Attentional engagement deficits in dyslexic children

Neuropsychologia 48 (2010) 3793–3801

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Attentional engagement deficits in dyslexic children Milena Ruffino a,b , Anna Noemi Trussardi a,c , Simone Gori a , Alessandra Finzi d , Sara Giovagnoli e , Deny Menghini d,f , Mariagrazia Benassi e , Massimo Molteni b , Roberto Bolzani e , Stefano Vicari d,f , Andrea Facoetti a,b,c,∗ a

Dipartimento di Psicologia Generale e Centro di Scienze Cognitive, Università di Padova, Italy Unità di Neuropsicologia dello Sviluppo, Istituto Scientifico “E. Medea” di Bosisio Parini, Lecco, Italy Unità di Neuropsichiatria Infantile, Azienda Ospedaliera di Bergamo, Italy d Dipartimento di Neuroscienze, Istituto Scientifico “Ospedale Pediatrico Bambino Gesù” di Roma, Italy e Dipartimento di Psicologia, Università di Bologna, Italy f Università Europea di Roma, Italy b c

a r t i c l e

i n f o

Article history: Received 22 February 2010 Received in revised form 29 August 2010 Accepted 3 September 2010 Available online 15 September 2010 Keywords: Reading disorders Spatio-temporal attention Sublexical route Graphemic parsing Temporo-parietal junction

a b s t r a c t Reading acquisition requires, in addition to appropriate phonological abilities, accurate and rapid selection of sublexical orthographic units by attentional letter string parsing. Spatio-temporal distribution of attentional engagement onto 3-pseudoletter strings was studied in 28 dyslexic and 55 normally reading children by measuring attentional masking (AM). AM refers to an impaired identification of the first of two sequentially presented masked objects (O1 and O2). In the present study, O1 was always centrally displayed, whereas the location of O2 (central or lateral) and the O1–O2 interval were manipulated. Dyslexic children showed a larger AM at the shortest O1–O2 interval and a sluggish AM recovery at the longest O1–O2 interval, as well as an abnormal lateral AM. More importantly, these spatio-temporal deficits of attentional engagement were selectively present in dyslexics with poor phonological decoding skills. Our results suggest that an inefficient spatio-temporal distribution of attentional engagement – probably linked to a parietal lobule dysfunction – might selectively impair the letter string parsing mechanism during phonological decoding. © 2010 Elsevier Ltd. All rights reserved.

1. Introduction Developmental dyslexia (DD) is a neurobiological disorder characterized by a difficulty in reading acquisition despite adequate intelligence, conventional education and motivation (American Psychiatric Association [APA], 1994). The prevailing view supports the hypothesis that DD results from a specific deficit of auditory-phonological perception, representation and phonological memory (see Gabrieli, 2009; Goswami, 2003; Tallal, 2004; Vellutino, Fletcher, Snowling, & Scanlon, 2004; Ziegler & Goswami, 2005, for reviews). Children and adults with DD show, indeed, deficits in the representation and manipulation of phonological information (e.g., poor speech-sound awareness, slow lexical retrieval and poor phonological short-term memory; see Ramus, 2003, for a review). These phonological deficits could interfere with one of the most criti-

∗ Corresponding author at: Dipartimento di Psicologia Generale, Università di Padova, Via Venezia 8, 35131 Padova, Italy. Tel.: +39 049 827 6675; fax: +39 049 827 6600. E-mail address: [email protected] (A. Facoetti). 0028-3932/$ – see front matter © 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.neuropsychologia.2010.09.002

cal skills for successful reading acquisition, such as phonological decoding (Share, 1995; Ziegler & Goswami, 2005). Phonological decoding is based on letter-sound reading and it allows children to make the connection between novel letter string sequences and words that are already stored in their phonological (spoken word) lexicon. The ability to assemble the phonological code for any string of letters allows the child to successfully decode and construct orthographic entries for thousands of new words during the first years of education (Share, 1995, 1999, 2004). Phonological decoding is the primary procedure used by beginning readers both for aloud and silent reading (e.g., Sprenger-Charolles, Siegel, Béchennec, & Serniclaes, 2003). A typical measure for phonological decoding is given by performance in nonword reading. Nonword reading skills are consistently impaired in DD children across different languages (Ziegler, Perry, Wyatt, Ladner, & SchülteKorne, 2003). Efficient phonological decoding requires accurate representations at the phoneme level (e.g., Harm & Seidenberg, 1999; Perry, Ziegler, & Zorzi, 2007; Ziegler & Goswami, 2005). In fact, a low-level auditory processing deficit in children with DD seems to impair speech-sound perception and more specifically its sublexical processing (see Goswami, 2003; Tallal, 2004; Wright, Bowen, & Zecker,

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2000, for reviews), which, in turn, would affect grapheme-tophoneme mapping and phonological short-term memory (Ramus, 2003). Goswami et al. (2002) reported, indeed, that children with DD are relatively insensitive to the rise times of amplitude envelope onsets in acoustic signals compared to normally reading children. The ability to detect this acoustic signal feature provides a non speech-specific mechanism for segmenting syllable onsets and rimes, a crucial precursor to the development of phoneme segmentation skills (Goswami et al., 2002). Experimental evidence has provided support for the idea that auditory attention is necessary for learning phonetic discriminations based on acoustic cues. This suggests that speech signal segmentation requires rapid shifting of auditory attention (Gordon, Eberhardt, & Rueckl, 1993; Francis, Kaganovich, & Driscoll-Huber, 2008). Consistently, auditory attention is impaired in children with DD (e.g., Asbjørnsen & Bryden, 1998; Facoetti, Trussardi, et al., 2010; Geiger et al., 2008; Renvall & Hari, 2002) as well as in children with specific language disorders (e.g., Stevens, Sanders, & Neville, 2006). Neurobiological evidence has shown that the left temporoparietal junction (TPJ) is crucial for auditory-phonological processing (see Pugh et al., 2000; Ramus, 2004, for reviews). However, activation of the left TPJ auditory phonological system occurs after the left occipito-temporal area’s involvement, during phonological decoding (e.g., Simos, Breier, Fletcher, Bergman, & Papanicolaou, 2000; Simos et al., 2002). The development of the visual-orthographic system (i.e., visual word form area, VWFA) reflects a specialization of the object recognition system that is particularly suitable for letter string processing (see McCandliss, Cohen, & Dehaene, 2003 for a review). VWFA seems to be hierarchically organized for visual orthographic processing: the posterior areas (occipital lobe) are specifically involved in low-level visual features and letter shape processing, while the anterior areas (ventral temporal lobe) are linked to more abstract letter string processing (McCandliss et al., 2003). Developmental reading disabilities could arise not only from a specific disorder of the auditory phonological system, but also from a visual-orthographic system dysfunction. In fact, a low-level visual processing (e.g., perceptual noise exclusion or attentional) deficit in children with DD seems to impair the visual-orthographic system (e.g., Bosse, Tainturier, & Valdois, 2007; Hari & Renvall, 2001; Hawelka, Huber, & Wimmer, 2006; Martelli, Di Filippo, Spinelli, & Zoccolotti, 2009; Sperling, Lu, Manis, & Seidenberg, 2005). The visual-orthographic system receives stimulus-driven (bottom-up) as well as goal-directed (top-down) attentional influence that modulates all visual processing levels from V1 to VWFA (see Corbetta & Shulman, 2002; Laycock & Crewther, 2008; Vidyasagar & Pammer, 2010a, for reviews). In particular, the letters string perceptual segmentation into its constituent graphemes (i.e., graphemic parsing) involves accurate and rapid attentional shifting (Cestnick & Coltheart 1999; Facoetti, Trussardi, et al., 2010; Facoetti et al., 2006; see Ans, Carbonnel, & Valdois, 1998; Perry et al., 2007, for computational studies). Before the letter-to-sound mapping mechanism is applied, irrelevant lateral letters should be filtered out by attentional shifting. Attentional shifting improves perception in several visual tasks, such as contrast sensitivity, texture segmentation and visual search, by intensifying the signal and enhancing spatial resolution as well as diminishing the noise effect outside the focus of attention (e.g., Boyer & Ro, 2007; Carrasco, Williams, & Yeshurum, 2002; Dosher & Lu, 2000; see Reynolds & Heeger, 2009, for a review). Attentional shifting can be considered as the resultant of the processing resources engagement mechanism onto the relevant object (e.g., letter or grapheme that has to be mapped to its correspondent speech-sound) and the subsequent disengagement mechanism from the previous object to the next one. Spatio-temporal proximity between letters causes a reduction in the letter identification accuracy (Bouma, 1970; see

Pelli, 2008, for a recent review) because of massive competition between resources’ processing (Potter, Staub, & O’Connor, 2002; see Keysers & Perrett, 2002, for a review). When the stimulus onset asynchrony (SOA) between two targets is short, the second target (T2) is often identified first (Potter et al., 2002). On the other hand, with larger SOAs, the probability that the first target (T1) is identified first increases (attentional blink, AB). Thus, a target attracts attentive processing resources rapidly, but in the first perceptual stage (i.e., at short SOAs) the attentional engagement is labile; consequently, T2 detection draws resources away from T1 (Potter et al., 2002). “Attentional masking” (AM) is described as the T1 accuracy changes in function of the SOA between targets (e.g., Kavcic & Daffy, 2003; Facoetti, Ruffino, Peru, Paganoni, & Chelazzi, 2008; see Fritz, Elhilali, David, & Shamma, 2007, for a recent review on auditory modality). However, almost no AM occurs if attention is rapidly engaged onto the object, whereas powerful AM ensues if attentional engagement on the object is delayed (e.g., Facoetti, 2001; van der Lubbe & Keuss, 2001; see Enns & Di Lollo, 2000, for a review). Although attentional shifting modulation in the presence of AB is still debated (see Nieuwenstein, Chun, van der Lubbe & Hooge, 2005, but Ghorashi, Enns, Spalek, & Di Lollo, 2009), attentional shifting modulation in the presence of the AM paradigm was recently shown by Corradi, Ruffino, Gori, and Facoetti (2010). Visual attentional shifting deficit has been repeatedly described in DD (see Facoetti, 2004; Hari & Renvall, 2001; Valdois, Bosse, & Tainturier, 2004; Vidyasagar & Pammer, 2010a for reviews) and more specifically in dyslexics with poor phonological decoding skills (e.g., Cestnick & Coltheart, 1999; Buchholz & McKone, 2004; Facoetti et al., 2006; Facoetti, Trussardi, et al., 2010; Kinsey, Rose, Hansen, Richardson, & Stein, 2004; Jones, Branigan, & Kelly, 2008; Roach & Hogben, 2007). Consistently with the multi-sensory “sluggish attentional shifting” hypothesis by Hari and Renvall (2001), as well as with the “perceptual noise exclusion deficit” by Sperling et al. (2005), children and adults with DD are specifically impaired from rapidly engaging their attention, showing both abnormal temporal (e.g., Di Lollo, Hanson, & McIntyre, 1983; Montgomery, Morris, Sevcik, & Clarkson, 2005) and lateral masking (e.g., Geiger et al., 2008; Martelli et al., 2009; Sperling et al., 2005; Spinelli, De Luca, Judica, & Zoccolotti, 2002). Indeed, temporal and lateral masking are probably supported by common attentional mechanisms (see Enns & Di Lollo, 2000, for a review). Evidence of sluggish attentional deployment in the visual modality for children and adults with DD is provided by AB (Buchholz & Aimola-Davies, 2007; Facoetti et al., 2008; Hari, Valta, & Uutella, 1999; Lallier, Donnadieu, Berger, & Valdois, 2010; Visser, Boden, & Giaschi, 2004), temporal order judgment (Hari, Renvall, & Tanskanen, 2001; Ja´skowski & Rusiak, 2008; Liddle, Jackson, Rorden, & Jackson, 2009), rapid multi-element presentation (Hawelka et al., 2006; Bosse et al., 2007) and spatial-cueing tasks (Brannan & Williams, 1987; Facoetti et al., 2006; Facoetti, Trussardi, et al., 2010; Roach & Hogben, 2007) that involve efficient spatio-temporal attentional shifting to rapidly displayed stimuli. In a cross-sectional study with typically developing children, Bosse and Valodis (2009) have shown that visual attention contributes to phonological decoding skills, independently from auditory-phonological processing in the first grade. Moreover, longitudinal studies have shown that visual attention shifting, in addition to speech-sound awareness, is one of the most important predictors of early reading abilities (e.g., Ferretti, Mazzotti, & Brizzolara, 2008; Plaza & Cohen, 2006; Facoetti, Corradi, Ruffino, Gori, & Zorzi, 2010a). All these longitudinal studies involve serial attentional processing of a single target identification in cluttered conditions. Finally, reading performance has been shown to improve following specific training for visual attention engage-

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ment in dyslexic children (e.g., Facoetti, Lorusso, Paganoni, Umiltà, & Mascetti, 2003; Geiger, Lettvin, & Fanhle, 1994). Thus, independently from an auditory-phonological disorder, visual attention engagement might play a critical role in the acquisition of spelling-to-sound mapping during letter string processing because it is crucially involved in parsing and identification of relevant sublexical orthographic units. The aim of this study is to verify whether a deficit of spatiotemporal distribution of attentional engagement in children with DD (i.e., “sluggish attentional shifting”; Hari & Renvall, 2001) supports the recent proposal of “perceptual noise exclusion deficit” in DD (e.g., Geiger et al., 2008; Martelli et al., 2009; Sperling et al., 2005; Ziegler, Pech-Georgel, George, & Lorenzi, 2009). In particular, a visual attentional engagement deficit would have a detrimental effect on the segmentation mechanism of the visual input (i.e., letter string) into components (i.e., graphemic parsing). Computational studies have shown that phonological assembly relies on efficient parsing and identification into grapheme units (e.g., Ans et al., 1998; Perry et al., 2007). Neuroimaging studies have highlighted an association between the cortical regions controlling attentional engagement and both typical and atypical reading development. Several studies employing phonological decoding tasks have shown reduced task related activation in areas surrounding the bilateral TPJ in dyslexics (see Eden & Zeffiro, 1998, for a review). While the left TPJ has been linked to auditory-phonological processing (Pugh et al., 2000; Ramus, 2004), the right TPJ is a crucial component of the network subserving the stimulus-driven control of attentional engagement (Corbetta & Shulman, 2002). TPJ activation changes have been observed during reading acquisition in normally-developing children (Hoeft et al., 2006; Turkeltaub, Gareau, Flowers, Zeffiro, & Eden, 2003), whereas some studies reported a right TPJ deficiency in dyslexics (e.g., Hoeft et al., 2006; Rumsey et al., 1997; Grünling et al., 2004). More generally, during phonological decoding, the visual attention network could apply a powerful modulation on sublexical visual unit processing in occipito-temporal areas (McCandliss et al., 2003). A specific relationship between impaired attentional engagement and letter-sound conversion in children with DD was already described in one of our previous studies (Facoetti et al., 2008). In that work, the ability to rapidly engage attention onto a visual object correlated with speed and accuracy of nonword reading (Facoetti et al., 2008). However, further investigation was required according to the following critical issues: (i) The former study investigated only the time-course (i.e., non-spatial attention), whereas the present study included also the spatial distribution of attentional engagement, testing a possible abnormal lateral AM. (ii) The former study investigated the time-course of attentional engagement in a small sample of children with DD (i.e., N = 13), making the dyslexic subtypes difference difficult to test. In the present study, a larger sample of children with DD (N = 28) was divided into two groups on the basis of their phonological decoding skills. This approach allows us to directly test the hypothesis of spatiotemporal attentional engagement deficit only in dyslexic children with poor phonological decoding skills. (iii) The attentional engagement paradigm presented by Facoetti et al. (2008) had letters as target stimuli; therefore, it could also measure a possible effect of reading disorder associated to DD. In particular, sluggish attentional engagement reported by Facoetti et al. (2008) might be explained by a primary letter identification deficit rather than by an attentional deficit per se. In the present study, we presented noletter targets to isolate a pure perceptual bottom-up deficit. (iv) In the former study, both the attentional engagement (i.e., AM) and the attentional disengagement (i.e., AB) were measured. This latter point is particularly important in addressing the issue of the relationship between a general attentional resources deficit and

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perceptual noise exclusion deficit in DD (see, for example, Badcock, Hogben, & Fletcher, 2008; Moores, Nicolson, & Fawcett, 2003, for a discussion). Sluggish attentional engagement and disengagement reported by Facoetti et al. (2008) might be explained by a general attentional resources deficit rather than by a specific attentional engagement deficit, given that children had to identify both the T1 and T2 in the same trial (i.e., divided attention in dual task). Thus, in the present study, we assessed the time-course of attentional engagement in dyslexic and normally reading children by measuring the identification of only the first of two sequentially presented masked objects. Our hypothesis suggests that if visual attention is sluggishly engaged in dyslexic children, a larger and prolonged AM is expected when the two objects are displayed in the same position. Moreover, an abnormal AM when the second object is laterally displayed is also predicted. More importantly, if an efficient spatiotemporal engagement of visual attention is specifically required for an accurate letter string parsing mechanism, we predict a specific abnormal AM in dyslexics with a phonological decoding deficit. 2. Methods 2.1. Participants 2.1.1. Dyslexic and normally reading children Spatio-temporal AM was studied in 28 children with DD (8 females and 20 males) and in 55 control children (23 females and 32 males) without reading difficulties. Dyslexic children were recruited at the Developmental Neuropsychology Units of two research hospitals (IRCCS “E. Medea”, Bosisio Parini, Lecco and IRCCS “Bambino Gesù”, Santa Marinella, Rome). These children had been diagnosed as dyslexics based on standard exclusion criteria (American Psychiatric Association, 1994). They were between 8 and 14 years old (mean age 10.99, 1.71 SD) and their performance (accuracy and/or speed) in reading was 2 SDs below the norm on at least one of the age-standardized Italian tests included in the battery (single word and nonword reading; Sartori, Job, & Tressoldi, 1995). Dyslexic participants were selected on the basis of: (1) a full scale IQ greater than 85, as measured by the Wechsler Intelligence Scale for Children-Revised (WISC-R, Wechsler, 1986); (2) normal or corrected-to-normal vision and hearing; (3) absence of neurological and/or psychiatric disorders; (4) absence of attention deficit disorder with hyperactivity (because of their high co-morbidity with DD), as evaluated through DSM-IV diagnostic criteria (American Psychiatric Association, 1994). Fifty-five chronological age-matched normally reading (NR) children ranging in age from 9 to 15 years (mean age 10.64, 1.5 SD) were also selected. These children were recommended as normal readers by their teachers and individually evaluated in a quiet room at school. DD and NR children were comparable for chronological age and Performance IQ, but were significantly different for both accuracy and speed of word and nonword reading (see Table 1 for details). Informed consent was obtained from each child and his or her parents. 2.1.2. Dyslexic children with and without phonological decoding deficit The ability to read nonwords aloud was measured on a standardized list of 48 Italian nonwords. Norms are available for both accuracy and fluency (Sartori et al., 1995). The latter is measured by total time (in seconds) spent on a specific list. As in the studies of Facoetti et al. (2006) and Facoetti, Trussardi, et al. (2010), dyslexic children were divided into two groups based on their accuracy in nonword reading. This choice is supported by the hypothesis that attentional engagement deficit in DD should impair the letter string parsing and identification mechanism, resulting in visuo-perceptual errors during phonological decoding. In particular, a dyslexic child was assigned to the DDN− group (where N− indicates poor nonword reading) if her/his Z-score in nonword reading accuracy was below −1.5 standard deviations on the standardized list of nonwords. All dyslexic children who did not meet the criterion for inclusion in the DDN− group were assigned to the DDN+ group (where N+ indicates near-to-normal nonword reading). The mean Z-score for nonword reading accuracy was −3.4 for the DDN− group whereas it was −.29 for the DDN+ group (p < .001). Of course, this does not mean that word reading accuracy is normal: a dissociation would be surprising in a shallow orthography like Italian. Indeed, the mean Z-score for DDN− was −3.6 for word reading accuracy. In contrast, children assigned to the DDN+ group showed a marked fluency deficit, but their decoding was accurate. That is, their reading accuracy was close to normal, but their speed was more than 2 standard deviations below the norm both for words and nonwords. Slow nonword reading performance in the DDN+ group is compatible with recent data (Facoetti et al., 2006; Facoetti, Trussardi, et al., 2010) suggesting that their phonological decoding is probably affected at the fluency level of grapheme-to-phoneme mapping (e.g., Denckla & Rudel, 1976; see Nicolson & Fawcett, 2007, for a review).

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Table 1 Mean (M) and standard deviation (SD) of age (in months), Performance IQ (Wechsler, 1993), word and nonword reading ability (errors and speed in Z-score; Sartori et al., 1995) in developmental dyslexic (DD; N = 28) and normally reading children (NR; N = 55). The bold values are with p < 0.05. DD (N = 28)

Age (months) Performance IQ Words reading (Z-score) Errors Speed Nonwords reading (Z-score) Errors Speed

NR (N = 55)

Comparison

M

SD

M

SD

t (81)

132 108

21 13

128 108

18 19

−0.94 0.01

p 0.35 0.99

−2.52 −3.86

2.53 3.65

0.50 0.59

0.43 0.63

8.64 8.83

<0.001 <0.001

−1.84 −3.34

1.95 3.09

0.65 0.52

0.59 0.72

8.76 8.85

<0.001 <0.001

Table 2 Mean (M) and standard deviation (SD) of age (in months), Performance IQ (Wechsler, 1993), word and nonword reading ability (errors and speed in Z-score; Sartori et al., 1995) in developmental dyslexics with (DDN−; N = 14) and without (DDN+; N = 14) phonological decoding deficit. The bold value is with p < 0.05. DDN− (N = 14)

Age (months) Performance IQ Words reading (Z-score) Errors Speed Nonwords reading (Z-score) Errors Speed

DDN+ (N = 14)

Comparison

M

SD

M

SD

t(26)

129 107

23 10

134 110

18 15

−0.60 0,52

p 0.55 0,61

−3.6 −5.35

2.83 4.28

−1.45 −2.36

1.69 2.12

2.43 2.35

0.07 0.21

−3.4 −4.08

1.52 4

−0.29 −2.61

0.62 1.62

7.08 1.27

<0.001 0.35

The importance of a speed deficit is well established in shallow orthographies (e.g., Tressoldi, Stella, & Faggella, 2001; Wimmer, 1993). DDN− and DDN+ children were comparable for chronological age and Performance IQ (see Table 2 for details).

2.2. Apparatus and stimuli The experiment was conducted in a dimly lit (luminance of 1.5 cd/m2) and quiet room. Participants were seated in front of a monitor; viewing distance was 40 cm. Fixation mark was a cross presented in the center of the screen (.3 deg of visual angle). Three 8-like figures (1 × .5 deg), comprising seven line segments, were first displayed centrally and laterally of the fixation mark (distance 1 deg), acting as a pre-mask. Two successive non-verbal objects (O1 and O2), obtained by removing three line segments from the “8” (see Fig. 1B), each followed by a post-mask, were presented. Participants viewed the sequence of stimuli binocularly. All visual stimuli were black (.6 cd/m2), whereas the background was white (119 cd/m2).

2.3. Procedures Each trial began with the onset of the fixation mark. Participants were instructed to keep their eyes on the fixation mark throughout the entire duration of the trial. After 500 ms, a blank (duration 100 ms) was presented and the 8-like figures were showed. After a variable time interval (125, 175, 225 and 275 ms), the O1 was presented for a duration of 100 ms at the central location and replaced by the post-mask. O2 was displayed for 100 ms after a variable SOA (i.e., 150, 250, 600 ms). O2 could be in the same location as O1 or in the lateral location (on the left side or on the right side) and it was replaced by the post-mask displayed for 500 ms (see Fig. 1A). At the end of the trial, participants were required to identify O1 choosing between the eight possible target stimuli displayed on the screen until their responses were given (see Fig. 1B). Each participant was instructed to use all the time they needed to identify the target as accurately as possible. Responses were pointed by participants and entered by the experimenter by pressing the corresponding key on the computer keyboard; no feedback was provided. The experimental session consisted of 90 trials (10 trials × 3 O2 locations × 3 O1–O2 SOAs).

3. Results 3.1. Dyslexic and normally reading children

Fig. 1. Schematic representation of the stimulus sequence for the spatio-temporal attention task.

The O1 identification mean (accuracy rate refers to the proportion of O1 correctly identified) was computed by a mixed analysis of variance (ANOVA) with a 3 × 3 × 2 design in which withinsubject factors were the O2 location (left, central and right) and the O1–O2 SOA (150, 250 and 600 ms), while the between-subject factor was the Group (normally reading and dyslexic children). We corrected for Greenhouse–Geisser those ANOVAs that did not respect homogeneity of variances. The degree of freedom values were kept the same but the p values changed according to the Greenhouse–Geisser correction. The results showed that the O2 location main effect was significant (F(2,162) = 45.24, p < .001). The O1–O2 SOA main effect was significant (F(2,162) = 20.47, p < .001). The Group main effect was also significant (F(1,81) = 4.62, p = .034). Moreover, the O2 location × O1–O2 SOA interaction was significant (F(4,324) = 5.29, p < .001). More importantly, the O2 location × O1–O2 SOA × Group interaction was also significant (F(4,324) = 2.98, p < .05; see Fig. 2). Planned comparison analyses were performed to investigate the spatio-

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Fig. 3. Mean O1 accuracy and standard error as a function of group (normally reading [NR], developmental dyslexic children with phonological decoding deficit [DDN−] and developmental dyslexic children without phonological decoding deficit [DDN+]), O2 position (left, central and right) and O1–O2 SOA (150, 250 and 600 ms).

Fig. 2. Mean O1 accuracy and standard errors as a function of Group (normally reading [NR] and developmental dyslexic [DD] children), O2 position (left, central and right) and O1–O2 SOA (150, 250 and 600 ms).

temporal dysfunctions of attentional engagement suggested by the O2 location × O1–O2 SOA × Group interaction. In normally reading children, the O1 identification was not affected by O1–O2 SOA when O2 was displayed on the left (F(2,108) = 2.43, p = .094) and on the right side (F(2,108) = 2, p = .143). In contrast, the O1 identification was affected by O1–O2 SOA when O2 was displayed at the same O1 location (F(2,108) = 13.62, p < .001). These results show the typical AM in the central location and no abnormal lateral AM (i.e., efficient spatio-temporal distribution of attentional engagement). In dyslexic children, O1 identification was affected when O2 was displayed at the same O1 location (F(2,54) = 9.03, p < .001). However, O1 identification never achieved the accuracy obtained when O2 was laterally displayed even at longest O1–O2 SOA (left, F(1,81) = 7.40, p < .01; right F(1,81) = 14.70, p < .001): that is, sluggish AM recovery. The O1–O2 SOA did not affect the O1 identification when the O2 was displayed on the left side (F < 1). In contrast, the O1–O2 SOA affected the O1 identification when O2 was displayed on the right side (F(2,54) = 6.37, p < .005). In the dyslexic group, O1 identification was lower when O2 was displayed on the right side than when O2 was displayed on the left side at 150 ms (F(1,81) = 6.80, p < .05), but not at 250 ms (F < 1) and at 600ms O1–O2 SOA (F(1,81) = 2.43, p = .124). The difference between the two groups when O2 was presented in the same location as O1 was significant at the first (F(1,81) = 4.73, p < .05) but not at the second O1–O2 SOA (F(1,81) = 2.32, p = .132. Moreover, at 150 ms O1–O2 SOA, there was no significant difference between dyslexics and controls on O1 performance when O2 was displayed on the left side (F < 1), demonstrating a similar visuo-perceptual baseline in two different reading groups. A larger AM in dyslexic children relative to normally reading children was found when performance of O1 with O2 in the central location was compared to performance of O1 with O2 in the left location (F(1,81) = 4.52, p < .05; see the Red line in Fig. 2). Moreover, O1 accuracy in the two groups was not significantly different when O2 was presented on the right side at (longest) 600 ms O1–O2 SOA (F < 1), demonstrating a similar visuo-perceptual baseline in the two different reading groups. However, O1 accuracy in the two groups was different when O2 was presented in the central location at (longest) 600 ms O1–O2 SOA (F(1,81) = 4.79, p < .05). A sluggish AM recovery in dyslexic children relative to normally reading children was found when performance of O1 with O2 in the central location was compared to performance of O1 with O2 on the right side (F(1,81) = 5.81, p < .05; see the Blue

line in Fig. 2). Finally, O1 accuracy in the two groups was different when O2 was presented on the right side at 250 ms O1–O2 SOA (F(1,81) = 6.92, p < .05). An abnormal lateral AM on the right side in dyslexic children relative to normally reading children was found when performance of O1 with O2 on the right side at 250 ms O1–O2 SOA was compared to performance of O1 with O2 on the right side at 600 ms O1–O2 SOA (F(1,81) = 10.85, p < .01; see the Green line in Fig. 2). 3.2. Normally reading, dyslexic with and without phonological decoding deficit Because O2 location × O1–O2 SOA × Group interaction was significant when the three groups of children (i.e., normal readers, DDN+ and DDN−) were entered in a 3 × 3 × 3 ANOVA (F(8,320) = 2.10, p = .035), the O1 identification mean was computed by two separated mixed ANOVAs with 3 × 3 × 2 design. In the first ANOVA, normally reading children were compared to DDN+ children, whereas in the second ANOVA, normally reading children were compared to DDN− children. Performance of the DDN+ group is similar to that of the normal readers (i.e., O2 location × O1–O2 SOA × Group interaction: F(4,268) = 1.76, p > .05) but not statistically different from that of the DDN− group (F > 1). 3.3. Normally reading and dyslexic children without phonological decoding deficit The O2 location main effect was significant (F(2,134) = 32.39, p < .001). The SOA main effect was also significant, (F(2,134) = 10.09, p < .001), whereas Group main effect was not significant (F < 1). No significant interaction was found (all instances of p > .14). See Fig. 3. 3.4. Normally reading and dyslexic children with phonological decoding deficit The O2 location main effect was significant (F(2,134) = 27.54, p < .001). The O1–O2 SOA main effect was also significant (F(2,134) = 15.59, p < .001). The Group main effect was also significant (F(1,67) = 5.87, p < .05). Moreover, the O2 location × O1–O2 SOA interaction was significant (F(4,268) = 6.31, p < .001). More importantly, the O2 location × O1–O2 SOA × Group interaction was also significant (F(4,268) = 3.05, p < .05; see Fig. 3). Planned comparison analyses were performed to investigate the spatio-temporal dysfunctions of attentional engagement suggested by the O2 location × O1–O2 SOA × Group interaction. In particular, O1 accuracy in the two groups was not significantly

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different when O2 was presented on the left side at (shortest) 150 ms O1–O2 SOA (F(1,67) = 1.15, p = .288), demonstrating a similar visuo-perceptual baseline in two different reading groups. However, O1 accuracy in the two groups was different when O2 was presented in the central location at (shortest) 150 ms O1–O2 SOA (F(1,67) = 7.97, p < .01). A larger AM in dyslexic children with a phonological decoding deficit in comparison to normally reading children was found (F(1,67) = 5.81, p < .05; see the Red line in Fig. 3). Moreover, O1 accuracy in the two groups was not significantly different when O2 was presented on the right side at (longest) 600 ms O1–O2 SOA (F < 1), demonstrating a similar visuo-perceptual baseline in two different reading groups. However, O1 accuracy in the two groups was different when O2 was presented in the central location at (longest) 600 ms O1–O2 SOA (F(1,67) = 3.36, although p = .071). A sluggish AM recovery in dyslexic children with a phonological decoding deficit in comparison to normally reading children is suggested (F(1,67) = 3.04, although p = .081; see the Blue line in Fig. 3). Finally, O1 accuracy in the two groups was different when O2 was presented on the right side at 250 ms O1–O2 SOA (F(1,67) = 7.80, p < .01). An abnormal lateral AM on the right side was found in dyslexic children with a phonological decoding deficit in comparison to normally reading children (F(1,67) = 12.59, p < .001; see the Green line in Fig. 3).

4. Discussion The aim of the present study was to investigate the role of attentional engagement efficiency required for an accurate letter string parsing and identification mechanism. For this purpose, the AM effect onto the central stimulus in a 3-pseudoletter string was measured in dyslexic and normally reading children. Our results indicated that normally reading children show a typical central AM recovery and no lateral AM, suggesting an efficient spatio-temporal engagement of visual attention during the processing of a central stimulus flanked by dynamic distractors. In contrast, dyslexic children showed an impaired O1 identification when O2 was centrally displayed at the shortest O1–O2 SOA as well as at the longest O1–O2 SOA, suggesting a larger AM and a sluggish AM recovery, respectively. Our results in dyslexics are interpreted as a consequence of the engagement deficit. We can exclude visual persistence (Di Lollo et al., 1983) because the masks should disrupt the retinal image. These results are consistent with the Facoetti et al. (2008) study conducted on a smaller sample of dyslexic children, which confirmed a sluggish engagement of non-spatial attention in DD. Moreover, in the present study, we found an impaired O1 identification in dyslexic children even when O2 was displayed laterally on the right side, suggesting an abnormal lateral AM. This deficit was present in children with dyslexia only at the shortest O1–O2 SOA. This result is in agreement with previous studies in parietal lesion patients presenting larger attentional disengage deficit at shorter cue-target SOAs that decreases when SOA increases (see Losier & Klein, 2001, for a review and meta-analysis). These data supported the left mini-neglect hypothesis in DD (i.e., a left inattention and/or a rightward attentional bias; e.g., Facoetti & Molteni, 2001; Facoetti et al., 2006; Hari et al., 2001; Buchholz & Aimola-Davies, 2005; Sireteanu, Goertz, Bachert, & Wandert, 2005; Liddle et al., 2009), supported by findings highlighting a rightward attentional bias both in children and adults with DD (Facoetti et al., 2006; Liddle et al., 2009). Thus, in addition to a sluggish engagement of non-spatial attention (see also Facoetti et al., 2008), dyslexic children show an inefficient spatial selection of visual stimuli. Globally, these results suggest, not only a “longer temporal”, but also a “larger spatial” window in which the attentional engagement is labile (Potter et al., 2002), coherently with a “perceptual noise exclusion deficit” in dyslexia (Sperling et al., 2005).

Fig. 4. Schematic representation simulating the spatio-temporal distribution of attentional engagement onto central letter in a 3-letter string for normal readers and poor phonological decoders. The ellipse represents the central letter resistance to the central O2 interference (with higher noise corresponding to higher interference), whereas the lateral contrast represents the interference on central letter from flanker letters (with higher contrast corresponding to higher interference). In comparison to normal readers, poor phonological decoders show: (i) a higher interference from central O2 at 150 ms O1–O2 SOA (i.e., larger AM); (ii) a higher interference from central O2 at 600 ms O1–O2 SOA (i.e., sluggish AM recovery); and (iii) a higher interference from lateral O2 (i.e., abnormal lateral AM).

4.1. Attentional engagement deficits and phonological decoding The attentional visual selection mechanism operating on letter strings appears to be a basic component of the phonological assembly process (see Ans et al., 1998; Perry et al., 2007; Whitney & Cornelissen, 2005, for computational studies; see McCandliss et al., 2003, for a review). Computational models of reading assume that some graphemic parsing forms achieve the level of representation on which the spelling-to-sound conversion mechanisms operate (see Zorzi, 2005, for a recent review). Regardless of how this process is exactly conceived (e.g., Ans et al., 1998; Perry et al., 2007; Whitney & Cornelissen, 2005), it requires the serial engagement of visual attention onto each sublexical unit. We investigated in dyslexics with poor phonological decoding skills (i.e., poor nonword readers) whether an efficient attentional engagement (measured by central and lateral AM) is specifically required for an accurate graphemic parsing process. Results show that only poor phonological decoders (DDN−) had a labile spatiotemporal engagement of visual attention onto a 3-pseudoletter string (see Fig. 4 for a schematic representation). Specifically, chronological age and IQ matched controls present in unimpaired O1 identification when O2 was centrally (vs. laterally) displayed at the longest SOA, whereas they show a mild O1 identification impairment when O2 was centrally (vs. laterally) displayed at short O1–O2 SOA. In contrast, DDN− showed impaired O1 identification when O2 was centrally displayed at every O1–O2 SOA, even at the longest O1–O2 SOA, suggesting that visual attention is sluggishly engaged (i.e., sluggish AM recovery). Moreover, we found a larger AM effect in DDN− because O1 identification was more impaired in DDN− than in controls at the shortest SOA O1–O2 when the two objects were displayed at the same central position. Finally, in controls, no AM effect was present when O2 was displayed laterally after O1 at each O1–O2 SOA. In contrast, only in DDN− was O1 identification impaired when O2 was displayed selectively on the

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right side at shorter O1–O2 SOAs, suggesting an abnormal lateral AM. These results highlight that AM deficits (i.e., sluggish AM recovery and larger AM as well as atypical lateral AM) were selectively present in dyslexics characterized by a specific deficit in phonological decoding skills. No significant difference in spatio-temporal AM was present in dyslexic children without phonological decoding deficit (DDN+) in comparison to normally reading children. 4.2. The role of noise exclusion deficits These attentional engagement deficits could be discussed referring to the recent hypothesis of multisensory perceptual noise exclusion deficit in DD (e.g., Sperling et al., 2005; Geiger et al., 2008; Martelli et al., 2009; Ziegler et al., 2009). This hypothesis describes an impaired ability to filter out visual and auditory perceptual noise to distinguish relevant sensory (i.e., signal) data from irrelevant (Sperling et al., 2005). In a recent study, Sperling, Lu, Manis and Seidenberg (2006) have highlighted that the presence of perceptual distractors (i.e., noise) in a motion detection task decreases dyslexics’ performance as compared to when noise is removed from the experimental setting. Moreover, experimental evidence has previously shown that dyslexics are impaired in detection of a brief visual signal rapidly followed by noise (e.g., Di Lollo et al., 1983; Visser et al., 2004; Facoetti et al., 2008) and that they are disturbed by lateral masking (e.g., Atkinson, 1991; Spinelli et al., 2002; Geiger et al., 2008; Martelli et al., 2009). Both temporal (Di Lollo et al., 1983; Visser et al., 2004; Facoetti et al., 2008) and spatial (e.g., Geiger et al., 2008) processing windows, in which noise interferes with the signal, appear to be broader in dyslexics (i.e., DDN−) compared to normally reading children. Thus, the broader spatio-temporal window in dyslexia could be a plausible effect of attentional engagement deficit specifically shown in dyslexics with poor phonological decoding skills (Hari & Renvall, 2001). In particular, a spatiotemporal attentional engagement deficit could influence the filter-out mechanism of irrelevant lateral letters during graphemic parsing (e.g., Cestnick & Coltheart, 1999; Facoetti et al., 2006; Facoetti, Trussardi, et al., 2010). 4.3. The role of temporal processing deficits Several authors suggested that the core deficit in DD is characterized by a reduced visual and auditory processing speed (i.e., a temporal processing deficit; see Farmer & Klein, 1995; Tallal, 2004, for reviews). According to the speed of processing deficit, the sluggish attentional engagement measured by AM could be interpreted as expression of the impaired ability to process rapid events. However, the time duration of visual objects (i.e., 100 ms) does not provide a satisfactory explanation for the attentional deficit, given that visual perception at two perceptual baselines (when O2 was rapidly presented on the left side and when O2 was more slowly presented on the right side) was found to be similar in the two groups. The deficit shown in the present study is related to the time interval between object presentation: that is, the time-course of attentional engagement. 4.4. Neurobiological substrate of attentional engagement deficits A possible neurobiological substrate of sluggish attentional engagement deficits can be a weakened or abnormal magnocellular (M) input to the dorsal visual stream (Livingstone, Rosen, Drislane, & Galaburda, 1991; see Boden & Giaschi, 2007; Stein & Walsh, 1997; Vidyasagar & Pammer, 2010a, for reviews) and a consequent dysfunction of TPJ. Deficits of the M system, albeit controversial (e.g., Amitay, Ben Yehudah, Banai, & Ahissar, 2002), could influence higher visual processing stages through the dorsal pathway and therefore lead to reading difficulties via attentional

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mechanisms (see Boden & Giaschi, 2007; Hari & Renvall, 2001; Stein & Walsh, 1997; Vidyasagar, 1999; Vidyasagar & Pammer, 2010a, for reviews). However, which specific attentional mechanisms are involved during letter string processing is debated. It is still unclear whether letter string processing is exclusively based on top-down attentional mechanisms (see Whitney, 2010, for a discussion) or a combination of bottom-up and top-down mechanisms (see Vidyasagar & Pammer, 2010b, for a discussion). A recent psychophysical study showed that M-based perceptual performance (i.e., coherent dot motion threshold) as well as attentional engagement skill was compromised in poor phonological decoders (Roach & Hogben, 2007). Importantly, recent longitudinal studies have shown that M (i.e., frequency doubling illusion) and dorsal pathway (i.e., coherent dot motion) functioning are important predictors of early reading abilities (e.g., Boets, Wouters, van Wieringen, De Smedt, & Ghesquière, 2008; Kevan & Pammer, 2009;). The TPJ is specifically involved in attention mechanisms control (Downar, Crawley, Mikulis, & Davis, 2000) and it is included in the right hemisphere of the cortical network (i.e., TPJ and ventral frontal cortex; Corbetta & Shulman, 2002). Other psychophysical findings (i.e., spatial cueing task, temporal order judgment and perception of line motion illusion; Hari et al., 2001; Roach & Hogben, 2007) suggest that dyslexic children and adults suffer from a left-side “mini-neglect” (e.g., Facoetti et al., 2006; Hari et al., 2001; Liddle et al., 2009). Moreover, the right TPJ plays a crucial role in selecting a target among interfering distractors, filtering out irrelevant perceptual noise (Friedman-Hill, Robertson, Desimone, & Ungerleider, 2003). Finally, in a functional magnetic resonance imaging study, it has been shown that there is a specific association between the right TPJ and visual-orthographic information representations during the early stages of reading acquisition (Turkeltaub et al., 2003). 5. Conclusion Our findings in dyslexics with poor phonological decoding are consistent with the predictions of computational and neurobiological models of reading which assume that attentional engagement – controlled by TPJ – is specifically involved in the sublexical spelling-to-sound mapping process. These visual attentional deficits frequently co-occur with the typically observed auditoryphonological disorders in DD (e.g., Menghini et al., 2010). Our results are open to different interpretations involving: (i) the attentional engagement (e.g., Facoetti et al., 2008); (ii) the time-course of attentional focusing (see Jefferies & Di Lollo, 2009 for a demonstration); or (iii) a combination between attentional shifting and focusing (see Castiello & Umiltà, 1990, for a demonstration). Acknowledgments This work was supported by grants from the Italian Ministry of University and Scientific Research (“PRIN2007” to A.F.), CARIPARO Foundation (“Borse di Dottorato CARIPARO 2009” to A.F.), and the University of Padova (“Assegni di Ricerca 2009” and “Progetto di Ateneo 2009” to A.F.). The contributions of staff members of Scientific Institutes as well as of children and their families, are gratefully acknowledged. We sincerely thank Andrea Peru and Leonardo Chelazzi for helpful discussions, Professor Maurizio Corbetta, and two anonymous reviewers for help in improving this manuscript. References American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders (4th ed.). Washington, DC: [DSM-IV]. Amitay, S., Ben-Yehudah, G., Banai, K., & Ahissar, M. (2002). Disabled readers suffer from visual and auditory impairments but not from a specific magnocellular deficit. Brain, 125, 2272–2285.

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