What is the Eye Doing during Reading Aloud? Eye-Voice Span in Acquired Dyslexia

Halm, K.; Ablinger, Irene; Ullmann, Anne; Solomon, Matthew; Radach, Ralph; Huber, Walter

doi:10.1016/j.sbspro.2011.09.260

Cited by 4 publications

(2 citation statements)

References 3 publications

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“…Since lexical route processes are less dependent from word length than the grapheme-to-phoneme conversion processes of the sublexical route (Weekes, 1997; Rastle and Coltheart, 1998, 1999; Ziegler et al, 2001; Juphard et al, 2004; Zoccolotti et al, 2005; Barton et al, 2014), we also test for frequency × length interactions on offset EVS and we expect that the offset EVS of low familiarity words show increased length effects than the offset EVS of low-familiarity ones. To our knowledge, word familiarity effects on EVS have only been investigated by Halm et al (2011), who found frequency effects on spatial EVS but not on temporal EVS. Since this paper is a very brief one, and the authors do not specify whether they measured the onset EVS or the offset EVS, uncertainty remains.…”

Section: Introductionmentioning

confidence: 97%

When the Eyes No Longer Lead: Familiarity and Length Effects on Eye-Voice Span

et al. 2016

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During oral reading, the eyes tend to be ahead of the voice (eye-voice span, EVS). It has been hypothesized that the extent to which this happens depends on the automaticity of reading processes, namely on the speed of print-to-sound conversion. We tested whether EVS is affected by another automaticity component – immunity from interference. To that end, we manipulated word familiarity (high-frequency, low-frequency, and pseudowords, PW) and word length as proxies of immunity from interference, and we used linear mixed effects models to measure the effects of both variables on the time interval at which readers do parallel processing by gazing at word N + 1 while not having articulated word N yet (offset EVS). Parallel processing was enhanced by automaticity, as shown by familiarity × length interactions on offset EVS, and it was impeded by lack of automaticity, as shown by the transformation of offset EVS into voice-eye span (voice ahead of the offset of the eyes) in PWs. The relation between parallel processing and automaticity was strengthened by the fact that offset EVS predicted reading velocity. Our findings contribute to understand how the offset EVS, an index that is obtained in oral reading, may tap into different components of automaticity that underlie reading ability, oral or silent. In addition, we compared the duration of the offset EVS with the average reference duration of stages in word production, and we saw that the offset EVS may accommodate for more than the articulatory programming stage of word N.

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Section: Introductionmentioning

confidence: 97%

When the Eyes No Longer Lead: Familiarity and Length Effects on Eye-Voice Span

et al. 2016

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“…FA has good accuracy in comparison to manual segmentation (Hosom, 2009;Malfrère et al, 2003;Yuan & Liberman, 2008). Time indices in segmentation files can be aligned with time stamps in the eye-movement record to determine asynchronies between gaze targets and speech production, the eye-voice span (EVS; Halm et al, 2011;Inhoff et al, 2011;Morton, 1964). EVS provides another target for simulation work, beyond pure gaze measures.…”

Section: C22 Speech Analysis/segmentationmentioning

confidence: 99%

Reading Fluency Matters: NIH R21 HD090460-01A1

Braze¹,

Gong²,

Nam³

2018

Preprint

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Fluency is an essential feature of skilled reading. Reading speed is an overt reflection of automaticity in decoding and similarly deft control over other component skills. There is considerable support for oral reading speed as a valid and reliable indicator of general reading skill. In fluent reading, whether oral or silent, processes of word recognition, comprehension, and eye movement control must be tightly coordinated, as characterized in several implemented models of eye movements over print (Reichle et al., 2013; Reilly & Radach, 2006; Engbert et al., 2005). Oral reading, in addition to those requirements shared with silent reading, places an additional demand on the reader: the need to produce accurate, prosodically appropriate speech in conjunction with the reading process itself. Both oral and silent reading rely not only on good control of component processes, but also on facile coordination of those processes (Berninger et al., 2001; Breznitz, 2003). Rapid automatized naming (RAN) is another fluencylike predictor of reading skills (see Bowey, 2005; Kirby et al., 2010; Hulme & Snowling, 2012; Cutting & Scarborough, 2006; Wolf et al., 2000). RAN’s task demands overlap considerably with reading, especially oral reading (Denckla & Rudel, 1974). Yet, the nature of the connections between the two capabilities remains uncertain. This research exploits commonalities between reading and RAN to help resolve uncertainties in their causes. We do so by working toward a unified, theoretical account of performance across tasks. Our main purpose is not to explain RAN as such, but a model of reading fluency that explicitly handles RAN will have distinct advantages over one that does not. It will be constrained by the need to accommodate both tasks, and so may provide insight into relationships between the two that will prove helpful in refining practical uses of RAN as a reading assessment. Our approach is two-fold. First, we will assess relationships among oral and silent reading, and standard indicators of reading skillin three critical periods in the development of skilled reading: a learning-to-read group (7-8yo), a transition-to-fluency group (10-11yo), and a full-literacy group (17-18yo). We will evaluate developmental differences between groups and individual differences within groups. We will apply new ways to investigate RAN performance and oral and silent reading fluency derived from analysis of gaze behavior, and of asynchronies in gaze and speech behaviors produced during these tasks (Jones et al., 2008). We make novel use of speech processing technology to facilitate alignment of speech and gaze signals to accommodate the large volume of speech data generated by the project. Second, we will implement a unified model of coordination among component processes for oral and silent reading(Reichle et al., 2009; Reichle, Pollatsek, et al., 2012). A successful unified model will serve as a testbed for investigating structural relationships among oral and silent reading, RAN and component skills.

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Reading patterns, reformulation and eye-voice span (IVS) in sight translation

Chmiel

Lijewska

2022

TIS

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The study examined how a required reformulation of a source text affects reading patterns in sight translation. We also tested how interpreters regulated their eye-voice span (IVS, understood as the delay between viewing the source language word and speaking it in the target language) in the task. Twenty-four professional conference interpreters sight translated (from Polish into English) a text with syntactically symmetrical sentences (that could be copied in the target language) or asymmetrical ones (that required reformulation). The participants’ output and eye movements were recorded. Mean IVS turned out to exceed 8s. Text viewing durations and IVS did not differ between symmetrical and asymmetrical sentences. In contrast, reformulated output structures generated larger IVS than the retained ones. Also, words appearing later in the text on the screen were associated with longer IVS. This shows that IVS is a complex construct and that reformulation affects reading patterns in sight translation.

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What is the Eye Doing during Reading Aloud? Eye-Voice Span in Acquired Dyslexia

Abstract: truefalse2016-03-16T23:02:09

Cited by 4 publications

References 3 publications

When the Eyes No Longer Lead: Familiarity and Length Effects on Eye-Voice Span

When the Eyes No Longer Lead: Familiarity and Length Effects on Eye-Voice Span

Reading Fluency Matters: NIH R21 HD090460-01A1

Reading patterns, reformulation and eye-voice span (IVS) in sight translation

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