Developmental dyslexia (DD) is an impairment in the acquisition of literacy skills despite normal intelligence, an absence of physical or psychological problems, and adequate formal education (DSM-IV, 1994), which is estimated to affect approximately 5-10% of school-aged children (Habib, 2000). Such reading and spelling problems limit career choices and professional opportunities (Snowling, 2000; Winner, von Karolyi, Malinsky, French, Seliger, Ross, et al., 2001). The neural basis or bases of developmental dyslexia is currently unknown. While the causes may be diverse, most dyslexic children demonstrate difficulty in phonological processing (Tallal, 1980) and rapid automatized naming (Denkla & Rudel, 1976).

Competing hypotheses for the neural basis of developmental dyslexia include: deficits in the rapid temporal processing of both auditory and visual stimuli, dysfunction in the magnocellular visual pathway, cerebellar dysfunction, and abnormalities in transient attention.

The temporal processing hypothesis derives from evidence indicating that dyslexics have difficulty in rapidly processing sequential information resulting in problems with phonological decoding, and hence reading (Tallal, 1980). Rapid temporal dot counting is more difficult for children with dyslexia than is spatial dot counting (Eden, Stein, Wood, & Wood, 1995). Longer interstimulus intervals (ISIs) are also needed to make temporal order judgements vs. spatial location judgements in poor readers (May, Williams, & Dunlap, 1988). In rapid serial visual presentation (RSVP) protocols the cognitive recovery time after target recognition is some 30% longer for dyslexic versus normal adults, when stimuli are presented in quick succession, indicating that processing speed and time to disengage attention seem compromised (Hari, Valta, & Uutela, 1999). More recently, both auditory gap detection and visual double flash detection performance has been shown to be inferior in dyslexic compared with normal reading children of the same age, indicative of a general, cross-modality temporal processing deficit in dyslexia (Van Ingelghem, van Wieringen, Wouters, Vandenbussche, Onghena, & Ghesquiere, 2001).

The magnocellular hypothesis proposes an anatomical and functional abnormality in the magnocellular (M) visual pathway from retina to brain as a cause of dyslexia. During the early 1980’s Lovegrove and colleagues proposed that individuals with dyslexia have visual impairments affecting the transient visual system (Lovegrove, Bowling, Badcock, & Blackwood, 1980). The impairment was identified on the basis of deficits in the contrast thresholds for low spatial frequency achromatic stimuli. These observations, coupled with the lowered motion and motion coherence sensitivity (Cornelissen, Richardson, Mason, Fowler, & Stein, 1995; Talcott, Hansen, Assoku, & Stein, 2000) as well as reduced brain activation in V5/MT+ to moving stimuli (Demb, Boynton, & Heeger, 1998; Eden, VanMeter, Rumsey, Maisog, Woods, & Zeffiro, 1996), led to the emergence of the magnocellular deficit theory (reviewed in Habib, 2000; Stein & Walsh, 1997). Recently this interpretation has been criticized by (Skottun, 2000), who notes that little has been made of the fact that the M stream projects to both the dorsal and ventral cortical streams.

Visual evoked potential (VEP) studies have not supported pre-cortical impairment of the M-pathway in dyslexics (Johannes, Kussmaul, Munte, & Mangun, 1996; Victor, Conte, Burton, & Nass, 1993) (but see Lehmkuhle, Garzia, Turner, Hash, & Baro, 1993); nor has direct measurement of the M-pathway contribution to the multi-focal VEP (Crewther, Crewther, Klistorner, & Kiely, 1999).

The cerebellar hypothesis proposes that the failure to learn to read fluently is representative of a generalized failure of automatization and is parsimoniously explained by cerebellar dysfunction. Children with dyslexia automatize temporal skills more slowly (Nicolson & Fawcett, 1993) and show neurological signs indicative of vestibulo-cerebellar dysfunction (Fawcett & Nicolson, 1999). Neuroimaging tests also indicate that dyslexia is associated with cerebellar impairment (reviewed in Nicolson, Fawcett, & Dean, 2001).

The parietal attention hypothesis links dyslexia with a deficit in transient and spatial attention. In performing visual search tasks, dyslexics tend to show longer response times (Eskenazi & Diamond, 1983), impaired accuracy (Casco & Prunetti, 1996) and a tendency not to focus visual attention as much as normal readers (Facoetti, Paganoni, & Lorusso, 2000a). Serial search strongly activates posterior parietal cortex (PPC) (Corbetta, Shulman, Miezin, & Petersen, 1995) and search speed is slowed by transcranial magnetic stimulation to this region (Ashbridge, Walsh, & Cowey, 1997). Search performance in dyslexics correlates with motion coherence thresholds (Iles, Walsh, & Richardson, 2000), suggesting a connection between lowered search capability and magnocellular dysfunction.

There is a strong overlap between the attentional hypothesis and the magnocellular hypothesis (at least in terms of visual attention), due to the fact that the magnocellular pathway is the major visual input to the dorsal cortical stream, including parietal cortex, which is one of the major sites of activation in attention-related tasks (Corbetta, Akbudak, Conturo, Snyder, Ollinger, Drury, et al., 1998), and that magnocellular neurons are characterized by transient response characteristics.

As reading is a spatio-temporal process, involving the sequential decoding of spatially arranged visual symbols, ability on spatio-temporal tasks such as change detection may have important implications for reading, but have yet to be examined in children with dyslexia. Tasks assessing change detection have recently emerged in the search literature in an attempt to systematically uncover the mechanisms underlying ‘change blindness’ (Rensink, O'Regan, & Clark, 1997). The nature of stimuli used in change detection experiments is wide-ranging, from simple geometrical figures to realistic dynamic scenes. However, even for simple shapes, a considerable degree of change blindness can be induced whenever there are more than a few items in the display (Rensink, 2002). Inserting a transient such as a flicker or a blank as the change is taking place, removes the salience of this target change, inducing ‘change blindness’ (O'Regan, Rensink, & Clark, 1999). Change detection rates are greatly improved when the target to be changed is cued during the blank ISI between the two pictures to be compared for change. Also, detecting the presence or absence of a change alone is less effortful than identifying the exact nature of the change (Becker, Pashler, & Anstis, 2000).

The neural correlates of change detection and change blindness have been recently identified with functional magnetic resonance imaging (fMRI) (Beck, Rees, Frith, & Lavie, 2001). Change detection activated parietal and right dorso-lateral prefrontal cortex as well as category-selective extrastriate cortex. Change detection is best distinguished from change blindness by enhanced activity bilaterally in parietal lobe and right dorsal-lateral pre-frontal cortex. The level of activation was highest in the right intraparietal sulcus (IPS) when change was consciously detected as opposed to when change was not detected (Beck et al., 2001).

While there have been no published reports of change detection in DD children, several studies involving dyslexic individuals have used comparison for difference between two displays, but mainly for the purpose of estimating memory performance. Thus Koenig et al (Koenig, Kosslyn, & Wolff, 1991) used visualization of remembered patterns in order to estimate spatial overlap. Dyslexic participants showed difficulty with letter forms, but as the authors point out the subjects are “integrating visual information stored in long-term memory”. Similarly Nelson and Warrington showed an impairment in dyslexia cf normal readers for verbal long-term memory functions (Nelson & Warrington, 1980). Allegretti and Puglisi used both immediate and remembered comparisons in a letter-search task, probing whether a letter in the first presentation matched any in a second presentation (Allegretti & Puglisi, 1986), again not a classical change detection task, requiring identification.

There is a continuing debate as to whether dyslexics show visual field (left/right) asymmetries on tasks of a visual spatial nature (Geiger & Lettvin, 1987; Klein, Berry, Briand, D'Entremont, & Farmer, 1990; Stein & Walsh, 1997). Recent evidence for a possible deficiency in right PPC functioning in dyslexia comes from findings of left inattention and right over-distractibility in recent visual flanker and reaction time tasks (Facoetti & Molteni, 2001; Facoetti & Turatto, 2000). Also, for line-motion and temporal two-dot judgements across the midline, dyslexics show a statistically significant right-sided bias (Hari, Renvall, & Tanskanen, 2001) leading to the terminology “mini-neglect” of the left visual field in poor readers.

Lower visual field (LVF) biases in normal human for reaching (Danckert & Goodale, 2001) and attentional resolution (He, Cavanagh, & Intriligator, 1996) have been related to the dorsal cortical stream and magnocellular dominance of peripersonal (LVF) space ((Previc, 1990; Previc, 1998) – reviewed in Danckert & Goodale, in press). Indeed, in the primate visual system some dorsal areas (e.g. V6A) are strongly devoted to LVF (Galletti, Fattori, Kuntz, & Gamberi, 1999). However, both visual search in normal adults (Christman & Niebauer, 1997) and change detection in normal-reading children (Rutkowski, Crewther, & Crewther, 2002) show an upper visual field (UpVF) advantage, presumably indicative of ventral pathway requirements for these tasks.

We aimed to investigate change detection performance in developmental dyslexic, learning disabled, and normal reading children, and to ascertain whether the provision of cues as an indicator of the position of likely change would be utilized to the same extent in the three groups. It was hypothesized that if there is a magnocellular-pathway/attentional dysfunction associated with dyslexia, then dyslexics would show impaired performance on a change detection task compared with normal readers. It was also suggested that the children with dyslexia would have greater difficulty utilizing brief cues, and that even when dyslexics detect change, they would identify a lower percentage of the changed items than do normal readers. In addition, dorsal pathway dysfunction should be accompanied by alterations in visual field detection biases.

86 children aged 7-16 drawn from three regions – city, suburban and rural – voluntarily participated in the current study (mean age ± standard error = 11.8 ± 0.1yr). The children were recruited from a wider subject pool involved in ongoing research into visual and attentional processes in reading and reading disorders. The Institutional Ethics Committee approved the study and informed consent was obtained from parents before testing commenced with any of the children. Children were screened for visual abnormalities and were excluded if any uncorrected binocular or refractive errors were present.

The reading age of some of the normal reading children had already been assessed by the Reading Progress Test (Vincent, Sadowsky, Saunders, & Reeves, 1977). All other children were administered the Neale Test of Reading Analysis (Neale, 1988). The two tests showed a high degree of overlap when correlated with a computerized measure of reading speed (“FastaReada” – coded in Authorware Professional, Macromedia) and hence reading ages were taken from either instrument without adjustment. Reading accuracy and the number of errors (mispronunciations, substitutions, refusals, additions, omissions and reversals) were quantified and compared to Australian age norms to estimate reading age. Children with a 2-year or more lag in reading for their chronological age were termed “Poor Readers”. Of these, two groups were formed: children with a mentation score within one standard deviation of the mean were defined as the Developmental Dyslexic (DD) group, while children with a mentation score below one standard deviation of the mean, formed the Learning Disability (LD) group. All children easily recognized the letters of the alphabet. Mentation scores were determined by performance on The Raven’s Coloured Progressive Matrix Test (Raven, Court, & Raven, 1990), a widely used measure of non-verbal intelligence, and which comprises 3 sets of 12 matrix puzzles of increasing difficulty. In each matrix, a segment is missing and the children are required to choose from 6 possibilities which segment best completes the pattern. Functional brain imaging demonstrates that the Raven’s Test activates many areas of the brain comprising a network of working memory areas (Prabhakaran, Smith, Desmond, Glover, & Gabrieli, 1997), with either left or right hemisphere activations dominating depending on whether the actual tasks involved analytic or figural reasoning or simple pattern matching. A group of children with normal reading skills for age (NR) was used, chronologically age-matched to the (DD) group. Preliminary data on the visual field preferences for change detection of 61 children with reading skill commensurate with age (across the range 7 – 13 years) have previously been published (Rutkowski et al., 2002)

The Change Detection task was custom programmed using Authorware 2.2 (Macromedia, Redwood City, USA), and was presented via an Apple iMac Computer with a 15 inch display monitor, running at 95 Hz screen refresh rate. The stimuli were placed at an eccentricity of 3.5° from the fixation cross, when viewing distance was 57 cm. Michelson contrast of the letters was 94%.

The task was based on that of Becker et al. (2000) which used 6 letter elements in a study of adults. We chose to use four elements in a square array (Figure 1), as our pilot studies indicated that children found the assimilation of information from 6 potential targets too difficult. The letter stimuli were sequentially drawn from the first 20 letters of the alphabet and could appear with equal probability at any of the four locations.

To enable a direct comparison of change detection performance across diagnostic and age groups a staircase parameter estimation by sequential testing (PEST) procedure was used to determine the threshold duration for the first display (P1-Time) which would allow each child to detect change at approximately 71% correct. The threshold P1-Time was taken as that after 6 reversals and was used within the second phase of the experimental paradigm, that examined cueing effects and possible asymmetries in change detection performance. Change detection response was recorded by a 2-alternate-forced-choice-box (Same/Different) that appeared on the right-hand side of the screen simultaneously with P2. The second stimulus remained on the screen until a response was made. If ‘Same’ was selected, the next trial began; if ‘Different’ was selected, the stimulus array disappeared and subsidiary questions were posed. Change identification response was recorded by a 4-alternate-forced-choice-response box (“What was the new letter?”) followed by another 4-alternate-forced-choice-response box (“What was the letter before it changed?”). These data were not used in the later analysis of change identification because they were gathered using variable P1-Times.

To examine the effects of cueing of position of change on change detection performance, 48 trials, 16 of each of 3 intermixed cue conditions (Cue 200, Cue 50, No Cue), were presented in a completely randomized order which included 50% null trials (no change). Exposure time for the first stimulus (P1-Time) was fixed for each individual to the value found in Phase 1. In Change/Cue trials, Cues were presented at 200 ms or 50 ms preceding the second presentation (P2), and always pointed to the location of the item to be changed (Figure 1). In NoChange/Cue trials, location of the cue was random. Change detection and identification performance for P1 and P2 were recorded as noted for Phase 1.

Children were seated at the computer prior to the experimenter giving instructions and demonstrating the task. Emphasis was placed on the importance of accuracy of detecting change, not reaction time after the appearance of the second stimulus, and children were informed that as performance improved, the task would get faster, making it harder to see whether any changes were being made to the letters. Children were closely supervised throughout the PEST component to ensure understanding of the task and compliance with instructions. If children were responding at random, reinforcement was given and the experiment was restarted. After finishing Phase 1, children were allowed a few minutes rest, and the instructions for the second phase of the experiment were given. Emphasis again was placed on the accuracy of detecting change rather than reaction time and children were clearly informed that if a cue appeared they needed to attend only to the cued location.

Data were screened prior to the commencement of analysis for outliers and errors in data entry. Normality and homogeneity of variance tests were conducted to ensure the assumptions underlying the use of analysis of variance were met. There were no violations, so data analysis proceeded without transformations.

The duration of exposure (P1-Time) of the first display necessary for threshold detection of change between displays is shown in Figure 2 for the three groups. Analysis of variance (ANOVA) for P1-Time indicates a significant main effect for group (F (2, 83)= 8.25, P = .0005). Comparisons between groups revealed that the NRs required significantly shorter presentation times to detect change when compared with DDs (Fisher’s PSLD, P = .008) and LDs (Fisher’s PLSD, P = .0003), all groups performing at the same level of accuracy (71 % correct).

For the second phase of the experiment, with P1-Time for each individual set to the value found in Phase 1, the effect of cue on change detection was investigated. Overall, despite the expectation of at least 71% correct detection, the presence of a cue did not manifestly increase the overall detection performance (Figure 3A). More strikingly, for the No Cue condition mean correct detection was 44 ± 3 %, against an expectation of 71% (a significant difference for each of the experimental groups, single sample t-test, P < .001 in each case). The provision of a cue 200 ms before the presentation of the second stimulus gave no advantage for change detection over a cue appearing only 50 ms before, for any of the groups, and thus these two cue conditions were combined for the purpose of analysis. Repeated measures ANOVA between Cue and No Cue conditions showed significantly reduced change detection performance (in the no cue condition) as illustrated in Figure 3A (F(1, 83) = 46.5, P < .0001), with a significant interaction between Group and Cue conditions (F(2, 83) = 4.7, P =.01). The DDs performed worse overall in both cued and uncued conditions, and post-hoc comparisons revealed a significant difference in the performance of the DDs and NRs (Fisher’s PLSD, P = .0057).

Performance for all groups was highly reliable under conditions when there was no change (Figure 3B) as illustrated by a mean overall correct rejection rate of 0.93 with no differences between groups.

For trials in which a change was correctly detected, children were asked to indicate the identity of the new letter (P2-ID) and that of the letter that had changed (P1-ID). Repeated measures (Cue/ No Cue) ANOVA on the first identification (P1-ID) demonstrated no significant effects for Cue or for Group, but showed a significant interaction (F(2, 72) = 3.24, P = .04). A similar analysis for the second identification (P2-ID) showed significant main effects for Cue (F(2, 72) = 3.69, P = .03) and experimental group (F(1, 72) = 4.29, P = .04). As Figure 4 illustrates, post-hoc testing showed that the NRs identified letters more accurately than either of the other groups and this was significant for the cued conditions (P1-ID, Fisher’s PLSD, NR vs DD, P =.04; P2-ID, Fisher’s PLSD, NR vs DD, P < 0.005, NR vs LD, P <.02).

Change detection performance was investigated across the four stimulus locations for the three experimental groups. Because of low trial numbers and randomized location presentation, two variables were created for each participant – an UpVF Bias (mean UpVF detection – mean LoVF detection) and RVF Bias (mean RVF detection – mean LVF detection) for both Cue and NoCue conditions, prior to analysis. A failure to correctly detect change at any one location excluded the data for that individual from further analysis, because visual field effects were calculated according to a difference equation which could not be calculated in the presence of an empty cell. Thus, only the data of 53 children were utilized in the analysis of visual field effects. A repeated measures ANOVA revealed that there was a significant main effect for visual field (F(3, 50) = 4.69, P = .004), however there was no significant main effect of experimental group.

We addressed the question of a possible left ‘mini-neglect’ in dyslexia (Hari et al., 2001) by testing whether the mean RVF Bias was different from zero (single value t-Test, Cued RVF Bias: Mean = -.001, t(63) = -0.03, p = .98; NoCue RVF Bias: Mean = -.05, t(63) = -0.98, p = .33). Non-significant figures indicated that there was no change detection biases to either the left or right hemifield for the cued or un-cued trials.

Similarly, on the basis of our findings of an upper visual field advantage in a group of normal reading children across a wider age range (Rutkowski et al., 2002), we tested the UpVF Bias variable and found a significant upper visual field bias for all groups in both cued and un-cued conditions (Cued condition: single value t-Test, mean = .119, t(63) = 3.36, p = 0.001; NoCue, mean = .124, t(56) = 2.63, p =.01).

This is the first time that change detection has been directly assessed in children with developmental dyslexia. In terms of procedure, the paper of Allegretti and Puglisi (1986) is perhaps closest, particularly in the immediate presentation condition. However, at no stage were their subjects asked whether a change occurred – it always did, with three letters being replaced by one, or vice versa. They were instead asked whether a letter in the first presentation matched any in a second presentation – an identity matching task, also having an element in common with visual search. Similarly, while Stanley and Hall’s early paper (Stanley & Hall, 1973) was indicative of early visual processing differences, the nature of the study was more of integration or impletion of letters than the detection of change, perhaps relating to the literature on visible persistence (Di Lollo, Hanson, & McIntyre, 1983; Lovegrove, Billing, & Slaghuis, 1978; Stanley, 1975).

Our experimental results indicate that developmental dyslexia is characterized by poor change detection. Children with dyslexia require substantially longer to detect change than chronological age-matched normally reading controls.

Change identity performance was considerably worse than change detection performance for all groups of children, especially in correct identification of the letter that had changed (P1-ID), giving a clear indication that detection of change rather than identification substantially determined the threshold for P1-Time. Performance for P2-ID was probably inflated because the second display remained on screen awaiting subject response. Thus correct P2-ID only required correct spatial localization of the changed item in order to determine the identity of the new item.

Finally, all groups demonstrated an upper visual field advantage for change detection.

One might query the choice of letter targets for a comparison between groups, one of which exhibits reading disability. It is clear from our population data, however, with mean reading age of the DDs being 7.4 years, and from direct observation of each individual, that recognition of single letters, per se, was not a problem with the DDs. Also, the idea that problems of dyslexic children are specific to words or even letters is not supported in the literature on the rapid automatized naming test (RAN) (Denkla & Rudel, 1976). Anderson et al (1984) showed both vocalization time and pause time means were significantly longer for the dyslexics on each of the four RAN subtests. Similarly, Fawcett & Nicolson (1994) showed lower naming speed for dyslexic children compared with age and IQ matched normal readers for all stimulus categories tested (colours, digits, letters, pictures), whether or not they required grapheme-to-phoneme conversion.

The discovery that dyslexics are less sensitive than the normal readers to change was hypothesized on the basis of a magnocellular/parietal dysfunction. In order to perform change detection at the same level as NRs, DDs required a longer time to process the first image of the four letter targets sufficiently to detect change. This raises the question of whether time to recognition is affected in DDs or whether the deficit is in the pathways sub-serving the alerting function. fMRI evidence points to dorsal pathway (as well as dorso-lateral pre-frontal cortex) activation in change detection (Beck et al., 2001). The magnocellular input via the dorsal pathway accounts for the great majority of the visual information projecting to the PPC which appears to be necessary for alerting of visual attention prior to the conscious detection of change (Beck et al., 2001). The notion that change detection is controlled through parietal cortex receives support from the finding of longer conjunction search times under conditions of trans cranial magnetic stimulation of right parietal cortex (Ashbridge et al., 1997). Rensink has clearly drawn parallels between change detection and visual search through experiments investigating whether the mechanisms for change detection is related to the attentional processes used in search for complex static patterns (Rensink, 2000).

The data presented in Figure 3 indicate that when subjects did not know whether or not to expect a cue, their change detection performance was strongly dependent on the appearance of a cue. Of the three groups, NRs were least cue-dependent, as performance was relatively stable across cued and un-cued trials. The change detection rate was expected to be approximately 71% correct for un-cued trials on the basis that the presentation time for the first stimulus in Phase 2 was identical to the P1-Time at change detection threshold found for each subject in Phase 1. Thus, higher levels of change detection performance were expected when cues were provided. This expectation was not borne out by the data. Cued performance around 70% correct for NRs and LDs (60% for the DDs) was observed. Presuming the cue direction was accurately perceived, chance performance would be 50% correct detection for a forced choice decision of change or no change. Considered in this way, the DDs were performing close to chance while both LDs and NRs benefited significantly. Overall, NoCue performance was worse than cued (ranging from around 60% correct for NRs to a little more than 30% for DDs and LDs). We suggest that the more complex experimental structure of the Phase 2 accounts for this lower than expected performance. Multiple strategies (Cue versus NoCue) are required in the second phase experiment compared with the first . If there was a cue, a rapid shift of attention in the direction of the cue would increase the chance of successful change detection, while if there was not a cue, then attention has to be distributed over the four letters to maximize the chance of success. The presence of a cue is likely to improve performance relative to Phase 1, while the higher cognitive load due to the dual strategy is likely to lower performance overall. The especially poor performance of the DD and LD groups for NoCue trials thus suggests a strategic reliance on the likelihood of a cue appearing, and an inability to rapidly switch attention or strategy. The situation is probably exacerbated by the fact that all of the subjects would be described as “novice” in the terminology of Braun (1998), who showed that novices perform poorly compared with expert or trained observers under conditions of increased cognitive load or dual task.

The cue created a transient disturbance, capturing attention in a way that the children may have had difficulty suppressing. It is possible that the observed effect was not a problem with cue utilization per se, but an inability to adequately monitor the required location for a change . This is consistent with Hari et. al’s finding that dyslexics take significantly longer to release attention after the recognition of a target in an attentional blink paradigm (Hari et al., 1999). In addition, dyslexics can attend to and perform recognition tasks as well as normal readers if given cues of longer durations, presumably because there is time enough to disengage attention (Facoetti, Paganoni, Turatto, Marzola, & Mascetti, 2000b). Encoding stimuli draws attention, and while the letters were not necessarily encoded to the point of conscious identification (on average, only 35% of trials where change was correctly detected were both of the letters correctly identified), it was proposed that under the conditions of this experiment, letter change cannot be captured globally, but rather requires a degree of local attention. If the dyslexic children were not able to shift attention from the cue to the intended location rapidly enough they would have been able to detect whether the cued letter changed.

Contrary to expectation, hemifield analysis on the change detection task failed to reveal any evidence consistent with the ‘mini-neglect’ finding in adult dyslexics (Hari et al., 2001). Change detection performance was not reduced in the left hemifield relative to the right for the dyslexic children (nor for the other groups). The resolution of this apparent conflict may lie in a fundamental difference between the mechanisms underlying temporal order judgment and change detection. In the temporal order judgment task of Hari, both of the elements are detected but the one lying in left hemifield suffers a 15 ms lag compared with right hemifield in adult dyslexics. In change detection the problem is one of detection, wherein a possible timing lag may not affect detection performance.

The discovery of an UpVF advantage for change detection in all groups of children is consistent with our previous findings of an UpVF bias in children reading at normal levels (Rutkowski et al., 2002). This conforms with a similar UpVF bias for complex visual search (Christman & Niebauer, 1997; Previc & Naegele, 2001). Previc originally proposed that the lower hemifield was concerned with near vision (peripersonal space) and that in a complimentary fashion the upper hemifield showed more ventral characteristics and was concerned with far vision (extrapersonal space) (Previc, 1990). We suggested (Rutkowski et al., 2002) that as the dorsal cortical stream receives greater input from LoVF and has greater attentional resolution there (Danckert & Goodale, 2001; He et al., 1996), visual masking by the simultaneous reappearance of the four letters and their place-holders may also be greater in LoVF, allowing ventral mechanisms associated with letter recognition to perform better for upper visual field presentation. Unfortunately, the fMRI study of Beck et al, while demonstrating the requirement of parietal activation for change detection, sheds no light on the question of relative activation to targets in UpVF compared with LoVF.

We proposed that change detection required an element of pre-conscious attention related to the coding of some attribute such as shape, but generally prior to letter identification in the reading process. To adequately detect change, one may have to, from a global perspective, quickly adapt to a local processing mode, to process the nature of the change. The dyslexics were unable to do this rapidly in the change detection task. When children learn to read, they have to decode a series of new letter images, without the benefit of much context. Following each saccadic eye movement, these images falling on the fovea, appear suddenly and with unknown identity. The more rapidly a child can process these changes, the more rapidly they are likely to both read and to perform change detection. Thus there emerges a possible rationale for the relationships found between reading ability and change detection.

Poorer change detection performance was demonstrated by the developmental dyslexic and learning disabled populations compared with the normal reading population, with age controlled between the groups. This gives an indication that there may be a closer relationship between fluent reading and rapid visual processing, as exhibited in change detection performance, than between reading and mentation.

We wish to acknowledge a grant (#A0000937) from the Australian Research Council, as well as support from the Andrew Fildes Foundation. Commercial relationships: None.