 300ms post-DP: β = 8.04, SE 284

= 1.88, lCI = 4.99, uCI = 12.32, BF01 < 0.01). Figure 3D shows that, with the gating 285

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segment becoming longer, the rating scores gradually reduce (lower scores 286

indicating more accurate and more confident identification). We examined whether 287

the mean score at the DP is equal to 3.5 (i.e. chance performance) and found strong 288

evidence supporting the null hypothesis, β = -0.02, SE = 0.04, lCI = -0.10, uCI = 0.06, 289

BF01 = 21.79, which is consistent with the accuracy results. Furthermore, in order to 290

refine the estimate of the time point at which participants recognise the stimuli with 291

enough confidence, we also investigated at what alignment point is there evidence 292

showing the mean score lower than 2 (i.e. participants indicating more confident 293

identification). We found moderate evidence supporting the null hypothesis (mean 294

score equals to 2) at 75ms post-DP (β = -0.09, SE = 0.08, lCI = -0.25, uCI = 0.07, 295

BF01 = 6.07), but extremely strong evidence in favour of the alternative hypothesis 296

at 150ms post-DP (β = -0.71, SE = 0.05, lCI = -0.79, uCI = 0.62, BF01 < 0.01). 297

These results show that critical acoustic information that supports confident word 298

recognition arrives between 75ms and 150ms post-DP. 299

Overall, the post-test gating study confirmed that the pre-DP segments of 300

correctly morphed stimuli are not distinguishable within each stimuli set. However, 301

since we found items that were not correctly morphed during this control study, we 302

did a thorough check of our stimuli and identified all the items with pre-DP acoustic 303

differences (16 words and 12 pseudowords), which resulted in 8.68% of all trials 304

presented in the MEG study. In order to double check our MEG study results, we 305

then removed all these problematic trials from the data and reanalysed the data 306

using the same methods as described in the method section. Fortunately, we did not 307

find any inconsistent pattern or significance in our behavioural or neural results 308

compared to those reported with all trials included (see Table 1-5). Therefore, we 309

kept the original MEG and behavioural results with all items included in this paper. 310

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311

Behavioural Data Analyses 312

Response times (RTs) were measured from the onset of the stimuli and inverse-313

transformed so as to maximise the normality of the data and residuals; Figures 314

report untransformed response times for clarity. Inverse-transformed RTs and error 315

rates were analysed using linear and logistic mixed-effect models respectively using 316

the lme4 package in R (Bates et al. 2014). Lexicality (word, pseudoword) and prime 317

type (unprimed, primed by same lexical status, primed by different lexical status) 318

were fixed factors, while participant and item were random factors. Maximal models 319

accounting for all random effects were attempted wherever possible, but reduced 320

random effects structures were applied when the full model did not converge (Barr et 321

al., 2013). Likelihood-ratio tests comparing the full model to a nested reduced model 322

using the Chi-Square distribution were conducted to evaluate main effects and 323

interactions. Significance of individual model coefficients were obtained using t 324

(reported by linear mixed-effect models) or z (reported by logistic mixed-effect 325

models) statistics in the model summary. One-tailed t statistics for RTs are also 326

reported for two planned contrasts: (1) word-primed versus unprimed conditions for 327

word targets, and (2) word-primed versus pseudoword-primed conditions for word 328

targets. 329

When assessing priming effects, we excluded data from target trials in which 330

the participant made an error in the corresponding prime trial, because it is unclear 331

whether such target items will be affected by priming given that the prime word was 332

not correctly identified. In addition, three trials with RTs shorter than the average pre-333

DP length (353ms) were removed from further analysis, since responses before 334

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words and pseudowords acoustically diverge are too quick to be valid lexical 335

decision responses. 336

337

MEG Data Acquisition, Processing and Analyses 338

Magnetic fields were recorded with a VectorView system (Elekta Neuromag) which 339

contains a magnetometer and two orthogonal planar gradiometers at each of 102 340

locations within a hemispherical array around the head. Although electric potentials 341

were recorded simultaneously using 68 Ag-AgCl electrodes according to the 342

extended 10-10% system, these EEG data were excluded from further analysis due 343

to excessive noise. All data were digitally sampled at 1 kHz. Head position were 344

monitored continuously using five head-position indicator (HPI) coils attached to the 345

scalp. Vertical and horizontal electro-oculograms were also recorded by bipolar 346

electrodes. A 3D digitizer (FASTRAK; Polhemus, Inc.) was used to record the 347

positions of three anatomical fiducial points (the nasion, left and right preauricular 348

points), HPI coils and evenly distributed head points for use in source reconstruction. 349

MEG Data were preprocessed using the temporal extension of Signal Source 350

Separation in MaxFilter software (Elekta Neuromag) to reduce noise sources, 351

normalise the head position over blocks and participants to the sensor array and 352

reconstruct data from bad MEG sensors. Subsequent processing was conducted in 353

SPM12 (https://www.fil.ion.ucl.ac.uk/spm/) and FieldTrip 354

(http://www.fieldtriptoolbox.org/) software implemented in MATLAB. The data were 355

epoched from -1100 to 2000ms time-locked to the DP and baseline corrected 356

relative to the -1100 to -700ms prior to the DP, which is a period before the onset of 357

speech for all stimuli (Figure 1C). Low-pass filtering to 40 Hz was conducted both 358

before and after robust averaging across trials (Litvak et al., 2011). A time window of 359

16

-150 to 0ms was defined for pre-DP comparisons based on the shortest pre-DP 360

stimuli length. A broad window of 0 to 1000ms was defined for post-DP comparisons, 361

which covered the possible period for lexicality and prime effects. After averaging 362

over trials, an extra step was taken to combine the gradiometer data from each 363

planar sensor pair by taking the root-mean square (RMS) of the two amplitudes. 364

Sensor data from magnetometers and gradiometers were analysed 365

separately. We converted the sensor data into 3D images (2D sensor x time) and 366

performed F tests for main effects across sensors and time (the term “sensors” 367

denotes interpolated sensor locations in 2D image space). Reported effects were 368

obtained with a cluster-defining threshold of p < .001, and significant clusters 369

identified as those whose extent (across space and time) survived p < 0.05 FWE-370

correction using Random Field Theory (Kilner & Friston, 2010). Region of interest 371

(ROI) analyses for the priming effect were then conducted over sensors and time 372

windows that encompassed the significant pseudoword>word cluster, orthogonal to 373

priming effects. When plotting waveforms and topographies, data are shown for 374

sensors nearest to the critical points in 2D image space. 375

Apart from the two planned contrasts mentioned above (see Behavioural Data 376

Analyses), which were applied to post-DP analysis, one-tailed t statistics was also 377

reported on the pre-DP planned contrast between unprimed and word-primed items. 378

379

Source Reconstruction 380

In order to determine the underlying brain sources underlying the sensor-space 381

effects, source reconstruction was conducted using SPM’s Parametric Empirical 382

Bayes framework (Henson et al., 2011). To begin with, we obtained T1-weighted 383

structural MRI (sMRI) scans from each participant on a 3T Prisma system (Siemens, 384

17

Erlangen, Germany) using an MPRAGE sequence. The scan images were 385

segmented and normalised to an MNI template brain in MNI space. The inverse of 386

this spatial transformation was then used to warp canonical meshes derived from 387

that template brain back to each subject’s MRI space (Mattout et al., 2007). Through 388

this procedure, canonical cortical meshes containing 8196 vertices were generated 389

for the scalp and skull surfaces. We coregistrated the MEG sensor data into the 390

sMRI space for each participant by using their respective fiducials, sensor positions 391

and head-shape points (with nose points removed due to the absence of the nose on 392

the T1-weighted MRI). Using the single shell model, the lead field matrix for each 393

sensor was computed for a dipole at each canonical cortical mesh vertex, oriented 394

normal to the local curvature of the mesh. 395

Source inversion was performed with all conditions pooled together using the 396

‘IID’ solution, equivalent to classical minimum norm, fusing the magnetometer and 397

gradiometer data (Henson et al, 2011). The resulting inversion was then projected 398

onto wavelets spanning frequencies from 1 to 40 Hz and from -150 to 0ms time 399

samples for pre-DP analysis and 400 to 900ms for post-DP analysis. This post-DP 400

time window was defined by overlapping temporal extent of the pseudoword > word 401

cluster between gradiometers and magnetometers. The total energy within these 402

time-frequency windows was summarised by taking the sum of squared amplitudes, 403

which was then written to 3D images in MNI space. 404

Reported effects for source analyses were obtained with a cluster-defining 405

threshold of p < 0.05 (FWE-corrected). And as in sensor space, ROI analyses were 406

conducted over significant sensors and time windows from the orthogonal 407

pseudoword>word cluster. Factorial ANOVA were carried out on main effects and 408

18

one-tailed paired t-tests on planned contrasts (see MEG Data Acquisition and 409

Processing). 410

411

412

413

Results 414

Behaviour 415

Response Times. As shown in Figure 4A and Table 1, factorial analysis of 416

lexicality (word, pseudoword) and prime type (unprimed, primed by same lexical 417

status, primed by different lexical status) indicated a significant main effect of 418

lexicality, in which RTs for pseudowords were significantly longer than for words, 419

X2(3) = 23.60, p < .001. In addition, there was a significant interaction between 420

lexicality and prime type, X2(2) = 10.73, p = .005. This interaction was followed up by 421

two separate one-way models for words and pseudowords, which showed a 422

significant effect of prime type for words, X2(2) = 10.65, p = .005, but not for 423

pseudowords, X2(2) = 1.62, p = .445. Consistent with the competitor priming results 424

from Monsell and Hirsh (1998), words that were primed by another word sharing the 425

same initial segments were recognised significantly more slowly than unprimed 426

words (for mean raw RTs see Fig 3A), β = 0.02, SE = 0.01, t(79.69) = 3.33, p < .001, 427

and more slowly than pseudoword-primed words, β = 0.02, SE = 0.01, t(729.89) = 428

2.37, p = .009. As mentioned earlier (see Introduction), both competitive- and 429

predictive-selection models predicted longer response times to word-primed target 430

words compared to unprimed words, it is hence critical to distinguish the two 431

accounts through further investigation of the MEG responses. 432

19

Accuracy. Figure 4B and Table 2 indicate that there was a trend towards more 433

lexical decision errors in response to words than to pseudowords, although this 434

lexicality effect was marginal, X2(3) = 7.31, p = .063. The error rates for words and 435

pseudowords were also affected differently by priming, as indicated by a significant 436

interaction between lexicality and prime type, X2(2) = 6.08, p = .048. Follow-up 437

analyses using two separate models for each lexicality type showed there was a 438

main effect of prime type for words, X2(2) = 13.95, p < .001, but not for pseudowords, 439

X2(2) = 1.93, p = .381. Since we had not anticipated these priming effects on 440

accuracy, post-hoc pairwise z tests were Bonferroni corrected for multiple 441

comparisons. These showed that pseudoword priming reliably increased the error 442

rate compared to the unprimed condition, β = 1.68, SE = 0.54, z = 3.14, p = .005, 443

and to the word-primed condition, β = 2.74, SE = 0.89, z = 3.07, p = .007. Although 444

no specific predictions on accuracy were made a priori by either competitive- or 445

predictive-selection model, it is worth noting that participants might have expected 446

pseudowords to be repeated given the increased error rate of responses to 447

pseudoword-primed target words. 448

449

MEG 450

In order to explore the impact of lexicality and competitor priming on neural 451

responses to critical portions of speech stimuli, both before and after they diverge 452

from each other, MEG responses were time-locked to the DP. All reported effects 453

are family-wise error (FWE)-corrected at cluster level for multiple comparisons 454

across scalp locations and time at a threshold of p < 0.05. We reported data from 455

gradiometers, magnetometers and source space wherever possible, since sensor x 456

time analyses help define the time-windows used by source localisation. Although 457

20

some minor effects were shown in only one of these analyses, our most interesting 458

effects are reliable in all three data types. 459

Pre-DP analyses. We assessed neural responses before the DP, during 460

which only the shared speech segments have been heard and hence the words and 461

pseudowords in each stimulus set are indistinguishable. Since there could not have 462

been any effect of lexical status pre-DP, only prime type effects were considered in 463

this analysis. Predictive- and competitive-selection accounts make opposite 464

predictions for pre-DP neural signals evoked by word-primed items compared to 465

unprimed items. We therefore conducted an F-test for neural differences between 466

these two conditions across the scalp and source spaces over a time period of -150 467

to 0ms before the DP. A significant cluster of 295 sensor x time points (p = .023) was 468

found in gradiometers over the mid-left scalp locations from -28 to -4ms (Figure 5A, 469

Table 3), in which unprimed items evoked significantly greater neural responses than 470

word-primed items. On the suggestion of a reviewer, and mindful of the potential for 471

these pre-DP neural responses to be modulated by post-DP information, we report 472

an additional analysis with a lengthened analysis time window of -150ms to 100ms. 473

Again, we found a significant unprimed > word-primed cluster of 313 sensor x time 474

points (p = .033) over the exact same locations in gradiometers from -28 to -3ms pre-475

DP, which confirmed that this pre-DP effect was not pushed forward by any post-DP 476

effect. We did not find any cluster showing stronger neural responses for word-477

primed items than unprimed items and no clusters survived correction for multiple 478

comparisons for magnetometer responses or for analysis in source space. 479

To further examine these results, we also conducted ROI analysis of 480

gradiometer signals evoked by unprimed and primed items averaged over the same 481

-150 to 0ms pre-DP time window but across the scalp locations that showed the 482

21

post-DP lexicality effect at which pseudowords elicited greater neural responses than 483

words (see Figure 6A). As shown in Figure 5B and Table 4, the results indicated that 484

unprimed items elicited significantly stronger neural responses than word-primed 485

items, t(21) = 2.41, p = .013, consistent with the whole-brain analysis. In particular, 486

the mid-left cluster shown in panel A partially overlaps with the post-DP 487

pseudoword>word cluster. The direction and location of these pre-DP neural 488

responses are in accordance with the predictive-selection account and inconsistent 489

with the competitive-selection account. A surprising finding is that post-hoc analysis 490

also showed greater neural responses evoked by unprimed items than pseudoword-491

primed items, t(21) = 2.69, p = .014, although we had not predicted these effects 492

from pseudoword primes. 493

Post-DP analyses. We then examined the post-DP response differences 494

between words and pseudowords (lexicality effect). The gradiometer sensors 495

showed a significant cluster of 39335 sensor x time points (p < .001) over the left 496

side of the scalp at 313-956ms post-DP (Figure 6A, Table 3). In this cluster, 497

pseudowords evoked a significantly stronger neural response than words. Similarly, 498

magnetometer sensors also detected a significant left-hemisphere cluster of 68517 499

sensor x time points (p < .001) at 359-990ms post-DP (Figure 6B, Table 3) showing 500

the same lexicality effect. We did not find any significant cluster in which words 501

evoked greater neural responses than pseudowords. These results are consistent 502

with findings from Gagnepain and colleagues (2012). To locate the likely neural 503

source of the effects found in sensor space, we conducted source reconstruction by 504

integrating gradiometers and magnetometers. As shown in Figure 6C, results from 505

source space showed that neural generators of the lexicality effect were estimated to 506

lie within the left superior temporal gyrus (STG, volume of 2315 voxels, p < .001, 507

22

peak at x = -46, y = -36, z = 0; x = -52, y = -34, z = -6; x = -56, y = -28, z = -10). This 508

location, and direction of response, is consistent with a sub-lexical (e.g. phonemic) 509

process being modulated by lexicality; in line with the predictive-selection account. 510

Next, we investigated whether the neural responses that were modulated by 511

lexicality were also influenced by prime type by conducting an ROI analysis which 512

tested the interaction between prime type and lexicality, as well as planned pairwise 513

comparisons of priming effects on words alone, using data averaged over the time 514

window and the sensor locations of the significant cluster shown in panel A and B 515

(Figure 6D & E, Table 5). Since these planned pairwise comparisons involve 516

responses to familiar words only (i.e. words that are word-primed vs unprimed, 517

words that are word-primed vs pseudoword-primed), they are orthogonal to the 518

lexicality effect that defined the pseudoword>word cluster and hence are not 519

confounded by task. The interaction was significant in both gradiometers, F(1.96, 520

41.11) = 7.30, p = .002, and magnetometers, F(1.90, 39.99) = 5.80, p = .007. 521

Specifically, there was a significant effect of prime type for words, F(1.93, 40.55) = 522

8.01, p = .001 (gradiometers), F(1.81, 37.96) = 5.61, p = .009 (magnetometers), such 523

that neural signals evoked by word-primed words were significantly stronger than 524

those evoked by unprimed words, t(21) = 2.22, p = .019 (gradiometers), t(21) = 3.33, 525

p = .002 (magnetometers), and pseudoword-primed words, t(21) = 3.70, p < .001 526

(gradiometers), t(21) = 2.64, p = .008 (magnetometers). In contrast, there was no 527

reliable main effect of prime type for pseudowords, F(1.94, 40.80) = 0.67, p = .514 528

(gradiometers), F(1.79, 37.61) = 0.80, p = .446 (magnetometers). 529

The corresponding tests performed on the source-reconstructed power within 530

the lexicality ROI of suprathreshold voxels (Figure 6F, Table 5) did not show a 531

reliable interaction effect between lexicality and competitor priming, F(1.56, 32.85) = 532

23

0.99, p = .360. Nevertheless, consistent with sensor space results, source power 533

indicated a significant effect of prime type for words, F(1.73, 36.42) = 3.77, p = .038, 534

but not pseudowords, F(1.62, 33.94) = 1.12, p = .326. Pairwise comparisons also 535

indicated that word-primed words evoked significantly greater source strength than 536

unprimed words, t(21) = 2.66, p = .007, though the effect between word-primed and 537

pseudoword-primed words was not significant, t(21) = 1.26, p = .110. Overall, in line 538

with behavioural results, neural responses evoked by words and pseudowords were 539

also influenced differently by prime type. Critically, competitor priming modulated the 540

post-DP neural responses evoked by words, but not those evoked by pseudowords, 541

and these effects were localised to the left STG regions that plausibly contribute to 542

sub-lexical processing of speech. This matches the pattern of responses proposed in 543

the predictive-selection model (see Figure 1F). As shown in Table 1 to 5, the pattern 544

and significance of the results did not change when items with pre-DP acoustic 545

differences identified through the gating post-test were excluded. 546

As encouraged by a reviewer, we also conducted whole brain analyses for the 547

competitor priming effects. We found a significant word-primed word > unprimed 548

word cluster of 1197 sensor x time points (p = .034) in magnetometers in the left 549

hemisphere within a time window of 426 - 466ms post-DP. In addition, we also found 550

a significant and a marginal word-primed word > pseudoword-primed word cluster in 551

gradiometers in the left hemisphere respectively of 527 sensor x time points (p 552

= .011) at 719-749ms and 471 sensor x time points (p = .053) at 315-336ms. These 553

topographies and time courses overlap with the pseudoword > word clusters and are 554

consistent with our ROI results. Hence, the ROI analyses have picked up the most 555

important findings from these whole-brain analyses. 556

24

To ensure that other response patterns were not overlooked, we also 557

investigated whether there was any lexicality by prime-type interaction at other 558

locations across the scalp and source spaces, and during other time periods. As 559

shown in Figure 7A, a significant cluster of Gradiometers at midline posterior scalp 560

locations were found at 397-437ms post-DP, in which the effect of priming was 561

significantly different for words and pseudowords. Figure 7B shows gradiometer 562

signals evoked by conditions of interest averaged over the spatial and temporal 563

extent of the significant cluster in panel A. To explore this profile, we computed an 564

orthogonal contrast to assess the overall lexicality effect (the difference between 565

words and pseudowords), and the result was marginal, F(1.00, 21.00) = 3.50, p 566

= .075. The effect of prime type was marginally significant for words, F(1.89, 39.78) = 567

3.08, p = .060, but significant for pseudowords, F(1.80, 37.85) = 7.14, p = .003. The 568

location and pattern of this interaction cluster were dissimilar to those predicted by 569

either competitive- or predictive-selection theories and no cluster survived correction 570

in magnetometer sensors or source space hence we did not consider this effect to 571

be as relevant or interpretable as our other findings. We report it here in the interest 572

of completeness and transparency. 573

Linking neural and behavioural effects. To further examine the relationship 574

between neural and behavioural response differences attributable to competitor 575

priming or lexicality, we conducted a single-trial regression analyses using linear 576

mixed-effect models that account for random intercepts and slopes for participants 577

and stimuli sets (grouped by their initial segments). We calculated behavioural RT 578

differences and neural MEG differences caused by: (1) lexicality. i.e. the difference 579

between pseudoword and word trials (collapsed over primed and unprimed 580

conditions) and (2) competitor priming, i.e. the difference between unprimed and 581

25

word-primed word trials, with MEG signals averaged over the spatial and temporal 582

extent of the post-DP pseudoword>word cluster seen in sensor space and the STG 583

peak voxel in source space (see Figure 6). We then assessed the relationship 584

between these behavioural and neural difference effects in linear mixed-effect 585

regression of single trials, with differences in RTs as the independent variable and 586

differences in MEG responses as the dependent variable. The analyses were 587

conducted using the lme4 package in R (Bates et al. 2014). 588

As shown in Figure 8A, we observed a significant positive relationship 589

between RTs and magnetometers on lexicality difference (β = 0.11, SE = 0.01, 590

t(23.31) = 7.77, p < .001), although associations between RTs and gradiometers or 591

source response were not significant. These observations from magnetometers 592

indicated that slower lexical decision times evoked by pseudowords were associated 593

with greater neural responses. Furthermore, the intercept parameter for the 594

magnetometers model was significantly larger than zero, β = 37.58, SE = 5.72, 595

t(23.09) = 6.57, p < .001. We can interpret this intercept as the neural difference that 596

would be predicted for trials in which there was no delayed response to 597

pseudowords compared to words. The significant intercept indicated a baseline 598

difference in neural responses to words and pseudowords, even in the absence of 599

any difference in processing effort (as indexed by lexical decision RTs). This 600

suggested the engagement of additional neural processes specific to pseudowords 601

regardless of the behavioural effect (cf. Taylor et al., 2014). 602

Figure 8B showed another significant positive relationship between RTs and 603

magnetometers on competitor priming difference (β = 0.15, SE = 0.02, t(38.85) = 604

7.89, p < .001), while relationships between RTs and gradiometers or source 605

response were again not significant. Interestingly, unlike for the lexicality effect, the 606

26

intercept in this competitor priming magnetometers model did not reach significance 607

(β = 12.88, SE = 7.27, t(21.33) = 1.77, p = .091). This non-significant intercept might 608

suggest that if word-primed words did not evoke longer RTs than unprimed words, 609

magnetometer signals would not be reliably different between the two conditions 610

either. Hence, consistent with predictive-selection accounts, the increased post-DP 611

neural responses in the STG caused by competitor priming was both positively 612

linked to and mediated by longer response times. 613

614

615

Discussion 616

In this study, we distinguished different implementations of Bayesian perceptual 617

inference by manipulating the prior probability of spoken words and examining 618

changes to neural responses. We replicated the competitor priming effect such that a 619

single prior presentation of a competitor word (e.g. hijack) delayed the recognition of 620

a similar-sounding word (e.g. hygiene), whereas this effect was not observed when 621

the prime or target was a pseudoword (e.g. hijure). Armed with this behavioural 622

evidence, we used MEG data to test the neural bases of two Bayesian theories of 623

spoken word recognition. 624

625

Competitive- vs predictive-selection 626

Competitive-selection accounts propose that word recognition is achieved through 627

direct inhibitory connections between representations of similar candidates (e.g. 628

McClelland & Elman, 1986). Priming boosts the activation of heard words and 629

increases lateral inhibition applied to neighbouring words, which delays their 630

27

subsequent identification. The effect of competitor priming is to increase lexical 631

uncertainty, and hence lexical-level neural responses, until later time points when 632

target words can be distinguished from the competitor prime (Figure 1C). In contrast, 633

predictive-selection accounts propose that word recognition is achieved by 634

subtracting predicted speech from heard speech and using computations of 635

prediction error to update lexical probabilities (Davis & Sohoglu, 2020). By this view, 636

predictions for segments that are shared between competitor primes and targets 637

(pre-DP segments) will be enhanced after presentation of prime words. Thus, 638

competitor priming will reduce the magnitude of prediction error, and hence neural 639

responses pre-DP (Figure 1F). Only when speech diverges from predictions (post-640

DP segments) will competitor-primed words evoke greater prediction error, leading to 641

increased neural response in brain areas involved in pre-lexical (e.g. phonemic) 642

processing of speech representing prediction error (Blank et al., 2018; Blank & Davis, 643

2016). Both these models involve multiple levels of representation and hence both 644

sub-lexical and lexical processes. However, our focus is primarily on lexical 645

processing within the competitive-selection framework and sub-lexical processing 646

within the predictive-selection framework. These are the critical processing levels 647

that 1) support word recognition, 2) are modulated by competitor priming effect and 3) 648

can potentially explain how slower behavioural responses will manifest in MEG 649

responses. 650

The direction and timing of changes to MEG responses associated with 651

competitor priming showed opposite effects pre- and post-DP. In the pre-DP period, 652

consistent with predictive-selection but contrary to competitive-selection mechanisms, 653

we saw decreased neural responses for word-primed items compared to unprimed 654

items. The initial, shared segments between prime (hijack) and target (hygiene) 655

28

words evoked a reduced response during early time periods in line with a reduction 656

in prediction error. However, during the post-DP period, competitor-primed words 657

evoked stronger neural responses than unprimed words in exactly the same 658

locations and time periods that showed increased responses to pseudowords (hijure) 659

compared to words. These post-DP response increases are in line with enhanced 660

processing difficulty for competitor-primed words and pseudowords due to greater 661

prediction error. Thus, the time course of the competitor priming neural effects – 662

showing reduced neural responses pre-DP and increased neural responses post-DP 663

– closely resembles the expected changes in prediction error (Figure 1F) based on 664

predictive-selection mechanisms. However, we note that post-DP effects reach 665

significance later than mismatch effects for written words (Dikker et al., 2010), 666

lexicality effects for spoken words (MacGregor et al., 2012), and phoneme surprisal 667

effects in connected speech (Brodbeck et al., 2018; Donhauser & Baillet, 2020). This 668

delay could be due to our morphing manipulation which removed coarticulation 669

before the divergence point, or due to neural effects being delayed for words in 670

isolation compared to connected speech (see Gwilliams & Davis, in press for review). 671

Further research to assess the latency of neural effects can help determine whether 672

they are sufficiently early to indicate bottom-up sensory signals as proposed by 673

predictive selection. 674

Effects of lexicality and competitor priming localised to the left STG; this brain 675

region has long been associated with lower-level sensory processing of speech (Yi 676

et al., 2019). Our observation of increased responses to pseudowords in STG 677

agrees with source-localised MEG findings (Gagnepain et al., 2012; Shtyrov et al., 678

2012) and a meta-analysis of PET and fMRI studies (Davis & Gaskell, 2009). This 679

location is also consistent with the proposal that lexical influences on segment-level 680

29

computations produce reliable neural differences between words and pseudowords 681

(Davis & Sohoglu, 2020). We take this localisation as further evidence in favour of 682

computations of segment prediction error as a critical mechanism underlying word 683

identification. 684

We further show using regression analyses that neural (MEG) and behavioural 685

(RT) effects of lexicality and competitor priming are linked on a trial-by-trial basis. 686

Trials in which pseudoword processing or competitor priming leads to larger 687

increases in RT also have greater post-DP neural responses. Links between 688

behavioural and neural effects of lexicality and competitor priming are once more in-689

line with the proposal that post-DP increases in prediction error are a key neural 690

mechanism for word and pseudoword processing and explain delayed behavioural 691

responses seen in competitor priming. Interestingly, lexicality and competitor priming 692

effects differ in terms of whether a reliable neural response difference would be seen 693

for trials with no baseline RT difference. While neural lexicality effects were 694

significant even for trials that did not show behavioural effects, the same was not 695

true for the competitor priming effect. These results indicate that, consistent with 696

predictive-selection accounts, the post-DP neural effect of competitor priming was 697

mediated by changes in behavioural RTs. In contrast, an increased neural response 698

to pseudowords was expected even in trials for which RTs did not differ between 699

pseudowords and words. We next consider the implications of these and other 700

findings for pseudoword processing. 701

How do listeners process pseudowords? 702

Participants identified pseudowords with a speed and accuracy similar to that seen 703

during recognition of familiar words. This is consistent with an optimally-efficient 704

30

language processing system (Marslen-Wilson, 1984; Zhuang et al, 2014), in which 705

pseudowords can be distinguished from real words as soon as deviating speech 706

segments are heard. Beyond this well-established behavioural finding, however, we 707

reported two seemingly contradictory observations concerning pseudoword 708

processing. 709

The first is that, while post-DP neural activity and response times for words 710

were modulated by competitor priming, processing of pseudowords was not similarly 711

affected. This might suggest that the prior probability of hearing a pseudoword and 712

the prediction error elicited by mismatching segments are not changed by our 713

experimental manipulations. This may be because pseudowords have a low or zero 714

prior probability and elicit maximal prediction errors that cannot be modified by a 715

single prime. Yet, memory studies suggest that even a single presentation of a 716

pseudoword can be sufficient for listeners to establish a lasting memory trace 717

(Mckone & Trynes, 1999; Arndt et al., 2008). However, it is possible that this memory 718

for pseudowords reflects a different type of memory (e.g. episodic memory) from that 719

produced by a word, with only the latter able to temporarily modify long-term, lexical-720

level representations and predictions for word speech segments (cf. Complementary 721

Learning Systems theories, McClelland et al., 1995; Davis & Gaskell, 2009). 722

A second observation is that, contrary to the null result for post-DP processing, 723

pseudoword priming reduced subsequent pre-DP neural responses evoked by target 724

items to a similar degree as word priming (Figure 5B). This pre-DP effect is 725

surprising given previous evidence suggesting that pseudowords must be encoded 726

into memory and subject to overnight, sleep-associated consolidation in order to 727

modulate the speed of lexical processing (Tamminen et al., 2010; James et al., 2017) 728

or neural responses (Davis & Gaskell, 2009; Landi et al. 2018). It might be that 729

31

neural effects seen for these pre-DP segments were due to changes to the 730

representation of familiar words that our pseudowords resembled, though these were 731

insufficient to modulate processing of post-DP segments. 732

733

Summary 734

Our work provides compelling evidence in favour of neural computations of 735

prediction error during spoken word recognition. Although previous work by 736

Gagnepain et al. (2012) provided evidence for the predictive-selection account, their 737

behavioural effects of consolidation on word recognition were obtained during 738

different tasks and different test sessions from neural responses. Our current study 739

goes beyond this previous work by adopting a single task (lexical decision) and using 740

a competitor priming paradigm that permits concurrent measurement of perceptual 741

outcomes and neural responses in a single session. This enables us to directly link 742

trials that evoked stronger neural signals in the STG to delayed RTs and hence 743

provide stronger evidence that both of these effects are caused by competitor 744

priming. 745

In addition, unlike previous work (Brodbeck et al. 2018; Donhauser & Baillet, 746

2020) which reported neural responses correlated with lexical entropy as well as 747

prediction error (surprisal), we did not find similarly equivocal evidence. These earlier 748

studies measured neural responses to familiar words in continuous speech 749

sequences such as stories or talks. It might be that effects of lexical entropy are 750

more apparent for connected speech than isolated words. However, since lexical 751

uncertainty (entropy) and segment-level predictability (segment prediction error or 752

surprisal) are highly correlated in natural continuous speech, these studies may be 753

32

less able to distinguish between the lexical and segmental mechanisms that we 754

assessed here. In contrast, our speech materials were carefully selected to change 755

lexical probability (through priming) and for priming to have opposite effects on 756

segment prediction error before and after DP. This manipulation provides evidence 757

in favour of predictive-selection mechanisms that operate using computations of 758

prediction error during spoken word recognition. 759

33

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932

933

41

Tables and Legends 934

Table 1. Behavioural RTs analyses on all data versus data excluding items with 935

pre-DP acoustic differences (identified in the gating post-test). Reported 936

pairwise effects (planned) are one-tailed. 937

All data Data with exclusion

Contrast X2 t p X2 t p

Lexicality 23.60 <.001 28.87 <.001

Lexicality-by-prime type 10.73 .005 8.52 .014

Word prime type 10.65 .005 8.57 .014

Word-word > word 3.33 <.001 3.00 .002

Word-word > pseudo-word 2.37 .009 2.30 .011

Pseudo prime type 1.62 .445 0.65 .720

Word-word, word-primed word; pseudo-word, pseudoword-primed word. 938

939

Table 2. Behavioural accuracy analyses on all data versus data excluding 940

items with pre-DP acoustic differences. Reported pairwise effects are 941

Bonferroni corrected. 942

All data Data with exclusion

Contrast X2 z p X2 z p

Lexicality 7.31 .063 6.40 .094

Lexicality-by-prime type 6.08 .048 6.98 .031

Word prime type 13.95 <.001 14.97 <.001

Pseudo-word > word 3.14 .005 3.03 .007

Pseudo-word > word-word 3.07 .007 3.05 .007

Pseudo prime type 1.93 .381 3.16 .206

42

Word-word, word-primed word; pseudo-word, pseudoword-primed word. 943

944

Table 3. Pre-DP MEG analyses of unprimed > word-primed items and Post-DP 945

MEG analyses of pseudoword > word on all data versus data excluding items 946

with pre-DP acoustic differences. Reported effects are FWE corrected at 947

cluster level at p < 0.05. 948

All data Data with exclusion

Time

window Modality

Cluster

PFWE-corr

Cluster

size

Latency

(ms)

Cluster

PFWE-corr

Cluster

size

Latency

(ms)

Pre-DP Grad .023 295 -28 to -4 .005 426 -25 to 0

Post-DP Grad <.001 39335 313 to 956 <.001 30811 320 to 775

Mag <.001 68517 359 to 990 <.001 69777 362 to 988

Source <.001 2315 400 to 900 <.001 2287 400 to 900

Grad, gradiometers; Mag, magnetometers. 949

950

Table 4. Pre-DP MEG ROI analyses on all data versus data excluding items 951

with pre-DP acoustic differences across gradiometer sensor locations that 952

showed post-DP pseudoword > word effect. Reported effects on unprimed > 953

word-primed items (planned) are one-tailed. 954

All data Data with exclusion

Contrast t p t p

Unprimed > word-primed 2.41 .013 2.57 .009

Unprimed > pseudo-primed 2.69 .014 3.14 .005

Unprimed, unprimed items; Word-primed, word-primed items; pseudo-primed, pseudoword-primed items. 955

956

43

Table 5. Post-DP MEG ROI analyses on all data versus data excluding items 957

with pre-DP acoustic differences. Reported pairwise effects (planned) are one-958

tailed. 959

All data Data with exclusion

Contrast Modality F t p F t p

Lexicality-by-prime type Grad 7.30 .002 6.12 .005

Mag 5.80 .007 3.77 .035

Source 0.99 .360 1.04 .354

Word prime type Grad 8.01 .001 6.18 .005

Mag 5.61 .009 4.46 .021

Source 3.77 .038 3.64 .039

Word-word > word Grad 2.22 .019 2.11 .023

Mag 3.33 .002 2.79 .006

Source 2.66 .007 2.51 .010

Word-word > pseudo-word Grad 3.70 <.001 3.60 <.001

Mag 2.64 .008 2.33 .015

Source 1.26 .110 1.39 .089

Pseudo prime type Grad 0.67 .514 0.57 .564

Mag 0.80 .446 0.37 .681

Source 1.12 .326 1.23 .300

Word-word, word-primed word; pseudo-word, pseudoword-primed word. Grad, gradiometers; Mag, magnetometers. 960

961

44

Figure Legends 962

Figure 1. Illustration of neural predictions based on competitive-selection and 963

predictive-selection models respectively for recognition of a word (hygiene) or 964

pseudoword (hijure) that is unprimed or primed by a similar-sounding word (hijack) or 965

pseudoword (higent). A. In a competitive-selection model, such as TRACE 966

(McClelland & Elman, 1986), word recognition is achieved through within-layer 967

lexical competition. B. Illustration of the competitive-selection procedure for word 968

(e.g. hygiene) and pseudoword (e.g. hijure) recognition. Phoneme input triggers the 969

activation of multiple words beginning with the same segments, which compete with 970

each other until one word is selected. No word can be selected when hearing a 971

pseudoword, though it would be expected that lexical probability (although not lexical 972

entropy) should be greater for words than for pseudowords. C. Illustration of neural 973

predictions based on lexical entropy. Lexical entropy gradually reduces to zero as 974

more speech is heard. Before the deviation point (hereafter DP) at which the prime 975

(hijack) and target (hygiene) diverge, these items are indistinguishable, and 976

competitor priming should transiently increase lexical entropy (shaded area). After 977

the DP, competitor priming should not affect entropy since prime and target words 978

can be distinguished. D. In a predictive-selection model such as the Predictive-979

Coding account (PC, Davis & Sohoglu, 2020), words are recognised by minimising 980

prediction error, which is calculated by subtracting the predicted segments from the 981

current sensory input. E. Illustration of the predictive-selection procedure during word 982

(e.g. hygiene) and pseudoword (e.g. hijure) recognition. Speech input evokes 983

predictions for the next segment (based on word knowledge as in B), which is then 984

subtracted from the speech input and used to generate prediction errors that update 985

lexical predictions (+ shows confirmed predictions that increase lexical probability, - 986

45

shows disconfirmed predictions that decrease lexical probability). F. Illustration of 987

neural predictions based on segment prediction error. Before the DP, priming of 988

initial word segments should strengthen predictions and reduce prediction error. 989

There will also be greater mismatch between predictions and heard speech for 990

competitor-primed words and hence primed words should evoke greater prediction 991

error than unprimed words (shaded area). This increased prediction error should still 992

be less than that observed for pseudowords, which should evoke maximal prediction 993

error regardless of competitor priming due to their post-DP segments being entirely 994

unpredictable. 995

996

Figure 2. Experimental design. A. Four different types of prime-target pairs. Each 997

pair was formed by two stimuli from the same quadruplet, separated by between 20 998

to 76 trials of items that do not share the same initial segments. B. Lexical decision 999

task. Participants made lexicality judgments to each item they heard via left hand 1000

button-press. The response time was recorded from the onset of the stimuli. As 1001

shown, items within each quadruplet are repeated after a delay of ~1-4 minutes 1002

following a number of other intervening stimuli. 1003

1004

Figure 3. Stimuli and post-test gating study results. A. Stimuli within the same 1005

quadruplet have identical onsets in STRAIGHT parameter space (Kawahara, 2006) 1006

and thus only diverge from each other after the deviation point (DP). MEG responses 1007

were time-locked to the DP. B. Stimuli length histogram. C. Bayes factor for chance 1008

level accuracy (BF01) at each post-DP alignment point of the stimuli in the post-test 1009

gating study. D. Mean rating score at each post-DP alignment point of the stimuli in 1010

the gating study. 1011

46

1012

Figure 4. Response time results (A) and accuracy results (B) of the lexical decision 1013

task. Bars are color-coded by lexicality and prime type on the x axis (words, blue 1014

frame; pseudowords, orange frame; unprimed, no fill; primed by same lexicality, 1015

consistent fill and frame colors; primed by different lexicality, inconsistent fill and 1016

frame colors). Bars show the subject grand averages, error bars represent ± within-1017

subject SE, adjusted to remove between-subjects variance (Cousineau, 2005). 1018

Statistical significance is shown based on generalised linear mixed-effects 1019

regression: * p<0.05, ** p<0.01, *** p<0.001. Statistical comparisons shown with 1020

solid lines indicate the lexicality by prime-type interaction and main effects of prime-1021

type for each lexicality, whereas comparisons with broken lines indicate the 1022

significance of pairwise comparisons. 1023

1024

Figure 5. Pre-DP results. A & B. Pre-DP response difference between items that are 1025

unprimed and primed by a word in MEG gradiometer sensors within -150 to 0ms (a 1026

time window at which words and pseudowords are indistinguishable). The 1027

topographic plots show F-statistics for the entire sensor array with the scalp locations 1028

that form two statistically significant clusters highlighted and marked with black dots. 1029

Waveforms represent MEG response averaged over the spatial extent of the 1030

significant cluster shown in the topography. The grey shade of waveforms represents 1031

± within-participant SE, adjusted to remove between-participants variance 1032

(Cousineau, 2005). C. ROI analysis of neural responses evoked by unprimed and 1033

primed items averaged over the same pre-DP time period of -150-0ms but across 1034

gradiometer sensor locations which showed the post-DP pseudoword>word lexicality 1035

effect (see Figure 5A). Bars are color-coded by prime type on the x axis (unprimed 1036

47

items, no fill; word-primed items, blue; pseudoword-primed items, orange; black 1037

frame indicates that words and pseudowords are indistinguishable). All error bars 1038

represent ± within-participant SE, adjusted to remove between-participant variance. 1039

Statistical significance: * p<0.05. 1040

1041

Figure 6. Post-DP results showing lexicality effects and corresponding ROI 1042

responses evoked by conditions of interest. A & B. Post-DP lexicality effects in MEG 1043

gradiometer and magnetometer sensors. The topographic plots show the statistically 1044

significant cluster with a main effect of lexicality (pseudoword > word). Waveforms 1045

represent MEG response averaged over the spatial extent of the significant cluster 1046

shown in the topography. The grey shade of waveforms represents ± within-1047

participant SE, adjusted to remove between-participants variance. C. Statistical 1048

parametric map showing the cluster (pseudoword > word) rendered onto an inflated 1049

cortical surface of the Montreal Neurological Institute (MNI) standard brain 1050

thresholded at FWE-corrected cluster-level p < 0.05, localised to the left STG. D, E & 1051

F. Post-DP ROI ANOVA on neural signals and source strength evoked by conditions 1052

of interest averaged over the time window and scalp locations of the significant 1053

cluster shown in panel A, B & C. Bars are color-coded by lexicality and prime type on 1054

the x axis (words, blue frame; pseudowords, orange frame; unprimed, no fill; primed 1055

by same lexicality, consistent fill and frame colors; primed by different lexicality, 1056

inconsistent fill and frame colors). All error bars represent ± within-participant SE, 1057

adjusted to remove between-participants variance. Statistical significance from 1058

ANOVAs: * p<0.05, ** p<0.01, *** p<0.001. Statistical comparisons shown with solid 1059

lines indicate the lexicality by prime-type interaction and main effects of prime-type 1060

48

for each lexicality, whereas comparisons with broken lines indicate the significance 1061

of planned pairwise comparisons. 1062

1063

Figure 7. Post-DP results showing lexicality-by-priming interaction effects in MEG 1064

gradiometers. A. The topographic plot shows F-statistics for the statistically 1065

significant cluster that showed an interaction between lexicality and prime type. 1066

Waveforms represent gradiometer responses averaged over the spatial extent of the 1067

significant cluster shown in the topography. The grey shade of waveforms represents 1068

± within-participant SE, adjusted to remove between-participants variance. B. 1069

Gradiometer signals evoked by conditions of interest averaged over temporal and 1070

spatial extent of the significant cluster in panel A. All error bars represent ± within-1071

participant SE, adjusted to remove between-participants variance. Statistical 1072

significance: ** p<0.01. The statistical comparison lines indicate main effects of 1073

prime type for each lexicality. The lexicality by prime-type interaction is statistically 1074

reliable as expected based on the defined cluster. 1075

1076

Figure 8. Single-trial linear mixed-effect models which accounted for random 1077

intercepts and slopes for participants and stimuli sets (grouped by initial segments) 1078

were constructed to compute the relationship between RTs and magnetometers on 1079

(A) lexicality difference (i.e. between pseudowords and words, collapsed over 1080

unprimed and primed conditions) and (B) competitor priming difference (i.e. between 1081

word-primed word and unprimed word conditions). Magnetometer responses were 1082

averaged over the time window and scalp locations of the significant post-DP 1083

pseudoword>word cluster (see Figure 6). β1 refers to the model slope, β0 refers to 1084

the model intercept. Statistical significance: *** p<0.001. 1085

49

1086

1087

Phonemes

AcousticFeatures

PredictiveSelection

(PC)

CompetitiveSelection(TRACE)

Phoneme Input: /ai/ /i:/ /n/

/æ/ -/e/ -… -/ai/ +… -/ / -… -/ / -

/d/ -… -/f/ -/k/ -… -

/d / +… -

/n/ +

DP

/n//i://d //ai//h/

/n//i:// //ai//h/Pre-DP

C

E

F

Lexi

cal E

ntro

py

Pred

ictio

n Er

ror

A

/æ/ -/i:/ +

Word

Phonemes

AcousticFeatures

Words

D

(1)

(1)

(1)

Words

LexicalCompetition

Top DownPredictions

PredictionError

Pre-DP

Post-DP

Post-DP

/ai/ /i:/ habithack…healthhelp…hijack…hobby…hygiene…

hide…high…hijackhike…hybridhydrate…hygiene…

hygienehijack

hygiene hygiene

DP

BWord(1)

Competitor Priming

Competitor Priming

/h/

/h/ /n/

DP

DP

WordUnprimed

Primed by Word hijack

Primed by Pseudoword higent

WordUnprimed

Primed by Word hijack

Primed by Pseudoword higent

High

Low

Prediction Error:

/h/ /j/ / /

? -/æ/ -/i:/ -

DP

/h/ /ai/ habithack…healthhelp…hijack…hobby…hygiene…

hide…high…hijackhike…hybridhydrate…hygiene…

hygienehijack

/j/‘hiju...’

/ /‘hijure’

DPPseudoword

Pseudoword

(2)

(2)

(2)

(2)

/ //j//d //ai//h/

Pred

ictio

n Er

ror

Pre-DP

/ // j// //ai//h/Pre-DP

Lexi

cal E

ntro

py

Post-DP

LexicalEntropy

High

Low

Competitor Priming

Post-DP

DP

/æ/ -/e/ -… -/ai/ +… -/ / -… -/ / -

/d/ -… -/f/ -/k/ -… -

/d / +… -

/ai/

/j/

DP

Pseudoword

Primed by Pseudoword higent

Primed by Word hijack

Unprimed

/

Pseudoword

Primed by Pseudoword higent

Primed by Word hijack

Unprimed

20-76 trials

A hijack æk/

hygiene in/

basef ef/

basis /

boycott /

boymid/

letto/let /

lettan/let /

Prime Target B

W = Word P = Pseudoword

hijack

1 2 3 4 50

lettoHear

Response

Time (Sec)

or oro

Trial Type Prime Word

Prime Pseudoword

123 124 1256 126

lettan

oro

Target Pseudoword

180 181 182 183

hygiene

oro

Target Word

... ... ... ...

W P P W

Strong Evidence f ev racy)

Strong Evidence for H1

1/100

1/30

1/10

1/3

1

3

10

30

100

DP DP+75ms DP+150ms DP+225ms DP+300ms

Baye

s Fa

ctor

Incorrect and confident

Correct and confident1

2

3

4

5

6

Mea

n R

atin

g Sc

ore

C D

A

0

40

Full Stimuli Length

0

40Pre-DP Length

0 100 200 300 400 500 600 700 800 900 10000

40 Post-DP Length

Time (ms)

B Deviation Point (DP)

l e tt er

l e tt uce

l e tt o

l e tt an

No.

of I

tem

s

DP DP+75ms DP+150ms DP+225ms DP+300ms

0 100 200 300 400 500 600Time (ms)

0.05

0.10

0.15

0.20

UnprimedWord

(Word Prime)Word Target

(Pseudo Prime)Word Target

Unprimed Pseudo

(Pseudo Prime)Pseudo Target

(Word Prime)Pseudo Target

1000

1050

1100

1150

1200

1250

Res

pons

e Ti

mes

(ms)

A

*** **

ns

**

0

Erro

r Rat

e

B

UnprimedWord

(Word Prime)Word Target

(Pseudo Prime)Word Target

Unprimed Pseudo

(Pseudo Prime)Pseudo Target

(Word Prime)Pseudo Target

**

*

Unprimed

Primed by Same

Lexicality

Primed by Different

Lexicality

Word

Pseudoword

**

**

ns***

0

10

20

30

40

50

~ -28 to -4ms

-400 -300 -200 -100 0 100

p = 1 p < .001

Unprimed ItemsWord-Primed Items

AA

mpl

itude

(fT/

cm)

Time (ms)

0

5

10

15

20

25

Am

plitu

de(fT

/cm

)

* *

B

Unprimeditems

Word-PrimedItems

Pseudoword-Primed Items

Pre-DP ROI Responses in Gradiometers

Unprimed - Primed by Word Pre-DP Responses in Gradiometers

DP

0

10

20

30

40

50

Am

plitu

de(fT

/cm

)

* ***

0

20

40

60

80

100

120

Am

plitu

de(fT

)

0

0.05

0.10

0.15

0.20

0.25

Sour

ce S

treng

th (a

rbitr

ary

units

)

Lexicality Effect in Magnetometers

Lexicality Effect in Source Space Localised to

Lexicality Effect in Gradiometers

Post-DP ROI Responses in Gradiometers

D

**

**

the Superior Temporal Gyrus (STG)

ns

Unprimed

Primed by Same

Lexicality

Primed by Different

Lexicality

Word

Pseudoword

UnprimedWord

(Word Prime)Word Target

(Pseudo Prime)Word Target

Unprimed Pseudo

(Pseudo Prime)Pseudo Target

(Word Prime)Pseudo Target

UnprimedWord

(Word Prime)Word Target

(Pseudo Prime)Word Target

Unprimed Pseudo

(Pseudo Prime)Pseudo Target

(Word Prime)Pseudo Target

UnprimedWord

(Word Prime)Word Target

(Pseudo Prime)Word Target

Unprimed Pseudo

(Pseudo Prime)Pseudo Target

(Word Prime)Pseudo Target

A

B

C

E Post-DP ROI Responses in Magnetometers

F Post-DP ROI Responses in Source Space

60

70

**

ns

** **

**

ns

**

ns

0

10

20

30

40

50

-1000 -500 0 500 1000

DP~313-956ms

Am

plitu

de (f

T/cm

)

Time (ms)

10.9

-10.9

fT/cm

Word

Pseudoword

Unprimed

Primed by Same

Lexicality

Primed by DifferentLexicality

0

DP~359-990ms

78.3

-78.3

fT

Am

plitu

de (f

T)

Time (ms)-1000 -500 500 1000

-40

0

40

80

120

*

-1000 0 500 10000

10

20

30

40

50DP~397-437ms

p = 1 p < .001

-500

Am

plitu

de (f

T/cm

)

0

10

20

30

40

50

Am

plitu

de (f

T/cm

)

UnprimedWord

(Word Prime)Word Target

(Pseudo Prime)Word Target

Unprimed Pseudo

(Pseudo Prime)Pseudo Target

(Word Prime)Pseudo Target

Unprimed

Primed by Same

Lexicality

Primed by DifferentLexicality

Word

Pseudoword

A B

Word

Pseudoword

ns **

Unprimed

Primed by Same

Lexicality

Primed by DifferentLexicality

Time (ms)

Lexicality-by-Priming Interaction in Gradiometers Interaction ROI Responses in Gradiometers

~

A

count

Association on Competitor Priming Difference (Word-Primed Word - Unprimed Word)

Association on Lexicality Difference (Pseudoword - Word)

Lexi

calit

y D

iffer

ence

in

Mag

neto

met

ers

Am

plitu

de (f

T)

78.3fT

count

Competitor Priming Difference in RTs (ms)

Com

petit

or P

rimin

g D

iffer

ence

in

Mag

neto

met

ers

Am

plitu

de (f

T)

Lexicality Difference in RTs (ms)

B Post-DP Pseudoword > Word Cluster in Magnetometers

β0 ***

β1 *** β1 ***

β0 ns