Alexandra Steinhilber

Julien Diard
email: julien.diard@univ-grenoble-alpes.fr

Emilie Ginestet

Sylviane Valdois

Visual attention modulates the transition from fine-grained, serial processing to coarser-grained, more parallel processing: a computational modeling study

Keywords: Visual attention, Bayesian modeling, Length effect, Orthographic learning 24

During reading acquisition, beginning readers transition from serial to more parallel processing. The acquisition of word specific knowledge through orthographic learning is critical for this transition. However, the processes by which orthographic representations are acquired and fine-tuned as learning progresses are not well understood. Our aim was to explore the role of visual attention in this transition through computational modeling. We used the BRAID-Learn model, a Bayesian model of visual word recognition, to simulate the orthographic learning of 700 4-to 10-letter English known words and novel words, presented 5 times each to the model. The visual attention quantity available for letter identification was manipulated in the simulations to assess its influence on the learning process. We measured the overall processing time and number of attentional fixations simulated by the model across exposures and their impact on two markers of serial processing, the lexicality and length effects, depending on visual attention quantity. Results showed that the two lexicality and length effects were modulated by visual attention quantity. The quantity of visual attention available for processing further modulated novel word orthographic learning and the evolution of the length effect on processing time and number of attentional fixations across repeated exposures to novel words. The simulated patterns are consistent with behavioral data and the developmental trajectories reported during reading acquisition. Overall, the model predicts that the efficacy of orthographic learning depends on visual attention quantity and that visual attention may be critical to explain the transition from serial to more parallel processing.

Introduction

Theoretical background

During learning to read, children move from slow serial processing to faster, more parallel word recognition [START_REF] Castles | Ending the Reading Wars: Reading Acquisition From Novice to Expert -Anne Castles, Kathleen Rastle, Kate Nation[END_REF]. This developmental trajectory was initially conceptualized as reflecting successive stages in reading acquisition [START_REF] Frith | Beneath the surface of developmental dyslexia[END_REF]. However, the self-teaching theory [START_REF] Share | Phonological recoding and self-teaching: sine qua non of reading acquisition[END_REF][START_REF] Share | Phonological Recoding and Orthographic Learning: A Direct Test of the Self-Teaching Hypothesis[END_REF][START_REF] Share | Self-teaching in normal and disabled readers[END_REF] proposed to replace this stage-based model by an item-based model according to which the transition from serial letter-by-letter to more parallel processing would apply at the level of each individual item word. According to this theory, the first time the child encounters a new printed word, this word would be serially processed through phonological recoding (i.e., translation of each orthographic unit into its spoken form). When phonological recoding is successful, then the input orthographic information can be memorized, leading to enriching the reader's word-19 specific orthographic knowledge. Although some or-20 thographic learning was demonstrated following a sin-21 gle encounter with the novel word, additional encoun-22 ters contribute to shape well-specified word-specific or-23 thographic representations [START_REF] Bowey | Phonological recoding and rapid orthographic learning in third-graders' silent reading: A critical test of the self-teaching hypothesis[END_REF] with higher VAS show higher reading fluency (Bosse 186 and [START_REF] Bosse | Influence of the visual attention span on child reading performance: a cross-sectional study[END_REF] and higher orthographic knowledge 187 [START_REF] Niolaki | Predictors of single word spelling in Englishspeaking children: A cross-sectional study[END_REF]. By reference to the "Theory of 188 Visual attention" [START_REF] Bundesen | Theory of visual attention[END_REF], VAS was found to 189 reflect the amount of visual attention available for mul-190 tielement processing [START_REF] Bogon | Parameter-based assessment of disturbed and intact components of visual attention in children with developmental dyslexia[END_REF]Dubois et al., 191 2010; [START_REF] Lobier | The Role of Visual Pro-cessing Speed in Reading Speed Development[END_REF]. Neuroimaging studies re-192 vealed that VAS related to the activation of the superior 193 parietal lobules within the dorsal attentional network 194 [START_REF] Lobier | Preorthographic character string processing and parietal cortex: A role for visual attention in reading[END_REF][START_REF] Peyrin | Superior parietal lobule dysfunction in a homogeneous group of dyslexic children with a visual attention span disorder[END_REF]Reilhac et al., 195 2013). Only a few behavioral studies have examined 196 whether VAS was involved in orthographic learning. In 197 an experiment conducted in adults, Ginestet et al. (2020) 198 showed that orthographic learning and eye movement 199 patterns across exposures were modulated by VAS. Us-200 ing a self-teaching paradigm without eye-movement 201 monitoring in children, [START_REF] Marinelli | The ability to learn new written words is modulated by language orthographic consistency[END_REF] showed (Juphard 209 et al., 2004;[START_REF] Valdois | A visual processing but no phonological disorder in a child with mixed dyslexia[END_REF][START_REF] Valdois | Phonological and visual processing deficits can dissociate in developmental dyslexia: Evidence from two case studies[END_REF]. In addition to (Valdois et al., 2021a) and the word 232 length effect in tasks of lexical decision, naming and 233 progressive demasking [START_REF] Ginestet | Modeling the length effect for words in lexical decision: The role of visual attention[END_REF]Saghiran 234 et al., 2020). The initial word recognition model was terns across repeated exposure to novel words by skilled readers. This was mainly due to the interaction of bottom-up sensory information modulated by visual attention and top-down lexical feedback from the newly acquired orthographic representation. However, the study focused on words of fixed length and attention quantity in the model was defined by its default value, thus remaining constant through simulations.

Our purpose in the present study was to provide a more plausible implementation of visual attention processing in BRAID-Learn. Indeed, behavioral studies have shown that VAS increased with age during childhood (from first to fifth grade) [START_REF] Bosse | Influence of the visual attention span on child reading performance: a cross-sectional study[END_REF] and that inter-individual variations in VAS accounted for differences in orthographic learning [START_REF] Ginestet | Orthographic learning of novel words in adults: effects of exposure and visual attention on eye movements[END_REF]. As VAS reflects the amount of visual attention available for processing [START_REF] Valdois | The visual attention span deficit in developmental dyslexia: Review of evidence for a visual-attention-based deficit[END_REF], this suggests that a plausible model of orthographic learning should be able to simulate the consequences of variations in visual attention quantity on processing. Our main contribution was thus to introduce a new visual attention quantity parameter in the model and examine the effect of attention quantity variations on orthographic learning through simulations.

Second, despite behavioral evidence that the length effect on word and pseudo-word reading decreases with reading expertise [START_REF] Marinelli | Costs and Benefits of Orthographic Inconsistency in Reading: Evidence from a Cross-Linguistic Comparison[END_REF][START_REF] Provazza | Double trouble: visual and phonological impairments in English dyslexic readers[END_REF][START_REF] Zoccolotti | Word length effect in early reading and in developmental dyslexia[END_REF], evidence is lacking on the evolution of length effects over repeated exposure to known or novel words in condition of orthographic learning. To fill this gap and provide new insights for future behavioral studies, we examined the model's predictions depending on the attention quantity available for processing when repeatedly exposed to known or novel words that varied in length. We used the model as an experimental substitute to study the length effect all other factors otherwise equal. For this purpose, a single set of words was considered as known words in a first series of simulations, in which the target words' orthographic information was part of the model's word knowledge, but as novel words in a second series of simulations conducted after removing target words' orthographic knowledge from the model database.

Assuming that higher visual attention quantity would allow the model to accurately identify more letters simultaneously, we expected longer stimuli to be more proficiently processed as attention quantity increases.

326

The BRAID-Learn model is a hierarchical, probabilistic 327 model, defined by a joint probability distribution over 328 its variables. As it is not relevant for the scope of the 329 current study, and as completely defining the model re-330 quires space, we do not describe entirely its mathemati-331 cal definition or its resulting properties here. However, 332 they can be found elsewhere [START_REF] Ginestet | Modélisation bayésienne et étude expérimentale du rôle de l'attention visuelle dans l'acquisition des connaissances lexicales orthographiques[END_REF]Phenix, 333 2018). Instead, in this section, we provide the necessary

Stimulus

Visual Attentional Submodel

Attentional modulation of information flow

Letter Perceptual Submodel

Dynamic models over letters

Perceived letters

Bidirectional information flow

Lexical Knowledge Submodel In previous simulations using either BRAID or anterior versions of the BRAID-Learn model, the total amount of attentional resources available for processing was implicitly equal to 1 (its default value). In the context of the present study, we have defined the parameter Q A to explicitly represent the attention quantity. It is a multiplicative coefficient applied to the distribution of attention with the precaution that the amount of attention allocated to each position cannot exceed 1.

Whatever the values of µ t A and σ t A , the higher Q A , the more attention is available for the processing of the attended letters, so that perceptual representations accumulate more identity evidence on these letters at each time-step, resulting, overall, in faster processing. The effect of parameter Q A on the attention value at each position is illustrated on Figure 2 (bottom plot).

Orthographic learning in BRAID-Learn

In the model, orthographic learning consists in the transfer of letter identity information from the perceptual submodel to the lexical knowledge submodel. As a result, orthographic learning is more efficient when perceptual information is of higher quality (i.e., providing enough information on letter identity at each position).

For the purpose of the current simulations, we consider that the model is given a task, in which the stimulus must be freely explored at each exposure, with after n + 1 exposures is as follows:

455

P(L n+1 | [W = w]) ∝ P(L n | [W = w]) × θ n × P(P | S ) + (1 -θ n ) × U .
456

If, on the contrary, the perceptual information does formed with an optimization approach was described 496 in a previous study [START_REF] Ginestet | Probabilistic modeling of orthographic learning based on visuo-attentional dynamics[END_REF]. However,

551

As previously [START_REF] Ginestet | Probabilistic modeling of orthographic learning based on visuo-attentional dynamics[END_REF], the entropy dif- scribed, attention dispersion σ t A is selected as a function of information accumulation speed, which is itself modulated by attention quantity Q A . Figure 3 (bottom plot)

illustrates the correlation between the two parameters on an independent experimental dataset. This dataset was composed of 200 8-letter words that were randomly extracted from the ELP database [START_REF] Balota | The English Lexicon Project[END_REF].

As illustrated, the smaller the visual attention quantity Q A , the smaller the adopted attentional dispersion σ t A .

In the rest of this paper, we consider Q A as our variable of interest, to study its effect on the predicted behavior, while σ t A is considered as a dependent, constrained variable.

Illustration: visuo-attentional exploration of the novel word "HOLPING". As can be seen on Figure 4 (left plot), during this period, identity evidence accumulates rapidly for the letter under the focus of attention and less so for other letters,

655

At the end of processing, lexical membership evaluation assessed the stimulus word as being a novel word, so that a new lexical representation was created. This lexical representation corresponds to knowledge accumulated on letter identity during processing. For the novel word "HOLPING", the new memory trace will be relatively complete, providing some identity information on all the letters of the input string. However, none of the input letters were perfectly identified at the first exposure (none reached Dirac probability) and some letters were better identified than others, thus leading the possibility to improve lexical knowledge for this item during subsequent exposures. To evaluate simulations, two measures characterizing processing at the first exposure are considered: a measure of processing time (in this example with the novel word "HOLPING", 353 iterations) and a measure of the number of attentional fixations during this processing time (here, 2).

Method

Material

Seven hundred words were selected from the model's lexical database to serve as stimuli for the current study.

The words varied in length from 4 to 10 letters. We used the Gurobi problem solver (Gurobi Optimization LLC, Beaverton, Oregon, USA; Gurobi Optimization, LLC 2021), to select one hundred words, for each length, so that they were matched in frequency and belonged to the Noun grammatical category. The selected words were of medium frequency, varying between 3.6 and 3.7 occurrences per million words (the average frequency of the whole lexicon was 3.63 occurrences per million words). To exclude any potential additional effect of neighborhood, all target word neighbors (i.e., all the words that differed from target words by a single letter) were excluded from the lexicon, thus resulting in a set of stimuli without orthographic neighbors. This removed 1,983 words from the 79,673 (2.5%) words of the lexicon. Removing the orthographic neighbors allowed studying the length effect while excluding confounding factors. Indeed, short words typically have many more orthographic neighbors than long words, so that the number of neighbors cannot be equated for sets of words that strongly differ in length.

For the current experiment, this set of 700 words was used twice. They were considered once as known words -thus belonging to the model's lexical word knowledge -and once as novel words, in which case they were removed from the model's lexical database. This was done to ensure a perfect matching between the characteristics of stimuli, independently of their status as known words or novel words; this also ensures that 706 stimuli considered as novel words are realistic, in the 707 sense that, for instance, they are orthographically legal.

708

The list of stimuli can be found in Appendix A.

787

The effect of Q A on stimuli processing is described in Quantity (Q A ): it was larger when Attention Quantity

928

The NAF was far more important for the longest than 929 the shortest words at the first (6.24 vs. 2.23 for the 10-

958

The advantage of computational modeling is to of- been attempted [START_REF] Bogon | Parameter-based assessment of disturbed and intact components of visual attention in children with developmental dyslexia[END_REF][START_REF] Bundesen | Theory of visual attention[END_REF]. [START_REF] Colotti | However, once again, direct evi-1136 dence that reduced processing speed or VAS affects the 1137 lexicality or length effects in developmental dyslexia is 1138 scarce[END_REF][START_REF] Joseph | Word length and landing position effects during reading in children and adults[END_REF][START_REF] Kliegl | Length, frequency, and predictability effects of words on eye movements in reading[END_REF] 1011 [START_REF] Loberg | Influence of reading skill and word length on fixation-related brain activity in school-aged children during natural reading[END_REF][START_REF] Mcdonald | Effects of number-of-letters on eye movements during reading are independent from effects of spatial word length[END_REF][START_REF] Rayner | Eye Movements in Reading and Information Processing: 20 Years of Research[END_REF]. [START_REF] Behrmann | However, we lack direct evidence that word 1117 processing and the oculomotor pattern in letter-by-letter 1118 readers are related to their perceptual processing speed 1119[END_REF][START_REF] Williams | Eye movements, word familiarity and vocabulary acquisition[END_REF] and show a 1014 larger length effect on these items than on known words 1015 [START_REF] Lowell | Word length effects on novel words: Evidence from eye movements[END_REF].

1016

In the same way, some general learning effects like [START_REF] Bosse | Developmental dyslexia: The visual attention span deficit hypothesis[END_REF][START_REF] Valdois | The visual attention span deficit in developmental dyslexia: Review of evidence for a visual-attention-based deficit[END_REF][START_REF] Valdois | The cognitive deficits responsible for developmental dyslexia: Review of evidence for a selective visual attentional disorder[END_REF]. ing [START_REF] Habekost | Clinical tva-based studies: A general overview[END_REF]. In particular, perceptual process-1112 ing speed is markedly reduced in letter-by-letter readers [START_REF] Zhao | The visual attention span deficit in chinese children with reading fluency difficulty[END_REF] suggest that VAS would contribute to the degree of reliance on serial processing.

To our knowledge, no study investigated the relationship between VAS (or processing speed) and the lexicality effect. [START_REF] Antzaka | Enhancing reading performance through action video games: the role of visual attention span[END_REF] examined skilled readers' pseudo-word reading in conditions of very brief presentation duration that prevented serial processing.

They showed that the adult readers who played action video games and had larger VAS than non-players could successfully read more pseudo-words through parallel processing. As the two groups of players and nonplayers were matched on text reading fluency, their findings might suggest that larger VAS is associated to a lower lexicality effect on processing times. Behavioral studies on orthographic learning should be particularly relevant to evaluate the link between visuoattentional resources and the shift from serial-to-moreparallel processing. Unfortunately, although available findings convincingly show incremental orthographic knowledge growth across repeated exposure to the same novel word [START_REF] Joseph | Examining incidental word learning during reading in children: The role of context[END_REF][START_REF] Joseph | Becoming a written word: Eye movements reveal order of acquisition effects following incidental exposure to new words during silent reading[END_REF][START_REF] Pagan | Learning words via reading: Contextual diversity, spacing, and retrieval effects in adults[END_REF][START_REF] Pellicer-Sanchez | Incidental L2 vocabulary acquisition from and while reading[END_REF], neither VAS nor perceptual processing speed were simultaneously measured. A single study provided some evidence of better orthographic learning skills in the group of participants with higher VAS [START_REF] Ginestet | Orthographic learning of novel words in adults: effects of exposure and visual attention on eye movements[END_REF].

Conclusion and perspectives

The main contribution of the present modeling study is twofold. First, the model provides a sophisticated description of the dynamics of visuo-attentional exploration during printed word processing. Second, it

shows how the interaction of visuo-attentional exploration and lexical knowledge contributes to the gradual strengthening of item-specific orthographic representations as learning progresses. Decrease of the lexicality and length effect across exposures suggests that the model captures some aspects of the transition from serial to more parallel processing. However, orthographic learning in the model is performed in the absence of any phonological processing. This drastically differs from previous modeling of orthographic learning through self-teaching [START_REF] Pritchard | A Computational Model of the Self-Teaching Hypothesis Based on the Dual-Route Cascaded Model of Reading[END_REF][START_REF] Ziegler | Modelling reading development through phonological decoding and self-teaching: implications for dyslexia[END_REF], in which successful phonological processing was critical to acquire new orthographic knowledge and explain the transition from serial to more parallel processing.

In this respect, BRAID-Learn more directly relates to the model of automaticity in reading proposed by [START_REF] Laberge | Toward a theory of automatic information processing in reading[END_REF]. LaBerge and Samuels , treatment, woodlouse, agreement, attention, candidate, cerebella, charabanc, charwoman, chiseller, cicatrice, developer, diathesis, driveller, duchesses, fooleries, forcemeat, forewoman, garrulity, germicide, gushiness, hothouses, ignorance, lactation, lazaruses, leucotomy, materials, noctiluca, obscurant, omnibuses, orangeade, packhorse, panatella, papyruses, peccaries, penknives, personnel, plasterer, poltroons, stokehold, striation, sucklings, suffusion, sulkiness, sunfishes, tailboard, telltales, territory, tigresses, wesleyans, youngster, zimmerman, zoologist 10-letter words: andromache, basketball, burckhardt, burlesques, categories, coagulants, conception, concretion, conversion, coronaries, corrigenda, crustiness, delphinium, employment, evaluation, flagellant, gingersnap, graphology, hobbyhorse, horseflesh, intactness, keypunches, lordliness, maidenhood, manageress, mortuaries, newsletter, pliability, postscript, preclusion, preference, properties, propionate, psychology, quintuplet, saleswomen, savageness, scrollwork, specialist, speleology, stonemason, submission, suspension, telephotos, terramycin, thrashings, threepence, truculence, undulation, vulgarians, alpenstock, anglomania, anointment, antiheroes, apoplexies, artfulness, assumption, bestiaries, braininess, businesses, clerestory, collieries, colloquies, conclusion, conference, dishabille, eisteddfod, foundation, giantesses, glossiness, goldfishes, hibiscuses, homoeopath, horselaugh, horsewoman, husbandman, industries, instrument, intendants, inwardness, irishwoman, mainstream, minuteness, parliament, petrolatum, preferment, presbytery, psalteries, reputation, resolution, rheumatics, scantlings, subsidizer, succulence, supplanter, swordstick, throughway, waterpower, workpeople, yellowness

  202that VAS contributed to promote orthographic learning. 203 Interestingly, VAS was further described as relating 204 to the length effect in reading. Lower length effects 205 on word and pseudo-word reading latencies were re-206 ported in individuals with higher VAS (van den Boer 207 et al., 2013) and exaggerated length effects were found 208 in individuals suffering from a VAS reduction

  210 visual acuity and crowding, these findings suggest that 211 visual attention might be involved not only in the way 212 words are processed (i.e., in a strict serial or more par-213 allel manner), but further in the capacity to acquire new 214 orthographic representations.215 1.2. The present study 216 The main contribution of the present study was to 217 investigate the role of visual attention in orthographic 218 learning using a modeling approach. For this purpose, 219 we started from BRAID, a word recognition model that 220 implements the three mechanisms of visual attention, 221 visual acuity and lateral interference that are known to 222 affect letter identification within strings (Ginestet et al., 223 2019; Phenix, 2018; Phénix et al., 2018; Saghiran et al., 224 2020). In BRAID, the spatial distribution of visual at-225 tention was modeled by a Gaussian probability distri-226 bution, so that the letters near the focus (i.e., peak) of 227 attention were better recognized while the number of 228 letters that were allocated attention was dependent on 229 attention dispersion. Computational studies have shown 230 that variations in visual attention dispersion modulated 231 word recognition

  More proficient processing was expected to result in shorter processing time (i.e., fewer iterations) and a smaller number of attentional fixations during the visuoattentional exploration of the input word. Novel words that do not benefit from top-down lexical knowledge at the first exposure, would be processed less efficiently 297 than known words; moreover this difference would be 298 magnified with low attention quantity. However, or-299 thographic learning being initiated at the first exposure, 300 novel word processing would improve across exposures 301 due to increasingly strengthened top-down support from 302 the newly acquired orthographic representation of the 303 target novel word. Assuming that higher attention quan-304 tity allows processing more letters efficiently, ortho-305 graphic knowledge acquisition would be more effective 306 at each exposure, leading to more proficient learning of 307 the novel word orthographic representation. This would 308 also result in a stronger length effect decrease, both on 309 processing times and number of attentional fixations, 310 across exposures as visual attention quantity is higher. 311 The rest of this paper is structured as follows. First, 312 we describe the BRAID-Learn model, with a particu-313 lar focus on the visual-attention component. Second, 314 we detail the material and procedure used in the exper-315 iment. Third, we present the simulation results, which 316 we discuss and relate to behavioral data.

  General outline of the model 319 The BRAID-Learn model shares the core of its ar-320 chitecture with the three-layer architecture used, among 321 others, by the classical Interactive Activation model 322 (IA; McClelland and Rumelhart, 1981). It also fea-323 tures an additional, original layer modeling visual at-324 tention, along with mechanisms for orthographic learn-325 ing. The resulting architecture is shown in Figure 1.

  four submodels. The letter sen-338 sory submodel focuses on low-level mechanisms in-339 volved in letter identification within the input string. 340 Letter identification at this level is modulated by inter-341 letter visual similarity, implemented through a let-342 ter confusion matrix adapted from experimental data 343 (Townsend, 1971) and by two mechanisms of visual 344 4 4 BRAID-Learn, box-model version, v2 "compacte"

397submodel.

  Due to the shape of the Gaussian distribu-398 tion, less evidence on letter identity accumulates in the 399 perceptual submodel when the distance from the atten-400 tional focus increases (Figure 2, top left). Letter iden-401 tity processing is further modulated by visual attention 402 dispersion. The smaller the attentional dispersion, the 403 more attention is concentrated around the attentional fo-404 cus, favoring efficient processing of a few letters, to the detriment of the others. The quality of perceptual representations is thus strongly modulated by the parameters of visual attention.

  no time-limit, until getting a precise enough perceptual representation of the input letter string. At the end of each exposure, the lexical membership mechanism evaluates whether perceptual information corresponds to a known word, by comparing the perceived letters to known words' letters. If this is the case, then the existing orthographic trace of the most likely word (a word recognition process, not detailed here, also proceeds in parallel) is updated by combining it with the perceptual representation of letters. Let us write (using a simplified notation) P(P | S ) the probability distribution about letters P given the stimulus letter sequence S , computed at the end of the exposure (i.e., the perceptual representation of letters), P(L n | [W = w]) the probability distribution over letters for word w in the set of known words W, after n exposures (i.e., the orthographic trace of word w), θ n the learning rate after n exposures (it decreases exponentially across exposures), and finally U the uniform distribution over the letter space. The probability distribution of the updated orthographic representation

457

  not correspond to any word in the lexicon, then, a new 458 orthographic trace is created. This trace is initialized 459 with the perceptual representation of letters at the end 460 of the first exposure. At each subsequent encounter with 461 the "novel" word, the corresponding orthographic trace 462 is gradually reinforced. Orthographic learning is said to 463 be successful when the trace of an already encountered 464 word is updated at subsequent encounters or when a new 465 trace is created for a novel word at the first encounter. 466 The influence of lexical feedback on letter perception 467 in the model is driven by lexical membership evalua-468 tion, so that the more likely the stimulus is to be a word, 469 the stronger the lexical feedback. As a result, grad-470 ual strengthening of the orthographic trace makes novel 471 word processing more and more efficient across expo-472 sures. A more detailed description of the mechanisms 473 of lexical feedback and trace creation and updating can474be found elsewhere[START_REF] Ginestet | Probabilistic modeling of orthographic learning based on visuo-attentional dynamics[END_REF].

  of visuo-attentional exploration in the 477 model is to favor efficient letter perception accumula-478 tion during processing. For this purpose, the model au-479 tomatically selects the visuo-attentional parameter val-480 ues that would allow gaining more information on letter 481 identity during a given exposure. The entropy of prob-482 ability distributions in the letter perceptual submodel is 483 computed to estimate the quality of perceptual repre-484 sentations. The entropy is close to maximal during the 485 first iterations of processing due to limited information 486 on letter identity within the input string. Conversely, it 487 would be small if letters were perfectly perceived (i.e., 488 if perceptual representations were Dirac probability dis-489 tributions). Thus, a decrease in entropy characterizes 490 letter identity information gain at the perceptual level. 491 Measuring entropy for each letter position allows iden-492 tifying those letters for which perceptual information is 493 lacking, thus indicating where attention should shift to 494 significantly decrease entropy. How this might be per-495

  space to predict the entropy de-499 crease for all possible combinations of visual attention 500 parameters. This was computationally costly. Here, 501 instead, we used a heuristic-based, approximate algorithm that provides visuo-attentional exploration behav-503 iors that are qualitatively comparable to those produced 504 by our previous algorithm (a quantitative assessment of 505 this approximation is beyond the scope of the current 506 paper). 507 Here, we more specifically focus on how location of 508 the attentional focus moves over the input string to in-509 crease the gain of information on letter identity at each 510 exposure and boost perceptual evidence accumulation. 511 We then expose how visual attention dispersion is af-512 fected during processing and then provide an illustra-513 tive example, through the processing of the novel word 514 "HOLPING". 515 Displacement of the visuo-attentional focus during ex-516 ploration. The heuristic algorithm proceeds as follows. 517 Initially, the position of the gaze and attentional fo-518 cus µ t A is set according to stimulus length and atten-519 tional quantity (note that gaze position always coincide 520 with the attentional focus position in the simulations). 521 Following eye movement behavioral findings (Rayner, 522 1998; Vitu et al., 1990), the attentional focus is located 523 slightly left of the word center, except for the smallest 524 value of attention quantity (Q A = 0.5), for which the 525 initial position is located on the first letter of the word. 526 This shift towards the beginning of words was motivated 527 by the fact that virtually a single letter could be pro-528 cessed at once in this condition, so that no information 529 could accumulate on the initial letter of the input stimu-530 lus when the focus of attention was located farther away 531 on the right. 532 Then, at each time-step, the difference in entropy, 533 between the probability distributions of the perceptual 534 representation of the letter under the attentional focus 535 and all other positions is computed. When this differ-536 ence exceeds a given threshold T shift (empirically set 537 to 1.5 nats, with 1 nat the unit for information quan-538 tity when entropy is computed using the natural loga-539 rithm, as we do, instead of the more usual bit when it 540 is computed with the base 2 logarithm), then a visuo-541 attentional shift is initiated towards that position. As 542 a result, except for the initial position of the focus of 543 attention, all subsequent displacements of the attention 544 focus are computed by the model depending on the 545 quality of identity evidence previously accumulated at 546 the perceptual level. As in the terminology of eye move-547 ment studies, we will refer to time intervals when atten-548 tion does not move as an "attentional fixation", between 549 attentional displacements, and therefore count the num-550 ber of attentional fixations.

Fig. 3 :

 3 Fig. 3: Illustration of the modulation of visual attention dispersion during exploration. Top left: Evolution of the letter entropy over time. The green curve represents the reference entropy profile; the other two represent entropy evolution when the model is presented with the word "IMAGE", for two different values of Q A . Top right: Values of dispersion parameter σ t A selected by the visual exploration algorithm, as a function of the entropy gain ratio between stimulus and reference processing at the end of the first attentional fixation. Bottom: Values of dispersion parameter σ t A selected by the visual exploration algorithm, as a function of parameter Q A . Color indicates how many words used each value of σ t A .

  Figure 4 illustrates the dynamics of visuo-attentional exploration (right plot) and how letter identity information evolves over time at the perceptual level (left plot), for the novel word "HOLPING" at the first exposure, with attention quantity Q A = 1. At the beginning of processing (iteration 0), the distribution of visual attention is characterized by a focus aligned on the third letter of the 7-letter input word and a default value dispersion σ t A = 1.75. During the 208 iterations of this first attentional fixation, letter identity information gradually accumulates at the perceptual level.

Fig. 4 :

 4 Fig.4: Illustration of the visuo-attentional exploration algorithm on stimulus "HOLPING". Left plot: Probability of perceived letters (yaxis) at each position, as a function of simulated time (x-axis). Each curve represents the probability value of the most likely letter hypothesis, at each position. Curves are color coded according to position (green curve for position 1, yellow curve for position 7, etc.). Curves are in thick lines when the focus of visual attention is on the position that they correspond to. Right plot: Evolution over time (y-axis) of the visuo-attentional distribution over the stimulus positions (x-axis). Letters at each positions are recalled at the bottom of the plot ("H" in position 1, etc.) Time indices indicated on the y-axis are beginnings of attentional fixations, for which the visuo-attentional distribution is the one depicted by the corresponding box plots, with its dispersion indicated by a number (e.g., between iterations 0 and 208, the focus of attention was on letter L at position 3; attention dispersion was 1.75). Box height indicates the attention allocated at each position).

  used to simulate the visuo-attentional 711 exploration of the 700 stimuli, twice each, as each was 712 once considered a known word and once as a novel 713 word, for a total of 1,400 simulations. This was re-714 peated for seven possible values of attention quantity 715 Q A (0.5, 0.75, 1, 1.25, 1.5, 1.75 and 2). In each sim-716 ulation, the same stimulus was presented five times to 717 the model: at each of these exposures, we simulated the 718 visual-attentional exploration of the stimulus, and the 719 subsequent updating of an existing orthographic trace, 720 or the creation of a new one. 721 From each simulated exposure, we measured two 722 variables of interest. First, a measure of Processing 723 Time (PT) was computed as the number of iterations 724 occurring before the termination criterion was met. Sec-725 ond, we measured the Number of Attentional Fixations 726 (NAF) performed by the model in the same time inter-727 val. The length effect was quantified by the slope be-728 tween performance on the two measures of interest for 729 the shortest and the longest items, item length being eswith a Gamma family and an inverse link.735To select the most appropriate link function, we tested 736 several possibilities ("identity", "inverse" and "log") 737 and analyzed the results of the subsequent models: 738 we chose the model that minimized both the result-739 ing AIC(Akaike Information Criterion;[START_REF] Akaike | Maximum likelihood identification of Gaussian autoregressive moving average models[END_REF] 740 and the Fisher Scoring (number of iterations required 741 for the model to converge). To analyze the NAF, we 742 followed the suggestion of Harris et al. (2012) and 743 used a generalized Poisson regression (vglm function; 744 R Core Team 2020), as the data were underdispersed 745 (dispersiontest function; R Core Team 2020

788

  the next two sections. We first focus on processing at the 789 first exposure to describe how Attention Quantity affects 790 PT and the NAF depending on Item Type (novel words 791 vs. known words) and Item Length (from 4 to 10 let-792 ters). Given the high level of performance of the model 793 for known words from the first exposure, in the second 794 section, we focus on novel word processing alone to de-795 scribe how the Item Length effect evolves across the five 796 exposures depending on Attention Quantity. Note that 797 all the results reported in the following sections were 798 derived from the same data set using a single statistical 799 model for each measure. They are presented in different 800 sections for the sake of clarity.

  Q A on PT and NAF for the two types of 804 items at the first exposure is illustrated in Figure 5. Keep 805 in mind that stimuli are of variable length, and thus in-806 duce very different PT and NAF. For the coherence of 807 the figure, and since we are not focusing on the length 808 effect for now, both PT and NAF were normalized by 809 word length. Novel words were processed slower than 810 known words (β = -5.6e-4, t = -14.70, p < .001). Re-811 gardless of Item Type, average PT decreased when At-812 tention Quantity increased (β = 6.5e-4, t = 59.60, p < 813 .001), varying from 188 iterations per letter on aver-814 age for Q A = 0.5 to 59 iterations per letter on av-815 erage for Q A = 2. More importantly, the Attention 816 Quantity (Q A ) by Item Type interaction was significant 817 (β = -1.1e-4, t = -8.80, p < .001), showing that PT 818 decreased more for novel words than for known words 819 as the Attention Quantity increased. Average PT var-820 ied from 261 iterations per letter for Q A = 0.5 to 70 821 iterations per letter for Q A = 2 for the novel words and 822 from 127 to 47 iterations per letter for the known words. 823 As a result, the difference in PT between known words 824 and novel words, that is the lexicality effect on PT, de-Q A ) by Item Type interaction was 829 significant (β = -0.078, z = -3.24, p = .001). Post-hoc 830 analysis showed that Attention Quantity (Q A ) affected 831 NAF for the novel words (β = -0.095, z = -6.47, p < 832 .001) but not for the known words (β = -0.016, z = 833 -0.86, p = .392). With respect to novel words, aver-834 age NAF varied from 1.17 NAF per letter for Q A = 0.5 835 to 0.44 NAF per letter for Q A = 2. With respect to 836 known words, average NAF varied from 0.52 NAF per 837 letter for Q A = 0.5 to 0.33 NAF per letter for Q A = 2. 838 Thus, the lexicality effect on NAF was modulated by 839 Attention Quantity, so that the difference in NAF be-840 tween known and novel words decreased when Atten-841 tion Quantity (Q A ) increased. Otherwise, the main Item 842 Type effect was significant; more attentional fixations 843 were observed on novel words than on known words 844 (β = 0.28, z = 2.87, p = .004). 845 At the first exposure, the effect of Q A on PT and NAF 846 for the two types of items depending on Item Length 847 is illustrated in Figure 6. This figure illustrates the 848 same data as the previous one, and corresponds to the 849 same statistical analyses. However, the graphical rep-850 resentation here focuses on the impact of Item Length 851 on the two measures of PT and NAF. With respect to 852 PT, the Item Length effect was modulated by Attention853

Fig. 5 :

 5 Fig. 5: Processing Time (PT, left) and Number of Attentional Fixations (NAF, right) per letter (y-axes), depending of Item Type (known words, in light blue, or novel words, in dark blue), as a function of visual Attention Quantity (Q A values, x-axes). For each measure, a "violin plot" depicts the distribution of obtained values, with wider portions indicating higher density of values. The central dot represents the median of the distribution of values.

Fig. 6 :Fig. 7 :Fig. 8 :Figure 8

 6788 Fig.6: Measures of visuo-attentional exploration (PT, top row and NAF, bottom row, on y-axes), at the first exposure, for known (left column) and novel words (right column), as a function of stimulus length (x-axes) and Attention Quantity Q A (colored curves, from blue (Q A = 0.5) to pink (Q A = 2.0)). Error bars represent the data's standard deviation. The curves are slightly shifted horizontally from each other to ensure that the error bars are readable in the presence of overlap.

930 and 4

 4 -letter words respectively) than at the fifth expo-931 sure (3.08 vs. 1.72) and the NAF difference between the 932 longest and the shortest words decreased faster across 933 Exposures when (Q A ) was lower. 934 5. Discussion 935 In the present paper, computational modeling was 936 used to examine the role of visual attention in the transi-937 tion from more serial to more parallel letter-string pro-938 cessing. We used the BRAID-Learn model, a model 939 of orthographic processing that includes word recogni-940 tion and orthographic learning mechanisms, as an ex-941 perimental substitute. 942 Simulations showed that lexicality and length effects 943 on PT and NAF decreased when larger visual attention 944 quantity was available for processing. Orthographic 945 learning was less successful when visual attention quan-946 tity was smaller and the input novel word longer. The 947 evolution patterns of orthographic processing across ex-948 posures were also affected by visual attention quantity. 949 Repeated exposure to the same novel word resulted in 950 a larger decrease of PT and NAF when the quantity of 951 visual attention was smaller. In the same way, smaller 952 visual attention quantity yielded a larger decrease of the 953 length effect on PT and NAF with repeated exposure 954 to the same novel word. Overall, the model predicts 955 that variations in visual attention quantity would signif-956 icantly affect letter string processing and orthographic 957 learning.

959

  fer the opportunity to examine the effect of a single pa-960 rameter manipulation, here visual attention quantity Q A , 961 on orthographic processing while controlling for all the 962 other effects, either inherent to the system (like visual 963 acuity or lateral interference) or to the input stimuli (like 964 frequency or lexical neighborhood). However, isolating 965 a single mechanism in this manner is easier in a compu-966 the amount of visual attention available for processing 968 is not easy to measure in humans, even though estimat-969 ing it in reference to the Theory of Visual Attention has 970

971

  Therefore, to evaluate the plausibility and relevance 972 of the model's predictions, we will concentrate on the 973 orthographic processing mechanisms that are responsi-974 ble for the simulated lexicality and length effects, first 975 without considering the effect of Q A variations. Second, 976 provided a close relationship between the model's gen-977 eral predictions and behavioral findings, we will discuss 978 to what extent the evolution of the lexicality and length 979 effects on PT and NAF depending on visual attention 980 quantity provides insights on the serial-to-more-parallel 981 transition and is compatible with available behavioral 982 evidence. 983 5.1. Lexicality and word length effects irrespective of 984 Q A 985 We focused on the two effects of lexicality and word 986 length, as markers of serial processing. The lexicality 987 effect in the model directly follows from top-down influ-988 ence of word knowledge that speeds up letter identifica-989 tion at the perceptual level and facilitates processing for 990 the input letter strings that match an orthographic repre-991 sentation. The length effect in the model follows from 992 the fact that the same amount of visual attention spreads 993 over the input letter string whatever its length, so that 994 less attention is allocated to each letter in longer stim-995 uli. As a result, letter identity information accumulates 996 less efficiently at the perceptual level for longer than 997 for shorter stimuli, which increases PT and NAF during 998 visuo-attentional exploration of the input string. How-999 ever, partial identity information accumulated at the per-1000 ceptual level through visuo-attentional exploration can 1001 be compensated by top-down lexical information, so 1002 that known words suffer lesser length effects than novel 1003 words, that have no orthographic representation (at the 1004 first exposure). These simulated length and lexicality 1005 effects, and their interaction, are coherent with many be-1006 havioral findings from studies on eye movements, word 1007 recognition and reading (Barton et al., 2014). In par-1008 ticular, longer fixation duration and a higher number of 1009 fixations are reported in longer than shorter words (Hau-1010 tala et al.

1012

  Readers spend more times fixating novel words (Chaf-1013 fin et al.

  in the present paper was to 1061 evaluate the influence of visual attention quantity on or-1062 thographic processing. The model predicts that the two 1063 lexicality and length effects are modulated by visual at-1064 tention quantity, thus suggesting that the total amount 1065 of visual attention available for processing further con-1066 tributes to the serial-to-more-parallel processing transi-1067 tity deployed for processing at the first attentional fix-1069 ation modulates the speed of letter identity perceptual 1070 identification and the number of letters that fall under 1071 the deployed attention. At the second fixation, visuo-1072 attentional dispersion is modulated according to previ-1073 ous information accumulation speed. Fast accumulation 1074 of identity information for the higher Q A values leads to 1075 adopt larger visual attention dispersion. A higher num-1076 ber of letters are then simultaneously identified at each 1077 new fixation, leading to more parallel processing. To the 1078 contrary, attentional dispersion is narrowed when iden-1079 tity information accumulated laboriously at the first at-1080 tentional fixation. Then, only a few letters can be suc-1081 cessfully identified at each subsequent fixation, leading 1082 to more serial processing. 1083 Although it is difficult to directly measure the vi-1084 sual attention quantity in humans, the impact of percep-1085 tual processing speed and multi-letter parallel process-1086 ing on behavioral performance have been investigated 1087 by reference to two theoretical frameworks, namely the 1088 Theory of Visual Attention (Bundesen, 1990; Bundesen 1089 and Habekost, 2014) and that of visual attention span 1090

1091

  Moreover, behavioral studies have established a link be-1092 tween perceptual processing speed and VAS, suggesting 1093 that lower VAS performance related to slower percep-1094 tual processing[START_REF] Bogon | Parameter-based assessment of disturbed and intact components of visual attention in children with developmental dyslexia[END_REF][START_REF] Dubois | Fractionating the multicharacter processing deficit in developmental dyslexia: Evidence from two case studies[END_REF] 1095[START_REF] Ginestet | Orthographic learning of novel words in adults: effects of exposure and visual attention on eye movements[END_REF][START_REF] Lobier | The Role of Visual Pro-cessing Speed in Reading Speed Development[END_REF]. The plausi-1096 bility of the model's predictions with respect to varia-1097 tions in visual attention quantity can therefore be ques-1098 tioned in the light of available behavioral evidence on 1099 how perceptual processing speed and VAS affect letter-1100 string processing and orthographic learning. 1101 The model predicts that individuals with smaller vi-1102 sual attention quantity would be more prone to rely on 1103 serial processing, thus showing higher lexicality and 1104 length effects on processing time and number of fix-1105 ations while reading. The studies carried out by ref-1106 erence to the Theory of Visual Attention (Bundesen, 1107 1990; Bundesen and Habekost, 2014) provide some 1108 support to this prediction. Perceptual processing speed 1109 was consistently found reduced in brain-damaged in-1110 dividuals showing excessive reliance on serial process-1111

(

  1974) emphasized the role of visual attention in the processing and memorization of increasingly large or-1223 thographic units during the course of learning to read. 1224 In the same way, in BRAID-Learn, the amount of vi-1225 sual attention quantity influences the size (in letter num-1226 ber) of the processed units (from individual letters to 1227 the whole word letter-string), so that the smaller the at-1228 tention quantity, the smaller the number of letters pro-1229 cessed as a whole. However, in the absence of imple-1230 mented phonological component, the predictive power 1231 of BRAID-Learn is limited. Addition of a phonologi-1232 cal module in BRAID-Learn, or the addition of visuo-1233 attentional processes in dual-route self-teaching models ness

  Top right: attention distribution for a few values of parameter σ t A , which defines attentional dispersion. Bottom: attention distribution for a few values of parameter Q A , which defines the total attention quantity available for processing.

		Dynamic models over lexical variables
		Recognized word
				Fig. 2: Illustration of visuo-attentional distributions on the 5-letter
				input word "IMAGE". Top left: attention distribution for a few values
				of parameter µ t A , which defines the position of attentional focus at time
		Fig. 1: Conceptual representation of the BRAID-Learn model. The	373	t. mechanism that evaluates lexical membership and al-
		four submodels are represented as colored blocks, and arrows repre-	374	lows determining whether the input stimulus is a known
		sent information flow or specific processes (blue arrows). This struc-ture is illustrated here on a 5-letter stimulus. See text for details.	375 376	word or a novel word. BRAID-Learn further includes a visuo-attentional
			377	submodel that controls the flow of information from the
	345 346 347 348	acuity and lateral interference. The acuity gradient pe-nalizes letter identification proportionally to the dis-tance of the letter to gaze position. Letter identification is further affected by interference from neighboring let-	378 379 380 381	sensory to the perceptual submodel. Given the key role of visual attention in letter identity processing and or-thographic learning, this submodel is described in more detail below.
	349	ters so that inner letters suffer more interference than
	350	outer letters.	382	2.2. The visuo-attentional submodel
	351	4 In the letter perceptual submodel, evidence about the	383	The visuo-attentional submodel acts as a filter be-
	352	identity of letters accumulates over time, to build dy-	384	tween the sensory and the perceptual submodels. Its
	353	namically evolving perceptual representations of the let-	385	main element is a Normal probability distribution, noted
	354 355	ters in the input string. These perceptual representations receive sensory information from the letter sensory sub-	386 387	P(A t | µ t A σ t A ), whose parameters µ t A and σ t A describe how visual attention is spatially distributed over the in-
	356 357	model, in a bottom-up manner. They are further influ-enced by top-down information from the lexical knowl-	388 389	put letter string: the µ t A parameter represents the po-sition of the focus of visual attention at time t (see
	358 359	edge submodel, so that identity information accumu-lates faster at the perceptual level for letters that be-	390 391	Figure 2, top left), whereas the standard deviation σ t A parameter characterizes visual attention dispersion (see
	360	long to previously known words. (Note that, in the con-	392	Figure 2, top right). Each letter in the input letter string,
	361	text of the current orthographic learning experiments,	393	and therefore each position of the stimulus, is allocated
	362	top-down knowledge about gradually improving ortho-	394	a certain amount of visual attention, defined by this
	363	graphic traces is facilitatory. However, this is not a gen-	395	probability distribution. The amount of visual attention
	364	eral property of the model. When top-down information	396	allocated to each position defines the amount of sensory
	365	from lexical knowledge is discongruent with the stimu-		evidence propagated from the sensory to the perceptual
	366	lus letters, for instance in priming simulations, it can
	367	slow down letter perception.)
	368	The lexical knowledge submodel is configured to rep-
	369	resent the spellings of a large database of words. The
	370	current simulations were run using a dataset of 79,673
	371	English words, taken from the English Lexicon Project
		(Balota et al., 2007). The submodel further includes a

Table 1 :

 1 Successful learning rate, in the learning simulation, for novel words (successful learning rate is 1.0 for words).

	Q A	Length	4L	5L	6L	7L	8L	9L	10L
	0.5		1.0	1.0	1.0	0.88	0.81	0.68	0.56
	0.75	1.0	1.0	1.0	0.97	0.95	0.80	0.73
	1		1.0	1.0	1.0	0.96	0.96	0.83	0.71
	1.25	1.0	1.0	1.0	0.97	0.97	0.85	0.80
	1.5		1.0	1.0	1.0	0.97	0.99	0.85	0.85
	1.75	1.0	1.0	1.0	0.97	0.99	0.91	0.88
	2		1.0	1.0	1.0	0.98	0.98	0.93	0.89

Open access availability for Supplementary Material files: https://osf.io/g8cbf/.

Attention Quantity Q A values. For novel words, ortho-768 graphic learning sometimes failed. This occurred when was smaller (β = -4.5e-5, t = -33.91, p < .001).

855

There was no larger Item Length effect on PTs for the 856 novel words than for the known words, as shown by the 857 non significant Item Type by Item Length interaction 858 (β = -2.8e-6, t = -0.61, p = .545). This is due to 4-letter words: acme, arak, ares, barb, bess, boon, brig, cell, chin, coup, dade, deer, dill, dyne, enos, gale, gaud, gent, hemp, joss, june, kivu, lear, leek, loch, buri, cony, lura, mali, marr, mink, moth, nara, huns, oath, peru, quod, role, rook, scut, slat, soul, tarn, tofu, topi, tosh, tree, vial, womb, yeas, aide, ainu, aryl, attu, oleg, bert, body, buna, byes, caff, capn, miry, dodd, dram, edam, feat, feds, fogg, ludo, fore, gogo, gown, grot, grub, hake, hume, husk, koan, lakh, pron, menu, mort, nett, orly, oxen, pane, pomp, quay, sham, sims, skit, talc, togs, tory, vail, vats, volt, weft, wold, yule 5-letter words: arabs, aroma, aspen, babel, baker, balsa, berry, blues, cache, chump, codex, compo, crust, dicks, dildo, flank, drake, fanny, dolly, greer, harem, horne, jonah, keane, lewis, loren, macon, males, maple, oasis, ozone, pansy, penis, photo, rabbi, clasp, rotor, rover, rumba, skull, sloan, snack, syrup, tamil, teeth, toque, trier, uncle, vigil, wayne, anvil, aorta, argos, aspic, atoll, attic, aught, blood, bourn, canoe, carey, chris, cleva, della, dinar, ernie, ether, folio, foyer, gibby, gusto, heron, highs, ivory, jones, katie, kurus, levin, maine, navvy, rhode, robot, sabra, sadie, saran, scuba, sewer, shank, sioux, skiff, slush, spoof, sprig, swath, tosca, twine, walls, weiss, whorl, wilde 6-letter words: ablaut, anklet, arrack, beeves, borage, centum, cicala, cotman, cowmen, czechs, dalton, dowser, flagon, gigolo, hotpot, howdah, icemen, kronor, krutch, kummel, lugger, mender, noshes, office, oxcart, pignut, poppet, ranker, rioter, sacker, sateen, scrota, seekin, shensi, stamen, street, sundew, tatian, tibiae, tomtom, torrio, tumuli, xavier, yeoman, yogurt, yonder, zenith, zephyr, zinnia, zombie, andrus, beirut, bistro, bustle, cactus, cartel, catgut, chukka, cicero, delvin, dibble, doddle, duenna, dustup, emblem, escudo, family, friend, fulmar, gasmen, gooier, guizot, hangup, hannah, hippie, hopper, howell, idiocy, jasper, lemons, newton, orgasm, persia, pulsar, quincy, rapist, rogues, rotter, runnel, sayers, schulz, sidney, sinker, strang, strata, varian, volume, wicket, wilson, yokuts 7-letter words: affaire, alumnae, anthill, autarky, barnaba, blanket, blemish, brooder, buildup, clayton, colonus, waiving, corrals, country, crystal, dawdler, decoder, divider, doublet, dresser, economy, egerton, erosion, evasion, firearm, flyways, francis, gingham, gouache, goulash, grenada, hormone, imagery, inkling, longbow, macedon, maurice, nemesis, newport, newsmen, oregano, panoply, pedicel, poussin, prowess, referee, seaport, stratum, virgule, vulture, antenna, babcock, beaches, bloomer, booklet, buttock, cabbage, calypso, concept, dilemma, diploma, dorothy, forrest, garrett, gazelle, gestapo, grafton, heckler, heywood, jackson, jenkins, lincoln, liqueur, luggage, mailman, mankind, mongrel, neilson, oranges, pattern, phantom, pitcher, pitfall, pointer, pompano, pretext, privacy, provost, sangria, schmidt, siberia, slipper, snowman, stinger, surgery, syrians, tremolo, untruth, valerie, virgins, 8-letter words: besieger, bombsite, bootlace, bullhide, cajolery, causerie, clifford, decoking, division, entresol, eyetooth, families, findsome, fireclay, gallants, glumness, gripsack, icefloes, infamies, lifebelt, lifebuoy, lummoxes, majority, mastoids, medicine, orchises, overplus, parterre, prattler, property, psalmody, putsches, quirinal, raciness, raillery, rankness, rockhall, tenpence, throstle, tidemark, toadyism, tollgate, transfer, turnspit, wigmaker, wineskin, wiriness, yugoslav, zeppelin, zimbabwe, addendum, botulism, boutique, bulgaria, cambodia, cassette, causeway, churches, commando, compiler, cupboard, deathbed, detritus, eyepiece, finisher, haitians, handbook, heraldry, holiness, ideology, instance, laxative, licensee, machismo, metaphor, musician, namesake, nebraska, plastics, pretense, proposal, roadster, rushmore, seedling, sherlock, softness, specimen, speeches, stimulus, tamarind, tasmania, tendency, theology, treasury, ugliness, universe, werewolf, westwood, winfield, woodside 9-letter words: ablatives, australia, blowflies, blutwurst, bourguiba, bowerbird, bridewell, cominform, companies, contriver, costumier, crimplene, cuckoldry, deauville, exhusband, flageolet, flashcube, abasement, fortifier, identikit, lobscouse, lowlander, lowliness, luckiness, lumbermen, luridness, lustiness, mistiness, moralizer, newspaper, nunneries, oratories, orrisroot, patricide, phagocyte, phalanges, polyether, punctilio, repletion, sandshoes, scenarist, september, sixtieths, smoochers, stridence, sunniness, technique, timid-