“Word recognition is the component of reading which involves the identification of individual words.”
“Words are the building blocks of language, and are the interface between written and spoken language. Recognition of the printed word is both essential to the important skill of reading and among the easiest routes for the experimenter to access higher cognition.”
“The definition of ‘grapheme’ is: the written representation of a phoneme. Thus the graphemes of THIGH are <TH> and <IGH>. Hence the correct term for the mappings of spellings to sounds is ‘grapheme-phoneme correspondences’ (GPCs).”
“Glushko (1981) gave two reasons for concluding that the dual-route theory of reading aloud was false. One was his finding that inconsistent nonwords such as BINT have significantly longer reading-aloud RTs than consistent nonwords such as BINK; he also pointed out here that errors with inconsistent nonwords often took the form of pronouncing them to rhyme with the irregular words from which they were derived: BINT read to rhyme with PINT, or HEAF to rhyme with DEAF. The other reason he gave was his finding that regular inconsistent words such as WAVE have significantly longer reading-aloud RTs than regular consistent words such as WADE.”
“the PbA (Pronunciation by Analogy) model was subsequently developed by Damper, Marchand and colleagues (see e.g. Damper & Eastmond, 1997). This is a genuinely computational model - i.e. a working computer program - which computes pronunciations from print for nonwords, regular words, and irregular words by a single uniform procedure whose first step is to find words in its orthographic lexicon that match the input string according to some orthographic criterion. This model has largely been developed in an engineering context, to try to improve the performance of automatic text-to-speech systems; but the model has also been offered as an account of how humans read aloud (e.g. by Marchand & Friedman, 2005).”
“Seidenberg and McClelland (1989) and the Triangle Models of Reading. The SM89 model contains three domains of representation: Orthography (0), Phonology (P), and Semantics (S). Each domain is connected to the other two, producing a triangular configuration: that is why this model, and other models subsequently developed using the same configuration, have come to be referred to as triangle models.”
“read low-frequency irregular words is a very difficult job for the direct O→P route and that input to P from the indirect O→S →P route is needed for low frequency irregular words to be correctly read. The O→S→P route not having been implemented, input from S to p was artificially introduced in such a way that when training was terminated, the O→p pathway was not able to read all irregular words by itself; it needed, especially for low-frequency irregular words, input from the S system.”
“Comparisons between the DRC and CDP+ model focus on the nonlexical routes of these models because the CDP+ lexical route has been imported from the DRC model, whereas the CDP+ nonlexical route is quite different from that of DRC.”
“As demonstrated by the Interactive Activation model, a lexical level of representation permits discriminating known from novel orthographic sequences. This approach has been adopted in many subsequent models, notably the Multiple Read-Out Model (Grainger & Jacobs, 1996), SOLAR (Davis, 1999), Dual-Route Cascaded (Coltheart et al., 2001), and Connectionist Dual Process + (Perry et al., 2007; Perry, Ziegler, & Zorzi, 2010). All of these models achieved word recognition through the use of lexicons that conform to Barlow’s (1972) definition of localist coding, where increased activation on a particular node corresponds to an increasing probability that a particular word was encountered.”
“Orthographic wordforms were selected from the set used in Sibley et al. (2008). This corpus was an intersection of the Wall Street Journal corpus (Marcus, Santorini, & Marcinkiewicz, 1993) and the CMU Pronunciation Dictionary (http://speech.cs.cmu.edu/cgi-bin/cmudict).”
“Word and nonword inputs produced distributions of activation over sets of lexical nodes, and lexical decisions were based on these distributions. Balota and Chumbley (1984) noted that lexical decisions can be conceptualized as a signal detection process, where a participant differentiates word and nonword distributions along a dimension of familiarity. Familiarity can be related to two aspects of lexical node activation distributions, i.e., heights of their peaks and spreads of their ‘valleys’, so to speak.”
“It has been repeatedly reported that the time needed to recognize a word is influenced by the stimuli’s length (Weekes, 1997; Ziegler, Perry, Jacobs, & Braun, 2001; Yap & Balota, 2009). This became a theoretically important phenomenon when Coltheart et al. (2001) proposed that length effects indicate a serial processes involvement in reading.”
“Words with more syllables take longer to recognize, before and after the variance associated with frequency and letter length are removed. This result is consistent with prior behavioral reports of syllabic length effects (Jared & Seidenberg, 1990; New, Ferrand, Pallier, & Brysbaert, 2006; Yap & Balota, 2009).”
“Word recognition latencies are related to the number of morphemes in a word (Yap, Balota, Sibley, & Ratcliff, 2012). Words with more morphemes are typically recognized slower, however, the direction of this effect reverses after the linear effects of letter length are removed. To illustrate, the word RUN tends to be recognized faster than REMODELED. However, REMODELED tends to be recognized faster than RAVISHING or REMINISCE, because it has the same number of letters but more morphemes (i.e., RE-MODEL-ED).”
“majority of English words are multisyllabic.”
“Recognizing a word involves finding a match between a coded version of the input stimulus and an internalized lexical representation. A central issue in the investigation of this process concerns the mechanism by which a match is found. On the one hand, parallel activation models based on the Interactive Activation model (McClelland Rumelhart, 1981) involve a system in which each letter increases the activation level of every word unit that contains that letter in the correct position, and decreases the activation level of every word unit that does not contain that letter in that position. The word unit that receives the most activation is the correct word unit. This approach essentially assumes that the input letter string is compared with every lexical representation in the lexicon simultaneously. The dominant examples of this approach are the Dual Route Cascaded Model (DRC) developed by Coltheart, Rastle, Perry, Langdon, and Ziegler (2001) and the Multi-Level Readout Model (MROM) developed by Grainger and Jacobs (1996). At the opposite extreme, search models assume that the comparison with the input is carried out one word at a time in a sequential fashion (Forster, 1976). In what follows, we will consider a third type of model that occupies a position intermediate between these two extremes, one that incorporates both parallel and sequential features.”
“evidence from EEG studies indicates that the semantic properties of words are not generated until about 160 ms after stimulus onset (e.g., Hauk, Davis, Ford, Pulvermüller, & Marsien-Wilson, 2006; Segalowitz & Zheng, 2009; Sereno, Rayner, & Posner, 1998).”
“The model is a parallel-serial hybrid model, hence referred to as PSM, and combines features of a standard parallel activation model with those of the entry-opening model of masked priming (Forster, 1999, 2009). The parallel activation component of PSM is based on a standard interactive-activation model, as described in McClelland and Rumelhart (1981). The input letter string activates word units by the usual mechanism - a letter in a given position sends activation to all word units that have that letter in the same position. The critical element in the design of the system is a device suggested by Sloman and Rumelhart (1992), namely a gatekeeper. This is a system that has excitatory and inhibitory connections to the actual connections between letter units and word units. This gives it the capacity to enable one set of connections between a letter unit and a subset of word units, while disabling all other connections between that letter unit and the remaining word units.”
“the gatekeeper is responsible for partitioning the lexicon. It is designed so that initially, the only letter-to-word connections that are enabled are those for the word units in the first partition. After activation of that partition is completed, the gatekeeper disables those connections (allowing the activation levels in the word units to decay), and enables the connections for the second partition. This procedure is repeated until all partitions have been activated.”
“The other component of PSM is based on the entry-opening model of masked priming (Forster, 1999, 2009). To link the two models, we need to distinguish between a word unit, and the lexical entry that is associated with it.”
“Words that occur frequently (e.g., HOME) are recognized faster than words that occur infrequently (e.g., EAGLE), despite the fact that both types of words may be perfectly familiar. This variable is by far the most important in terms of the percentage of variance accounted for (e.g., Balota, Cortese, Sergent-Marshall, Spieler, & Yap, 2004).”
“the connections between letter units and word units could be stronger for high-frequency words, so that activation builds up in those word units more rapidly. This is the approach taken in the SOLAR model (Davis, 2010).”
“subjects are unable to accurately report short sentences with a presentation rate of 16 words per second, i.e., 62.5 ms per word (Forster, 1970).”
“Most contemporary models of visual word recognition are based on interactive-activation or similar frameworks that propose that the input is compared with all words in the vocabulary simultaneously.”
“An alternative parallel-serial model (PSM) proposes that at any given time, only a subset of words is under consideration, these subsets being considered in a sequential fashion, the sequence being linked to frequency of occurrence (or a correlated variable). Access to these subsets of word units is controlled by a gatekeeper mechanism.”
“Masked cross-language translation priming occurs with a prime-target SOA of 50 ms, yet electrophysiological evidence suggests that semantic interpretation of a word requires at least 150 ms. This implies that the prime continues to be processed after it has been replaced by the target. This is difficult (if not impossible) to implement in a standard interactive activation model, but is readily explained in PSM.”
“It is proposed that priming occurs at two sites. Form priming occurs in word units, but there is an additional semantic component that occurs at the lexical entry associated with the word unit.”
"Key advantages of this approach are: 1) An explanation of how multiple words can be active to process phrases and sentences. 2) A principled account of the functional form of frequency effects.
- An explanation of ‘intervenor’ effects, which are difficult to reconcile with activation accounts. 4) Providing a mechanism to explain mounting evidence that semantic variables affect access time."
“Brysbaert, M., Lange, M., & Wijnendaele, I. V. (2000). The effects of age-of-acquisition and frequency-of-occurrence in visual word recognition: Further evidence from the Dutch language. European Journal of Cognitive Psychology, 12, 65-85.”
“Coltheart, M., Rastle, K., Perry, C., Langdon, R., & Ziegler, J. (2001). DRC: A dual route cascaded model of visual word recognition and reading aloud. Psychological Review, 108, 204-256.”
“Davis, C. J. (2010). The spatial coding model of visual word identification. Psychological Review, 117, 713-758.”
“Gardner, M. K., Rothkopf, E. Z., Lapan, R., & Lafferty, T. (1987). The word frequency effect in lexical decision: Finding a frequency-based component. Memory & Cognition, 15, 24-28.”
“Kennedy, A., Pynte, J. L., & Ducrot, S. P. (2002). Parafoveal-on-foveal interactions in word recognition. The Quarterly Journal of Experimental Psychology A: Human Experimental Psychology, 55A, 1307-1337.”
“Morrison, C. M. & Ellis, A. W. (1995). Roles of word frequency and age of acquisition in word naming and lexical decision. Journal of Experimental Psychology: Learning, Memory, and Cognition, 21, 116-133.”
“Norris, D. (2006). The Bayesian Reader: Explaining word recognition as an optimal Bayesian decision process. Psychological Review, 113, 327-357.”
“Perea, M. & Lupker, S. J. (2004). Can CANISO activate CASINO? Transposed-letter similarity effects with nonadjacent letter positions. Journal of Memory and Language, 51, 231-246.”
“Rayner, K. (1998). Eye movements in reading and information processing: 20 years of research. Psychological Bulletin, 124, 372-422.”
“Rayner, K., Balota, D. A., & Pollatsek, A. (1986). Against parafoveal semantic preprocessing during eye fixations in reading. Canadian Journal of Psychology, 40, 473-483.”
“Visual word recognition tasks have been used by researchers since the 1950s. It wasn’t until 40 years ago that Meyer and Schvaneveldt (1971) coined the term lexical decision task (LDT) which has been used ever since. There has been a wide range of theoretical and empirical aims in studies using the LDT; hence, there has been some variability in the methodology used in the task (e.g., presentation and response modalities, priming, masked vs unmasked presentation). In spite of these variations, the defining aspect of the task is a binary classification of a stimulus as a ‘word’ or as a ‘nonword’.”
“In the IA model, words are represented by single units. This, however, is not the only way to implement lexical representations within the neural networks framework; words can also be represented by patterns of activation across the network units”
“Bayesian Approaches. Bayesian models of the LDT provide an implementation of the lexical aspect of the LDT, and share the sequential accumulation of evidence process with the diffusion model. Wagenmakers et al. (2004) and Norris (2006, 2009) are good examples of this approach.”
“Perhaps the most comprehensive model of the LDT is the Bayesian Reader (Norris & Kinoshita, in press; Norris, 2006, 2009). The ambitious scope of the Bayesian Reader (BR) includes accounts of the representation of the lexicon (Norris, 2006), the decision mechanism for the LDT (Norris, 2009), priming (Norris & Kinoshita, 2008) and the encoding of letter position (Norris, Kinoshita, & van Casteren, 2010).”
“In the BR model, the stimulus is represented in a multi-dimensional perceptual space. Each word is represented by a concatenation of vectors; each of these vectors (with size = 26) represents a letter and concatenates (size = 26 x N) to represent a string of letters of length N that is the input to the model.”
“Shiffrin, R. M. & Steyvers, M. (1997). A model for recognition memory: REM: Retrieving Effectively from Memory. Psychonomic Bulletin & Review, 4, 145-166.”
“Shiffrin, R. M. & Steyvers, M. (1998). The effectiveness of retrieval from memory. In M. Oaksford & N. Chater (Eds). Rational models of cognition (pp. 73-95). Oxford, England: Oxford University Press.”
“Within cognitive science, one might argue that words are a fundamental building block in psychology. Words have been central to developments in computational modeling (McClelland & Rumelhart, 1981), cognitive neuroscience (e.g., Petersen, Fox, Posner, Mintun, & Raichle, 1988, 1989), memory (Craik & Lockhart, 1972), psycholinguistics (Pinker, 1999), among many other areas. Words are wonderful stimuli because they have a relatively limited set of constituents (e.g., letters/phonemes) that can be productively rearranged to capture virtually all the meaning that humans convey to each other. In this light, one might argue that words, like cells for biologists, are a major building block of cognitive science.”
“The Semantic Priming Project (SPP) The SPP (Hutchison, Balota, Cortese, Neely, Niemeyer, & Bengson, 2011) is an attempt to greatly extend the methodology of Hutchison et al. (2008) to a broader range of items and subjects. Like its predecessor, the ELP, the SPP is a National Science Foundation funded collaborative effort among four universities (Montana State University; University of Albany, SUNY; University of Nebraska, Omaha; and Washington University in St Louis) to investigate a wide range of both item and individual differences in semantic priming.”
“Adelman, J. S., Marquis, S. J., Sabatos-De Vito, M. G., & Estes, Z. (2012). The unexplained nature of reading. Manuscript submitted for publication.”
“Baayen, R. H., Felaman, L. B., & Schreuder, R. (2006). Morphological influences on the recognition of monosyllabic monomorphemic words. Journal of Memory & Language, 55, 290-313.”
“Balota, D. A., Pilotti, M., & Cortese, M. J. (2001). Subjective frequency estimates for 2,938 monosyllabic words. Memory & Cognition, 29, 639-647.”
“Balota, D. A., Cortese, M. J., Sergent-Marshall, S. D., Spieler, D. H., & Yap, M. J. (2004). Visual word recognition of single-syllable words. Journal of Experimental Psychology: General, 133, 283-316.”
“Becker, C. A. (1979). Semantic context and word frequency effects in visual word recognition. Journal of Experimental Psychology: Human Perception & Performance, 5, 252-259.”
“Becker, C. A. (1980). Semantic context effects in visual word recognition: An analysis of semantic strategies. Memory & Cognition, 8, 493-512.”
“Bueno, S. & Frenk-Mastre, C. (2008). The activation of semantic memory: Effects of prime exposure, prime-target relationship, and task demands. Memory & Cognition, 36, 882-898.”
“Burgess, C. (1998). From simple associations to the building blocks of language: Modeling meaning in memory with the HAL model. Behavior Research Methods: Instruments & Computers, 30, 188-198.”
“Burgess, C. & Livesay, K. (1998). The effect of corpus size in predicting RT in a basic word recognition task: Moving on from Kucera and Francis. Behavior Research Methods, Instruments, & Computers, 30, 272-277.”
“Cortese, M. J. & Fugett, A. (2004). Imageability ratings for 3,000 monosyllabic words. Behavior Research Methods, Instruments & Computers, 36, 384-387.”
“Cortese, M. J. & Khanna, M. M. (2008). Age of acquisition ratings for 3000 monosyllabic words. Behavior Research Methods, 40, 791-794.”
“Cortese, M. J., Khanna, M. M., & Hacker, S. (2010) Recognition memory for 2,578 monosyllabic words. Memory, 18, 595-609.”
“Craik, F. I. M. & Lockhart, R. S. (1972). Levels of processing: A framework for memory research. Journal of Verbal Learning & Verbal Behavior, 11, 671-684.”
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“Dufau, S., Dunabeitia, J. A., Moret-Tatay, C., McGonigal, A., Peeters, D., Alaorio, F., Balota, D. A., Brysbaert, M., Carreiras, M., Ferrand, L., Ktoir, M., Perea, M., Rastle, K., Sasburg, O., Yap, M. J., Ziegler, J. C., & Grainger, J. (2011). Smart phone, smart science: How the use of Smartphones can revolutionize research in cognitive science. PLOS ONE 6(9): e24974.”
“Ferrand, L. & New, B. (2003). Semantic and associative priming in the mental lexicon. In P. Bonin (Ed.), Mental lexicon: Some words to talk about words (pp. 25-43). Hauppage, NY: Nova Science Publishers.”
“Forster, K. I. (2000). The potential for experimenter bias effects in word recognition experiments. Memory & Cognition, 28, 1109-1115.”
“Frost, R., Katz, L., & Bentin, S. (1987). Strategies for visual word recognition and orthographical depth: A multilingual comparison. Strategies, 13, 104-115.”
“Glanzer, M., Adams, J. K., Iverson, G. J., & Kim, K. (1993). The regularities of recognition memory. Psychological Review, 100, 546-567.”
“Kang, S. H. K., Yap, M. J., Tse, C-S., & Kurby, C. A. (2011). Semantic size does not matter: ‘Bigger’ words are not recognised faster. The Quarterly Journal of Experimental Psychology, 64, 1041-1047.”
“Lemhöefer, K., Dijkstra, A., Schriefers, Н., Baayen, R. H., Grainger, J., & Zwitserlood, P. (2008). Native language influences on word recognition in a second language: A megastudy. Journal of Experimental Psychology: Learning, Memory, & Cognition, 34, 12-31.”
“Neely, J. H. (1991). Semantic priming effects in visual word recognition: A selective review of current findings and theories. In Besner, D. & Humphreys, G. W. (Eds) Basic processes in reading: Visual word recognition. (pp. 264-336). Hillsdale, NJ, USA: Lawrence Erlbaum Associates, Inc.”
“New, B., Ferrand, L., Pallier, C., & Brysbaert, M. (2006). Re-examining word length effects in visual word recognition: New evidence from the English Lexicon Project. Psychonomic Bulletin & Review, 13, 45-52.”
“Norris, D. (2006). The Bayesian reader: Explaining word recognition as an optimal Bayesian decision process. Psychological Review, 113, 327-357.”
“Petersen, S. E., Fox, P. T., Posner, M. I., Mintun, M., & Raichle, M. E. (1988). Positron emission tomographic studies of the cortical anatomy of single-word processing. Nature, 331, 585-589.”
“Petersen, S. E., Fox, P. T., Posner, M. I., Mintun, M., & Raichle, M. E. (1989). Positron emission tomographic studies of the processing of single words. Journal of Cognitive Neuroscience, 1, 153-170.”
“Pinker, S. (1999). Words and rules: The ingredients of language. New York: HarperCollins.”
“Rey, A., Courieu, P., Schmidt-Weigand, F., & Jacobs, A. M. (2009). Item performance in visual word recognition. Psychonomic Bulletin & Review, 16, 600-608.”
“Yap, M. J., Rickard Liow, S. J., Jalil, S. B., & Faizal, S. S. B. (2010b). The Malay lexicon project: A database of lexical statistics for 9,592 words. Behavior Research Methods, 42, 992-1003.”
“Yap, M. J., Tse, C-S., & Balota, D. A. (2009). Individual differences in the joint effects of semantic priming and word frequency revealed by RT distributional analyses: The role of lexical integrity. Journal of Memory and Language, 61, 303-325.”
“Ziegler, J. & Goswami, U. (2005). Reading acquisition, developmental dyslexia, and skilled reading across languages: A psycholinguistic grain size theory. Psychological Bulletin, 131, 3-29.”
“Word Frequency. This is among the earliest studied variables in visual word recognition (Cattell, 1886), and among the most studied in cognitive psychology. Frequency is sometimes measured in occurrences or parts per million (opm), or otherwise in raw count, the number of times the word occurred in the sample of language (corpus) from which the estimate is taken. A frequent word is read more quickly and accurately than its rare counterpart. This effect shows diminishing returns: the RT difference between a 1 opm word and a 10 opm word is more than that between all opm word and a 20 opm word.”
“Contextual Diversity. When assembling a frequency count, several documents are examined for the number of times a word occurs. Contextual diversity (or context frequency or context variability) is the number of documents in which the word occurs, indicating the variety of contexts or situations in which the word has been experienced - TORNADO and OUTLOOK are similar in frequency, but OUTLOOK occurs in far more contexts. We (Adelman, Brown, & Quesada, 2006) have claimed that this variable accounts for the existence of word frequency effects; words occurring in more contexts take less time to read (and are also usually more frequent).”
“Orthographic Neighbourhood Size. Orthographic neighbours are words that may be formed from one another by replacing one letter with another in its place (e.g., DOVE and COVE are orthographic neighbours). The neighbourhood size of a word is the number of neighbours it has (Coltheart’s N; Coltheart, Davelaar, Jonasson, & Besner, 1977).”
“Phonological Neighbourhood Size. Visual word recognition researchers usually define phonological neighbours analogously to orthographic neighbours, but with phonemes in place of letters! (e.g., HEART is a phonological neighbour of CART). Phonological N is also claimed to be facilitatory (Yates, 2005).”
“Bigram Frequency. Although the effect on word recognition is not clear, it is still fairly common to control the frequency with which adjacent letter pairs are used; for instance NT is common, but UA is rare. The count is usually based on word types (not their frequency) and may take into account the position of the bigram within the word.”
“Morphological Properties. A word’s morphological structure - its construction from components (morphemes) that have meaning (e.g., WALK = WALK + ED or HEADBOARD = HEAD + BOARD) - and its morphological family - those words sharing the same baseword (e.g., WALK, WALKS, WALKED, WALKING, WALKER) - may also be important.”
“Elexicon. The Elexicon project web site (http://elexicon.wustl.edu/; Balota, Yap, Cortese, Hutchison, Kessler, Loftis, et al., 2007) contains many of the variables that are relevant to control for many words, as well as data from word naming and lexical decision.”
“CELEX (Baayen, Piepenbrock, & Gulikers, 1995) contains commonly used phonological transcriptions, frequencies, part of speech information, and morphological transcriptions.”
“Lexicall (currently at http://lexicall.widged.com/) provides access or links to a variety of databases, which include the Gilhooly and Logie (1980) norms for age of acquisition, imagery, concreteness, familiarity, and ambiguity measures, the Clark and Paivio (2004) norms for imageability and familiarity, and the Cortese and Fugett (2004) imageability norms, among others.”
“MRC Psycholinguistic Database. Among the databases in the Lexicall list is the MRC database (Wilson, 1988). This database has been widely used, because it brought together several contemporary sources in one place. However, many of those sources have been extended or superseded.”
“Adelman, J. S. & Brown, G. D. A. (2007). Phonographic neighbors, not orthographic neighbors, determine word naming latencies. Psychonomic Bulletin & Review, 14, 455-459.”
“Adelman, J. S. & Brown, G. D. A. (2008). Methods of testing and diagnosing models: Single and dual route cascaded models of word naming. Journal of Memory and Language, 59, 524-544.”
“Baayen, R. H., Feldman, L. B., & Schreuder, R. (2006). Morphological influences on the recognition of monosyllabic monomorphemic words. Journal of Memory and Language, 53, 496-512.”
“Baayen, R. H., Davidson, D. J., & Bates, D. M. (2008). Mixed-effects modeling with crossed random effects for subjects and items. Journal of Memory and Language, 59, 390-412.”
“Balota, D. A., Cortese, M. J., Sergent-Marshall, S. D., Spieler, D. II., & Yap, M. J. (2004). Visual word recognition of single-syllable words. Journal of Experimental Psychology:”
“Brysbaert, M. & New, B. (2009). Moving beyond Kucera and Francis: A critical evaluation of current word frequency norms and the introduction of a new and improved word frequency measure for American English. Behavior Research Methods, 41, 977-990.”
“Burgess, C. & Livesay, K. (1998). The effect of corpus size in predicting reaction time in a basic word recognition task: Moving on from Kucera and Francis. Behavior Research Methods, Instruments and Computers, 30, 211-257.”
“Cattell, J. M. (1886). The time taken up by cerebral operations. Mind, 11, 377-392. Retrieved from http://psychclassics.yorku.ca.”
“Clark, H. H. (1973). The language-as-fixed-effect fallacy: A critique of language statistics in psychological research. Journal of Verbal Learning and Verbal Behavior, 12, 335-359.”
“Coltheart, M., Curtis, B., Atkins, P., & Haller, M. (1993). Models of reading aloud: Dual-route and parallel-distributed-processing approaches. Psychological Review, 100, 589-608.”
“Coltheart, M., Rastle, K., Perry, C., Langdon, R., & Ziegler, J. (2001). DRC: A dual route cascaded model of visual word recognition and reading aloud. Psychological Review, 108, 204-256.”
“Cortese, M. J. & Fugett, A. (2004). Imageability ratings for 3,000 monosyllabic words. Behavior Research Methods, 36, 384-387.”
“Forster, K. I. (2000). The potential for experimenter bias effects in word recognition experiments. Memory & Cognition, 28, 1109-1115.”
“Gilhooly, K. & Logie, R. (1980). Age of acquisition, imagery, concreteness, familiarity, and ambiguity measures for 1,944 words. Behavior Research Methods and Instrumentation, 12, 395-427.”
“Rastle, K. & Coltheart, M. (1999). Serial and strategic effects in reading aloud. Journal of Experimental Psychology: Human Perception and Performance, 25, 482-503.”
“Whitney, C. (2001). How the brain encodes the order of letters in a printed word: The SERIOL model and selective literature review. Psychonomic Bulletin & Review, 8, 221-243.”
“Yap, M. J. & Balota, D. A. (2009). Visual word recognition of multisyllabic words. Journal of Memory and Language, 60, 502-529.”
“Yarkoni, T., Balota, D., & Yap, M. (2008). Moving beyond Coltheart’s N: A new measure of orthographic similarity. Psychonomic Bulletin & Review, 15, 971- 979.”
“The question to what extent word reading is lateralised got a major impetus from modern neuroscience techniques. Two particularly interesting studies were published by Cohen and colleagues (Cohen, Dehaene, Naccache, Lehericy, Dehaene-Lambertz, Henaff, et al., 2000, Cohen, Lehericy, Chochon, Lemer, Rivaud, & Dehaene, 2002). In these studies, Cohen et al. showed that a region in the left occipito-temporal junction was crucially involved in visual word recognition (Figure 7.2). This region was active independent of the position of the word in the visual field and, in particular, whether or not the word was initially projected to the left hemisphere. Cohen et al. called this area the ‘visual word form area’ (VWFA) and claimed that information from written words had to pass through it to access the associated semantic and phonological memory representations.”
“Figure 7.2. Figure of the left hemisphere showing the frontal areas active in word generation and the visual word form area, as postulated by Cohen and colleagues. Posterior to the visual word form area is a part of the occipital cortex, the middle occipital gyrus, that is also particularly active in written word recognition. It is left lateralised in typical healthy participants as well (Gold & Rastle, 2007), but was not correlated with the activity in the frontal language areas in Cai et al. (2010).”
“The lateralisation of the visual word form area most likely has an impact on parafoveal word recognition. This is word recognition a few letter positions to the left or to the right of the fixation location (central vision is usually referred to as foveal vision). Indeed, the organisation of the visual system is such that stimuli in the left visual field (LVF) are initially sent to the right brain half, whereas stimuli in the right visual field (RVF) are sent to the left brain half. This is because the optic fibres from the nasal hemiretina (i.e., the side towards the nose cross at the optic chiasm and project to the contralateral cerebral hemisphere (Figure 7.3).”
“The fact that words are recognised better in RVF than LVF was first documented in the 1950s, although the effect initially was not attributed to cerebral dominance but to reading-related attentional processes. Mishkin and Forgays (1952) investigated the left-right differences for English and Yiddish words (the latter is a language read from right to left), and reported a RV advantage for English words, but a tendency towards an LVF advantage for Yiddish words (a finding shortly afterwards reported by Orbach, 1952, as well).”
“Parafoveal word recognition plays a role in text reading, as can be concluded from studies in which the upcoming words are masked until the eyes land on them. Eye movements in reading are characterised by a sequence of fixations and short fast eye movements, called saccades (see the chapter by Schotter and Rayner in the accompanying volume, Chapter 4). Verbal information is extracted during the fixations and mainly consists of the word being fixated, but also of the word next to it and sometimes the second next word. Rayner, Well, Pollatsek, and Bertera (1982, Experiment 1) concluded this from an English reading study in which three viewing conditions were compared: (1) a condition in which none of the upcoming words next to the currently fixated word was visible; (2) a condition in which one word was visible in the right parafovea; and (3) a condition in which two parafoveal words were visible. Reading rate in the condition with no parafoveal preview was 212 words per minute; in the condition with one parafoveal word visible it was 309 words per minute; and in the condition with two parafoveal words visible it was 339 words per minute, close to the reading speed when the full text was visible all the time (348 words per minute). The finding that reading is more efficient when participants have information of the words next to the one they are currently fixating is called the parafoveal preview benefit effect (e.g., Rayner, 1998).”
“Because central vision occupies a large part of the visual cortex, which in addition is less susceptible to strokes, central vision has the highest chances of surviving brain injury. This explains why macular sparing is so variable, going from nearly 0° to over 5° (e.g., McFadzean, Hadley, & Condon, 2002; Trauzettel-Klosinski & Reinhard, 1998).”
“the critical role of the visual word form area as the gateway to visual word perception.”
“Bourne, V.J. (2006). The divided visual field paradigm: Methodological considerations. Laterality, 11, 373-93.”
“Bowers, J.S., Davis, C.J., & Hanley, D.A. (2005). Automatic semantic activation of embedded words: Is there a ‘hat’ in ‘that’? Journal of Memory and Language, 52, 131-143.”
“Bryden, M.P. (1982). Laterality: Functional asymmetry in the intact brain. New York: Academic Press.”
“Bryden, M.P., Hecaen, H., & De Agostini, M. (1983). Patterns of cerebral organization. Brain and Language, 20, 249-262.”
“Brysbaert, M. (2004). The importance of interhemispheric transfer for foveal vision: A factor that has been overlooked in theories of visual word recognition and object perception. Brain and Language, 88, 259-267.”
“Brysbaert, M. & Nazir, T.A. (2005). Visual constraints on written word recognition: Evidence from the optimal viewing position effect. Journal of Research in Reading, 28, 216-228.”
“Bunt, A.H. & Minckler, D.S. (1977). Foveal sparing: New anatomical evidence for bilateral representation of the central retina. Archives of Ophthalmology, 95, 1445-1447.”
“Cai, Q., Lavidor, M., Brysbaert, M., Paulignan, Y., & Nazir, T.A. (2008). Cerebral lateralization of frontal lobe language processes and lateralization of the posterior visual word processing system. Journal of Cognitive Neuroscience, 20, 672-681.”
“Cohen, L., Dehaene, S., Naccache, L., Lehericy, S., Dehaene-Lambertz, G., Henaff, M.A., & Michel, F. (2000). The visual word form area: Spatial and temporal characterization of an initial stage of reading in normal subjects and posterior split-brain patients. Brain, 123, 291-307.”
“Cornellisen, P.L., Kringelbach, M.L., Ellis, A. W., Whitney, C., Holliday, I.E., & Hansen, P.C. (2009). Activation of the left inferior frontal gyrus in the first 200 ms of reading: Evidence from magnetoencephalography (MEG). Plos One, 4, Article e5359.”
“Ellis, A.W. & Brysbart, M. (2010). Split fovea theory and the role of the two cerebral hemispheres in reading: A review of the evidence. Neuropsychologia, 48, 353-365.”
“Ellis, A.W., Brooks, J., & Lavidor, M. (2005). Evaluating a split fovea model of visual word recognition: Effects of case alternation in the two visual fields and in the left and right halves of words presented at the fovea. Neuropsychologia, 43, 1128-1137.”
“Gazzaniga, M.S. (1983). Right-hemisphere language following brain bisection: A 20-year perspective. American Psychologist, 38, 525-537.”
“Gold, B.T. & Rastle, K. (2007). Neural correlates of morphological decomposition during visual word recognition. Journal of Cognitive Neuroscience, 19, 1983-1993.”
“Hillis, A.E., Newhart, M., Heidler, J., Barker, P., Herskovits, E., & Degaonkar, M. (2005). The roles of the ‘visual word form area’ in reading. Neurolmage, 24, 548-559.”
“Hunter, Z.R., Brysbaert, M., & Knecht, S. (2007). Foveal word reading requires interhemispheric communication. Journal of Cognitive Neuroscience, 19, 1373-1387.”
“Ibrahim, R. & Eviatar, Z. (2009). Language status and hemispheric involvement in reading: Evidence from trilingual Arabic speakers tested in Arabic, Hebrew, and English. Neuropsychology, 23, 240-254.”
“Jordan, T.R. & Paterson, K. (2009). Reevaluating split-fovea processing in visual word recognition: A critical assessment of recent research. Neuropsychologia, 47, 2341-2353.”
“Lavidor, M. & Walsh, V. (2004). The nature of foveal representation. Nature Reviews Neuroscience, 5, 729-735.”
“Lavidor, M., Ellis, A.W., Shillcock, R., & Bland, T. (2001). Evaluating a split processing model of visual word recognition: Effects of word length. Cognitive Brain Research, 12, 265-272.”
“Lavidor, M., Ellis, A.W., & Pansky, A. (2002). Case alternation and length effects in lateralized word recognition: Studies of English and Hebrew. Brain and Cognition, 50, 257-271.”
“Lavidor, M., Hayes, A., Shillock, R., & Ellis, A.W. (2004). Evaluating a split processing model of visual word recognition: Effects of orthographic neighborhood size. Brain and Language, 88, 312-320.”
“McCormick, S., Davis, C.J., & Brysbaert, M. (2010). Embedded words in visual word recognition: Does the left hemisphere see the rain in brain? Journal of Experimental Psychology: Learning, Memory, and Cognition, 36, 1256-1266.”
“Mishkin, M. & Forgays, D.G. (1952). Word recognition as a function of retinal locus. Journal of Experimental Psychology, 43, 43-48.”
speed reading exercises (promo)
“Mohr, B., Pulvermuller, F., & Zaidel, E. (1994a). Lexical decision after left, right, and bilateral presentation of function words, content words and non-words: Evidence for interhemispheric interaction. Neuropsychologia, 32, 105-124.”
“Mohr, B., Endrass, T., Hauk, O., & Pulvermuller, F. (2007). ERP correlates of the bilateral redundancy gain for words. Neuropsychologia, 45, 2114-2124.”
“Orbach, J. (1952). Retinal locus as a factor in the recognition of visually perceived words. American Journal of Psychology, 65, 555-562.”
“Perea, M. & Lupker, S.J. (2004). Can CANISO activate CASINO? Transposed-letter similarity effects with nonadjacent letter positions. Journal of Memory and Language, 51, 231-246.”
“Pujol, J., Deus, J., Losilla, J.M., & Capdevila, A. (1999). Cerebral lateralization of language in normal left-handed people studied by functional MRI. Neurology, 52, 1038-1043.”
“Rayner, K. (1998). Eye movements in reading and information processing: 20 years of research. Psychological Bulletin, 124, 372-322.”
“Rayner, K., Well, A.D., Pollatsek, A., & Bertera, J.H. (1982). The availability of useful information to the right of fixation in reading. Perception & Psychophysics, 31, 537-550.”
“Shillcock, R., Ellison, T.M., & Monaghan, P. (2000). Eye-fixation behavior, lexical storage and visual word recognition in a split processing model. Psychological Review, 107, 824-851.”
“Van der Haegen, L., Brysbaert, M., & Davis, C.J. (2009). How does interhemispheric communication in visual word recognition work? Deciding between early and late integration accounts of the split fovea theory. Brain and Language, 108, 112-121.”
“Whitney, C. (2001). How the brain encodes the order of letters in a printed word: The SERIOL model and selective literature review. Psychonomic Bulletin & Review, 8, 221-243.”
“So why are words easier to identify than their constituent letters even if word recognition proceeds via the constituent letters? One clear answer to this question was provided by McClelland and Rumelhart (1981) with their interactive-activation model (IAM) of context effects on letter perception. There are two important ingredients in the IAM, and either one of them alone can account for the word superiority effect in a letter-based account of word recognition. These are cascaded processing and interactive processing. Cascaded processing can account for the word superiority effect by having activation build up faster in word representations than letter representations, even if the latter are the first to receive any bottom-up activation input. This can occur due to the greater amount of activation input at the word level through the convergence of inputs that are separated at the letter level. Grainger and Jacob’s (1994) dual readout model provided an account of the word superiority effect within the framework of a non-interactive version of the IAM. Superior identification of letters in words compared to letters in pseudowords is due to identification of the word enabling correct identification of its constituent letters via read-out from a whole-word orthographic representation in long-term memory. According to this non-interactive account of the word superiority effect, the pseudoword superiority effect (superior identification of letters in orthographically regular, pronounceable nonwords compared with irregular unpronounceable nonwords) is due to misperceiving the pseudoword as a real word (see Grainger & Jacobs, 2005, for a detailed discussion of this possibility).”
“Figure 8.3. Adaptation of Riesenhuber and Poggio’s (1999) model of object identification to the case of letter perception (Grainger et al., 2008).”
“What is most noticeable is the sharp drop in acuity within the 1-2° of the fovea, the area that will be involved in processing words that are fixated. Indeed, under standard reading conditions, a five-letter word is about 1 cm long and therefore spans about 1.4° of visual angle at a reading distance of 40 cm. This implies that for a centrally fixated word there is a very sharp, almost linear drop in acuity as one moves from the fixated letter to the outer letters (first and last letter) of the word. Furthermore, given that words up to about eight letters in length are typically read in a single fixation, this represents about 93% of English words that a skilled reader is likely to encounter (estimated from a token frequency word form count).”
“bank of letter detectors that enable parallel independent identification of letters in a string (Dehaene, Cohen, Sigman, & Vinckier, 2005; Grainger, Granier, Farioli, Van Assche, & van Heuven, 2006; Grainger & van Heuven, 2003).”
“Initial support for the hypothesized reduction in crowding with letters as a function of exposure to print was provided by Atkinson, Anker, Evans, Hall, and Pimm-Smith (1988), who found greater crowding effects with letter targets and a combination of horizontal and vertical flankers in 3-year-old children compared with 5-7-year-old children.”
“Kwon, Legge, and Dubbels (2007) found a strong correlation between the development of reading speed and the size of the visual span (number of letters that can be identified without eye movements). Since Pelli et al. (2007) have shown that crowding is the critical factor determining the size of the visual span, one can again conclude that reduced crowding in letter strings is a key factor at play in the development of reading fluency.”
“With the focus on silent word reading, the general goal of our modeling efforts is to account for how, given the constraints on letter-in-string visibility, plus the temporal constraints imposed by reading rate (about 250 ms per word), the skilled reader optimizes uptake of information from the printed word stimulus in order to recover the appropriate semantic and syntactic information necessary for text comprehension. The dual-route approach acknowledges that two different types of constraint affect processing along the two routes. Both types of constraint are driven by the frequency with which different combinations of letters occur in printed words. On the one hand, frequency determines the probability with which a given combination of letters belongs to the word being read, as opposed to all other words. Letter combinations that are encountered less often in other words are more diagnostic of the identity of the word being processed. On the other hand, frequency of co-occurrence enables the formation of higher-order representations (chunking) in order to diminish the amount of information that is processed via data compression. Letter combinations that often occur together can be usefully grouped to form higher-level orthographic representations such as multi-letter graphemes (<TH>, <CH>, <AI>) and morphemes (ING, ER), and therefore facilitate the contact between orthographic representations and preexisting phonological and morphological representations during the course of reading acquisition. This dual-route approach to orthographic processing is illustrated in Figure 8.7.”
“Figure 8.7 A dual-route approach to orthographic processing (Grainger & Ziegler, 2011). Starting from location-specific letter detectors, two fundamentally different types of location-invariant sublexical orthographic code are computed. A coarse-grained code optimizes the mapping of orthography to semantics by selecting letter combinations that are the most informative with respect to word identity, irrespective of letter contiguity. A fine-grained code optimizes processing via the chunking of frequently co-occurring contiguous letters.”
“Fundamentally different types of orthographic processing are performed by these two routes, since they are geared to use frequency of occurrence in diametrically opposite ways. The two routes differ notably in terms of the level of precision with which letter position information is coded. In one route, a coarse-grained orthographic code is computed in order to rapidly home in on a unique word identity, and corresponding semantic representations (the fast track to semantics). Given variations in visibility across letters in a string, the key hypothesis here is that the best way to optimize performance is to adapt processing to the constraints imposed by variations in letter visibility and variations in the amount of information carried by different letter combinations. That is, to code for combinations of the most visible letters that best constrain word identity. This fits with the idea of coding for contiguous and non-contiguous letter combinations proposed in recent models of orthographic processing (so-called ‘open-bigram’ coding: Grainger & van Heuven, 2003; Grainger & Whitney, 2004; Whitney, 2001; see Dandurand, Grainger, Duñabeitia, & Granier, 2011, for further discussion, and Adelman, 2011, for a similar proposal). Empirical evidence in favor of this type of coarse orthographic coding has been obtained using the masked priming paradigm in the form of robust priming effects with transposed-letter primes (e.g., gadren-GARDEN: Perea & Lupker, 2004; Schoonbaert & Grainger, 2004), and subset and superset primes (e.g., grdn-GARDEN, gamrdsen-GARDEN: Grainger et al., 2006; Peresotti & Grainger, 1999; Welvaert, Farioli, & Grainger, 2008; Van Assche & Grainger, 2006). Not only does open-bigram coding provide a natural explanation for these empirical demonstrations of flexible orthographic processing, but when combined with the constraints of letter visibility and informativeness it can also account for more subtle variations in orthographic priming effects as a function of the position of orthographic overlap and the precise letters involved (e.g., consonants vs vowels; Dandurand et al., 2011).”
“Input to word representations is determined by computing the match between the spatial code for the input and the stored spatial codes for known words (see Davis, 2010, and this volume, Chapter 9, for details). Davis (2010) showed that an implemented version of the SOLAR model provided an excellent fit to a large set of benchmark phenomena. Furthermore, Hannagan, Dupoux, and Christophe (2011) showed that the spatial coding scheme of the SOLAR model satisfied more priming constraints than alternative coding schemes, albeit with a greater number of free parameters. The fact that precise within-word letter order information is computed in order to generate an activation gradient in the SOLAR model means that this model has the level of precision necessary for computing contiguous letter combinations such as those that occur in complex graphemes. This level of precision is necessary for the transformation of a sublexical orthographic code into a phonological code, as postulated in various dual-route accounts of visual word recognition.”
“In languages that use an alphabetic script to represent written language, printed words are recognized via their component letters. This does not mean that you need to identify each component letter before you can start to identify the word. As soon as information starts to accrue in feature and letter representations this is transferred to higher-levels in the manner of cascaded processing. This can explain why it is easier to identify a letter embedded in a real word as opposed to letters in nonwords, the so-called ‘word superiority effect’.”
“Word-shape information might play a role in certain specific conditions, but there is little evidence at present for generalized use of this type of information. It should be remembered that finding a difference in the ability to read text printed in MixEd cAsE versus homogeneous case does not necessarily imply a role for word shape information.”
“The very first phase of orthographic processing proper involves the mapping of retinotopic features onto letter representations - most likely simultaneously for each letter position. This mapping process is constrained by two main factors: visual acuity and crowding. The combination of these two factors explains the typical W-shaped serial position function for letter-in-string identification, with highest accuracy at initial and final positions as well as for the letter that is fixated. Due to the sharp drop in visual acuity, accuracy becomes worse as one moves away from fixation, except for the first and last letter in the string, that suffer less crowding than the inner letters.”
“Location-invariant word recognition requires a word-centered orthographic code, hence the hard problem of orthographic processing: how to enable the transition from retinotopic coordinates to word-centered coordinates for letter position coding. There is considerable evidence that this location-invariant word-centered orthographic code is robust to small chagnes in letter odrer and the inserstion and remval of lettrs. One way to achieve such flexibility is to code for ordered pairs of letters while ignoring to a certain extent whether these letters are adjacent or not. Alternatively one could assign an approximate position code to each individual letter.”
“This proposed flexibility in orthographic processing contrasts with the level of precision that is required to make contact with preexisting phonological and morphological representations during reading acquisition. Therefore, a complete account of orthographic processing must be able to simultaneously exhibit the kind flexibility revealed notably by research on orthographic priming, as well as being able to accurately code for the presence of the letter T immediately before the letter H in the grapheme <TH> or the E before the R in the suffix ER.”
“It should be noted that when reading text of average difficulty, skilled readers will typically fixate the majority of words, skipping only short high-frequency words, and the majority of fixated words are only fixated once. Furthermore, the distribution of initial fixation positions (or landing sites) has a strong peak near the centre of the word (the preferred viewing position, Rayner, 1979).”
“Adelman, J.S. (2011). Letters in time and retinotopic space. Psychological Review, 118, 570-582.”
“Adelman, J.S., Marquis, S.J., & Sabatos-DeVito, M.G. (2010). Letters in words are read simultaneously, not in left-to-right sequence. Psychological Science, 21, 1799- 1801.”
“Averbach, E. & Coriell, A.S. (1961). Short-term memory in vision. Bell Telephone Technical Journal, 40, 19-31.”
“Bendahman, L. A., Vitu, F., and Grainger, J. (2010). Ultra-fast processing of printed words? Perception, 39 (ECVP Abstract Supplement), 147.”
“Bouma, H. (1970). Interaction effects in parafoveal letter recognition. Nature, 226, 177-8.”
“Bouma, H. (1973). Visual interference in the parafoveal recognition of initial and final letters of words. Vision Research, 13, 767-782.”
“Cattell, J. (1886). The time it takes to see and name objects. Mind, 11, 53-65.”
“Chanceaux, M., Vitu, F., Bendahman, L., Thorpe, S., & Grainger, J. (2011). Word processing speed in peripheral vision measured with a saccadic choice task. Vision Research, 56, 10-19.”
“Chung, S.T.L. (2007). Learning to identify crowded letters: Does it improve reading speed? Vision Research, 47, 3150-3159.”
“Coltheart, M., Curtis, B., Atkins, P., & Haller, M. (1993). Models of reading aloud: Dual-route and parallel-distributed-processing approaches. Psychological Review, 100, 589-608.”
“Coltheart, M., Rastle, K., Perry, C., Langdon, R., & Ziegler, J.C. (2001). DRC: A dual-route cascaded model of visual word recognition and reading aloud. Psychological Review, 108, 204-256.”
“Courrieu, P., Farioli, F., & Grainger, J. (2004). Inverse discrimination time as a perceptual distance for alphabetic characters. Visual Cognition, 11, 901-919.”
“Dandurand, F., Grainger, J., Duñabeitia, J.A., & Granier, J.P. (2011). On coding non-contiguous letter combinations. Frontiers in Cognitive Sciences, 2, 136. DOI: 10.3389/ fpsyg.2011.00136.”
“Davis, C.J. (2010). The spatial coding model of visual word recognition. Psychological Review, 117, 713-758.”
“Dehaene, S., Cohen, L., Sigman, M., & Vinckier, F. (2005). The neural code for written words: a proposal. Trends in Cognitive Sciences, 9, 335-341.”
“Diependaele, K., Ziegler, J., & Grainger, J. (2010). Fast phonology and the bi-modal interactive activation model. European Journal of Cognitive Psychology, 22, 764-778.”
“Ducrot, S. & Grainger, J. (2007). Deployment of spatial attention to words in central and peripheral vision. Perception & Psychophysics, 69, 578-590.”
“Estes, W.K. (1972). Interactions of signal and background variables in visual processing. Perception & Psychophysics, 12, 278-286.”
“Finkbeiner, M. & Coltheart, M. (2009). Letter recognition: From perception to representation. Cognitive Neuropsychology, 26(1), 1-6.”
“Fischer-Baum, S., McCloskey, M., & Rapp, B. (2010). Representation of letter position in spelling: Evidence from acquired dysgraphia. Cognition, 115, 466-490.”
“Fiset, D., Blais, C., Ethier-Majcher, C., Arguin, M., Bubu, D., & Gosselin, F. (2007). Features for uppercase and lowercase letter identification. Psychological Science, 19, 1161-1168.”
“Gomez, P., Ratcliff, R., & Perea, M. (2008). The overlap model: A model of letter position coding. Psychological Review, 115, 577-601.”
“Gosselin, F. & Schyns, P.G. (2001). Bubbles: A technique to reveal the use of information in recognition. Vision Research, 41, 2261-2271.”
“Grainger, J. & Ferrand, L. (1994). Phonology and orthography in visual word recognition: Effects of masked homophone primes. Journal of Memory and Language, 33, 218-233.”
“Grainger, J. & Holcomb, P. J. (2009). Watching the word go by: on the time-course of component processes in visual word recognition. Language and Linguistics Compass, 3, 128-156.”
“Grainger, J. & Jacobs, A.M. (1994). A dual read-out model of word context effects in letter perception: Further investigations of the word superiority effect. Journal of Experimental Psychology: Human Perception and Performance, 20, 1158-1176.”
“Grainger, J. & van Heuven, W. (2003). Modeling letter position coding in printed word perception. In P. Bonin (Ed.), The Mental Lexicon (pp. 1-24). New York: Nova Science Publishers.”
“Grainger, J. & Whitney, C. (2004). Does the huamn mind raed wrods as a lohe? Trends in Cognitive Sciences, 8, 58-59.”
“Grainger, J. & Ziegler, J. (2008). Cross-code consistency effects in visual word recognition. In E.L. Grigorenko and A. Naples (Eds) Single-word reading: Biological and behavioral perspectives (pp. 129-157). Mahwah, NJ: Lawrence Erlbaum Associates.”
“Grainger, J., Granier, J.P., Farioli, F., Van Assche, E., & van Heuven, W.J. (2006). Letter position information and printed word perception: the relative-position priming constraint. Journal of Experimental Psychology: Human Perception and Performance, 32, 865-884.”
“Grainger, J., Rey, A., & Dufau, S. (2008). Letter perception: From pixels to pandemonium. Trends in Cognitive Sciences, 12, 381-387.”
“Grainger, J., Tydgat, I., & Isselé, J. (2010). Crowding affects letters and symbols differently. Journal of Experimental Psychology: Human Perception and Performance, 36, 673-688.”
“Hammond, E.J. & Green, D.W. (1982). Detecting targets in letter and non-letter arrays. Canadian Journal of Psychology, 36, 67-82.”
“Hinton, G.E. (2007). Learning multiple layers of representation. Trends in Cognitive Sciences, 11, 428-434.”
“Huckauf, A. & Nazir, T.A. (2007). How odgernwi becomes crowding: Stimulus-specific learning reduces crowding. Journal of Vision, 7(2): 18, 1-12.”
“Jacobs, A.M., Nazir, T.A., & Heller, O. (1989). Letter perception in peripheral vision: a temporal discrimination matrix using eye movements. Perception & Psychophysics, 46, 95-102.”
“Jacobs, A.M., Rey, A., Ziegler, J.C., & Grainger, J. (1998). MROM-P: An interactive activation, multiple read-out model of orthographic and phonological processes in visual word recognition. In J. Grainger & A.M. Jacobs (Eds), Localist connectionist approaches to human cognition (pp. 147- 188). Mahwah, NJ: Erlbaum.”
“Just, M.A. & Carpenter, P.A. (1987). The Psychology of Reading and Language Comprehension. Newton, MA: Allyn and Bacon, Inc.”
“Kirchner, H. & Thorpe, S.J. (2006). Ultra-rapid object detection with saccadic eye movements: visual processing speed revisited. Vision Research, 46, 1762-1776.”
“Ktori, M., Grainger, J., & Dufau, S. (2012). Letter string processing and visual short-term memory. Quarterly Journal of Experimental Psychology, in press, Doi: 10.1080/174702 18.2011.611889.”
“Kwon, M.Y. & Legge, G.E. (2006). Developmental changes in the size of the visual span for reading: effects of crowding. Journal of Vision, 6, 1003a (Abstract).”
“Kwon, M.Y., Legge, G.E., & Dubbels, B.R. (2007). Developmental changes in the visual span for reading. Vision Research, 47, 2889-2890.”
“Lavidor, M. (2010). Whole-word shape effect in dyslexia. Journal of Research in Reading, 34, 443-454.”
“Levi, D.M. (2008). Crowding - an essential bottleneck for object recognition: A minireview. Vision Research, 48, 635-654.”
“McCandliss, B.D., Cohen, L., & Dehaene, S. (2003). The visual word form area: Expertise for reading in the fusiform gyrus. Trends in Cognitive Sciences, 13, 155-161.”
“McClelland, .L. & Rumelhart, D.E. (1981). An interactive activation model of context effects in letter perception: Part 1. An account of basic findings. Psychological Review, 88, 375-407.”
“Mason, M. (1975). Reading ability and letter search time: Effects of orthographic structure defined by single-letter positional frequency. Journal of Experimental Psychology, 104, 146-166.”
“Mason, M. (1982). Recognition time for letters and nonletters: Effects of serial position, array size, and processing order. Journal of Experimental Psychology: Human Perception andPerformance, 8, 724-738.”
“Massaro, D.W. & Cohen, M.M. (1994). Visual, orthographic, phonological, and lexical influences in reading. Journal of Experimental Psychology: Human Perception and Performance, 20, 1107- 1128.”
“Mayall, K., Humphreys, G. W., & Olson, A. (1997). Disruption to word or letter processing? The origins of case-mixing effects. Journal of Experimental Psychology: Learning, Memory, and Cognition, 23, 1275-1286.”
“Paap, K., Newsome, S.L., McDonald, J.E., & Schvaneveldt, R.W. (1982). An activation-verification model for letter and word recognition: The word superiority effect. Psychological Review, 89, 573-594.”
“Pelli, D.G. & Tillman, K.A. (2007). Parts, wholes, and context in reading: A triple dissociation. PLOS ONE, 2(8): e680. doi:10.1371/journal.pone.0000680”
“Pelli, D.G., Burns, C.W., Farrell, B., & Moore-Page, D.C. (2006). Feature detection and letter identification. Vision Research, 46, 4646-4674.”
“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.”
“Perea, M. & Rosa, E. (2002). Does ‘whole-word shape’ play a role in visual word recognition? Perception & Psychophysics, 64, 785-794.”
“Peressotti, F. & Grainger, J. (1999). The role of letter identity and letter position in orthographic priming. Perception & Psychophysics, 61, 691-706.”
“Pitchford, N.J., Ledgeway, T., & Masterson, J. (2008). Effect of orthographic processes in letter position encoding. Journal of Research in Reading: Special Issue - Orthographic Processes in Reading, 31, 97-116.”
“Podgorny, P. & Garner, W. (1979). Reaction time as a measure of inter- and intraobject visual similarity: Letters of the alphabet. Perception & Psychophysics, 26, 37-52.”
“Rayner, K. (1979). Eye guidance in reading: Fixation location within words. Perception, 8, 21-30.”
“Rey, A., Dufau, S., Massol, S., & Grainger, J. (2009). Testing computational models of letter perception with item-level ERPs. Cognitive Neuropsychology, 26, 7-22.”
“Riesenhuber, M. & Poggio, T. (1999). Hierarchical models of object recognition in cortex. Nature Neuroscience, 2, 1019-1025.”
“Schoonbaert, S. & Grainger, J. (2004). Letter position coding in printed word perception: Effects of repeated and transposed letters. Language and Cognitive Processes, 19, 333-367.”
“Seidenberg, M.S. & McClelland, J.L. (1989). A distributed, developmental model of word recognition and naming. Psychological Review, 96, 523-568.”
“Solomon, J.A. & Pelli, D.G. (1994). The visual filter mediating letter identification. Nature, 369, 395-397.”
“Stevens, M. & Grainger, J. (2003). Letter visibility and the viewing position effect in visual word recognition. Perception & Psychophysics, 65, 133-151.”
“Tydgat, I. & Grainger, J. (2009). Serial position effects in the identification of letters, digits and symbols. Journal of Experimental Psychology: Human Perception and Performance, 35, 480-498.”
“Wheeler, D. (1970). Processes in word recognition. Cognitive Psychology, 1, 59-85.”
“Whitney, C. (2001). How the brain encodes the order of letters in a printed word: the SERIOL model and selective literature review. Psychonomic Bulletin and Review, 8, 221-243.”
“Whitney, C. & Cornelissen, P. (2008). SERIOL reading. Language and Cognitive Processes, 23, 143-164.”
“Williamson, K., Scolari, M., Jeong, S., Kim, & Awh, E. (2009). Experiencedependent changes in the topography of visual crowding. Journal of Vision, 9, 1-9.”
“Wolford, G. & Hollingsworth, S. (1974). Retinal location and string position as important variables in visual information processing. Perception & Psychophysics, 16, 437-442.”
“Ziegler, J.C., Pech-Georgel, C., Dufau, S., & Grainger, J. (2010). Rapid processing of letters, digits, and symbols: What purely visual-attentional deficit in developmental dyslexia? Developmental Science, 13, 8-14.”
“A similarity neighborhood will be defined as the set of words in the language from which a given stimulus word is indistinguishable after a specified loss of information about the stimulus word. The similarity neighborhood of a word thus always includes the word itself. Words other than the stimulus belonging to a neighborhood will be called neighbors.”
“More specifically, Landauer and Streeter focussed on what they called one-letter substitution neighbours: words that could be formed by replacing a single letter of the stimulus. For example, the neighbours of WORD include CORD, WARD, WOOD, and WORK. The neighbourhood size of a stimulus was defined to be the count of such neighbours (plus the stimulus word itself, in their formulation).”
“Variables such as word frequency, familiarity, age-of-acquisition, imageability, spelling-sound consistency - all of which affect the speed with which words are processed in various tasks - have no bearing on nonwords.”
“the similarity neighbourhood of a letter string is based not only on words formed via letter substitutions but also those formed via letter transpositions. Indeed, there is some evidence that such transposition neighbours are closer in similarity space than are substitution neighbours. It can be concluded that it takes some time to resolve letter position uncertainty, often more time than is required to resolve uncertainty regarding the identity of the letters.”
“the specific similarity of two-letter strings depends on the nature of the orthographic input coding scheme. The standard approach, based on position-specific coding, explains the similarity of substitution neighbours, but doesn’t explain why transposition neighbours are more similar than double substitution neighbours. The chief alternatives to the standard approach are open-bigram coding and spatial coding. Attempts to adjudicate between these alternatives are the subject of much ongoing research (e.g., Davis, 2010; Davis & Bowers, 2006; Grainger, 2008; Whitney, 2008).”
“The most well-known example of a competitive network model of visual word identification is the interactive activation (IA) model (McClelland & Rumelhart, 1981); other models in this framework that have explicitly simulated inhibitory neighbour effects have been discussed by Davis (1999, 2010) and Grainger and Jacobs (1996). According to such models, visual word identification involves competition between lexical representations - it is this competition that underlies the lexical selection process that chooses the learned representation that best matches the input stimulus.”
“A very different prediction is made by parallel distributed processing (PDP) models of visual word recognition (e.g., Plaut, McClelland, Seidenberg, & Patterson, 1996; Seidenberg McClelland, 1989). These models do not include localist lexical representations for words; instead, all lexical knowledge is encoded with the distributed pattern of connections between nodes. Over the course of training these connections are modified, so that words and sublexica patterns that been encountered very frequently will be better represented in the model’s weights. Words that are orthographically similar will tend to share many connections, and thus training on one word tends to strengthen connections that are relevant not only to that word but also to orthographically similar words. This aspect of PDP models suggests that orthographic similarity should exert a facilitatory effect on visual word recognition. Sears, Hino, and Lupker (1999a) reported simulations of two influential PDP models (the Seidenberg & McClelland, 1989 model and the Plaut et al., 1996 model) and confirmed that both models predict a facilitatory effect of having a higher frequency orthographic neighbour.”
“Acha, J. & Perea, M. (2008). The effect of neighborhood frequency in reading: Evidence with transposed-letter neighbors. Cognition, 108, 290-300.”
“Balota, D.A. & Chumbley, J.I. (1984). Are lexical decisions a good measure of lexical access? The role of word frequency in the neglected decision stage. Journal of Experimental Psychology: Human Perception and Performance, 10, 340-357.”
“Balota, D.A., Cortese, M.J., Sergent-Marshall, S.D., Spieler, D.H., & Yap, M.J. (2004). Visual word recognition of single-syllable words. Journal of Experimental Psychology: General, 133, 283-316.”
“Bowers, J.S., Davis, C.J., & Hanley, D.A. (2005). Interfering neighbours: The impact of novel word learning on the identification of visually similar words. Cognition, 97, 45-54.”
“Carreras, M., Vergara, M., & Perea, M. (2007). ERP correlates of transposed-letter similarity effects: Are consonants processed differently from vowels? Neuroscience Letters, 419, 219-224.”
“Chambers, S.M. (1979). Letter and order information in lexical access. Journal of Verbal Learning and Verbal Behavior, 18, 225-241.”
“Davis, C.J. (1999). The self-organising lexical acquisition and recognition (SOLAR) model of visual word recognition. Doctoral dissertation, University of New South Wales.”
“Davis, C.J. (2010). The spatial coding model of visual word identification. Psychological Review, 117, 713-58.”
“Davis, C.J. & Bowers, J. S. (2006). Contrasting five different theories of letter position coding: Evidence from orthographic similarity effects. Journal of Experimental Psychology-Human Perception and Performance, 32, 535-557.”
“Forster, K.I. & Hector, J. (2002). Cascaded versus noncascaded models of lexical and semantic processing: the turple effect. Memory and Cognition, 30, 1106-1116.”
“Gomez, P., Ratcliff, R., & Perea, M. (2008). The overlap model: A model of letter position coding. Psychological Review, 115, 577-600.”
“Grainger, J.& Jacobs, A.M. (1996). Orthographic processing in visual word recognition: A multiple read-out model. Psychological Review, 103, 518-565.”
“Grainger, J. & van Heuven, W.J.B. (2003). Modeling letter position coding in printed word perception. In P. Bonin (Ed.), The mental lexicon (pp. 1-23). New York: Nova Science.”
“Grainger, J., O’Regan, J.K., Jacobs, A.M., & Segui, J. (1989). On the role of competing word units in visual word recognition: The neighbourhood frequency effect. Perception & Psychophysics, 51, 49-56.”
“Johnson, N.F. & Pugh, K.R. (1994). A cohort model of visual word recognition. Cognitive Psychology, 26, 240-346.”
“Johnson, R.L. (2009). The quiet clam is quite calm: Transposed-letter neighborhood effects on eye movements during reading. Journal of Experimental Psychology: Learning, Memory, and Cognition, 35, 943-969.”
“Landauer, T. & Streeter, L.A. (1973). Structural differences between common and rare words: Failure of equivalence assumptions for theories of word recognition. Journal of Verbal Learning and Verbal Behavior, 12, 119-131.”
“Pollatsek, A., Perea, M., & Binder, K. (1999). The effects of neighborhood size in reading and lexical decision. Journal of Experimental Psychology: Human Perception and Performance, 25, 1142- 1158.”
“Rayner, K., White, S.J., Johnson, R.L., & Liversedge, S.P. (2006). Raeding wrods with jubmled lettres. Psychological Science, 17, 192-193.”
“Sears, C.R., Hino, Y., & Lupker, S.J. (1995). Neighborhood size and neighborhood frequency effects in word recognition. Journal of Experimental Psychology: Human Perception and Performance, 21, 876-900.”
“Seidenberg, M.S. & McClelland, J.L. (1989). A distributed, developmental model of word recognition and naming. Psychological Review, 96, 523-568.”
“Stadthagen-Gonzales, H., Bowers, J.S., & Damian, M.F. (2004). Age-of-acquisition effects in visual word recognition: Evidence from expert vocabularies. Cognition, 93, B11-B26.”
“Whitney, C. (2001). How the brain encodes the order of letters in a printed word: The SERIOL model and selective literature review. Psychonomic Bulletin and Review, 8, 221-243.”
“Whitney, C. (2008). A comparison of the SERIOL and SOLAR theories of letter-position encoding. Brain & Language, 107, 170-178.”
“Yates, M., Locker, L. Jr, & Simpson, G.B. (2004). The influence of phonological neighborhood on visual word perception. Psychonomic Bulletin and Review, 11, 452-457.”
“inner speech is one. Word identification is another. Baddeley and Lewis (1981) Showed that these two components could be partially independent.”
“Readers typically recognize words 40-50 ms faster with parafoveal preview than when preview is denied (Rayner, 2009; Rayner, Liversedge, & White, 2006). The parafoveal preview paradigm manipulates the relation of parafoveal information to the upcoming word. If readers process the information parafoveally, then congruent previews will facilitate recognition once the word is fixated. Thus, parafoveal preview studies measure effects of the initial information that skilled readers process during word recognition and provide singular insights into early lexical access processes.”
“eye movement evidence extends the findings of many isolated word experiments to word recognition in context by indicating that skilled readers routinely activate phonological information during silent reading. The fact that the critical word (on which measurement is taken) is not visibly marked as different compared to the rest of the sentence means that the reader uses standard reading processes rather”
“than some special strategic process. Another sense of ‘routine’ is that phonological processes apply not only to a subset of unfamiliar words, but to words in general. Experiments using several paradigms have demonstrated phonological effects for low, moderate, and high frequency words (Ashby, 2006; Ashby, Treiman, Kessler, & Rayner, 2006; Miellet & Sparrow, 2004; McCutchen & Perfetti, 1982; Newman, Jared, & Haigh (in press); Perfetti et al., 1992; Rayner, Sereno, Lesch, & Pollatsek, 1995). Such general effects are expected given the automaticity of parafoveal processing, which gains phonological information about a word before its frequency is determined.”
“Miellet, S. & Sparrow, L. (2004). Phonological codes are assembled before word fixation: Evidence from boundary paradigm in sentence reading. Brain & Language, 90, 299- 310.”
“Newman, R. L., Jared, D., & Haigh, C.A. (in press). Does phonology play a role when skilled readers read high-frequency words? Evidence from ERPs. Language and Cognitive Processes.”
“Pammer, K., Hansen, P. C., Kringelback, M. L., Holliday, I., Barnes, G., Hillebrand, A., Singh, K. D., & Cornelissen, P. L. (2004). Visual word recognition: The first half second. Neurolmage, 22, 1819-1825.”
“Perfetti, C. A. & Bell, L. (1991). Phonemic activation during the first 40 ms of word identification: Evidence from backward masking and masked priming. Journal of Memory and Language, 30, 473-85.”
“Perfetti, C. A. & Tan, L. H. (1998). The time course of graphic, phonological, and semantic activation in Chinese character identification. Journal of Experimental”
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