These initial findings were interpreted as evidence for a form of morpho‐orthographic decomposition based on the mere appearance of morphological structure (Rastle et al., 2004). They went on to be replicated across a number of studies in different Indo‐European languages (see Rastle & Davis, 2008, for review), bringing them into line with discoveries concerning masked morphological priming in Semitic languages (Boudelaa & Marslen‐Wilson, 2001; Frost, Forster, & Deutsch, 1997). Further research established that this form of decomposition arises rapidly and prior to lexical identification. Longtin and Meunier (2005) reported that the masked priming effects yielded by morphologically structured nonwords (e.g., darkism – DARK) were of the same magnitude as those observed for semantically transparent derived words (e.g., darkness – DARK). This was the case irrespective of whether the nonword primes were syntactically legal (e.g., quickify – QUICK) or illegal (e.g., sportation – SPORT). Finally, research indicates that this form of decomposition is robust to regular orthographic alterations that characterize morphologically complex words (McCormick, Rastle, & Davis, 2008). Masked morphological priming effects are observed when primes cannot be parsed perfectly into constituents because of a missing e (e.g., adorable – ADORE), a shared e (e.g., writer – WRITE), or a reduplicated consonant (e.g., metallic – METAL). These effects are of a similar magnitude to those using primes that can be parsed perfectly into morphemic constituents, and also arise when primes do not share a meaningful relationship with stems (e.g., badger – BADGE; McCormick et al., 2008).
Research investigating morphological priming effects in children of different ages has suggested that morpho‐orthographic segmentation may reflect a form of reading expertise. Beyersmann and colleagues reported that English speaking (Beyersmann, Castles, & Coltheart, 2012) and French speaking (Beyersmann, Grainger, Casalis, & Ziegler, 2015) primary school children show robust masked morphological priming effects, but only when morphological primes have a semantic relationship with targets. Beyersmann et al. (2012) found no evidence of priming based on the appearance of morphological structure (e.g., corner – CORN) in children between the ages of 8 and 10. Similar findings were observed for Hebrew primary school children between the ages of 9 and 12: Robust masked morphological priming when primes were semantically related to targets, but weak or null priming when they were not (Schiff, Raveh, & Fighel, 2012). These findings suggest that perhaps morpho‐orthographic segmentation is a form of analysis that is acquired only after extensive reading experience. This conclusion is consistent with work by Andrews and Lo (2013) investigating individual differences in masked priming amongst university students. They found that the morpho‐orthographic pattern is modulated by vocabulary and spelling ability, with pseudo‐morphological priming being stronger in people with good spelling skills.
Theoretical Accounts of Morphological Processing
The body of data previously described is consistent with the notion of a hierarchically organized system relating spellings to meanings, in which the analysis of morphological information plays a vital role. In terms of time course, early processing is characterized by segmentation of printed words into their likely morphemic constituents; hence, masked morphological priming reflects segmentation based on the mere appearance of morphological structure, irrespective of the lexical status of the stimulus and the semantic relationship of the stimulus to its stem. Later processing is characterized by activation of semantic information; hence, morphological priming using fully visible primes reflects meaningful relationships between prime and target.
Lavric and colleagues (2012) tested this account by studying brain potentials as participants made lexical decisions to morphologically complex words (e.g., darkness), pseudo‐morphological words (e.g., corner), and nonmorphological stimuli (e.g., brothel). Results revealed that within the first 190 milliseconds, neural responses to stimuli in the two morphologically structured conditions were similar, and both differed significantly from neural responses to stimuli in the nonmorphological condition. Evidence of semantic involvement was observed 60 to 70 milliseconds later, when neural responses to stimuli in the pseudo‐morphological condition diverged from those in the other two conditions (Lavric, Elchlepp, & Rastle, 2012). The authors suggested that this second phase of recognition is suggestive of some type of process to repair the incorrect segmentation (e.g., corner is not “someone who corns”). These findings support a hierarchical model in which morphological decomposition is based initially on an orthographic analysis of morphemes, and is only later constrained by semantic information (see also Whiting, Shtyrov, & Marslen‐Wilson, 2014, for similar results using MEG methods). Conversely, these findings would appear to rule out any account in which the analysis of morphological information arises subsequent to lexical identification, such as the supra‐lexical model (Giraudo & Grainger, 2000).
Most theories of morphological processing are located within a localist tradition of modeling. These theories conceptualize morphemes as a layer of representation that resides between letter units and an orthographic lexicon (e.g., Crepaldi, Rastle, Coltheart, & Nickels, 2010; Taft, 1994; Taft & Nguyen‐Hoan, 2010). On these accounts, letter strings comprising a morphological structure such as darkness and corner would activate constituent morphemes prior to the activation of whole words, as in Figure 5.2. It is the activation of these units prior to lexical identification that is presumed to underlie the morpheme frequency effect and the morpheme interference effect. Likewise, activation of a morpheme representation due to presentation of a morphologically structured prime should facilitate later recognition of a morphologically related target. However, this type of model has never been specified computationally, and attempting to do so would immediately reveal gaps in our understanding and implementational challenges. One example of this is provided by the research testing whether sensitivity to morphemes is position specific (Crepaldi et al., 2010). This research emerged directly from an initial attempt by Davide Crepaldi and myself to specify this theory computationally within an interactive activation framework. We had not even considered the question until faced with a decision about how to implement a morpheme layer of representation in this model.
Figure 5.2 Theory of morphological decomposition based on classical localist approach.
Figure 5.3 A theory of morphological processing based on a distributed‐connectionist approach.
Alternative approaches to morphological processing are superior because they are computationally specified, but their ability to account for critical data has not been established. One perspective arises within the distributed‐connectionist modeling tradition (Harm & Seidenberg, 2004; Plaut & Gonnerman, 2000; Rueckl & Raveh, 1999; see Figure 5.3). These models reject the notion of explicit morphological representations. Instead, in learning the mapping between orthography and meaning, these models come to represent morphologically complex words componentially – that is, in terms of their morphemes (Plaut & Gonnerman, 2000). This means that the distributed representations for morphologically complex words such as darkness will overlap those of their stems (dark), providing scope for modeling morphological effects (Plaut & Gonnerman,