Semantic Transparency in Free Stems: The Effect of Orthography-Semantics Consistency on Word Recognition

The role of morphology in novel word learning: a registered report

The majority of the new words that we learn every day as adults are morphologically complex; yet, we do not know much about the role of morphology in novel word learning. In this study, we tackle this issue by comparing the learning of: (i) suffixed novel words (e.g. flibness); (ii) novel words that end in non-morphological, but frequent letter chunks (e.g. fliban); and (iii) novel words with non-morphological, low-frequency endings (e.g. flibov). Words are learned incidentally through sentence reading, while the participants' eye movements are monitored. We show that morphology has a facilitatory role compared with the other two types of novel words, both during learning and in a post-learning recognition memory task. We also showed that participants attributed meaning to word parts (if flibness is a state of happiness, then flib must mean happy), but this process was not specifically triggered by the presence of a suffix (flib must also mean happy in fliban and flibov), thus suggesting that the brain tends to assume similar meanings for similar words and word parts.

Systematic mappings of sound to meaning: A theoretical review

The form of a word sometimes conveys semantic information. For example, the iconic word gurgle sounds like what it means, and busy is easy to identify as an English adjective because it ends in -y. Such links between form and meaning matter because they help people learn and use language. But gurgle also sounds like gargle and burble, and the -y in busy is morphologically and etymologically unrelated to the -y in crazy and watery. Whatever processing effects gurgle and busy have in common likely stem not from iconic, morphological, or etymological relationships but from systematicity more broadly: the phenomenon whereby semantically related words share a phonological or orthographic feature. In this review, we evaluate corpus evidence that spoken languages are systematic (even when controlling for iconicity, morphology, and etymology) and experimental evidence that systematicity impacts word processing (even in lieu of iconic, morphological, and etymological relationships). We conclude by drawing attention to the relationship between systematicity and low-frequency words and, consequently, the role that systematicity plays in natural language processing.

Malay Lexicon Project 3: The impact of orthographic-semantic consistency on lexical decision latencies

Theories of word processing propose that readers are sensitive to statistical co-occurrences between spelling and meaning. Orthographic-semantic consistency (OSC) measures provide a continuous estimate of the statistical regularities between spelling and meaning. Here we examined Malay, an Austronesian language that is agglutinative. In Malay, stems are often repeated in other words that share a related meaning (e.g., sunyi/quiet; ke-sunyi-an/silence; makan/eat; makan-an/foods). The first goal was to expand an existing large Malay database by computing OSC estimates for 2,287 monomorphemic words. The second goal was to explore the impact of root family size and OSC on lexical decision latencies for monomorphemic words. Decision latencies were collected for 1,280 Malay words of various morphological structures. Of these, data from 1,000 monomorphemic words were analysed in a series of generalised additive mixed models (GAMMs). Root family size and OSC were significant predictors of decision latencies, particularly for lower frequency words. We found a facilitative effect of root family size and OSC. Furthermore, we observed an interaction between root family size and OSC in that an effect of OSC was only apparent in words with larger root families. This interaction has not yet been explored in English but has the potential to be a new benchmark effect to test distributional models of word processing.

On the Connection Between Language Change and Language Processing

Previous studies provided evidence for a connection between language processing and language change. We add to these studies with an exploration of the influence of lexical-distributional properties of words in orthographic space, semantic space, and the mapping between orthographic and semantic space on the probability of lexical extinction. Through a binomial linear regression analysis, we investigated the probability of lexical extinction by the first decade of the twenty-first century (2000s) for words that existed in the first decade of the nineteenth-century (1800s) in eight data sets for five languages: English, French, German, Italian, and Spanish. The binomial linear regression analysis revealed that words that are more similar in form to other words are less likely to disappear from a language. By contrast, words that are more similar in meaning to other words are more likely to become extinct. In addition, a more consistent mapping between form and meaning protects a word from lexical extinction. A nonlinear time-to-event analysis furthermore revealed that the position of a word in orthographic and semantic space continues to influence the probability of it disappearing from a language for at least 200 years. Effects of the lexical-distributional properties of words under investigation here have been reported in the language processing literature as well. The results reported here, therefore, fit well with a usage-based approach to language change, which holds that language change is at least to some extent connected to cognitive mechanisms in the human brain.

How trial-to-trial learning shapes mappings in the mental lexicon: Modelling lexical decision with linear discriminative learning

Trial-to-trial effects have been found in a number of studies, indicating that processing a stimulus influences responses in subsequent trials. A special case are priming effects which have been modelled successfully with error-driven learning (Marsolek, 2008), implying that participants are continuously learning during experiments. This study investigates whether trial-to-trial learning can be detected in an unprimed lexical decision experiment. We used the Discriminative Lexicon Model (DLM; Baayen et al., 2019), a model of the mental lexicon with meaning representations from distributional semantics, which models error-driven incremental learning with the Widrow-Hoff rule. We used data from the British Lexicon Project (BLP; Keuleers et al., 2012) and simulated the lexical decision experiment with the DLM on a trial-by-trial basis for each subject individually. Then, reaction times were predicted with Generalized Additive Models (GAMs), using measures derived from the DLM simulations as predictors. We extracted measures from two simulations per subject (one with learning updates between trials and one without), and used them as input to two GAMs. Learning-based models showed better model fit than the non-learning ones for the majority of subjects. Our measures also provide insights into lexical processing and individual differences. This demonstrates the potential of the DLM to model behavioural data and leads to the conclusion that trial-to-trial learning can indeed be detected in unprimed lexical decision. Our results support the possibility that our lexical knowledge is subject to continuous changes.

Is There an Early Morphological Decomposition during L2 Lexical Access? A Meta-Analysis on the Morphological Priming Effect

A considerable body of experimental data currently exists on the representation and processing of derived words. However, no theoretical account has led to a consensus so far, due in part to inconsistencies in empirical results which show either the presence or the absence of signs of early morphological decomposition during lexical access. In this paper, we present the results of a meta-analysis that sought to examine the robustness of the masked morphological priming effect (MMP) in native and non-native speakers. This effect is indexed by faster responses to targets preceded by morphologically related primes vs. unrelated primes (e.g., fighter-FIGHT < needle-FIGHT), and is perhaps the most widespread effect used to test whether speakers of a given language are sensitive to the morphological components of words at early stages of lexical access. To this end, we selected 10 masked priming lexical decision studies (16 experiments) conducted with native and non-native speakers. Variables such as prime duration and level of L2 proficiency were considered in the analyses to assess their impact on the MMP effect. Results showed significant MMP effects, which were restricted to native speakers. No modulations were found for the prime duration. Results are interpreted in light of prevalent models of complex word processing.

Prediction at the intersection of sentence context and word form: Evidence from eye-movements and self-paced reading

A key issue in language processing is how we recognize and understand words in sentences. Research on sentence reading indicates that the time we need to read a word depends on how (un)expected it is. Research on single word recognition shows that each word also has its own recognition dynamics based on the relation between its orthographic form and its meaning. It is not clear, however, how these sentence-level and word-level dynamics interact. In the present study, we examine the joint impact of these sources of information during sentence reading. We analyze existing eye-tracking and self-paced reading data (Frank et al., 2013, Behavior Research Methods, 45[4], 1182-1190) to investigate the interplay of sentence-level prediction (operationalized as Surprisal) and word Orthography-Semantics Consistency in activating word meaning in sentence processing. Results indicate that both Surprisal and Orthography-Semantics Consistency exert an influence on several reading measures. The shape of the observed interaction differs, but the results give compelling indication for a general trade-off between expectations based on sentence context and cues to meaning from word orthography.

Very early and late form-to-meaning computations during visual word recognition as revealed by electrophysiology

We used a large-scale data-driven approach to investigate the role of word form in accessing semantics. By using distributional semantic methods and taking advantage of an ERP lexical decision mega-study, we investigated the exact time dynamic of semantic access from printed words as driven by orthography-semantics consistency (OSC) and phonology-semantics consistency (PSC). Generalized Additive Models revealed very early and late OSC-by-PSC interactions, visible at 100 and 400 msec, respectively. This pattern suggests that, during visual word recognition: a) meaning is accessed by means of two distinct and interactive paths - the orthography-to-meaning and the orthography-to-phonology-to-meaning path -, which mutually contribute to recognition since early stages; b) the system may exploit a dual mechanism for semantic access, with early and late effects associated to a fast-coarse and a slow-fine grained semantic analysis, respectively. The results also highlight the high sensitivity of the visual word recognition system to arbitrary form-meaning relations.

Characterizing the semantic and form-based similarity spaces of the mental lexicon by means of the multi-arrangement method

Collecting human similarity judgments is instrumental to measuring and modeling neurocognitive representations (e.g., through representational similarity analysis) and has been made more efficient by the multi-arrangement task. While this task has been tested for collecting semantic similarity judgments, it is unclear whether it also lends itself to phonological and orthographic similarity judgments of words. We have extended the task to include these lexical modalities and compared the results between modalities and against computational models. We find that similarity judgments can be collected for all three modalities, although word forms were considered more difficult to sort and resulted in less consistent inter- and intra-rater agreement than semantics. For all three modalities we can construct stable group-level representational similarity matrices. However, these do not capture significant idiosyncratic similarity information unique to each participant. We discuss the potential underlying causes for differences between modalities and their effect on the application of the multi-arrangement task.

Not just form, not just meaning: Words with consistent form-meaning mappings are learned earlier

By leveraging Phonology-to-Semantics Consistency (PSC), which quantifies form-meaning systematicity as the semantic similarity between a target word and its phonological nearest neighbours, we document a unique effect of systematicity on Age of Acquisition (AoA). This effect is also found after controlling for the effect of neighbourhood density measured for word forms and lexical semantics and several other standard predictors of AoA. Moreover, we show that the effect of systematicity is not reducible to iconicity. Finally, we extensively probe the reliability of this finding by testing different statistical models, analysing systematicity in phonology and orthography and implementing random baselines, reporting a robust, unique negative effect of systematicity on AoA, such that more systematic words tend to be learned earlier. We discuss the findings in the light of studies on non-arbitrary form-meaning mappings and their role in language learning, focusing on the analogical process at the interface of form and meaning upon which PSC is based and how it could help children infer the semantics of novel words when context is scarce or uninformative, ultimately speeding up word learning.

From decomposition to distributed theories of morphological processing in reading

The morphological structure of complex words impacts how they are processed during visual word recognition. This impact varies over the course of reading acquisition and for different languages and writing systems. Many theories of morphological processing rely on a decomposition mechanism, in which words are decomposed into explicit representations of their constituent morphemes. In distributed accounts, in contrast, morphological sensitivity arises from the tuning of finer-grained representations to useful statistical regularities in the form-to-meaning mapping, without the need for explicit morpheme representations. In this theoretically guided review, we summarize research into the mechanisms of morphological processing, and discuss findings within the context of decomposition and distributed accounts. Although many findings fit within a decomposition model of morphological processing, we suggest that the full range of results is more naturally explained by a distributed approach, and discuss additional benefits of adopting this perspective.

Masked Morphological Priming and Sensitivity to the Statistical Structure of Form–to–Meaning Mapping in L2

In one’s native language, visual word identification is based on early morphological analysis and is sensitive to the statistical structure of the mapping between form and meaning (Orthography–to–Semantic Consistency, OSC). How these mechanisms apply to a second language is much less clear. We recruited L1 Italian–L2 English speakers for a masked priming task where the relationship between prime and target was morphologically transparent, e.g., employer–EMPLOY, morphologically opaque, e.g., corner-CORN, or merely orthographic, e.g., brothel–BROTH. Critically, participants underwent thorough testing of their lexical, morphological, phonological, spelling, and semantic proficiency in their second language. By exploring a wide spectrum of L2 proficiency, we showed that this factor critically qualifies L2 priming. Genuine morphological facilitation only arises as proficiency grows, while orthographic priming shrinks as L2 competence increases. OSC was also found to modulate priming and interact with proficiency, providing an alternative way of describing the transparency continuum in derivational morphology. Overall, these data illustrate the trajectory towards a fully consolidated L2 lexicon and show that masked priming and sensitivity to OSC are key trackers of this process.

Knowledge of Statistics or Statistical Learning? Readers Prioritize the Statistics of their Native Language Over the Learning of Local Regularities

A large body of evidence suggests that people spontaneously and implicitly learn about regularities present in the visual input. Although theorized as critical for reading, this ability has been demonstrated mostly with pseudo-fonts or highly atypical artificial words. We tested whether local statistical regularities are extracted from materials that more closely resemble one’s native language. In two experiments, Italian speakers saw a set of letter strings modelled on the Italian lexicon and guessed which of these strings were words in a fictitious language and which were foils. Unknown to participants, words could be distinguished from foils based on their average bigram frequency. Surprisingly, in both experiments, we found no evidence that participants relied on this regularity. Instead, lexical decisions were guided by minimal bigram frequency, a cue rooted in participants’ native language. We discuss the implications of these findings for accounts of statistical learning and visual word processing.

EXPRESS: Are there Task-specific Effects in Morphological Processing? Examining Semantic Transparency Effects in Semantic Categorization and Lexical Decision

Current theories of morphological processing include form-then-meaning accounts, form-with-meaning accounts, and connectionist theories. Form-then meaning accounts argue that the morphological decomposition of complex words is based purely on orthographic structure, while form-with meaning accounts argue that decomposition is influenced by the semantic properties of the stem. Connectionist theories, on the other hand, argue that morphemes are encoded as statistical patterns of occurrences between form and meaning. The weight of evidence from the literature thus far suggests that morphological decomposition is best explained by form-then-meaning accounts. That said, conflicting empirical findings exist, and more importantly, semantic transparency effects in morphological processing have been examined almost exclusively with the lexical decision task, in which participants discriminate between words and nonwords. Consequently, the extent to which observed results reflect the specific demands of the lexical decision task remains unclear. The present study extends previous work by testing if the processing dynamics of early morphological processing are moderated by task requirements. Using the masked morphological priming paradigm, this hypothesis was tested by examining semantic transparency effects for a common set of words across semantic categorization and lexical decision. In both tasks, priming was stronger for transparent (e.g., painter-PAINT) than opaque (e.g., corner-CORN) prime-target pairs; these results speak against form-then-meaning accounts. These findings further inform theories of morphological processing and underscore the importance of examining the interplay between task-general and task-specific mechanisms.

Is there such a thing as a 'good statistical learner'?

A growing body of research investigates individual differences in the learning of statistical structure, tying them to variability in cognitive (dis)abilities. This approach views statistical learning (SL) as a general individual ability that underlies performance across a range of cognitive domains. But is there a general SL capacity that can sort individuals from 'bad' to 'good' statistical learners? Explicating the suppositions underlying this approach, we suggest that current evidence supporting it is meager. We outline an alternative perspective that considers the variability of statistical environments within different cognitive domains. Once we focus on learning that is tuned to the statistics of real-world sensory inputs, an alternative view of SL computations emerges with a radically different outlook for SL research.

Form and Function: A Study on the Distribution of the Inflectional Endings in Italian Nouns and Adjectives

Inflectional values, such as singular and plural, sustain agreement relations between constituents in sentences, allowing sentence parsing and prediction in online processing. Ideally, these processes would be facilitated by a consistent and transparent correspondence between the inflectional values and their form: for example, the value of plural should always be expressed by the same ending, and that ending should only express plural. Experimental research reports higher processing costs in the presence of a non-transparent relation between forms and values. While this effect was found in several languages, and typological research shows that consistency is far from common in morphological paradigms, it is still somewhat difficult to precisely quantify the transparency degree of the inflected forms. Furthermore, to date, no accounts have quantified the transparency in inflection with regard to the declensional classes and the extent to which it is expressed across different parts of speech, depending on whether these act as controllers of the agreement (e.g., nouns) or as targets (e.g., adjectives). We present a case study on Italian, a language that marks gender and number features in nouns and adjectives. This work provides measures of the distribution of forms in the noun and adjective inflection in Italian, and quantifies the degree of form-value transparency with respect to inflectional endings and declensional classes. In order to obtain these measures, we built Flex It, a dedicated large-scale database of inflectional morphology of Italian, and made it available, in order to sustain further theoretical and empirical research.

Developmental Dyslexia, Reading Acquisition, and Statistical Learning: A Sceptic's Guide

Many theories have been put forward that propose that developmental dyslexia is caused by low-level neural, cognitive, or perceptual deficits. For example, statistical learning is a cognitive mechanism that allows the learner to detect a probabilistic pattern in a stream of stimuli and to generalise the knowledge of this pattern to similar stimuli. The link between statistical learning and reading ability is indirect, with intermediate skills, such as knowledge of frequently co-occurring letters, likely being causally dependent on statistical learning skills and, in turn, causing individual variation in reading ability. We discuss theoretical issues regarding what a link between statistical learning and reading ability actually means and review the evidence for such a deficit. We then describe and simulate the "noisy chain hypothesis", where each intermediary link between a proposed cause and the end-state of reading ability reduces the correlation coefficient between the low-level deficit and the end-state outcome of reading. We draw the following conclusions: (1) Empirically, there is evidence for a correlation between statistical learning ability and reading ability, but there is no evidence to suggest that this relationship is causal, (2) theoretically, focussing on a complete causal chain between a distal cause and developmental dyslexia, rather than the two endpoints of the distal cause and reading ability only, is necessary for understanding the underlying processes, (3) statistically, the indirect nature of the link between statistical learning and reading ability means that the magnitude of the correlation is diluted by other influencing variables, yielding most studies to date underpowered, and (4) practically, it is unclear what can be gained from invoking the concept of statistical learning in teaching children to read.

The processing of pseudoword form and meaning in production and comprehension: A computational modeling approach using linear discriminative learning

Pseudowords have long served as key tools in psycholinguistic investigations of the lexicon. A common assumption underlying the use of pseudowords is that they are devoid of meaning: Comparing words and pseudowords may then shed light on how meaningful linguistic elements are processed differently from meaningless sound strings. However, pseudowords may in fact carry meaning. On the basis of a computational model of lexical processing, linear discriminative learning (LDL Baayen et al., Complexity, 2019, 1-39, 2019), we compute numeric vectors representing the semantics of pseudowords. We demonstrate that quantitative measures gauging the semantic neighborhoods of pseudowords predict reaction times in the Massive Auditory Lexical Decision (MALD) database (Tucker et al., 2018). We also show that the model successfully predicts the acoustic durations of pseudowords. Importantly, model predictions hinge on the hypothesis that the mechanisms underlying speech production and comprehension interact. Thus, pseudowords emerge as an outstanding tool for gauging the resonance between production and comprehension. Many pseudowords in the MALD database contain inflectional suffixes. Unlike many contemporary models, LDL captures the semantic commonalities of forms sharing inflectional exponents without using the linguistic construct of morphemes. We discuss methodological and theoretical implications for models of lexical processing and morphological theory. The results of this study, complementing those on real words reported in Baayen et al., (Complexity, 2019, 1-39, 2019), thus provide further evidence for the usefulness of LDL both as a cognitive model of the mental lexicon, and as a tool for generating new quantitative measures that are predictive for human lexical processing.

Consistency measures individuate dissociating semantic modulations in priming paradigms: A new look on semantics in the processing of (complex) words

In human language the mapping between form and meaning is arbitrary, as there is no direct connection between words and the objects that they represent. However, within a given language, it is possible to recognise systematic associations that support productivity and comprehension. In this work, we focus on the consistency between orthographic forms and meaning, and we investigate how the cognitive system may exploit it to process words. We take morphology as our case study, since it arguably represents one of the most notable examples of systematicity in form-meaning mapping. In a series of three experiments, we investigate the impact of form-meaning mapping in word processing by testing new consistency metrics as predictors of priming magnitude in primed lexical decision. In Experiment 1, we re-analyse data from five masked morphological priming studies and show that orthography-semantics-consistency explains independent variance in priming magnitude, suggesting that word semantics is accessed already at early stages of word processing and that crucially semantic access is constrained by word orthography. In Experiments 2 and 3, we investigate whether this pattern is replicated when looking at semantic priming. In Experiment 2, we show that orthography-semantics-consistency is not a viable predictor of priming magnitude with longer stimulus onset asynchrony (SOA). However, in Experiment 3, we develop a new semantic consistency measure based on the semantic density of target neighbourhoods. This measure is shown to significantly predict independent variance in semantic priming effect. Overall, our results indicate that consistency measures provide crucial information for the understanding of word processing. Specifically, the dissociation between measures and priming paradigms shows that different priming conditions are associated with the activation of different semantic cohorts.

The Form of Morphemes: MEG Evidence From Masked Priming of Two Hebrew Templates

Studies of lexical access have benefited from comparisons between languages like English, which shows concatenative morphology, and Semitic languages showing non-concatenative morphology of roots and patterns. Morphological decomposition in Semitic has previously been probed using masked priming, originally developed to investigate concatenative morphology. However, studies conducted on Semitic languages have often targeted Semitic-specific questions, such as whether the root and the verbal template prime lexical access. The overall consequence of these studies for our understanding of lexical access remains unclear. In two experiments on Hebrew using MEG, we demonstrate that a verbal form which is orthographically and phonologically indistinguishable from non-verbal forms is primed by other verbs in the same template but not by similar nouns and adjectives. These results suggest that masked priming taps into more than just visual forms but reflects morphological content, even if this content is abstract, showing no distinct orthographic or phonological marking.

From sound to meaning: Phonology-to-Semantics mapping in visual word recognition

In the present study, the role of phonological information in visual word recognition is investigated by adopting a large-scale data-driven approach that exploits a new consistency measure based on distributional semantics methods. A recent study by Marelli, Amenta, and Crepaldi (2015) showed that the consistency between an orthographic string and the meanings to which it is associated in a large corpus is a relevant predictor in lexical decision experiments. Exploiting irregular mappings between orthography and phonology in English, we were able to compute a phonology-to-semantics consistency measure that dissociates from its orthographic counterpart and tested both measures on lexical decision data taken from the British Lexicon Project (Keuleers et al., 2012). Results showed that both orthography and phonology are activated during visual word recognition. However, their contribution is crucially determined by the extent to which they are informative of the word semantics, and phonology plays a crucial role in accessing word meaning.

Examining word processing via a megastudy of conditional reading aloud

Results from a megastudy on conditional reading aloud for 2,145 monosyllabic words are reported. In stepwise regression analyses, the predictor variables accounted for over 66% of the reaction time (RT) variance. Linear mixed effect modelling on log RT indicated that every variable that related to RT in either reading aloud or lexical decision also related to RT in conditional reading aloud. Notably, differences among tasks were observed. Specifically, lexical decision showed stronger reliance on semantic information than the other two tasks, but conditional reading aloud also showed strong reliance on semantic information. Interestingly, feedback consistency affected reading aloud and conditional reading but not lexical decision. Pairwise correlations revealed that conditional reading aloud performance showed moderately strong relationships to lexical decision and reading aloud performance, whereas reading aloud and lexical decision performance were weakly related to each other. Conditional reading aloud produces reliable data that can be used to examine word processing. Theoretical challenges moving forward include how to best conceptualise and model processes involved in this task.

Discrimination in lexical decision

In this study we present a novel set of discrimination-based indicators of language processing derived from Naive Discriminative Learning (ndl) theory. We compare the effectiveness of these new measures with classical lexical-distributional measures-in particular, frequency counts and form similarity measures-to predict lexical decision latencies when a complete morphological segmentation of masked primes is or is not possible. Data derive from a re-analysis of a large subset of decision latencies from the English Lexicon Project, as well as from the results of two new masked priming studies. Results demonstrate the superiority of discrimination-based predictors over lexical-distributional predictors alone, across both the simple and primed lexical decision tasks. Comparable priming after masked corner and cornea type primes, across two experiments, fails to support early obligatory segmentation into morphemes as predicted by the morpho-orthographic account of reading. Results fit well with ndl theory, which, in conformity with Word and Paradigm theory, rejects the morpheme as a relevant unit of analysis. Furthermore, results indicate that readers with greater spelling proficiency and larger vocabularies make better use of orthographic priors and handle lexical competition more efficiently.

Social Media and Language Processing: How Facebook and Twitter Provide the Best Frequency Estimates for Studying Word Recognition

Corpus‐based word frequencies are one of the most important predictors in language processing tasks. Frequencies based on conversational corpora (such as movie subtitles) are shown to better capture the variance in lexical decision tasks compared to traditional corpora. In this study, we show that frequencies computed from social media are currently the best frequency‐based estimators of lexical decision reaction times (up to 3.6% increase in explained variance). The results are robust (observed for Twitter‐ and Facebook‐based frequencies on American English and British English datasets) and are still substantial when we control for corpus size.