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  1. Jukka Hyönä (2012). The Role of Visual Acuity and Segmentation Cues in Compound Word Identification. Frontiers in Psychology 3.
    Studies are reviewed that demonstrate how the foveal area of the eye constrains how compound words are identified during reading. When compound words are short, their letters can be identified during a single fixation, leading to the whole-word route dominating word recognition from early on. Hence, visually marking morpheme boundaries by hyphens slows down processing by encouraging morphological decomposition when holistic processing is a feasible option. In contrast, the decomposition route dominates the early stages of identifying long compound words. Thus, (...)
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  2. Jukka Hyönä & Raymond Bertram (2012). Flexible Letter-Position Coding is Unlikely to Hold for Morphologically Rich Languages. Behavioral and Brain Sciences 1 (1):28-29.
    We agree with Frost that flexible letter-position coding is unlikely to be a universal property of word recognition across different orthographies. We argue that it is particularly unlikely in morphologically rich languages like Finnish. We also argue that dual-route models are not overly flexible and that they are well equipped to adapt to the linguistic environment at hand.
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  3. Jukka Hyönä & Raymond Bertram (2003). Future Challenges to E-Z Reader: Effects of OVP and Morphology on Processing Long and Short Compounds. Behavioral and Brain Sciences 26 (4):488-489.
    We argue that although E-Z Reader does a good job in simulating many basic facts related to readers' eye movements, two phenomena appear to pose a challenge to the model. The first has to do with word length mediating the way compound words are identified; the second concerns the effects of initial fixation position in a word on eye behavior.
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  4. Janice M. Keenan, Jukka Hyönä & Johanna K. Kaakinen (2003). Incorporating Semantics and Individual Differences in Models of Working Memory. Behavioral and Brain Sciences 26 (6):742-742.
    Ruchkin et al.'s view of working memory as activated long-term memory is more compatible with language processing than models such as Baddeley's, but it raises questions about individual differences in working memory and the validity of domain-general capacity estimates. Does it make sense to refer to someone as having low working memory capacity if capacity depends on particular knowledge structures tapped by the task?
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