Review  |   January 2015
How the visual aspects can be crucial in reading acquisition: The intriguing case of crowding and developmental dyslexia
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Journal of Vision January 2015, Vol.15, 8. doi:10.1167/15.1.8
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      Simone Gori, Andrea Facoetti; How the visual aspects can be crucial in reading acquisition: The intriguing case of crowding and developmental dyslexia. Journal of Vision 2015;15(1):8. doi: 10.1167/15.1.8.

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Developmental dyslexia (DD) is the most common neurodevelopmental disorder (about 10% of children across cultures) characterized by severe difficulties in learning to read. According to the dominant view, DD is considered a phonological processing impairment that might be linked to a cross-modal, letter-to–speech sound integration deficit. However, new theories—supported by consistent data—suggest that mild deficits in low-level visual and auditory processing can lead to DD. This evidence supports the probabilistic and multifactorial approach for DD. Among others, an interesting visual deficit that is often associated with DD is excessive visual crowding. Crowding is defined as difficulty in the ability to recognize objects when surrounded by similar items. Crowding, typically observed in peripheral vision, could be modulated by attentional processes. The direct consequence of stronger crowding on reading is the inability to recognize letters when they are surrounded by other letters. This problem directly translates to reading at a slower speed and being more prone to making errors while reading. Our aim is to review the literature supporting the important role of crowding in DD. Moreover, we are interested in proposing new possible studies in order to clarify whether the observed excessive crowding could be a cause rather than an effect of DD. Finally, we also suggest possible remediation and even prevention programs that could be based on reducing the crowding in children with or at risk for DD without involving any phonological or orthographic training.

Developmental dyslexia
Individuals with developmental dyslexia (DD) present difficulties with accurate or fluent word recognition and spelling despite adequate instruction, intelligence, and sensory abilities. DD is characterized by difficulties with decoding while comprehension is more intact (American Psychiatric Association, 1994). DD represents the tail of a normal distribution of word reading ability (e.g., Shaywitz, Escobar, Shaywitz, Fletcher, & Makuch, 1992). Prevalence estimates depend on the definition of DD; however, it seems that around 10% of the population can be classified as an individual with DD. A significant male predominance is consistently found with a ratio that ranges between 1.5 to three males per one female. DD presents some important comorbidity with attention deficit hyperactivity disorder, developmental dyscalculia, specific language impairment (SLI), and speech–sound disorder (see Peterson & Pennington, 2012, for a recent review). 
Following earlier descriptions of high familial aggregation of the disorder (Hallgren, 1950), substantial heritability typical of a complex neurodevelopmental trait has been reported (Fisher & De Fries, 2002; Plomin & Kovas, 2005). Since the early 1980s, at least nine DD risk loci have been mapped to chromosomes 1, 2, 3, 6, 15, 18, and X, and candidate DD genes have been consistently reported (for reviews, see Carrion-Castillo, Franke, & Fisher, 2013; Paracchini, Scerri, & Monaco, 2007; Scerri & Schulte-Körne, 2010). Moreover, recent studies provide evidence that gene-by-environment (e.g., Mascheretti et al., 2013) and gene-by-gene (Harold et al., 2006; Ludwig et al., 2008; Powers et al., 2013) analysis can be exploited for the study of the DD etiology and assist in defining a neurodevelopmental and theoretical molecular-signaling network contributing to DD etiology (Poelmans, Buitelaar, Pauls, & Franke, 2011). 
The phonological awareness theory
DD is often correlated with an impaired phonological awareness, which refers to the ability to perceive and manipulate the sounds of spoken words (Goswami & Bryant, 1990; Mattingly, 1972) and involves not only discriminating speech sounds, but also explicitly acting upon them (Castles & Coltheart, 2004). The phonological awareness theory is the most traditional approach adopted to explain DD, and it is still the dominant view. Impaired phonological processing is largely assumed to be the core deficit in DD (e.g., Hornickel & Kraus, 2013; see Gabrieli, 2009; Goswami, 2003, for reviews). A suggested hypothesis is that a phonological awareness deficit impairs the ability to map speech sounds onto homologous visual letters, preventing the attainment of fluent reading (see Vellutino, Fletcher, Snowling, & Scanlon, 2004, for a review). Recent studies suggest that comorbidity with DD is mediated by shared causative and neurocognitive risk factors (e.g., Franceschini, Gori, Ruffino, Pedrolli, & Facoetti, 2012; Franceschini et al., 2013). 
The main issue related to this hypothesis is that no published study has been able to clearly call for a causality effect between phonological awareness and a reading disorder. In DD research, the commonly accepted metrics to prove a causal relationship between a neurocognitive function and DD are longitudinal and remediation studies. 
Studies have reported that the phonological deficit in children with DD is still present when compared to reading level (RL) controls: younger, typical readers matched to the dyslexics on reading level (e.g., Bruck & Treiman, 1990; Stanovich & Siegel, 1994; Swan & Goswami, 1997). These results suggest a causal link between phonology and DD. However, the use of the RL control can only be a first step in research aimed at delineating the causal factors in reading difficulties. Subsequent longitudinal and remediation studies are necessary to determine for a causal link (Goswami & Bryant, 1989). To date, there are no longitudinal and remediation studies that investigate the phonological skills in DD that have controlled for existing literacy skills and grapheme-to-phoneme mapping in their participants and for the effect of these skills on phonological awareness tasks (Castles & Coltheart, 2004). Moreover, specific phonological awareness training does not automatically transfer to better reading abilities (e.g., Agnew, Dorn, & Eden, 2004; Galuschka, Ise, Krick, & Schulte-Körne, 2014; Strong, Torgerson, Torgerson, & Hulme, 2011), which, therefore, does not suggest a direct causal link between phonological awareness and reading abilities. In other words, the hypothesis that DD arises specifically from a deficit of phonological awareness is controversial because of the circular relationship between reading ability and phonological skills acquisition (Vidyasagar & Pammer, 2010). 
Interestingly, Dehaene et al. (2010) measured brain responses to spoken and written language in adults of variable literacy by using fMRI. Literacy enhanced phonological activation to speech sound in the planum temporal and superior temporal cortex (STC). Other studies have demonstrated that learning to read in adulthood can significantly affect the structure of the same brain areas that are important for typical readers (e.g., Carreiras et al., 2009). The brain also changes when literacy is acquired in adulthood (Carreiras et al., 2009; Dehaene et al., 2010). These results demonstrated that reading acquisition in both childhood and adulthood can profoundly refine the neurobiological organization of the auditory–phonological reading network (see Blomert, 2011, for a review). Based on the aforementioned studies, an interesting question is “Could the functional and structural impairments characterizing the phonological network in individuals with DD be a consequence of the widespread lack of reading experience that is commonly observed in individuals with DD?” It is known that a child with DD reads in 1 year the same number of words read by a typical reader in 2 days (Cunningham & Stanovich, 1998). Thus, findings of relatively less gray matter volume (GMV) in DD may represent the consequence of a limited reading experience. Consistent with previous reports, Krafnick, Flowers, Luetje, Napoliello, and Eden (2014) reported that individuals with DD showed less GMV in multiple left and right hemisphere regions, including the left superior temporal sulcus, when compared with age-matched controls. However, not all of these differences emerged when dyslexics were compared with controls matched on reading abilities with only right precentral gyrus GMV remaining significant in the second anaysis (Krafnick et al., 2014). These results indicate that the GMV differences in DD reported before are in large part the outcome of experience (e.g., disordered reading experience) compared with controls with only a fraction of the differences being driven by DD per se. Consistently, Clark et al. (2014) found in their longitudinal study, based on structural MRI, that abnormalities in the reading network are the consequence of having different reading experiences whereas the neuroanatomical precursors of DD are predominantly in primary sensory cortices. 
Interestingly, it could be that the phonological awareness deficit is a cause of SLI, which presents high comorbidity with DD (Brizzolara et al., 2006; Brizzolara et al., 2011; Chilosi et al., 2011). However, the phonological awareness deficit may not be the cause of DD itself. Considering that most of the study of DD did not exclude children with a history of SLI, the high comorbidity with DD could potentially conciliate the presence of supporting results about the causal role of phonological awareness in DD (e.g., RL design) and the absence of well-controlled studies employing powerful causal methods (longitudinal and remediation design). However, a direct consequence of controlling for history of SLI would be excluding a large number of children with DD, raising the inevitable question: Does this procedure tell us something reliable about the causes of DD? It seems pretty clear from the literature that DD is, indeed, a complex disorder characterized by a large number of deficits that combine so that the final outcome passes the threshold of diagnosis (e.g., Menghini et al., 2010). Consequently, the research of the “pure DD deficit” seems to be unsuccessful in explaining this complex disorder. Moreover, it was demonstrated that children with SLI also reported visuo-temporal attentional deficits (e.g., Dispaldro et al., 2013) showing that SLI is not exclusively a language disorder. 
Traditional remediation approach for DD
Until now, the most common approach in DD remediation has been to devise sophisticated programs that train subskills of reading, especially phonological awareness. The typical tasks in phonological awareness training are phoneme deletion, phoneme counting, phoneme blending, phoneme reversal, syllable segmentation, rhyme oddity, and rhyme judgment (Castles & Coltheart, 2004). Results showed that the improvements in phonological awareness unfortunately do not automatically transfer to better reading abilities (e.g., Agnew et al., 2004; Galuschka et al., 2014; Strong et al., 2011). 
Blau, van Atteveldt, Ekkebus, Goebel, and Blomert (2009) used fMRI to investigate the neural processing of letters and speech sounds in unisensory (visual or auditory) and multisensory (audiovisual congruent and audiovisual incongruent) conditions in adults with DD. The data revealed that the STC was underactivated for the integration of letters and speech sounds. This reduced audiovisual integration was able to predict the phonological awareness task performance. Another fMRI study by Blau et al. (2010) showed that letter-to-speech sound integration is an emergent property of learning to read that does not properly develop in children with DD. Thus, the phonological deficits in DD might be a consequence of the reading failure, resulting from a deviant interactive specialization of the neural systems dedicated to the letter-to-speech sound integration (see Johnson, 2011; Karmiloff-Smith, 1998, for reviews). Learning to read visual words requires, indeed, a novel integration of two neurocognitive systems: a visual system that allows the recognition of a visual word in a clutter of letter features and a phonological language system that is able to recognize the spoken words from a crowd of phonetic features (Schlaggar & McCandliss, 2007). Dehaene et al. (2010) showed that literacy enhanced the left fusiform activation together with enhancing the visual responses in the occipital cortex, including V1. These results demonstrated that reading acquisition can profoundly refine cortical organization in both the auditory–phonological and the visual–orthographic network (see Blomert, 2011, for a review). The remediation approach based on explicit, systematic instruction on letter-to-speech integration, also called “phonics training,” appears to be the most efficient treatment in English-speaking individuals with DD (McArthur et al., 2012). By comparing the efficiency of different types of training for DD remediation, a recent meta-analysis revealed that phonics instruction is not only the most frequently investigated treatment approach, but also the only approach whose efficacy on reading and spelling performance in children and adolescents with DD is statistically confirmed (Galuschka et al., 2014). 
In sum, the same old dominant view that attempted to explain DD with a single cause represented by the phonological awareness deficit remains controversial no matter how same, old, and dominant it appears at first glance; on the other hand, moving the focus more onto the letter-to-speech sound integration deficit seems to be revitalizing this traditional approach to DD. 
DD: Some new fresh air
In parallel with studies supporting the phonological hypothesis in DD, new perspectives, not necessarily opposite to the dominant view, introduced fresh air into the constant fight against DD and its consequent costs. The general idea is that a lower-level deficit can be linked to DD together with the deficit in phonoloy (Goswami, Power, Lallier, & Facoetti, 2014). 
Rapid auditory processing theory
One mild deficit that is often associated with DD seems to be at the level of auditory processing. More specifically, it seems that rapid auditory processing is defective in individuals with DD (Tallal, 1980, 2004). The inability to correctly process two sounds in a fast sequence can directly translate into future reading problems (e.g., Benasich & Tallal, 2002; Benasich, Thomas, Choudhury, & Leppanen, 2002). To some extent, these findings of temporal processing difficulties in the auditory system could be considered a possible neuronal basis for the phonological theory (e.g., Choudhury, Lappenen, Leevers, & Benasich, 2007; Benasich, Choudhury, Reale-Bonilla, & Roesler, 2014). Some pretty popular auditory perception trainings were developed in order to try to rehabilitate the reading difficulties in DD. These auditory perception trainings are language-based programs containing speech that is acoustically modified, similar to those used by speech and language therapists, in order to “cross-train” many different skills at the same time (Tallal, 2000). Although rather successful, the improvements in auditory perception were similar to what was found with phonological awareness training and do not automatically transfer into better reading abilities (e.g., Agnew et al., 2004; Galuschka et al., 2014; Strong et al., 2011). 
Temporal sampling framework
More recently, results showing a rapid auditory processing deficit were integrated with findings on neural oscillatory mechanisms related to the temporal sampling of speech in an innovative approach to DD termed the “temporal sampling framework” (TSF) by Goswami (2011). In sum, Goswami (2011) suggests that deficits in syllabic perception at relatively low frequencies in the range of delta/theta (4–10 Hz) is the critical basis for the reading disability in DD. This hypothesis is supported by findings that show the possible role of neuronal oscillations in speech perception (Luo & Poeppel, 2007; Poeppel, Idsardi, & Van Wassenhove, 2008). Even if this approach was presented as a possible neurophysiological substrate of the phonological deficit of DD, the TSF can also be applied to the various stages of processing within the visual system, way before the phonological processing stage as suggested by Vidyasagar (2013) and successfully tested by Gori, Cecchini, Bigoni, Molteni, and Facoetti (2014b). This leads to consideration of TSF with an even more broad approach that can also integrate several low-level deficits known in DD (Gori et al., 2014b; Pammer, 2014; Vidyasagar, 2013). 
The magnocellular–dorsal theory
Another dominant, albeit controversial (e.g., Amitay, Ben-Yehudah, Banai, & Ahissar, 2002; Olulade, Napoliello, & Eden, 2013; Sperling, Lu, Manis, & Seidenberg, 2005) theory is known as the magnocellular–dorsal (M–D) theory of DD (Livingstone, Rosen, Drislane, & Galaburda, 1991; Stein & Walsh, 1997), which stems from the observation that a high percentage of reading disabled children are impaired in the specific visual M–D pathway (see Boden & Giaschi, 2007; Facoetti, 2012; Gori & Facoetti, 2014; Stein & Walsh, 1997; Vidyasagar & Pammer, 2010, for reviews). The M–D pathway originates in the ganglion cells of the retina, passes through the M-layer of the lateral geniculate nucleus (LGN), and finally reaches the occipital and parietal cortices (Maunsell & Newsome, 1987). The M–D stream is considered blind to colors and responds optimally to contrast differences, low spatial frequencies, high temporal frequencies, and both real and illusory motion (e.g., Gori, Giora, & Stubbs, 2010; Gori, Giora, Yazdanbakhsh, & Mingolla, 2011; Gori, Hamburger, & Spillmann, 2006; Gori & Yazdanbakhsh, 2008; Livingstone & Hubel, 1987; Morrone et al., 2000; Ruzzoli et al., 2011; Yazdanbakhsh & Gori, 2011), which is also, surprisingly, perceived by animals without a cortex, such as fish (Gori, Agrillo, Dadda, & Bisazza, 2014a). Individuals with DD are less sensitive than typically reading controls to luminance patterns and motion displays with high temporal and low spatial frequencies (e.g., Eden et al., 1996), visual features that are known to be associated with the M–D pathway. However, they perform similarly to the controls on tasks preferentially associated with the parvocellular–ventral pathway (Gori et al., 2014b), such as those involving color and form (Merigan & Maunsell, 1993). The M–D theory can also be embedded in its multisensory (i.e., visual and auditory) version, called the temporal processing hypothesis, which suggests that children with DD have specific deficits in processing rapidly presented sensory stimuli in either the visual or auditory modalities (see Farmer & Klein, 1995; Hari & Renvall, 2001, for reviews). Importantly, the M–D temporal hypothesis explicitly claims that phonological decoding deficits in individuals with DD could arise from impairments in dynamic sensory processing of visual and auditory stimuli (e.g., Facoetti et al., 2010b; Gori et al., 2014b; Ruffino et al., 2010, 2014). It has been reported that up to 75% of dyslexic individuals show visual temporal processing deficits (Lovegrove, Martin, & Slaghuis, 1986). Moreover, a postmortem study showed that in the brain of individuals with dyslexia the M neurons of the LGN were significantly smaller than those found in normal readers' brains, and the P neurons did not differ between the two groups (Livingstone et al., 1991). This study recently received strong support from the first in vivo study (Giraldo-Chica, Hegarty, & Schneider, in press) showing smaller LGN volume in a larger sample of individuals with DD compared to controls. Recently, Gori et al. (2014b) and Gori et al. (in press) demonstrated, for the first time, that children with DD showed a lower performance in both a task that taps the M (i.e., spatial frequency doubling illusion; Kelly, 1966) and one that taps the D (i.e., rotating tilted lines illusion, Gori & Hamburger, 2006; Gori & Yazdanbakhsh, 2008; Yazdanbakhsh & Gori, 2008, and the accordion grating, Gori et al., 2011; Gori, Giora, Yazdanbakhsh, & Mingolla, 2013; Yazdanbakhsh & Gori, 2011) portion of the M-D pathway, not only in comparison with an age-matched control group, but also with a RL control group. Some longitudinal studies provided strong evidence in the direction of a causal link between a prereading M–D deficit and future reading acquisition (e.g., Boets, Vandermosten, Cornelissen, Wouters, & Ghesquière, 2011; Boets, Wouters, van Wieringen, De Smedt, & Ghesquière, 2008; Kevan & Pammer, 2008; 2009). These studies supported the hypothesis that the M–D deficit is not caused by lack of reading abilities (effect of DD) but should be considered a core deficit of DD. Gori et al. (in press) also showed the first reported association between a genetic variance (the DCDC2-Intron deletion) and an M–D deficit in both individuals with DD and typical readers. The DCDC2-Intron deletion is a proved DD genetic risk factor (e.g., Marino et al., 2011; Marino et al., 2012; Marino et al., 2014; Mascheretti et al., 2013; Mascheretti et al., in press; Meng et al., 2005; Riva, Marino, Giorda, Molteni, & Nobile, in press). According to recent studies, the M–D pathway also seems to be specifically involved in audiovisual detection enhancements (e.g., Harrar et al., 2014; Pérez-Bellido, Soto-Faraco, & Lopez-Moliner, 2013), suggesting an additional causal link between the M–D deficit and the basic cross-modal integration dysfunction in individuals with DD. Interestingly, the M–D deficit in individuals with DD was found also in logographic languages, such as Chinese (e.g., Zhao, Qian, Bi, & Coltheart, 2014). Gori and Facoetti (2014) recently stressed the importance of showing the positive effects of a rehabilitation approach based on an M–D stream deficit. If an M–D stream deficit is really a cause of DD, it is expected that specific M–D stream training would be able to improve not only M–D functioning, but also reading abilities in individuals with DD. 
In summary, some studies failed to confirm differences in high temporal, low spatial frequency stimuli perception between individuals with DD and controls (e.g., Johannes, Kussmaul, Münte, & Mangun, 1996; Schulte-Körne & Bruder, 2010; Victor, Conte, Burton, & Nass, 1993; Williams, Stuart, Castles, & McAnally, 2003, for a review). Nevertheless, sometimes questionable choices in the stimulus parameters (e.g., relative low temporal frequencies) were adopted (Stein, 2012). More importantly, around 90% of studies that specifically looked for subcortical visual M-cell deficits in individuals with DD confirmed mild M impairments in tests employing low contrast, high temporal, and low spatial frequency as recently reported by Stein (2012) in his very comprehensive literature review. 
The attentional deficit theory
Interestingly, although Wright, Conlon, and Dyck (2012) suggested that magnocellular sensitivity and visual spatial attention deficits might be independent of one another, deficits in the M-pathway could influence higher visual processing stages by the D-stream. Therefore, reading difficulties could come out due to an impaired attentional orienting system (Boden & Giaschi, 2007; Hari & Renvall, 2001; Vidyasagar & Pammer, 2010), which is anatomically contained in the D-stream. Accordingly, neuroimaging studies of both typical and atypical reading development have consistently implicated regions that are known to subserve the visual attention orienting system (see Corbetta & Shulman, 2002, 2011, for reviews). Based on that, Vidyasagar (1999), probably for the first time, proposed that an attentional deficit could be the basis of DD. 
Several studies employing phonological decoding tasks have shown deficient task-related activation in areas surrounding the bilateral frontoparietal attentional system in dyslexics (see Eden & Zeffiro, 1998, for a review). Although the left frontoparietal system has been linked to auditory word form processing (Pugh et al., 2000), the right frontoparietal system is a crucial component of the network subserving automatic attentional shifting (Corbetta & Shulman, 2002, 2011). Thus, developmental changes in activation of the right frontoparietal system have been linked to reading acquisition in typically developing children (Turkeltaub, Gareau, Flowers, Zeffiro, & Eden, 2003), and some studies have observed a right frontoparietal system dysfunction in dyslexics (e.g., Hoeft et al., 2006). A recent study using all-brain and data-driven analysis has shown divergent connectivity within the visual pathway and between visual association areas and prefrontal attention areas in adults and children with DD (Finn et al., 2014). Moreover, adults with DD have shown that high-frequency, repetitive transcranial magnetic stimulation improved nonword reading accuracy over the left and right inferior parietal lobules (Costanzo, Menghini, Caltagirone, Oliveri, & Vicari, 2013). Interestingly, children with autism spectrum disorders (e.g., Ronconi, Basso, Gori, & Facoetti, 2014; Ronconi et al., 2013a; Ronconi et al., 2012; Ronconi, Gori, Ruffino, Molteni, & Facoetti, 2013b) and with SLI (Dispaldro et al., 2013) also presented attentional focusing disorders showing how attentional dysfunction can be at the basis of different developmental outcomes. 
Some aforementioned data leads us to propose the M–D stream deficit as a possible neurobiological substrate of the spatial and temporal attentional deficit in DD, which is one of the current leading theories in explaining DD. Visual attention deficit is now considered a cause of DD, independent from the auditory–phonological abilities (Franceschini et al., 2012; Gabrieli & Norton, 2012). The visual–orthographic system receives bottom-up as well as goal-top-down attentional influence that modulates all visual processing levels from V1 to the visual word form area (see Corbetta & Shulman, 2002; 2011; Facoetti, 2012; Laycock & Crewther, 2008; McCandliss, Cohen, & Dehaene, 2003; Vidyasagar & Pammer, 2010, for reviews). 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 reducing the noise effect outside the focus of attention (e.g., Boyer & Ro, 2007; Carrasco, Williams, & Yeshurun, 2002; Dosher & Lu, 2000; Facoetti, 2001; Facoetti & Molteni, 2000; Montani, Facoetti, & Zorzi, 2014; Yeshurun & Rashal, 2010; see Bellocchi, Muneaux, Bastien-Toniazzo, & Ducrot, 2013; Reynolds & Chelazzi, 2004; Reynolds & Heeger, 2009, for reviews). Attentional shifting can be considered the result of the engagement mechanism onto the relevant object (e.g., the letter or grapheme that has to be mapped to its corresponding speech sound) and the subsequent disengagement mechanism from the previous object to the next one. Visual attentional shifting deficit has been systematically found in DD (see Facoetti, 2004, 2012; Hari & Renvall, 2001; Valdois, Bosse, & Tainturier, 2004; Vidyasagar & Pammer, 2010, for reviews) and more specifically in dyslexics with poor phonological decoding skills (e.g., Buchholz & McKone, 2004; Cestnick & Coltheart, 1999; Facoetti et al., 2010b; Facoetti et al., 2006; Jones, Branigan, & Kelly, 2008; Kinsey, Rose, Hansen, Richardson, & Stein, 2004; Roach & Hogben, 2007; Ruffino, Gori, Boccardi, Molteni, & Facoetti, 2014; Ruffino et al., 2010). Moreover, some evidence points toward a difficulty in excluding distracting stimuli. Sperling et al. (2005, 2006) found that the performance of adults in a visual motion detection task only correlated with reading ability in conditions with low signal-to-noise ratios. Using a visual search paradigm, Roach and Hogben (2004, 2007) measured psychophysical thresholds of individuals with DD and controls to detect a tilted target stimulus among vertical distractors showing an ineffective noise exclusion. Consistent with the multisensory “sluggish attentional shifting” (SAS) hypothesis (Hari & Renvall, 2001) and the “perceptual noise exclusion deficit” (Sperling et al., 2005), children and adults with DD are specifically impaired from rapidly engaging their attention, showing abnormal temporal masking (e.g., Di Lollo, Hanson, & McIntyre, 1983; Montgomery, Morris, Sevcik, & Clarkson, 2005; Ruffino et al., 2014; Ruffino et al., 2010). Evidence of SAS in the visual modality for children and adults with DD is provided by attentional blink results (e.g., Buchholz & Aimola-Davies, 2007; Facoetti, Ruffino, Peru, Paganoni, & Chelazzi, 2008; Hari, Valta, & Uutela, 1999; Lallier, Donnadieu, & Valdois, 2010; Visser, Boden, & Giaschi, 2004), temporal order judgment (Jaśkowski & Rusiak, 2008; Liddle, Jackson, Rorden, & Jackson, 2009), rapid multielement presentation (Bosse, Tainturier, & Valdois, 2007; Hawelka, Huber, & Wimmer, 2005), and spatial cueing tasks (Brannan & Williams, 1987; Facoetti, Lorusso, Cattaneo, Galli, & Molteni, 2005; Facoetti et al., 2010b; Facoetti et al., 2006; Roach & Hogben, 2007; Ruffino et al., 2014) that involve efficient spatial and temporal attentional shifting to rapidly displayed stimuli. Moreover, contrarily to what was recently affirmed by Goswami (2015), longitudinal studies and studies with pre-reading children at risk for DD have shown that visual attention shifting is one of the most important predictors of early reading abilities (e.g., Facoetti, Corradi, Ruffino, Gori, & Zorzi, 2010a; Ferretti, Mazzotti, & Brizzolara, 2008; Franceschini et al., 2012; Plaza & Cohen, 2007). In addition, the relationship between attentional skills in preschooler children and their future reading abilities resulted fully independent from phonological processing (Franceschini et al., 2012). These results clearly rule out the possible explanation suggested by Goswami (2015) about a supposed major role of the reading experience in explaining the attentional deficit found in children with DD. 
It is proposed that the core neural deficit underlying DD is the fundamental multimodal attentional mechanism (which affects both visual and auditory perception) that mediates efficient orthographic–phonological binding (Gori & Facoetti, 2014; Hari & Renvall, 2001). Some intervention studies have clearly shown that both auditory and visual shifting of attention can be improved by training in children with both DD and/or SLI (e.g., Facoetti, Lorusso, Paganoni, Umiltà, & Mascetti, 2003; Geiger, Lettvin, & Fahle, 1994; Stevens, Fanning, Coch, Sanders, & Neville, 2008). In particular, these studies consistently demonstrated that the inhibitory aspects of attention—that are crucial for perceptual noise exclusion—can be remediated with appropriate rehabilitation programs (Facoetti et al., 2003; Geiger et al., 1994). In fact, even the so-called phonologically based treatment programs that are typically used to rehabilitate DD (e.g., Olulade et al., 2013) have to make use of fundamental auditory attentional mechanisms. Recently, Franceschini et al. (2013) showed that playing action video games (AVG) for only 12 hr improved children's reading abilities, more so than 1 year of spontaneous reading development and more than or equal to highly demanding traditional reading treatments. These results were the outcome of an attentional training based on the AVGs that transferred directly to better reading abilities. After the AVG training, attentional and reading improvements were highly correlated even after controlling for phonological training–induced changes, showing how unfounded the phonological interpretation of these results recently suggested by Goswami (2015) was. Consequently, attentional training was found to be a crucial method to remediate DD independently from auditory–phonological approaches. 
Finally, before beginning the next chapter of this review, it is important to remind the reader that there are several other visual aspects that were found to be relevant in DD that are out of the scope of this review but that are well summarized in the recent book edited by Stein and Kapoula (2012). 
In summary, it seems clear that DD is a very complex disorder that is well described by a multifactorial and probabilistic model (Menghini et al., 2010). Inside this model, the visual aspects play a crucial role, and based on the scientific evidence, it is now time to seriously evaluate them even before reading acquisition. This approach could allow early identification and even prevention of DD based on prereading trainings. Among the visual aspects that are often associated with DD, one of the most prominent has not yet been mentioned: the crowding effect, which will be discussed in the following section. 
DD: The intriguing case of crowding
Several studies have suggested that individuals with DD suffer from crowding more than similarly aged control readers (e.g., Bouma & Legein, 1977; Callens, Whitney, Tops, & Brysbaert, 2013; Geiger & Lettvin, 1987; Martelli, Di Filippo, Spinelli, & Zoccolotti, 2009; Moll & Jones, 2013; Montani, Facoetti, & Zorzi, in press; Moores, Cassim, & Talcott, 2011; Perea et al., 2012; Pernet, Valdois, Celsis, & Démonet, 2006; Spinelli, De Luca, Judica, & Zoccolotti, 2002; Zorzi et al., 2012). However, some outstanding questions about the link between crowding and DD remain unanswered: 
    Is the observed excessive crowding in individuals with DD a cause or a simple effect of DD?
    Can training that aims to ameliorate the crowding resistance directly lead to better reading abilities in individuals with DD?
    Can training that aims to ameliorate the crowding resistance during the prereading stage reduce future DD incidence?
Visual crowding
Visual crowding occurs when an object becomes more difficult to identify when it is surrounded by other objects than when it is presented in isolation (see Pelli, 2008; Pelli & Tillman, 2008; Whitney & Levi, 2011, for reviews). Crowding is a universal perceptual phenomenon, not restricted to vision or reading. It can occur with simple objects, such as orientation gratings, and also with complex objects, such as letters and faces (Pelli & Tillman, 2008; Whitney & Levi, 2011). Recognition is impaired when objects are closer than a critical spacing (e.g., Yu, Cheung, Legge, & Chung, 2007), which is the distance between objects at which target recognition is restored (Martelli et al., 2009). Critical spacing is proportional to eccentricity. Thus, object identification is increasingly limited as objects are displayed at larger eccentricities (Bouma, 1970). However, crowding is independent of print size (Pelli et al., 2007). Crowding is also a different phenomenon from ordinary masking, with which the target disappears (Pelli, Palomares, & Majaj, 2004). On the contrary, the target remains visible in the typical crowding display, but it is ambiguous, mushed with the flankers. Moreover, the crowding effect extends over a larger region in comparison to what is observed in an ordinary masking display (Pelli et al., 2004). Finally, crowding is also independent from the surrounding suppression, in which a mask has the orientation preferred by the neuron but appears outside its receptive field as suggested by Levi, Hariharan, and Klein (2002) and demonstrated by Petrov, Popple, and McKee (2007) although both phenomena share several common properties (Petrov, Carandini, & McKee, 2005; Petrov & McKee; 2006; Petrov et al., 2007). 
Possible basis of visual crowding
After many years of scientific investigation, the neural mechanisms of crowding remain debated, and several theories have been proposed to explain this phenomenon. Some theories stressed the role of the early visual cortical interaction in accounting crowding. Based on these theories, crowding occurs when the target and flanker overlap within the same neural unit or they are represented by different populations of neurons with long-range horizontal connections (Flom, Heath, & Takahashi, 1963; Levi, 2008; Levi, Klein, & Aitsebaomo, 1985; Pelli, 2008). On the other hand, other theories argue that crowding could be the result of a limit in the resolution of spatial attention (He, Cavanagh, & Intriligator, 1996; Intriligator & Cavanagh, 2001; Strasburger, 2005; Yeshurun & Rashal, 2010). Some studies showed no or small effects of attentional cueing on crowding (Nazir, 1992; Scolari, Kohnen, Barton, & Awh, 2007; Wilkinson, Wilson, & Ellemberg, 1997); however, these studies did not control for the interactions between crowding and masking (see Whitney & Levi, 2011, for a review). After controlling that the cue did not mask the target, the attentional modulation on crowding seemed to be present (Yeshurun & Rashal, 2010). Moreover, although crowding is usually thought of as a spatial phenomenon, it also occurs in the time domain (see Whitney & Levi, 2011, for a review). It remains unclear if there is an independent mechanism specifically devoted to processing temporal crowding, but the effects of spatial crowding are correlated with those of temporal crowding (Bonneh, Sagi, & Polat, 2007), supporting the involvement of spatiotemporal and attentional mechanisms in crowding (e.g., Chakravarthi & Cavanagh, 2007; Yeshurun & Rashal, 2010). However, Freeman and Pelli (2007) proposed a bottom-up interpretation that could also fit, for example, the results by Intriligator and Cavanagh (2001) in a parsimonious model involving only low-level mechanisms. Dakin, Bex, Cass, & Watt (2009) argued that crowding does not specifically reflect an attention phenomena. On the other hand, Petrov and Meleshkevich (2011a, 2011b), based on their study on anisotropies and asymmetries in crowding, suggest that spatial attention is intimately involved in the mechanism of crowding. 
Although several psychophysical studies were conducted, only a few neurophysiological studies have attempted to investigate the neural mechanisms of crowding (e.g., Anderson, Dakin, Schwarzkopf, Rees, & Greenwood, 2012; Bi, Cai, Zhou, & Fang, 2009; Chen et al., 2014; Fang & He, 2008; Freeman, Donner, & Heeger, 2011; Millin, Arman, Chung, & Tjan, 2014). Some fMRI studies (Anderson et al., 2012; Freeman et al., 2011; Kwon, Bao, Millin, & Tjan, 2014; Millin et al., 2014) showed that crowding attenuated the activation in the early visual cortex (e.g., V1). However, it is unclear whether the attenuation originates in V1 or it is a result of top-down feedback from higher cortical areas due to the low temporal resolution of fMRI. Recently, Chen et al. (2014) performed event-related potential and fMRI experiments in order to measure the cortical interaction between the target and flankers in humans. Their results showed that the crowding magnitude was strongly associated with an early suppressive cortical interaction originating in V1. As reported by these authors, spatial attention plays a critical role in the manifestation of this suppression showing that attention-dependent V1 suppression contributes to crowding at a very early stage of visual processing. Another recent study (Chicherov, Plomp, & Herzog, 2014) investigated the neural substrate of crowding using high-density EEG. These authors showed that crowding might reflect processes in high-level visual areas, such as the lateral occipital cortex. Their results suggest that crowding occurs when elements are grouped into wholes (e.g., Gori & Spillmann, 2010) and cannot be fully attributed to lower cortical areas such as V1. 
Thus, the contribution of attention and of more general top-down feedback on the crowding effect remains, to date, debated. The dorsal stream role in modulating the ventral stream activation related to crowding is yet to be proved, and future studies need to be done in order to shed light on this topic. 
Visual crowding and reading
Specifically, when objects are letters, which is the main focus of this review, the situation did not seem to change at all. The distance between letters (measured center-to-center) is the critical spacing (Martelli et al., 2009), which scales with eccentricity (Bouma, 1970). In the periphery of the visual field, more letters within words printed at fixed spacing will be unrecognizable because of crowding (Bouma, 1973). Consequently, the longer a word, the stronger the effect of crowding (Martelli et al., 2009). Crowding mostly affects peripheral vision in normal adult readers (Pelli et al., 2007), but it also affects central vision in school-aged children (Jeon, Hamid, Maurer, & Lewis, 2010). It is well known that letter identification is a fundamental step in visual word recognition and reading aloud (e.g., McClelland & Rumelhart, 1981; Pelli, Farell, & Moore, 2003; Perry, Ziegler, & Zorzi, 2007). Parsing of a letter string into its constituent graphemes is a key component of phonological decoding (Perry et al., 2007), which, in turn, is fundamental for reading acquisition (Goswami, 2003; Ziegler & Goswami, 2005). 
There is growing evidence that children with DD are more influenced by crowding than age-matched controls even under optimal viewing conditions. In the pioneering study by Bouma and Legein (1977), children with DD and typical readers were investigated. Recognition scores of isolated or embedded letters were compared in both foveal and parafoveal vision. No difference was found between the two groups in isolated letters whereas the children with DD were impaired in the embedded letters condition. Interestingly, individual scores of embedded letters were correlated with reading skills. The so-called Bouma's law of crowding predicts an uncrowded central window through which we can read and a crowded periphery through which we cannot (Bouma, 1970). Crowding and eccentricity determine reading rate. Typical readers are limited by letter spacing (crowding) and not font size (acuity) during ordinary text reading under adequate illumination (Pelli et al., 2007). 
Geiger and Lettvin (1987) compared individuals with DD and typical readers in briefly presented letters and letter string identification across a large portion of the visual field. Although the individuals with DD showed a markedly wider area of correct identification in the peripheral field, they had a reduced accuracy for letter identification in and near the foveal field in comparison with typical readers. These results were interpreted as abnormal lateral masking in the near foveal field for individuals with DD. According to these authors, letters are self-masking: The different distinct parts of a letter mask each other. These findings suggest that individuals with DD present a peculiar spatial distribution of lateral masking across central and peripheral vision (see also Goolkasian & King, 1990; Lorusso et al., 2004; but Klein, Berry, Briand, D'Entremont, & Farmer, 1990). However, other studies that specifically investigated the crowding effect across eccentricity in individuals with DD and typical readers (e.g., the aforementioned Bouma & Legein, 1977, and the described below Martelli et al., 2009, studies) found that the disadvantage in letter identification for individuals with DD is present also in the periphery. 
Spinelli et al. (2002) studied the effect of crowding on word identification in typical readers and in individuals with DD. These authors presented words either alone or embedded inside other words. Vocal reaction times of individuals with DD were slower and more sensitive to the presence of the surrounding stimuli than controls. Similar results were obtained by using the same task for isolated versus crowded strings of symbols. Interestingly, a moderate increase in interletter spacing produced faster vocal reaction times in individuals with DD whereas no effect was present in the controls. More recently, Martelli et al. (2009) tested the hypothesis that crowding effects could be responsible for the slow reading rate characterizing DD. They measured contrast thresholds for identifying letters and words as a function of stimulus duration. Thresholds were higher in individuals with DD in comparison with controls for words at a limited time exposure, but not for letters, confirming the original study by Bouma and Legein (1977). It is important to note that, with long exposure time, the thresholds were similar in the two groups, suggesting possible temporal effects of spatial attention (Facoetti et al., 2010b). Pernet et al. (2006) investigated the influence of feature analysis, memory access, and stimulus type (Latin letters, Korean letters, and geometrical figures) on crowding in typical and dyslexic readers. Participants with DD showed poorer performance than controls in memory access and a reduced identification with the crowding. Poorer performance in readers with DD may reflect impaired parafoveal/low-level processing during feature integration that may have worsened in the condition with flankers due to spatial attentional disorder. 
Martelli et al. (2009) measured the spacing between a target letter and two flankers at a fixed level of performance as a function of eccentricity and size. With eccentricity, the critical spacing was significantly larger in the DD group in comparison with controls. Critical spacing was independent of stimulus size in both groups. The authors concluded that word analysis in individuals with DD is slowed because of greater crowding effects, which limit letter identification in multiletter arrays across the visual field. Crowding accounts for a large variance of children with DD slow reading speed. However, after controlling for crowding, the reading rate of children with DD remains slower than what was observed in typical readers. The persistent slow reading rate observed in DD can be simply explained in terms of a reduced reading experience as a consequence to DD itself. Crowding might not only slow down reading speed (Martelli et al., 2009; Pelli et al., 2007; Yu et al., 2007), but also might induce reading errors because crowding is accompanied by a percept that is thought to reflect pooling of features from the target and the flankers (Whitney & Levi, 2011). 
In sum, several behavioral and psychophysical studies showed that individuals with DD are abnormally affected by crowding and that crowding is modulated by the spacing between objects. It could also be argued that the different spatial distribution in crowding observed in individuals with DD can be explained by their well-documented attentional deficit that could modulate the crowding effect (e.g., Petrov & Meleshkevich, 2011a, 2011b). However, the effect of attentional cuing on crowding in the absence of position uncertainty has not been shown yet. Consequently, it is unclear if the excessive crowding found in DD can be fully attributed to the attentional deficit in DD. Further studies should be conducted in order to isolate the crowding effect in DD by controlling for attention. 
All these findings lead to the prediction that extra-large interletter spacing in words should reduce crowding and immediately ameliorate reading performance in individuals with DD. However, the previous studies did not control with RL participants. As mentioned above, the comparison with RL controls is the first step to call for a causal link between a neurocognitive aspect and DD. To our knowledge, the only published study including RL controls is the one by Zorzi et al. (2012). These authors showed that a simple manipulation of letter spacing substantially improved text reading performance on the fly (without any training) in a large, unselected sample of Italian and French children with DD. In contrast, the RL controls did not show any improvement with the extra-large spacing. This result is congruent with the previous study by Spinelli et al. (2002) in which a moderate increase of the spacing between letters improved reading only in individuals with DD. Perea et al. (2012) also demonstrated that slight increases in interletter spacing improved the readability of texts aimed at children, especially those with DD. These results seem very relevant because extra-large letter spacing might help to break the vicious circle by making the reading material more easily accessible for children with DD. Recently, Schneps, Thomson, Chen, Sonnert, & Pomplun (2013a) and Schneps et al. (2013b) showed that reducing crowding by presenting fewer words in a line on a small screen improved reading abilities. Some authors interpreted this reading improvement as a consequence of the reduced amount of attention necessary to perform the task (Schneps et al., 2013a; Schneps et al., 2013b; Zorzi et al., 2012). However, this interpretation is challenged by a study on typical readers (Lee, Kwon, Legge, & Gefroh, 2010) in which the improvement in reading abilities in the periphery found by Chung (2004) after a training was correlated to reduced crowding but not to improvement in spatial attention in peripheral vision (Lee et al., 2010). 
Nevertheless, all the previous reported studies investigating crowding in DD have mainly used letters or letter-like stimuli, yet it is already known that individuals with DD could have difficulties in processing such linguistic stimuli. Moores and colleagues (2011; see also Cassim, Talcott, & Moores, 2014, for evidence in a nonsearch task) measured the accuracy of the target orientation in an array of different numbers of—and differently spaced—vertically oriented distractors in adults with DD and controls. Results showed that adults with DD presented larger effects of crowding and a stronger impact of the increased numbers of distractors. These perceptual–attentional variables correlated significantly with reading and spelling. These findings extended the previous results of crowding in DD from letters to nonlinguistic and noncomplex stimuli. Although the crowding in DD is almost exclusively studied in the visual modality, there are some works that showed crowding is also different in individuals with DD in other modalities. Geiger et al. (2008) examined the performance of children with and without DD in two analogous recognition tasks: one visual and the other auditory. Individuals with DD showed more crowding near the center in comparison with typical readers. Both groups performed comparably in recognizing centrally spoken stimuli presented without peripheral interference, but in the presence of a surrounding speech mask, individuals with DD recognized the central stimuli significantly less well than typical readers. The authors suggest that these data showed how peculiar crowding is in DD in both visual and auditory modalities. Moreover, Grant et al. (2000) showed a deficit in tactile perception in individuals with DD that can be considered a homologue of the excessive crowding observed in the visual modality. 
Future goals for visual crowding and reading
As reported above, three important questions remain open regarding visual crowding and reading; here we would like to suggest future research projects that may answer them in quite a conclusive fashion. 
The first question that urgently needs an answer is “Is the peculiar crowding often associated with DD a cause of the reading disabilities or a simple effect of DD?” An answer to this question is crucial because, although the effects of DD on the brain and on consequent behavior can be interesting, the main aim of DD research is to find all the possible causes of DD in order to train them to actively reduce DD incidence. Our proposal is to implement a longitudinal study in which the crowding will be measured at the prereading stage, reaching even the infant stage (Farzin, Rivera, & Whitney, 2010), and the same children will be followed the next years until the diagnosis of DD can be done (which varies depending on language transparency). If the amount of crowding measured at the prereading level is a predictor of future reading abilities, a causal link between crowding and reading will be demonstrated as previously shown for the attentional deficit in DD (e.g., Franceschini et al., 2012). 
Assuming that this crucial question will have a positive answer with results showing that crowding is causally linked to DD, the next question that immediately comes to mind is “Can a training able to improve crowding resistance directly lead to better reading abilities?” 
Being able to answer this question will have two positive effects: To strengthen the causal link between crowding and DD and to provide a possible remediation program for DD based on crowding resistance training that can be integrated with the preexisting treatments in order to reduce the symptoms of DD. Interestingly, Geiger et al. (1994) tested a new method for DD remediation based on the learning of a “visual strategy” by a specific attentional focusing training. The experimental group improved reading skills significantly more than the control group. The ratio between central and peripheral crowding also changed after the attentional training. Recently, Franceschini et al. (2013) found that the reading abilities in children with DD improves after playing AVG. AVG are known to reduce the crowding effect in typical readers (Green & Bavelier, 2007); consequently, it could be interesting to test the crowding before and after the AVG training and to correlate the reading and the crowding resistance improvements to see whether less crowding will result in better reading. Another possibility could be to reduce crowding with a perceptual learning approach and test if it will lead to better reading abilities. Gori and Facoetti (2014) already proposed employing perceptual learning to improve the M–D stream functioning in individuals with DD. It is known, indeed, that perceptual learning can also reduce crowding (Chung, 2004; Chung, Levi, & Tjan, 2005; Chung, Li, & Levi, 2012; Y. He, Legge, & Yu, 2013; Hussain, Webb, Astle, & McGraw, 2012; Lee et al., 2010), being, at least on paper, a perfect candidate to be employed as a training procedure for the aforementioned aim. Moreover, it would be interesting to test, after the training, if the reduced crowding will be correlated to a better performance in both attentional and M–D tasks, which provides important information about the relationship of those deficits in causing DD. 
Assuming the previous questions will be answered positively and results will show that training procedures to reduce crowding produce a direct improvement in reading abilities, the remaining question will be “Could a training at the prereading stage based on increasing crowding resistance in children at risk for DD reduce the incidence of future DD?” Future studies are needed to answer this exciting question. On paper, this answer has less of a chance to be a positive one. It is, indeed, complicated by the fact that it requires a combination of a longitudinal study with a training study. On the other hand, it could also be the most important answer because the ultimate common goal in DD research is reducing the incidence of DD before the manifestation of its symptoms. 
In summary, this review article aimed to connect experts of vision sciences and reading in order to better understand the role of crowding in reading disability, and pave the way for studies to be able to (a) demonstrate a causal link between crowding and DD, (b) identify a risk of DD early, (c) produce new remediation trainings, and (d) project ambitious prevention programs that potentially could stem from new insights in the topic covered by this review. 
The authors would like to thank Julia R. Duggan, Sara Mascheretti, and Sara Bertoni for their helpful comments on the manuscript. This work was funded with grants from the CARIPARO Foundation (“Progetti di Eccellenza CARIPARO 2012-2014 rep. no. 1873/2012” to A. F. and S. G.) and the University of Padua (“Senior Post Doc Researcher 2014-2016” to S. G.). 
Commercial relationships: none. 
Corresponding author: Simone Gori. 
Address: Developmental and Cognitive Neuroscience Lab, Department of General Psychology, University of Padua, Padua, Italy. 
Agnew J. A. Dorn C. Eden G. F. (2004). Effect of intensive training on auditory processing and reading skills. Brain and Language, 88, 21–25. [CrossRef] [PubMed]
American Psychiatric Association. (1994). Task force on DSM-iv. DSM-iv: Diagnostic and statistical manual of mental disorders and failing reading development. Washington, D.C.: APA.
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. [CrossRef] [PubMed]
Anderson E. J. Dakin S. C. Schwarzkopf D. S. Rees G. Greenwood J. A. (2012). The neural correlates of crowding-induced changes in appearance. Current Biology, 22, 1199–1206. [CrossRef] [PubMed]
Bellocchi S. Muneaux M. Bastien-Toniazzo M. Ducrot S. (2013). I can read it in your eyes: What eye movements tell us about visuo-attentional processes in developmental dyslexia. Research in Developmental Disabilities, 34, 452–460. [CrossRef] [PubMed]
Benasich A. A. Choudhury N. A. Reaple-Bonilla T. Roesler C. P. (2014). Plasticity in developing brain: Active auditory exposure impacts prelinguistic acoustic mapping. Journal of Neuroscience, 34 (40), 13349–13363. [CrossRef] [PubMed]
Benasich A. A. Tallal P. (2002). Infant discrimination of rapid auditory cues predicts later language impairment. Behavioral Brain Research, 136 (1), 31–49. [CrossRef]
Benasich A. A. Thomas J. J. Choudhury N. Leppanen P. H. (2002). The importance of rapid auditory processing abilities to early language development: Evidence from converging methodologies. Developmental Psychology, 10 (3), 278–292. [CrossRef]
Bi T. Cai P. Zhou T. Fang F. (2009). The effect of crowding on orientation-selective adaptation in human early visual cortex. Journal of Vision, 9 (11): 13, 1–10,, doi:10.1167/9.11.13. [PubMed] [Article] [PubMed]
Blau V. Reithler J. van Atteveldt N. Seitz J. Gerretsen P. Goebel R. Blomert L. (2010). Deviant processing of letters and speech sounds as proximate cause of reading failure: A functional magnetic resonance imaging study of dyslexic children. Brain, 133, 868–879. [CrossRef] [PubMed]
Blau V. van Atteveldt N. Ekkebus M. Goebel R. Blomert L. (2009). Reduced neural integration of letters and speech sounds links phonological and reading deficits in adult dyslexia. Current Biology, 19, 503–508. [CrossRef] [PubMed]
Blomert L. (2011). The neural signature of orthographic-phonological binding in successful and failing reading development. Neuroimage, 57, 695–703. [CrossRef] [PubMed]
Boden C. Giaschi D. (2007). M-stream deficits and reading-related visual processes in developmental dyslexia. Psychological Bulletin, 133, 346–366. [CrossRef] [PubMed]
Boets B. Vandermosten M. Cornelissen P. Wouters J. Ghesquière P. (2011). Coherent motion sensitivity and reading development: Changing relations in the transition from pre-reading to reading stage. Child Development, 82, 854–869. [CrossRef] [PubMed]
Boets B. Wouters J. van Wieringen A. De Smedt B. Ghesquière P. (2008). Modelling relations between sensory processing, speech perception, orthographic and phonological ability, and literacy achievement. Brain and Language, 106 (1), 29–40. [CrossRef] [PubMed]
Bonneh Y. S. Sagi D. Polat U. (2007). Spatial and temporal crowding in amblyopia. Vision Research, 47 (14), 1950–1962. [CrossRef] [PubMed]
Bosse M. L. Tainturier M. J. Valdois S. (2007). Developmental dyslexia: The visual attention span deficit hypothesis. Cognition, 104 (2), 198–230. [CrossRef] [PubMed]
Bouma H. (1970). Interaction effects in parafoveal letter recognition. Nature, 226 (5241), 177–178. [CrossRef] [PubMed]
Bouma H. (1973). Visual interference in the parafoveal recognition of initial and final letters of words. Vision Research, 13 (4), 767–782. [CrossRef] [PubMed]
Bouma H. Legein C. P. (1977). Foveal and parafoveal recognition of letters and words by dyslexics and by average readers. Neuropsychologia, 15, 69–80. [CrossRef] [PubMed]
Boyer J. Ro T. (2007). Attention attenuates metacontrast masking. Cognition, 104 (1), 135–149. [CrossRef] [PubMed]
Brannan J. R. Williams M. C. (1987). Allocation of visual attention in good and poor readers. Perception & Psychophysics, 41 (1), 23–28.
Brizzolara D. Chilosi A. Cipriani P. Di Filippo G. Gasperini F. Mazzotti S. Zoccolotti P. (2006). Do phonologic and rapid automatized naming deficits differentially affect dyslexic children with and without a history of language delay? A study of Italian dyslexic children. Cognitive Behavior and Neurology, 19 (3), 141–149.
Brizzolara D. Gasperini F. Pfanner L. Cristofani P. Casalini C. Chilosi A. M. (2011). Long-term reading and spelling outcome in Italian adolescents with a history of specific language impairment. Cortex, 47 (8), 955–973.
Bruck M. Treiman R. (1990). Phonological awareness and spelling in normal children and dyslexics: The case of initial consonant clusters. Journal of Experimental Child Psychology, 50 (1), 156–178.
Buchholz J. Aimola-Davies A. (2007). Attentional blink deficits observed in dyslexia depend on task demands. Vision Research, 47 (10), 1292–1302.
Buchholz J. McKone E. (2004). Adults with dyslexia show deficits on spatial frequency doubling and visual attention tasks. Dyslexia, 10 (1), 24–43.
Callens M. Whitney C. Tops W. Brysbaert M. (2013). No deficiency in left-to-right processing of words in dyslexia but evidence for enhanced visual crowding. The Quarterly Journal of Experimental Psychology, 66 (9), 1803–1817.
Carrasco M. Williams P. E. Yeshurun Y. (2002). Covert attention increases spatial resolution with or without masks: Support for signal enhancement. Journal of Vision, 2 (6): 4, 467–479,, doi:10.1167/2.6.4. [PubMed] [Article]
Carreiras M. Seghier M. L. Baquero S. Estévez A. Lozano A. Devlin J. T. Price C. J. (2009). An anatomical signature for literacy. Nature, 461 (7266), 983–986.
Carrion-Castillo A. Franke B. Fisher S. E. (2013). Molecular genetics of dyslexia: An overview. Dyslexia, 19 (4), 214–240.
Cassim R. Talcott J. B. Moores E. (2014). Adults with dyslexia demonstrate large effects of crowding and detrimental effects of distractors in a visual tilt discrimination task. PLoS One, 9 (9), e106191.
Castles A. Coltheart M. (2004). Is there a causal link from phonological awareness to success in learning to read? Cognition, 91 (1), 77–111.
Cestnick L. Coltheart M. (1999). The relationship between language-processing and visual-processing deficits in developmental dyslexia. Cognition, 71 (3), 231–255.
Chakravarthi R. Cavanagh P. (2007). Temporal properties of the polarity advantage effect in crowding. Journal of Vision, 7 (2): 11, 1–13,, doi:10.1167/7.2.11. [PubMed] [Article]
Chen J. He Y. Zhu Z. Zhou T. Peng Y. Zhang X. Fang F. (2014). Attention-dependent early cortical suppression contributes to crowding. The Journal of Neuroscience, 34 (32), 10465–10474.
Chicherov V. Plomp G. Herzog M. H. (2014). Neural correlates of visual crowding. Neuroimage, 93 (1), 23–31.
Chilosi A. M. Brizzolara D. Lami L. Pizzoli C. Gasperini F. Pecini C. Zoccolotti P. (2011). Reading and spelling disabilities in children with and without a history of early language delay: A neuropsychological and linguistic study. Child Neuropsychol, 15 (6), 582–604.
Choudhury N. Lappanen P. N. Leevers H. J. Benasich A. A. (2007). Infant information processing and family history of specific language impairment: Converging evidence for RAP deficits from two paradigms. Developmental Science, 10 (2), 213–236.
Chung S. T. (2004). Reading speed benefits from increased vertical word spacing in normal peripheral vision. Optometry Vision Science, 81 (7), 525–535.
Chung S. T. Levi D. M. Tjan B. S. (2005). Learning letter identification in peripheral vision. Vision Research, 45, 1399–1412.
Chung S. T. Li R. W. Levi D. M. (2012). Learning to identify near-acuity letters, either with or without flankers, results in improved letter size and spacing limits in adults with amblyopia. PLoS One, 7, e35829.
Clark K. A. Helland T. Specht K. Narr K. L. Manis F. R. Toga A. W. Hugdahl K. (2014). Neuroanatomical precursors of dyslexia identified from pre-reading through to age 11. Brain, 137 (12), 3136–3141.
Corbetta M. Shulman G. L. (2002). Control of goal-directed and stimulus-driven attention in the brain. Nature Reviews Neuroscience, 3, 201–215. [CrossRef]
Corbetta M. Shulman G. L. (2011). Spatial neglect and attention networks. Annual Review Neuroscience, 34, 569–599. [CrossRef]
Costanzo F. Menghini D. Caltagirone C. Oliveri M. Vicari S. (2013). How to improve reading skills in dyslexics: The effect of high frequency rTMS. Neuropsychologia, 51 (14), 2953–2959. [CrossRef]
Cunningham A. E. Stanovich K. E. (1998). What reading does for the mind. American Education, 22, 8–15.
Dakin S. C. Bex P. J. Cass J. R. Watt R. J. (2009). Dissociable effects of attention and crowding on orientation averaging. Journal of Vision, 9 (11): 28, 1–16,, doi:10.1167/9.11.28. [PubMed] [Article]
Dehaene S. Pegado F. Braga L. W. Ventura P. Nunes Filho G. Cohen L. (2010). How learning to read changes the cortical networks for vision and language. Science, 330, 1359–1364. [CrossRef]
Di Lollo V. Hanson D. McIntyre J. S. (1983). Initial stages of visual information processing in dyslexia. Journal of Experimental Psychology: Human, Perception & Performance, 9, 923–935.
Dispaldro M. Leonard L. B. Corradi N. Ruffino M. Bronte T. Facoetti A. (2013). Visual attentional engagement deficits in children with specific language impairment and their role in real-time language processing. Cortex, 49 (8), 2126–2139.
Dosher B. A. Lu Z. L. (2000). Noise exclusion in spatial attention. Psychological Science, 11 (2), 139–146.
Eden G. F. VanMeter J. W. Rumsey J. M. Maisog J. M. Woods R. P. Zeffiro T. A. (1996). Abnormal processing of visual motion in dyslexia revealed by functional brain imaging. Nature, 382, 66–69.
Eden G. F. Zeffiro T. A. (1998). Neural systems affected in developmental dyslexia revealed by functional neuroimaging. Neuron, 21 (2), 279–282. [PubMed]
Facoetti A. (2001). Facilitation and inhibition mechanisms of human visuospatial attention in a non-search task. Neuroscience Letters, 298 (1), 45–48. [PubMed]
Facoetti A. (2004). Reading and selective spatial attention: Evidence from behavioral studies in dyslexic children. In Tobias D. (Ed.), Trends in dyslexia research (pp. 35–71). New York: Nova Biomedical Books.
Facoetti A. (2012). Spatial attention disorders in developmental dyslexia: Towards the prevention of reading acquisition deficits. In Stein J. Kapoula Z. (Eds.), Visual aspect of dyslexia (pp. 123–136). Oxford, UK: Oxford University Press.
Facoetti A. Corradi N. Ruffino M. Gori S. Zorzi M. (2010a). Visual spatial attention and speech segmentation are both impaired in preschoolers at familial risk for developmental dyslexia. Dyslexia, 16, 226–239.
Facoetti A. Lorusso M. L. Cattaneo C. Galli R. Molteni M. (2005). Visual and auditory attentional capture are both sluggish in children with developmental dyslexia. Acta Neurobiologiae Experimentalis, 65, 61–72. [PubMed]
Facoetti A. Lorusso M. L. Paganoni P. Umiltà C. Mascetti G. G. (2003). The role of visuospatial attention in developmental dyslexia: Evidence from a rehabilitation study. Brain Research, Cognitive Brain Research, 15 (2), 154–164.
Facoetti A. Molteni M. (2000). Is attentional focusing an inhibitory process at distractor location? Brain Research, Cognitive Brain Research, 10, 185–188.
Facoetti A. Ruffino M. Peru A. Paganoni P. Chelazzi L. (2008). Sluggish engagement and disengagement of non-spatial attention in dyslexic children. Cortex, 44, 1221–1233. [PubMed]
Facoetti A. Trussardi A. N. Ruffino M. Lorusso M. L. Cattaneo C. Galli R. Zorzi M. (2010 b). Multisensory spatial attention deficits are predictive of phonological decoding skills in developmental dyslexia. Journal of Cognitive Neuroscience, 22, 1011–1025. [PubMed]
Facoetti A. Zorzi M. Cestnick L. Lorusso M. L. Molteni M. Paganoni P. Mascetti G. G. (2006). The relationship between visuo-spatial attention and nonword reading in developmental dyslexia. Cognitive Neuropsychology, 23, 841–855. [PubMed]
Fang F. He S. (2008). Crowding alters the spatial distribution of attention modulation in human primary visual cortex. Journal of Vision, 8 (9): 6, 1–9,, doi:10.1167/8.9.6. [PubMed] [Article]
Farmer M. E. Klein R. M. (1995). The evidence for a temporal processing deficit linked to dyslexia: A review. Psychonomic Bulletin & Review, 2 (4), 460493.
Farzin F. Rivera S. M. Whitney D. (2010). Spatial resolution of conscious visual perception in infants. Psychological Science, 21 (10), 1502–1509.
Ferretti G. Mazzotti S. Brizzolara D. (2008). Visual scanning and reading ability in normal and dyslexic children. Behavioral Neurology, 19 (1–2), 87–92.
Finn E. S. Shen X. Holahan J. M. Scheinost D. Lacadie C. Papademetris X. Constable R. T. (2014). Disruption of functional networks in dyslexia: A whole-brain, data driven analysis of connectivity. Biological Psychiatry, 76 (5), 397–404.
Fisher S. E. De Fries J. C. (2002). Developmental dyslexia: Genetic dissection of a complex cognitive trait. Nature Reviews Neuroscience, 3 (10), 767–780.
Flom M. C. Heath G. G. Takahashi E. (1963). Contour interaction and visual resolution: Controlateral effects. Science, 142 (3594), 979–980.
Franceschini S. Gori S. Ruffino M. Pedrolli K. Facoetti A. (2012). A causal link between visual spatial attention and reading acquisition. Current Biology, 22, 814–819.
Franceschini S. Gori S. Ruffino M. Viola S. Molteni M. Facoetti A. (2013). Action video games make dyslexic children read better. Current Biology, 23, 462–466.
Freeman J. Donner T. H. Heeger D. J. (2011). Inter-area correlations in the ventral visual pathway reflect feature integration. Journal of Vision, 11 (4): 15, 1–23,, doi:10.1167/11.4.15. [PubMed] [Article]
Freeman J. Pelli D. G. (2007). An escape from crowding. Journal of Vision, 7 (2): 22, 1–14,, doi:10.1167/7.2.22. [PubMed] [Article]
Gabrieli J. D. (2009). Dyslexia: A new synergy between education and cognitive neuroscience. Science, 325, 280–283.
Gabrieli J. D. Norton E. S. (2012). Reading abilities: Importance of visual-spatial attention. Current Biology, 22 (9), R298–R299.
Galuschka K. Ise E. Krick K. Schulte-Körne G. (2014). Effectiveness of treatment approaches for children and adolescents with reading disabilities: A meta-analysis of randomized controlled trials. PLoS One, 9 (2), e89900.
Geiger G. Cattaneo C. Galli R. Pozzoli U. Lorusso M. L. Facoetti A. Molteni M. (2008). Wide and diffuse perceptual modes characterize dyslexics in vision and audition. Perception, 37 (11), 1745–1764.
Geiger G. Lettvin J. Y. (1987). Peripheral vision in persons with dyslexia. New England Journal of Medicine, 316 (20), 1238–1243.
Geiger G. Lettvin J. Y. Fahle M. (1994). Dyslexic children learn a new visual strategy for reading: A controlled experiment. Vision Research, 34 (9), 1223–1233.
Giraldo-Chica M. Hegarty J. P. II Schneider K. A. (in press). Morphological differences in the lateral geniculate nucleus associated with dyslexia. Neuroimage Clinical.
Goolkasian P. King J. (1990). Letter identification and lateral masking in dyslexic and average readers. American Journal of Psychology, 103 (4), 519–538.
Gori S. Agrillo C. Dadda M. Bisazza A. (2014a). Do fish perceive illusory motion? Scientific Reports, 4, 6443, doi:10.1038/srep06443.
Gori S. Cecchini P. Bigoni A. Molteni M. Facoetti A. (2014b). Magnocellular-dorsal pathway and sub-lexical route in developmental dyslexia. Frontiers in Human Neurosciences, 8, 460.
Gori S. Facoetti A. (2014). Perceptual learning as a possible new approach for remediation and prevention of developmental dyslexia. Vision Research, 99, 78–87.
Gori S. Giora E. Stubbs D. A. (2010). Perceptual compromise between apparent and veridical motion indices: The unchained-dots illusion. Perception, 39, 863–866.
Gori S. Giora E. Yazdanbakhsh A. Mingolla E. (2011). A new motion illusion based on competition between two kinds of motion processing units: The accordion grating. Neural Network, 24, 1082–1092.
Gori S. Giora E. Yazdanbakhsh A. Mingolla E. (2013). The novelty of the accordion grating. Neural Network, 39, 52. [CrossRef]
Gori S. Hamburger K. (2006). A new motion illusion: The rotating-tilted-lines illusion. Perception, 35 (6), 853–857. [CrossRef]
Gori S. Hamburger K. Spillmann L. (2006). Reversal of apparent rotation in the Enigma-figure with and without motion adaptation and the effect of T-junctions. Vision Research, 46, 3267–3273. [CrossRef]
Gori S. Mascheretti S. Giora E. Ronconi L. Ruffino M. Quadrelli E. Marino C. (in press). The DCDC2 intron 2 deletion impairs illusory motion perception unveiling the selective role of magnocellular-dorsal stream in reading (dis)ability. Cerebral Cortex, doi:10.1093/cercor/bhu234.
Gori S. Spillmann L. (2010). Detection vs. grouping thresholds for elements differing in spacing, size and luminance. An alternative approach towards the psychophysics of Gestalten. Vision Research, 50, 1194–1202. [CrossRef]
Gori S. Yazdanbakhsh A. (2008). The riddle of the rotating-tilted-lines illusion. Perception, 37, 631–635. [CrossRef]
Goswami U. (2003). Why theories about developmental dyslexia require developmental designs. Trends in Cognitive Science, 7, 534–540. [CrossRef]
Goswami U. (2011). A temporal sampling framework for developmental dyslexia. Trends in Cognitive Science, 15 (1), 3–10. [CrossRef]
Goswami U. (2015). Sensory theories of developmental dyslexia: Three challenges for research. Nature Reviews Neuroscience, 16 (1), 43–54.
Goswami U. Bryant P. (1989). The interpretation of studies using the reading level design. Journal of Literacy Research, 21 (4), 413–424. [CrossRef]
Goswami U. Bryant P. (1990). Phonological skills and learning to read. Journal of Child Psychology and Psychiatry, 32 (7), 1173–1176.
Goswami U. Power A. J. Lallier M. Facoetti A. (2014). Oscillatory “temporal sampling” and development dyslexia: Toward an over-archid theoretical framework. Frontiers in Human Neuroscience, 8, 904. [CrossRef]
Grant A. C. Zangaladze A. Thiagarajah M. C. Sathian K. (1999). Tactile perception in developmental dyslexia: A psychophysical study using gratings. Neuropsychologia, 37 (10), 1201–1211. [CrossRef]
Green C. S. Bavelier D. (2007). Action-video-game experience alters the spatial resolution of vision. Psychological Science, 18, 88–94. [CrossRef]
Hallgren B. (1950). Specific dyslexia (congenital word-blindness): A clinical and genetic study. Acta Psychiatrica et Neurologica. Supplementum, 65, 1–287.
Hari R. Renvall H. (2001). Impaired processing of rapid stimulus sequences in dyslexia. Trends in Cognitive Science, 5, 525–532. [CrossRef]
Hari R. Valta M. Uutela K. (1999). Prolonged attentional dwell time in dyslexic adults. Neuroscience Letters, 271, 202–204. [CrossRef]
Harold D. Paracchini S. Scerri T. Dennis M. Cope N. Hill G. Monaco A. P. (2006). Further evidence that the KIAA0319 gene confers susceptibility to developmental dyslexia. Molecular Psychiatry, 11 (12), 1085–1091. [CrossRef]
Harrar V. Tammam J. Pérez-Bellido A. Pitt A. Stein J. Spence C. (2014). Multisensory integration and attention in developmental dyslexia. Current Biology, 24 (5), 531–535. [CrossRef]
Hawelka S. Huber C. Wimmer H. (2005). Impaired visual processing of letter and digit strings in adult dyslexic readers. Vision Research, 46 (5), 718–723.
He S. Cavanagh P. Intriligator J. (1996). Attentional resolution and the locus of visual awareness. Nature, 383 (6598), 334–337. [CrossRef]
He Y. Legge G. E. Yu D. (2013). Sensory and cognitive influences on the training-related improvement of reading speed in peripheral vision. Journal of Vision, 13 (7): 14, 1–14,, doi:10.1167/13.7.14. [PubMed] [Article]
Hoeft F. Hernandez A. McMillon G. Taylor-Hill H. Martindale J. L. Meyler A. Gabrieli J. D. (2006). Neural basis of dyslexia: A comparison between dyslexic and nondyslexic children equated for reading ability. Journal of Neuroscience, 26, 10700–10708. [CrossRef] [PubMed]
Hornickel J. Kraus N. (2013). Unstable representation of sound: A biological marker of dyslexia. Journal of Neuroscience, 33, 3500–3504. [CrossRef] [PubMed]
Hussain Z. Webb B. S. Astle A. T. McGraw P. V. (2012). Perceptual learning reduces crowding in amblyopia and in the normal periphery. Journal of Neuroscience, 32, 474–480. [CrossRef] [PubMed]
Intriligator J. Cavanagh P. (2001). The spatial resolution of visual attention. Cognitive Psychology, 43 (3), 171–216. [CrossRef] [PubMed]
Jaśkowski P. Rusiak P. (2008). Temporal order judgment in dyslexia. Psychological Research, 72 (1), 65–73. [PubMed]
Jeon S. T. Hamid J. Maurer D. Lewis T. L. (2010). Developmental changes during childhood in single-letter acuity and its crowding by surrounding contours. Journal of Experimental Child Psychology, 107 (4), 423–437. [CrossRef] [PubMed]
Johannes S. Kussmaul C. L. Münte T. F. Mangun G. R. (1996). Developmental dyslexia: Passive visual stimulation provides no evidence for a magnocellular processing defect. Neuropsychologia, 34 (11), 1123–1127. [CrossRef] [PubMed]
Johnson M. H. (2011). Interactive specialization: A domain-general framework for human functional brain development? Developmental Cognitive Neuroscience, 1 (1), 7–21. [CrossRef] [PubMed]
Jones M. W. Branigan H. P. Kelly M. L. (2008). Visual deficits in developmental dyslexia: Relationships between non-linguistic visual tasks and their contribution to components of reading. Dyslexia, 14 (2), 95–115. [CrossRef] [PubMed]
Karmiloff-Smith A. (1998). Development itself is the key to understanding developmental disorders. Trends in Cognitive Science, 2 (10), 389–398. [CrossRef]
Kelly D. (1966). Frequency doubling in visual responses. JOSA, 56, 1628–1632. [CrossRef]
Kevan A. Pammer K. (2008). Visual deficits in pre-readers at familial risk for dyslexia. Vision Research, 48, 2835–2839. [CrossRef] [PubMed]
Kevan A. Pammer K. (2009). Predicting early reading skills from pre-reading measures of dorsal stream functioning. Neuropsychologia, 47, 3174–3181. [CrossRef] [PubMed]
Kinsey K. Rose M. Hansen P. Richardson A. Stein J. (2004). Magnocellular mediated visual-spatial attention and reading ability. NeuroReport, 15 (14), 2215–2218. [CrossRef] [PubMed]
Klein R. Berry G. Briand K. D'Entremont B. Farmer M. (1990). Letter identification declines with increasing retinal eccentricity at the same rate for normal and dyslexic readers. Perception & Psychophysics, 47 (6), 601–606.
Krafnick A. J. Flowers D. L. Luetje M. M. Napoliello E. M. Eden G. F. (2014). An investigation into the origin of anatomical differences in dyslexia. Journal of Neuroscience, 34 (3), 901–908.
Kwon M. Bao P. Millin R. Tjan B. S. (in press). Radial-tangential anisotropy of crowding in the early visual areas. Journal of Neurophysiology, 112 (10), 2413–2422.
Lallier M. Donnadieu S. Valdois S. (2010). Visual attentional blink in dyslexic children: Parameterizing the deficit. Vision Research, 50, 1855–1861.
Laycock R. Crewther S. G. (2008). Towards an understanding of the role of the ‘magnocellular advantage' in fluent reading. Neuroscience and Biobehavioral Reviews, 32 (8), 1494–1506.
Lee H. W. Kwon M. Legge G. E. Gefroh J. J. (2010). Training improves reading speed in peripheral vision: Is it due to attention? Journal of Vision, 10 (6): 18, 1–15,, doi:10.1167/10.6.18. [PubMed] [Article] [CrossRef]
Levi D. M. (2008). Crowding–An essential bottleneck for object recognition: A minireview. Vision Research, 48 (5), 635–654. [CrossRef]
Levi D. M. Hariharan S. Klein S. A. (2002). Suppressive and facilitatory spatial interactions in peripheral vision: Peripheral crowding is neither size invariant nor simple contrast masking. Journal of Vision, 2 (2): 3, 167–177,, doi:10.1167/2.2.3. [PubMed] [Article]
Levi D. M. Klein S. A. Aitsebaomo A. P. (1985). Vernier acuity, crowding and cortical magnification. Vision Research, 25 (7), 963–977. [CrossRef]
Liddle E. B. Jackson G. M. Rorden C. Jackson S. R. (2009). Lateralized temporal order judgement in dyslexia. Neuropsychologia, 47 (14), 3244–3254. [CrossRef]
Livingstone M. S. Hubel D. H. (1987). Psychophysical evidence for separate channels for the perception of form, color, movement, and depth. Journal of Neuroscience, 7, 3416–3468.
Livingstone M. S. Rosen G. D. Drislane F. W. Galaburda A. M. (1991). Physiological and anatomical evidence for a magnocellular defect in developmental dyslexia. Proceedings of the National Academy of Sciences, USA, 88, 7943–7947. [CrossRef]
Lorusso M. L. Facoetti A. Pesenti S. Cattaneo C. Molteni M. Geiger G. (2004). Wider recognition in peripheral vision common to different subtypes of dyslexia. Vision Research, 44, 2413–2424. [CrossRef]
Lovegrove W. Martin F. Slaghuis W. (1986). A theoretical and experimental case for a visual deficit in specific reading disability. Cognitive Neuropsychology, 3, 225–267. [CrossRef]
Ludwig K. U. Schumacher J. Schulte-Körne G. König I. R. Warnke A. Plume E. Hoffmann P. (2008). Investigation of the DCDC2 intron 2 deletion/compound short tandem repeat polymorphism in a large German dyslexia sample. Psychiatric Genetics, 18 (6), 310–312. [CrossRef]
Luo H. Poeppel D. (2007). Phase patterns of neuronal responses reliably discriminate speech in human auditory cortex. Neuron, 54 (6), 1001–1010. [CrossRef]
Marino C. Mascheretti S. Riva V. Cattaneo F. Rigoletto C. Rusconi M. Molteni M. (2011). Pleiotropic effects of DCDC2 and DYX1C1 genes on language and mathematics traits in nuclear families of developmental dyslexia. Behavior Genetics, 41 (1), 67–76. [CrossRef]
Marino C. Meng H. Mascheretti S. Rusconi M. Cope N. Giorda R. Gruen J. R. (2012). DCDC2 genetic variants and susceptibility to developmental dyslexia. Psychiatry Genetic, 22, 25–30. [CrossRef]
Marino C. Scifo P. Della Rosa P. A. Mascheretti S. Facoetti A. Perani D. (2014). The DCDC2/intron 2 deletion and white matter disorganization: Focus on developmental dyslexia. Cortex, 57, 227–243. [CrossRef]
Martelli M. Di Filippo G. Spinelli D. Zoccolotti P. (2009). Crowding, reading, and developmental dyslexia. Journal of Vision, 9 (4): 14, 1–18,, doi:10.1167/9.4.14. [PubMed] [Article]
Mascheretti S. Bureau A. Battaglia M. Simone D. Quadrelli E. Croteau J. Marino C. (2013). An assessment of gene-by-environment interactions in developmental dyslexia-related phenotypes. Genes, Brain and Behavior, 12, 47–55. [CrossRef]
Mascheretti S. Marino C. Simone D. Quadrelli E. Riva V. Cellino M. R. Battaglia M. (in press). Putative risk factors in developmental dyslexia: A case-control study of Italian children. Journal of Learning Disabilities, doi:10.1177/0022219413492853.
Mattingly I. G. (1972). Speech cues and sign stimuli. American Scientist, 60 (3), 327–337.
Maunsell J. H. R. Newsome W. T. (1987). Visual processing in monkey extrastriate cortex. Annual Review of Neuroscience, 10, 363–401. [CrossRef]
McArthur G. Eve P. M. Jones K. Banales E. Kohnen S. Anandakumar T Castles A. (2012). Phonics training for English-speaking poor readers. Cochrane Database of Systematic Reviews, 12, CD009115.
McCandliss B. D. Cohen L. Dehaene S. (2003). The visual word form area: Expertise for reading in the fusiform gyrus. Trends in Cognitive Science, 7 (7), 293–299. [CrossRef]
McClelland J. L. Rumelhart D. E. (1981). An interactive activation model of context effects in letter perception: I. An account of basic findings. Psychological Review, 88 (5), 375–407. [CrossRef]
Meng H. Smith S. D. Hager K. Held M. Liu J. Olson R. K. Gruen J. R. (2005). DCDC2 is associated with reading disability and modulates neuronal development in the brain. Proceedings of the National Academy of Sciences, USA, 102 (47), 17053–17058. [CrossRef]
Menghini D. Finzi A. Benassi M. Bolzani R. Facoetti A. Giovagnoli S. Vicari S. (2010). Different underlying neurocognitive deficits in developmental dyslexia: A comparative study. Neuropsychologia, 48 (4), 863–872. [CrossRef]
Merigan W. H. Maunsell J. H. (1993). How parallel are the primate visual pathways? Annual Review of Neuroscience, 16, 369–402. [CrossRef]
Millin R. Arman A. C. Chung S. T. Tjan B. S. (2014). Visual crowding in V1. Cerebral Cortex, 24 (12), 3107–3115.
Moll K. Jones M. (2013). Naming fluency in dyslexic and nondyslexic readers: Differential effects of visual crowding in foveal, parafoveal, and peripheral vision. Quarterly Journal of Experimental Psychology, 66 (11), 2085–2091. [CrossRef]
Montani V. Facoetti A. Zorzi M. (2014). Spatial attention in written world perception. Frontiers in Human Neuroscience, 8, 42, doi:10.3389/fnhum.2014.00042.
Montani V. Facoetti A. Zorzi M. (in press). The effect of decreased interletter spacing on orthographic processing. Psychonomic Bulletin & Review.
Montgomery C. R. Morris R. D. Sevcik R. A. Clarkson M. G. (2005). Auditory backward masking deficits in children with reading disabilities. Brain and Language, 95 (3), 450–456. [CrossRef]
Moores E. Cassim R. Talcott J. B. (2011). Adults with dyslexia exhibit large effects of crowding, increased dependence on cues, and detrimental effects of distractors in visual search tasks. Neuropsychologia, 49, 3881–3890. [CrossRef]
Morrone M. C. Tosetti M. Montanaro D. Fiorentini A. Cioni G. Burr D. C. (2000). A cortical area that responds specifically to optic flow, revealed by fMRI. Nature Neuroscience, 3 (12), 1322–1328. [CrossRef]
Nazir T. A. (1992). Effects of lateral masking and spatial precueing on gap-resolution in central and peripheral vision. Vision Research, 32, 771–777. [CrossRef]
Olulade O. A. Napoliello E. M. Eden G. F. (2013). Abnormal visual motion processing is not a cause of dyslexia. Neuron, 79, 1–11. [CrossRef]
Pammer K. (2014). Temporal sampling in vision and the implications for dyslexia. Frontiers in Human Neuroscience, 7, 933. [CrossRef] [PubMed]
Paracchini S. Scerri T. Monaco A. P. (2007). The genetic lexicon of dyslexia. Annual Review of Genomics and Human Genetics, 8, 57–79. [CrossRef] [PubMed]
Pelli D. G. (2008). Crowding: A cortical constraint on object recognition. Current Opinion in Neurobiology, 18 (4), 445–451. [CrossRef] [PubMed]
Pelli D. G. Farell B. Moore D. C. (2003). The remarkable inefficiency of word recognition. Nature, 423 (6941), 752–756. [CrossRef] [PubMed]
Pelli D. G. Palomares M. Majaj N. J. (2004). Crowding is unlike ordinary masking: Distinguishing feature integration from detection. Journal of Vision, 4 (12): 12, 1136–1169,, doi:10.1167/4.12.12. [PubMed] [Article] [PubMed]
Pelli D. G. Tillman K. A. (2008). The uncrowded window of object recognition. Nature Neuroscience, 11 (10), 1129–1135. [CrossRef] [PubMed]
Pelli D. G. Tillman K. A. Freeman J. Su M. Berger T. D. Majaj N. J. (2007). Crowding and eccentricity determine reading rate. Journal of Vision, 7 (2): 20, 1–36,, doi:10.1167/7.2.20. [PubMed] [Article]
Perea M. Gomez P. (2012). Subtle increases in interletter spacing facilitate the encoding of words during normal reading. PLoS One, 7 (10), e47568.
Perea M. Panaderó V. Moret-Tatay C. Góméz P. (2012). The effects of inter-letter spacing in visual-word recognition: Evidence with young normal readers and developmental dyslexics. Learning and Instruction, 22, 420–430. [CrossRef]
Pérez-Bellido A. Soto-Faraco S. Lopez-Moliner J. (2013). Sound-driven enhancement of vision: Disentangling detection-level from decision-level contributions. Journal of Neurophysiology, 109 (4), 1065–1077. [CrossRef] [PubMed]
Pernet C. Valdois S. Celsis P. Démonet J. F. (2006). Lateral masking, levels of processing and stimulus category: A comparative study between normal and dyslexic readers. Neuropsychologia, 44 (12), 2374–2385. [CrossRef] [PubMed]
Perry C. Ziegler J. C. Zorzi M. (2007). Nested incremental modeling in the development of computational theories: The CDP+ model of reading aloud. Psychological Review, 114 (2), 273–315. [CrossRef] [PubMed]
Peterson R. L. Pennington B. F. (2012). Developmental dyslexia. Lancet, 379 (9830), 1997–2007. [CrossRef] [PubMed]
Petrov Y. Carandini M. McKee S. (2005). Two distinct mechanisms of suppression in human vision. Journal of Neuroscience, 25, 8704–8707. [CrossRef] [PubMed]
Petrov Y. McKee S. P. (2006). The effect of spatial configuration on surround suppression of contrast sensitivity. Journal of Vision, 6 (3): 4, 224–238,, doi:10.1167/6.3.4. [PubMed] [Article] [PubMed]
Petrov Y. Meleshkevich O. (2011 a). Asymmetries and idiosyncratic hot spots in crowding. Vision Research, 51, 1117–1123. [CrossRef] [PubMed]
Petrov Y. Meleshkevich O. (2011 b). Locus of spatial attention determines inward-outward anisotropy in crowding. Journal of Vision, 11 (4): 1, 1–11,, doi:10.1167/11.4.1. [PubMed] [Article] [CrossRef] [PubMed]
Petrov Y. Popple A. V. McKee S. P. (2007). Crowding and surround suppression: Not to be confused. Journal of Vision, 7 (2): 12, 1–9,, doi:10.1167/7.2.12. [PubMed] [Article]
Plaza M. Cohen H. (2007). The contribution of phonological awareness and visual attention in early reading and spelling. Dyslexia, 13, 67–76. [CrossRef] [PubMed]
Plomin R. Kovas Y. (2005). Generalist genes and learning disabilities. Psychological Bulletin, 131 (4), 592–617. [CrossRef] [PubMed]
Poelmans G. Buitelaar J. K. Pauls D. L. Franke B. (2011). A theoretical molecular network for dyslexia: Integrating available genetic findings. Molecular Psychiatry, 16 (4), 365–382. [CrossRef] [PubMed]
Poeppel D. Idsardi W. J. Van Wassenhove V. (2008). Speech perception at the interface of neurobiology and linguistics. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 363 (1493), 1071–1086. [CrossRef]
Powers N. R. Eicher J. D. Butter F. Kong Y. Miller L. L. Ring S. M. Gruen J. R. (2013). Alleles of a polymorphic ETV6 binding site in DCDC2 confer risk of reading and language impairment. American Journal of Human Genetics, 93 (1), 19–28. [CrossRef] [PubMed]
Pugh K. R. Mencl W. E. Shaywitz B. A. Shaywitz S. E. Fulbright R. K. Constable R. T. Gore J. C. (2000). The angular gyrus in developmental dyslexia: Task-specific differences in functional connectivity within posterior cortex. Psychological Science, 11 (1), 51–56. [CrossRef] [PubMed]
Reynolds J. H. Chelazzi L. (2004). Attentional modulation of visual processing. Annual Review of Neuroscience, 27, 611–647. [CrossRef] [PubMed]
Reynolds J. H. Heeger D. J. (2009). The normalization model of attention. Neuron, 61 (2), 168–185. [CrossRef] [PubMed]
Riva V. Marino C. Giorda R. Molteni M. Nobile M. (in press). The role of DCDC2 genetic variants and low socioeconomic status in vulnerability to attention problems. European Child & Adolescent Psychiatry.
Roach N. V. Hogben J. H. (2004). Attentional modulation of visual processing in adult dyslexia: A spatial-cuing deficit. Psychological Science, 15 (10), 650–654. [CrossRef] [PubMed]
Roach N. V. Hogben J. H. (2007). Impaired filtering of behaviourally irrelevant visual information in dyslexia. Brain, 130 (3), 771–785. [CrossRef] [PubMed]
Ronconi L. Basso D. Gori S. Facoetti A. (2014). TMS on right frontal eye fields induces an inflexible focus of attention. Cerebral Cortex, 24 (2), 396–402. [CrossRef] [PubMed]
Ronconi L. Gori S. Giora E. Ruffino M. Molteni M. Facoetti A. (2013a). Deeper attentional masking by lateral objects in children with autism. Brain and Cognition, 82, 213–218. [CrossRef]
Ronconi L. Gori S. Ruffino M. Franceschini S. Urbani B. Molteni M. Facoetti A. (2012). Decreased coherent motion discrimination in autism spectrum disorder: The role of attentional zoom-out deficit. PLoS One, 7, e49019.
Ronconi L. Gori S. Ruffino M. Molteni M. Facoetti A. (2013b). Zoom-out attentional impairment in children with autism spectrum disorder. Cortex, 49, 1025–1033. [CrossRef]
Ruffino M. Gori S. Boccardi D. Molteni M. Facoetti A. (2014). Spatial and temporal attention are both sluggish in poor phonological decoders with developmental dyslexia. Frontiers in Human Neurosciences, 8, 331.
Ruffino M. Trussardi A. N. Gori S. Finzi A. Giovagnoli S. Menghini D. … Facoetti, A. (2010). Attentional engagement deficits in dyslexic children. Neuropsychologia, 8, 3793–3801. [CrossRef]
Ruzzoli M. Gori S. Pavan A. Pirulli C. Marzi C. A. Miniussi C. (2011). The neural basis of the Enigma illusion: A transcranial magnetic stimulation study. Neuropsychologia, 49, 3648–3655. [CrossRef] [PubMed]
Scerri T. S. Schulte-Körne G. (2010). Genetics of developmental dyslexia. European Child and Adolescent Psychiatry, 19 (3), 179–197. [CrossRef] [PubMed]
Schlaggar B. L. McCandliss B. D. (2007). Development of neural systems for reading. Annual Review of Neuroscience, 30, 475–503. [CrossRef] [PubMed]
Schneps M. H. Thomson J. M. Chen C. Sonnert G. Pomplun M. (2013a). E-readers are more effective than paper for some with dyslexia. PLoS One, 8, e75634.
Schneps M. H. Thomson J. M. Sonnert G. Pomplun M. Chen C. Heffner-Wong A. (2013b). Shorter lines facilitate reading in those who struggle. PLoS One, 8, e71161.
Schulte-Körne G. Bruder J. (2010). Clinical neurophysiology of visual and auditory processing in dyslexia: A review. Clinical Neurophysiology, 121 (11), 1794–1809. [CrossRef] [PubMed]
Scolari M. Kohnen A. Barton B. Awh E. (2007). Spatial attention, preview, and popout: Which factors influence critical spacing in crowded displays? Journal of Vision, 7 (2): 7, 1–23,, doi:10.1167/7.2.7. [PubMed] [Article] [PubMed]
Shaywitz S. E. Escobar M. D. Shaywitz B. A. Fletcher J. M. Makuch R. (1992). Evidence that dyslexia may represent the lower tail of a normal distribution of reading ability. New England Journal of Medicine, 326 (3), 145–150. [CrossRef] [PubMed]
Sperling A. J. Lu Z. L. Manis F. R. Seidenberg M. S. (2005). Deficits in perceptual noise exclusion in developmental dyslexia. Nature Neuroscience, 8 (7), 862–863. [CrossRef] [PubMed]
Sperling A. J. Lu Z. L. Manis F. R. Seidenberg M. S. (2006). Motion-perception deficits and reading impairment: It's the noise, not the motion. Psychological Science, 17 (12), 1047–1053. [CrossRef] [PubMed]
Spinelli D. De Luca M. Judica A. Zoccolotti P. (2002). Crowding effects on word identification in developmental dyslexia. Cortex, 38, 179–200. [CrossRef] [PubMed]
Stanovich K. E. Siegel L. S. (1994). Phenotypic performance profile of children with reading disabilities: A regression-based test of the phonological-core variable-difference model. Journal of Educational Psychology, 86 (1), 24–53. [CrossRef]
Stein J. (2012). Visual contributions to reading difficulties: The Magnocellular theory. In Stein J. Kapoula Z. (Eds.), Visual aspect of dyslexia (pp. 171–197). Oxford, UK: Oxford University Press.
Stein J. Kapoula Z. (2012). Visual aspects of dyslexia. Oxford, UK: Oxford University Press.
Stein J. Walsh V. (1997). To see but not to read: The magnocellular theory of dyslexia. Trends in Neuroscience, 20, 147–152. [CrossRef]
Stevens C. Fanning J. Coch D. Sanders L. Neville H. (2008). Neural mechanisms of selective auditory attention are enhanced by computerized training: Electrophysiological evidence from language-impaired and typically developing children. Brain Research, 1205, 55–69. [CrossRef] [PubMed]
Strasburger H. (2005). Unfocused spatial attention underlies the crowding effect in indirect form vision. Journal of Vision, 5 (11): 8, 1024–1037,, doi:10.1167/5.11.8. [PubMed] [Article] [PubMed]
Strong G. K. Torgerson C. J. Torgerson D. Hulme C. (2011). A systematic metaanalytic review of evidence for the effectiveness of the ‘Fast ForWord' language intervention program. Journal of Child Psychology and Psychiatry, 52 (3), 224–235. [CrossRef] [PubMed]
Swan D. Goswami U. (1997). Picture naming deficits in developmental dyslexia: The phonological representations hypothesis. Brain and Language, 56, 334–353. [CrossRef] [PubMed]
Tallal P. (1980). Auditory temporal perception, phonics, and reading disabilities in children. Brain and Language, 9 (2), 182–198. [CrossRef] [PubMed]
Tallal P. (2000). The science of literacy: From the laboratory to the classroom. Proceedings of the National Academy of Sciences, USA, 97 (6), 2402–2404. [CrossRef]
Tallal P. (2004). Improving language and literacy is a matter of time. Nature Reviews Neuroscience, 5 (9), 721–728. [CrossRef] [PubMed]
Turkeltaub P. E. Gareau L. Flowers D. L. Zeffiro T. A. Eden G. F. (2003). Development of neural mechanisms for reading. Nature Neuroscience, 6 (7), 767–773. [CrossRef] [PubMed]
Valdois S. Bosse M. L. Tainturier M. J. (2004). The cognitive deficits responsible for developmental dyslexia: Review of evidence for a selective visual attentional disorder. Dyslexia, 10 (4), 339–363. [CrossRef] [PubMed]
Vellutino F. R. Fletcher J. M. Snowling M. J. Scanlon D. M. (2004). Specific reading disability (dyslexia): What have we learned in the past four decades? Journal of Child Psychology and Psychiatry, 45, 2–40. [CrossRef] [PubMed]
Victor J. D. Conte M. M. Burton L. Nass R. D. (1993). Visual evoked potentials in dyslexics and normals: Failure to find a difference in transient or steady-state responses. Visual Neuroscience, 10 (5), 939–946. [CrossRef] [PubMed]
Vidyasagar T. R. (1999). A neuronal model of attentional spotlight: Parietal guiding the temporal. Brain Research: Brain Research Reviews, 30 (1), 66–76. [CrossRef] [PubMed]
Vidyasagar T. R. (2013). Reading into neuronal oscillations in the visual system: Implications for developmental dyslexia. Frontiers in Human Neuroscience, 7, 811, doi:10.3389/fnhum.2013.00811.
Vidyasagar T. R. Pammer K. (2010). Dyslexia: A deficit in visuo-spatial attention, not in phonological processing. Trends in Cognitive Science, 14, 57–63. [CrossRef]
Visser T. A. Boden C. Giaschi D. E. (2004). Children with dyslexia: Evidence for visual attention deficits in perception of rapid sequences of objects. Vision Research, 44, 2521–2535. [CrossRef] [PubMed]
Whitney D. Levi D. M. (2011). Visual crowding: A fundamental limit on conscious perception and object recognition. Trends in Cognitive Science, 15 (4), 160–168. [CrossRef]
Wilkinson F. Wilson H. R. Ellemberg D. (1997). Lateral interactions in peripherally viewed texture arrays. Journal of the Optical Society of America A: Optics, Image Science, and Vision, 14, 2057–2068. [CrossRef]
Williams M. J. Stuart G. W. Castles A. McAnally K. I. (2003). Contrast sensitivity in subgroups of developmental dyslexia. Vision Research, 43 (4), 467–477. [CrossRef] [PubMed]
Wright C. M. Conlon E. G. Dyck M. (2012). Visual search deficits are independent of magnocellular deficits in dyslexia. Annual Dyslexia, 62 (1), 53–69. [CrossRef]
Yazdanbakhsh A. Gori S. (2008). A new psychophysical estimation of the receptive field size. Neuroscience Letters, 438, 246–251. [CrossRef] [PubMed]
Yazdanbakhsh A. Gori S. (2011). Mathematical analysis of the accordion grating illusion: A differential geometry approach to introduce the 3D aperture problem. Neural Network, 24, 1093–1101. [CrossRef]
Yeshurun Y. Rashal E. (2010). Precueing attention to the target location diminishes crowding and reduces the critical distance. Journal of Vision, 10 (10): 16, 1–12,, doi:10.1167/10.10.16. [PubMed] [Article]
Yu D. Cheung S. H. Legge G. E. Chung S. T. L. (2007). Effect of letter spacing on visual span and reading speed. Journal of Vision, 7 (2): 2, 1–10,, doi:10.1167/7.2.2. [PubMed] [Article]
Zhao J. Qian Y. Bi H.-Y. Coltheart M. (2014). The visual magnocellular-dorsal dysfunction in Chinese children with developmental dyslexia impedes Chinese character recognition. Scientific Report, 4 (7068), 1–7, doi:10.1038/srep07068.
Ziegler J. C. Goswami U. (2005). Reading acquisition, developmental dyslexia, and skilled reading across languages: A psycholinguistic grain size theory. Psychological Bulletin, 131 (1), 3–29. [CrossRef] [PubMed]
Zorzi M. Barbiero C. Facoetti A. Lonciari I. Carrozzi M. Montico M. Ziegler J. C. (2012). Extra large letter spacing improves reading in dyslexia. Proceedings of the National Academy of Sciences, USA, 109, 11455–11459. [CrossRef]

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