Research ReportA neural index of inefficient evidence accumulation in dyslexia underlying slow perceptual decision making
Introduction
Developmental dyslexia (or dyslexia) is a neurodevelopmental disorder characterized by difficulties with accurate or fluent word reading. Although diagnostic symptoms are circumscribed to below age-expected reading, individuals frequently present with a range of deficits outside of reading, including processing of visual stimuli. Increasing evidence suggests that individuals with dyslexia experience difficulties processing visual stimuli at the sensory/perceptual level (Stein, 2001) and deficits in higher-order attentional functions that guide what visual information is processed and when (Vidyasagar & Pammer, 2010). However, evidence for these deficits is controversial, with mixed results frequently reported across studies (Ramus, 2003; Stein, 2018).
Typically, research paradigms assessing visual processing require a decision to be made about a visual stimulus, with performance measured behaviorally (e.g., response accuracy and/or reaction time). Accordingly, poor performance, commonly attributed to either sensory or attentional deficits, may arise from dysfunction at any point along the perception-to-action continuum. Yet, little effort has been dedicated to investigating the role that perceptual decision-making may play in accounting for differences between individuals with dyslexia and their typically reading peers. Perceptual decision-making encompasses multiple processing stages from perceiving visual stimuli, selecting features whilst inhibiting irrelevant information, mentally representing information and accumulating relevant evidence to prepare, initiate and execute subsequent motor actions (Gold & Shadlen, 2007; Joo et al., 2016; Resulaj, et al., 2009). Using indices derived from electroencephalographic (EEG) data, researchers have identified neural metrics indexing information flow across the perception to action hierarchy: low level sensory processing (steady state visual evoked potentials; SSVEPs), pre-target attentional bias (posterior α power), attention orienting (N2), evidence accumulation (centro-parietal positive decision signal; CPP) and execution of a motor response (β; Kelly & O'Connell, 2013; Loughnane et al., 2016; Newman et al., 2013; O'Connell et al., 2012). For example, the N2 is a negative deflection evoked in the occipital-temporal region of the contralateral cortex at the presentation of visual stimuli linked to both the selection of a target and suppression of distractor items argued to reflect allocation of attentional resources (Luck et al., 1997). In addition, the CPP has been shown to build during decision formation in line with sensory evidence strength and is associated with a corresponding response in an individual upon meeting a threshold (Kelly & O'Connell, 2013; Loughnane et al., 2016; Newman et al., 2013; O'Connell et al., 2012; Steinemann et al., 2018). The fine temporal resolution of EEG may therefore help to identify the locus of visual processing dysfunction in dyslexia and in turn, isolate the mechanisms that may be contribute to poor reading.
Outside of studies examining electrophysiological correlates in response to linguistic or auditory stimuli, neural markers of visual perceptual decision-making have not been extensively examined in dyslexia. Instead, researchers have focused on isolated stages of processing, such as motion detection and contrast perception (for reviews see Laycock et al., 2008, Laycock et al., 2007, and Schulte-Körne & Bruder, 2010). Here, the most consistent findings are prolonged latencies and smaller amplitudes of visually evoked potentials elicited by motion stimuli (Schulte-Körne et al., 2004) alongside dysfunctional lateralisation of components such as the N2 (Jednoróg, et al., 2011) thought to reflect sensory impairments in magnocellular functioning. Importantly, the attentional disorder of the magnocellular-dorsal pathway has been demonstrated using both longitudinal and rehabilitative approaches, suggesting a causal link with dyslexia (Bertoni et al., 2019; Carroll et al., 2016; Fraceschini et al., 2013, 2017; Gori et al., 2016). However, not all results demonstrating magnocellular dysfunctional have been replicated (Johannes, et al., 1996; Victor et al., 1993). Beyond early sensory detection, there is electrophysiological evidence to suggest that individuals with dyslexia orient their attention differently towards visual material (Wijers, et al., 2005), although others have argued for a deficit in sustaining visual attention (Van der Lubbe et al., 2019). More recently, behavioural evidence using drift diffusion modelling indicates that poor readers display suboptimal decision-making (O'Brien, et al., 2020), suggesting that processes beyond initial detection or perception of a visual stimulus may also be impaired in dyslexia (Joo, et al., 2017).
A significant issue in dyslexia research is that the direction of the relationship between reading and visual processing deficits is unclear. Some researchers claim deficits in attending to and processing visual material can account for reading difficulties (Franceschini, et al., 2012) whereas others have argued that differences between individuals with dyslexia and typically reading controls may be the consequence, rather than the cause, of poor reading (Goswami, 2003, 2015). The latter line of reasoning arises from evidence showing that learning to read has a flow-on effect for visual and attentional abilities (Chokron & Agostini, 1995; Kermani, et al., 2018). Accordingly, visual deficits may arise subsequent to reduced reading practice and suboptimal reading experience as supplementary visual and attentional skills are not refined to the same degree as in typically developing children. In this case, all children with immature reading systems would be expected to show visual deficits regardless of whether they meet age-expected reading benchmarks rather than representing a dyslexia-specific deficit.
Individuals with dyslexia can present with different types of reading difficulties. Contemporary models posit that reading requires the contribution of two overall processing pathways, the sub lexical and the lexical routes, which differ in their contributions depending on word frequency and regularity (Coltheart, et al., 1993). Hence, individuals with dyslexia may present with reading profiles that vary considerably in terms of the relative strength/weakness of sublexical and lexical processes (Castles, et al., 2006; Castles, et al., 1999; Jackson & Coltheart, 2001; Ziegler et al., 2008). Different visual processing deficits in dyslexia may also be specific to a particular type of reading difficulty. For instance, poorer nonword readers (i.e., sublexical dyslexics) have been shown to exhibit reduced sensitivity to visual motion stimuli with low spatial frequencies at the sensory level (Borsting et al., 1996; Slaghuis & Ryan, 1999), slower time courses for visual stimulus processing (Gori et al., 2014), longer response latencies in attentional masking paradigms (Franceschini et al., 2012; Ruffino, et al., 2014) and poorer inhibition of visual targets in the right visual field indicative of a visuospatial mini-neglect (Facoetti et al., 2006) compared to both control participants and other dyslexics with intact nonword reading. In contrast, reduced visual attention span and rapid naming have been associated with lexical dyslexics with reduced accuracy in their report of multi-object strings (Lassus-Sangosse et al., 2008). There is also evidence, however, that visual deficits are not consistently linked to a specific subtype of dyslexia (Lukov et al., 2015; Ridder, et al., 2001). Thus, differentiation of visual deficits on the basis of reading subtype remains unclear. A significant limitation of these past studies is that differences were examined between discrete sub-groups of individuals with dyslexia. Accordingly, research findings are based on the assumption that all participants in these sub-groups are alike, failing to account for individual variations across the spectrum of relative lexical and sublexical skills.
Here we utilized an EEG perceptual decision-making framework to isolate distinct neural markers of information processing which contribute to cognitive deficits in dyslexia. The aims were threefold: (1) isolate the neural markers underpinning poor performance in dyslexia using a random dot-motion paradigm; (2) ascertain whether deficits are unique to children with dyslexia or if they are associated with immature reading skills using comparisons to both age- and reading-matched controls; and (3) investigate whether any identified deficits vary as a function of reading profile using a continuous measure of relative lexical and sublexical abilities. We provide electrophysiological evidence that children with dyslexia - particularly those with relatively poorer lexical abilities - have a reduced rate of evidence accumulation but remain slow in their overt response suggesting a dyslexia-specific impairment in effectively sampling and processing evidence about visual motion stimuli.
Section snippets
Participants
An initial sample of 217 participants with dyslexia was recruited from paediatric learning difficulty clinics and community support groups. Eighty-one control participants were recruited from schools and the community via advertisements. Individuals were eligible if they were right-handed, aged between 8 and 16 years, had normal or corrected to normal vision, normal hearing, no history of developmental delay, ADHD, intellectual disability, autism spectrum disorder, behavioural or emotional
Behavioural measures
There were no significant main effects of group on measures of accuracy or false alarm rate. There was a main effect of group for reaction time, F(2,65) = 6.74, p = .002, η2 = .172. The DD group responded significantly more slowly to targets than both the AM controls, t(51), = −3.05, p = .004, 95% CI [-157.73, −32.53], and RM controls, t(45), = −2.87, p = .006, 95% CI [-149.56, −26.26]. There was also an interaction for reaction time between target hemifield and response hand, F(1,65) = 8.18, p
Discussion
Here we show that slowed perceptual decision-making in response to motion stimuli is a deficit that is specific to children with dyslexia, particularly those with relatively poorer lexical abilities. That is, only children with dyslexia demonstrated reduced reaction times compared with younger, age-appropriate readers with equally immature reading systems. Further, our EEG analysis suggests that the neural basis of this deficit appears related to a slower build-up rate, and a possible
Credit author statement
Nicole R. Stefanac: conceptualization, methodology, investigation, writing – original draft and project administration.
Shou-Han Zhou: software, formal analysis, data curation and writing – review and editing.
Megan M. Spencer-Smith: resources, writing – review and editing and supervision.
Redmond O'Connell: validation, writing – review and editing.
Mark A. Bellgrove: resources, writing – review and editing, funding acquisition and supervision.
Funding
This work was supported by funding by the ARC Future Fellowship FT130101488 to MAB and ARC Discovery Project DP150100986 to MAB and ROC. MAB is currently supported by a Senior Research Fellowship (Level B) from the National Health and Medical Research Council of Australia.
Declaration of competing interest
The authors report no competing interests.
Acknowledgements
We thank the participants and parents for their generous involvement in the study and the interns that assisted throughout the recruitment and testing process. We also thank Professor Anne Castles for her kind contributions.
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