to refer to a long hair growing from the face of a mammal, or infrequently, to a very tiny amount (e.g., she won the race by a whisker). Likewise, while some affixes are highly consistent across all words in which they occur (e.g., ‐ness virtually always transforms an adjective to a noun), others are not (e.g., ‐age in vicarage, postage, and bandage). Even where morphologically complex words are consistently related to the meanings of their stems, the specific way that affixes transform the meanings of stems can vary. For example, while the words snowman, patrolman, and milkman are all men (of sorts), the ‐man component functions quite differently in each word: a snowman is a man made of snow, but a milkman is not made of milk; a patrolman patrols, but a snowman does not snow.
These examples suggest that the regularity that morphology brings to the spelling‐meaning mapping is graded rather than all‐or‐none. However, research has only recently begun to quantify the nature of this regularity. Emerging research has revealed a striking relationship between English suffixes and grammatical category, with most suffixes being highly diagnostic of this aspect of meaning (Ulicheva et al., 2020). For example, the suffix ‐ous virtually always denotes adjective status (e.g., nervous, envious, glamorous), and adjectives ending in the sound sequence /Əs/ must be spelled ‐ous (see also Berg & Aronoff, 2017). The sound sequence /Əs/ is virtually always spelled another way if the word is not an adjective (e.g., service, princess, haggis). This relationship means that a superficial inspection of a suffixed English word already reveals an important aspect of its meaning: whether it is an object, property, or act.
Intriguingly, at least in English, it seems that regularity between spelling and sound is sacrificed to express these powerful regularities between spelling and meaning. If English spelling offered a perfect transcription of the sounds of words, then one might spell the words busted, snored, and kicked as bustid, snord, and kict (Rastle, 2019a). Yet, the spelling system admits spelling‐sound inconsistency (i.e., the spelling ‐ed can be pronounced in many ways) in order to transmit an important piece of information about meaning (i.e., the spelling ‐ed indicates the past). This trade‐off is ubiquitous in English spelling (Ulicheva et al., 2020), and means that meaningful morphological information is highly visible, significantly more than in spoken English (Rastle, 2019a; Ulicheva et al., 2020). Further analyses are required to quantify the strength of the relationship between spelling and meaning in other languages.
Morphology and the Spelling‐Meaning Mapping
Theoretical models propose that readers map orthographic representations onto meaning via two pathways. One pathway relates orthographic representations to semantic representations directly, while the other achieves this mapping via phonological representations (Harm & Seidenberg, 2004; see Seidenberg, this volume). Meta‐analyses of neuroimaging data are consistent with these models, revealing dorsal (spelling‐sound‐meaning; see Figure 5.1) and ventral (spelling‐meaning) pathways for reading (Taylor, Rastle, & Davis, 2013; see also Hoffman, Ralph, & Woollams, 2015; Yeatman, this volume). Though there is wide agreement that phonological decoding plays a vital role in the computation of meaning (particularly during the initial stages of learning to read), it is also recognized that skilled reading ultimately requires rapid, direct computation of meaning from a printed stimulus. The acquisition of this spelling‐meaning mapping is sometimes called orthographic learning (Castles & Nation, 2006; Castles & Nation, this volume).
Figure 5.1 Dual pathway model of reading.
Our understanding of the nature of the spelling‐meaning mapping and its acquisition is relatively poor (Nation, 2009; Nation, 2017; Taylor et al., 2015), perhaps because it has been difficult to find a clear behavioural measure of this pathway (Seidenberg, 2011). Behavioral and electrophysiological data suggest that semantic information is activated rapidly in visual word recognition (Balota et al., 2004; Carreiras, Armstrong, Perea, & Frost, 2014), although these semantic effects may be at least in part driven by processing along the phonological pathway (van Orden, 1987). However, there is also evidence that by the age of 7, children activate semantic information from subword orthographic patterns in reading, irrespective of whether those subword patterns share pronunciation with the carrier word (e.g., the crow in crown; Nation & Cocksey, 2009). These data provide evidence for a direct mapping between spelling and meaning.
Neural models conceptualize the ventral (spelling‐to‐meaning) pathway as supporting reading expertise (Dehaene‐Lambertz, Monzalvo, & Dehaene, 2018; McCandliss, Cohen, & Dehaene, 2003). Likewise, longitudinal data suggest that sensitivity along this pathway continues to develop into adolescence (Ben‐Shachar, Dougherty, Deutch, & Wandell, 2011) as reliance shifts from the dorsal pathway to the ventral pathway (Pugh et al., 2000). More recent neuroimaging research has revealed a hierarchical posterior‐to‐anterior gradient whereby representations of visual symbols become increasingly invariant as they are transformed to meaningful information (Taylor, Davis, & Rastle, 2019).
Morphology brings an important dimension to thinking about the nature of the spelling‐meaning mapping. Recent estimates suggest that the average 20‐year‐old English speaker recognizes around 71,400 printed words (Brysbaert, Stevens, Mandera, & Keuleers, 2016). If the spelling‐meaning mappings were wholly arbitrary, this would require children to learn over 12 new printed words each day (assuming that learning to read begins at age 4); surely, an unfeasible task. However, if we remove inflectional variants of stems (e.g., cleans, cleaning, cleaned), this reduces the learning challenge to 42,000 printed words. If we go further and remove derivational variants (e.g., unclean, cleanliness, cleanly), this reduces the learning challenge to 11,100 stems (Brysbaert, et al., 2016; see also Nagy & Anderson, 1984), or to just under two new words each day. Though this figure seems more feasible, it is still over double the number of distinct spellings required for full literacy in Chinese (Katz & Frost, 1992), providing an indication of the difficulty of acquiring the spelling‐meaning mapping. Crucially, these revised figures also presume that the learner has sufficient knowledge of morphological relationships to recognize that printed words such as cleanly, unclean, cleanliness, and cleaned are variations of clean.
The previous discussion suggests that acquiring knowledge of morphological relationships may be a vital part of developing a direct mapping between spellings and meanings. Strong causal evidence for this hypothesis is lacking. However, neural evidence using MEG (Lewis, Solomyak, & Marantz, 2011; Solomyak & Marantz, 2010), fMRI (Devlin, Jamison, Matthews, & Gonnerman, 2004; Gold & Rastle, 2007), and diffusion tensor imaging (Yablonski, Rastle, Taylor, & Ben‐Shachar, 2019) converge in suggesting that morphological computations engage ventral (spelling‐meaning) pathway regions of the reading system (Rastle, 2019a).
Morphological Analysis in Skilled Reading
There is strong evidence that morphological information is analysed during the recognition of printed words. Much of this evidence derives from three paradigms: the morpheme frequency paradigm, the morpheme interference paradigm, and the morphological priming paradigm. This evidence is reviewed briefly in the following section (see also Amenta & Crepaldi, 2012 for fuller review).
The term morphological decomposition is typically used to describe the analysis of morphemic information. This term has usually been used to refer to a process of segmenting words into morphemic constituents (e.g., segmenting unclean into the constituents [un‐] + [clean]) during the recognition process (e.g., Taft, 1994; Taft & Forster, 1975). However, the term morphological decomposition can also refer to the way that morphologically complex words are represented in distributed‐connectionist networks (e.g., Rastle & Davis,