Discrimination in lexical decision

Age-Related Diversification and Specialization in the Mental Lexicon: Comparing Aggregate and Individual-Level Network Approaches

The mental lexicon changes across the lifespan. Prior work, aggregating data among individuals of similar ages, found that the aging lexicon, represented as a network of free associations, becomes more sparse with age: degree and clustering coefficient decrease and average shortest path length increases. However, because this work is based on aggregated data, it remains to be seen whether or not individuals show a similar pattern of age-related lexical change. Here, we demonstrate how an individual-level approach can be used to reveal differences that vary systematically with age. We also directly compare this approach with an aggregate-level approach, to show how these approaches differ. Our individual-level approach follows the logic of many past approaches by comparing individual data as they are situated within population-level data. To do this, we produce a conglomerate network from population-level data and then identify how data from individuals of different ages are situated within that network. Though we find most qualitative patterns are preserved, individuals produce associates that have a higher clustering coefficient in the conglomerate network as they age. Alongside a reduction in degree, this suggests more specialized but clustered knowledge with age. Older individuals also reveal a pattern of increasing distance among the associates they produce in response to a single cue, indicating a more diverse range of associations. We demonstrate these results for three different languages: English, Spanish, and Dutch, which all show the same qualitative patterns of differences between aggregate and individual network approaches. These results reveal how individual-level approaches can be taken with aggregate data and demonstrate new insights into understanding the aging lexicon.

Emerging Roots: Investigating Early Access to Meaning in Maltese Auditory Word Recognition

In Semitic languages, the consonantal root is central to morphology, linking form and meaning. While psycholinguistic studies highlight its importance in language processing, the role of meaning in early lexical access and its representation remain unclear. This study investigates when meaning becomes accessible during the processing of Maltese verb forms, using a computational model based on the Discriminative Lexicon framework. Our model effectively comprehends and produces Maltese verbs, while also predicting response times in a masked auditory priming experiment. Results show that meaning is accessible early in lexical access and becomes more prominent after the target word is fully processed. This suggests that semantic information plays a critical role from the initial stages of lexical access, refining our understanding of real-time language comprehension. Our findings contribute to theories of lexical access and offer valuable insights for designing priming studies in psycholinguistics. Additionally, this study demonstrates the potential of computational models in investigating the relationship between form and meaning in language processing.

On humans' (explicit) intuitions about the meaning of novel words

Pseudowords offer a unique opportunity to investigate how humans deal with new (verbal) information. Within this framework, previous studies have shown that, at the implicit level, humans exploit systematic associations in the form-meaning interface to process new information by relying on (sub-lexical) contents already mapped in semantic memory. However, whether speakers exploit such processes in explicit decisions about the meanings elicited by unfamiliar terms remains an open, important question. Here, we tested this by leveraging computational models that are able to induce semantic representations for out-of-vocabulary stimuli. Across two experiments, we demonstrate that participants' guesses about pseudoword meanings in a 2AFC task consistently align with the model's predictions. This indicates that humans' ability to extract meaningful knowledge from complex statistical patterns can affect explicit decisions.

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.

Effects of individual differences in text exposure on sentence comprehension

Linguistic experience plays a clear role in accounting for variability in sentence comprehension behavior across individuals and across sentence types. We aimed to understand how individual differences in reading experience predict reading behavior. Corpus analyses revealed the frequencies with which our experimental items appeared in written and spoken language. We hypothesized that reading experience should affect sentence comprehension most substantially for sentence types that individuals primarily encounter through written language. Readers with more text exposure were faster and more accurate readers overall, but they read sentence types biased to written language particularly faster than did readers with less text exposure. We see clear effects of text exposure on sentence comprehension in ways that allow explicit links between written and spoken corpus statistics and behavior. We discuss theoretical implications of effects of text exposure for experience-based approaches to sentence processing.

Lateralized reading in the healthy brain: A behavioral and computational study on the nature of the visual field effect

Despite its widespread use to measure functional lateralization of language in healthy subjects, the neurocognitive bases of the visual field effect in lateralized reading are still debated. Crucially, the lack of knowledge on the nature of the visual field effect is accompanied by a lack of knowledge on the relative impact of psycholinguistic factors on its measurement, thus potentially casting doubts on its validity as a functional laterality measure. In this study, an eye-tracking-controlled tachistoscopic lateralized lexical decision task (Experiment 1) was administered to 60 right-handed and 60 left-handed volunteers and word length, orthographic neighborhood, word frequency, and imageability were manipulated. The magnitude of visual field effect was bigger in right-handed than in left-handed participants. Across the whole sample, a visual field-by-frequency interaction was observed, whereby a comparatively smaller effect of word frequency was detected in the left visual field/right hemisphere (LVF/RH) than in the right visual field/left hemisphere (RVF/LH). In a subsequent computational study (Experiment 2), efficient (LH) and inefficient (RH) activation of lexical orthographic nodes was modelled by means of the Naïve Discriminative Learning approach. Computational data simulated the effect of visual field and its interaction with frequency observed in the Experiment 1. Data suggest that the visual field effect can be biased by word frequency. Less distinctive connections between orthographic cues and lexical/semantic output units in the RH than in the LH can account for the emergence of the visual field effect and its interaction with word frequency.

An exploration of error-driven learning in simple two-layer networks from a discriminative learning perspective

Error-driven learning algorithms, which iteratively adjust expectations based on prediction error, are the basis for a vast array of computational models in the brain and cognitive sciences that often differ widely in their precise form and application: they range from simple models in psychology and cybernetics to current complex deep learning models dominating discussions in machine learning and artificial intelligence. However, despite the ubiquity of this mechanism, detailed analyses of its basic workings uninfluenced by existing theories or specific research goals are rare in the literature. To address this, we present an exposition of error-driven learning - focusing on its simplest form for clarity - and relate this to the historical development of error-driven learning models in the cognitive sciences. Although historically error-driven models have been thought of as associative, such that learning is thought to combine preexisting elemental representations, our analysis will highlight the discriminative nature of learning in these models and the implications of this for the way how learning is conceptualized. We complement our theoretical introduction to error-driven learning with a practical guide to the application of simple error-driven learning models in which we discuss a number of example simulations, that are also presented in detail in an accompanying tutorial.

COVIDisgust: Language processing through the lens of partisanship

Disgust is an aversive reaction protecting an organism from disease. People differ in how prone they are to experiencing it, and this fluctuates depending on how safe the environment is. Previous research has shown that the recognition and processing of disgusting words depends not on the word’s disgust per se but rather on individual sensitivity to disgust. However, the influence of dynamically changing disgust on language comprehension has not yet been researched. In a series of studies, we investigated whether the media’s portrayal of COVID-19 will affect subsequent language processing via changes in disgust. The participants were exposed to news headlines either depicting COVID-19 as a threat or downplaying it, and then rated single words for disgust and valence (Experiment 1; N = 83) or made a lexical decision (Experiment 2; N = 86). The headline type affected only word ratings and not lexical decisions, but political ideology and disgust proneness affected both. More liberal participants assigned higher disgust ratings after the headlines discounted the threat of COVID-19, whereas more conservative participants did so after the headlines emphasized it. We explain the results through the politicization and polarization of the pandemic. Further, political ideology was more predictive of reaction times in Experiment 2 than disgust proneness. High conservatism correlated with longer reaction times for disgusting and negative words, and the opposite was true for low conservatism. The results suggest that disgust proneness and political ideology dynamically interact with perceived environmental safety and have a measurable effect on language processing. Importantly, they also suggest that the media’s stance on the pandemic and the political framing of the issue may affect the public response by increasing or decreasing our disgust.

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.

Asymmetric Morphological Priming Among Inflected and Derived Verbs and Nouns in Greek

The present study examined differences between inflectional and derivational morphology using Greek nouns and verbs with masked priming (with both short and long stimulus onset asynchrony) and long-lag priming. A lexical decision task to inflected noun and verb targets was used to test whether their processing is differentially facilitated by prior presentation of their stem in words of the same grammatical class (inflectional morphology) or of a different grammatical class (derivational morphology). Differences in semantics, syntactic information, and morphological complexity between inflected and derived word pairs (both nouns and verbs) were minimized by unusually tight control of stimuli as permitted by Greek morphology. Results showed that morphological relations affected processing of morphologically complex Greek words (nouns and verbs) across prime durations (50–250ms) as well as when items intervened between primes and targets. In two of the four experiments (Experiments 1 and 3), inflectionally related primes produced significantly greater effects than derivationally related primes suggesting differences in processing inflectional versus derivational morphological relations, which may disappear when processing is less dependent on semantic effects (Experiment 4). Priming effects differed for verb vs. noun targets with long SOA priming (Experiment 3), consistent with processing differences between complex words of different grammatical class (nouns and verbs) when semantic effects are maximized. Taken together, results demonstrate that inflectional and derivational relations differentially affect processing complex words of different grammatical class (nouns and verbs). This finding indicates that distinctions of morphological relation (inflectional vs. derivational) are not of the same kind as distinctions of grammatical class (nouns vs. verbs). Asymmetric differences among inflected and derived verbs and nouns seem to depend on semantic effects and/or processing demands modulating priming effects very early in lexical processing of morphologically complex written words, consistent with models of lexical processing positing early access to morphological structure and early influence of semantics.

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.

Memory for affixes in a long-lag priming paradigm

Psycholinguistic research on the processing of morphologically complex words has largely focused on debates about how/if lexical stems are recognized, stored, and retrieved. Comparatively little processing research has investigated similar issues for functional affixes. In Word or Lexeme Based Morphology (Aronoff 1994), affixes are not representational units on par with stems or roots. This view is in stark contrast to the claims of linguistic theories like Distributed Morphology (Halle & Marantz 1993), which assign rich representational content to affixes. We conducted a series of eight visual lexical decision studies, evaluating effects of derivational affix priming along with stem priming, identity priming, form priming, and semantic priming at long and short lags. We find robust and consistent affix priming (but not semantic or form priming) with lags up to 33 items, supporting the position that affixes are morphemes, i.e., representational units on par with stems. Intriguingly, we find only weaker evidence for the long-lag stem priming effect found in other studies. We interpret this potential asymmetry in terms of the salience of different morphological contexts for recollection memory.

Scaling Laws for Phonotactic Complexity in Spoken English Language Data

Two prominent statistical laws in language and other complex systems are Zipf's law and Heaps' law. We investigate the extent to which these two laws apply to the linguistic domain of phonotactics-that is, to sequences of sounds. We analyze phonotactic sequences with different lengths within words and across word boundaries taken from a corpus of spoken English (Buckeye). We demonstrate that the expected relationship between the two scaling laws can only be attested when boundary spanning phonotactic sequences are also taken into account. Furthermore, it is shown that Zipf's law exhibits both high goodness-of-fit and a high scaling coefficient if sequences of more than two sounds are considered. Our results support the notion that phonotactic cognition employs information about boundary spanning phonotactic sequences.

Durational Differences of Word-Final /s/ Emerge From the Lexicon: Modelling Morpho-Phonetic Effects in Pseudowords With Linear Discriminative Learning

Recent research has shown that seemingly identical suffixes such as word-final /s/ in English show systematic differences in their phonetic realisations. Most recently, durational differences between different types of /s/ have been found to also hold for pseudowords: the duration of /s/ is longest in non-morphemic contexts, shorter with suffixes, and shortest in clitics. At the theoretical level such systematic differences are unexpected and unaccounted for in current theories of speech production. Following a recent approach, we implemented a linear discriminative learning network trained on real word data in order to predict the duration of word-final non-morphemic and plural /s/ in pseudowords using production data by a previous production study. It is demonstrated that the duration of word-final /s/ in pseudowords can be predicted by LDL networks trained on real word data. That is, duration of word-final /s/ in pseudowords can be predicted based on their relations to the lexicon.

Prediction and error in early infant speech learning: A speech acquisition model

In the last two decades, statistical clustering models have emerged as a dominant model of how infants learn the sounds of their language. However, recent empirical and computational evidence suggests that purely statistical clustering methods may not be sufficient to explain speech sound acquisition. To model early development of speech perception, the present study used a two-layer network trained with Rescorla-Wagner learning equations, an implementation of discriminative, error-driven learning. The model contained no a priori linguistic units, such as phonemes or phonetic features. Instead, expectations about the upcoming acoustic speech signal were learned from the surrounding speech signal, with spectral components extracted from an audio recording of child-directed speech as both inputs and outputs of the model. To evaluate model performance, we simulated infant responses in the high-amplitude sucking paradigm using vowel and fricative pairs and continua. The simulations were able to discriminate vowel and consonant pairs and predicted the infant speech perception data. The model also showed the greatest amount of discrimination in the expected spectral frequencies. These results suggest that discriminative error-driven learning may provide a viable approach to modelling early infant speech sound acquisition.

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.

Direct Versus Indirect Causation as a Semantic Linguistic Universal: Using a Computational Model of English, Hebrew, Hindi, Japanese, and K'iche' Mayan to Predict Grammaticality Judgments in Balinese

The aim of this study was to test the claim that languages universally employ morphosyntactic marking to differentiate events of more- versus less-direct causation, preferring to mark them with less- and more- overt marking, respectively (e.g., Somebody broke the window vs. Somebody MADE the window break; *Somebody cried the boy vs. Somebody MADE the boy cry). To this end, we investigated whether a recent computational model which learns to predict speakers' by-verb relative preference for the two causatives in English, Hebrew, Hindi, Japanese, and K'iche' Mayan is able to generalize to a sixth language on which it has never been trained: Balinese. Judgments of the relative acceptability of the less- and more-transparent causative forms of 60 verbs were collected from 48 native-speaking Balinese adults. The composite crosslinguistic computational model was able to predict these judgments, not only for verbs that it had seen, but also--in a split-half validation test--to verbs that it had never seen in any language. A "random-semantics" model showed only a relatively small decrement in performance with seen verbs, whose behavior can be learned on a verb-by-verb basis, but achieved zero correlation with human judgments when generalizing to unseen verbs. Together, these findings suggest that Balinese conceptualizes directness of causation in a similar way to these unrelated languages, and therefore constitute support for the view that the distinction between more- versus less-distinct causation constitutes a morphosyntactic universal.

Vocabulary Size Influences Spontaneous Speech in Native Language Users: Validating the Use of Automatic Speech Recognition in Individual Differences Research

Previous research has shown that vocabulary size affects performance on laboratory word production tasks. Individuals who know many words show faster lexical access and retrieve more words belonging to pre-specified categories than individuals who know fewer words. The present study examined the relationship between receptive vocabulary size and speaking skills as assessed in a natural sentence production task. We asked whether measures derived from spontaneous responses to everyday questions correlate with the size of participants' vocabulary. Moreover, we assessed the suitability of automatic speech recognition (ASR) for the analysis of participants' responses in complex language production data. We found that vocabulary size predicted indices of spontaneous speech: individuals with a larger vocabulary produced more words and had a higher speech-silence ratio compared to individuals with a smaller vocabulary. Importantly, these relationships were reliably identified using manual and automated transcription methods. Taken together, our results suggest that spontaneous speech elicitation is a useful method to investigate natural language production and that automatic speech recognition can alleviate the burden of labor-intensive speech transcription.

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.

Of mice and men: Speech sound acquisition as discriminative learning from prediction error, not just statistical tracking

Despite burgeoning evidence that listeners are highly sensitive to statistical distributions of speech cues, the mechanism underlying learning may not be purely statistical tracking. Decades of research in animal learning suggest that learning results from prediction and prediction error. Two artificial language learning experiments test two predictions that distinguish error-driven from purely statistical models; namely, cue competition - specifically, Kamin's (1968) 'blocking' effect (Experiment 1) - and the predictive structure of learning events (Experiment 2). In Experiment 1, prior knowledge of an informative cue blocked learning of a second cue. This finding may help explain second language learners' difficulty in acquiring native-level perception of non-native speech cues. In Experiment 2, learning was better with a discriminative (cue-outcome) order compared to a non-discriminative (outcome-cue) order. Experiment 2 suggests that learning speech cues, including reversing effects of blocking, depends on (un)learning from prediction error and depends on the temporal order of auditory cues versus semantic outcomes. Together, these results show that (a) existing knowledge of acoustic cues can block later learning of new cues, and (b) speech sound acquisition depends on the predictive structure of learning events. When feedback from prediction error is available, this drives learners to ignore salient non-discriminative cues and effectively learn to use target cue dimensions. These findings may have considerable implications for the field of speech acquisition.

Opacity, Transparency, and Morphological Priming: A Study of Prefixed Verbs in Dutch

A basic question for the study of the mental lexicon is whether there are morphological representations and processes that are independent of phonology and semantics. According to a prominent tradition, morphological relatedness requires semantic transparency: semantically transparent words are related in meaning to their stems, while semantically opaque words are not. This study examines the question of morphological relatedness using intra-modal auditory priming by Dutch prefixed verbs. The key conditions involve semantically transparent prefixed primes (e.g., aanbieden 'offer', with the stem bieden, also 'offer') and opaque primes (e.g., verbieden 'forbid'). Results show robust facilitation for both transparent and opaque pairs; phonological (Experiment 1) and semantic (Experiment 2) controls rule out the possibility that these other types of relatedness are responsible for the observed priming effects. The finding of facilitation with opaque primes suggests that morphological processing is independent of semantic and phonological representations. Accordingly, the results are incompatible with theories that make semantic overlap a necessary condition for relatedness, and favor theories in which words may be related in ways that do not require shared meaning. The general discussion considers several specific proposals along these lines, and compares and contrasts questions about morphological relatedness of the type found here with the different but related question of whether there is morphological decomposition of complex forms or not.

Perceptual salience of derivational suffixes in visual word recognition

This study analyzes the role of derivational suffixes in visual word recognition, tracking the eye movements of 31 participants in a sentence-reading task in Spanish. Perceptual salience of suffixes was operationalized as the proportion of letters represented by the suffixes with respect to the full words, that is, we relate the number of letters comprising the suffixes to the number of letters in the words in which they appear. The results reveal a significant role in first fixation duration of both word frequency - the more frequent the word, the shorter the fixations, and perceptual salience - the more salient the suffix, the longer the fixations. Moreover, in gaze duration, our results show a main effect of word length - the longer the word, the longer the fixations; word frequency; and significant interactions between word frequency and perceptual salience of suffixes on the one hand - the effect of word frequency is only significant when perceptual salience of suffixes is high, and between word frequency and word length on the other hand - the frequency effect decreases as word length increases. Overall results are interpreted in the light of the dual route models by which full-form and morphological processing interactively cooperate in visual word recognition.

Native speakers like affixes, L2 speakers like letters? An overt visual priming study investigating the role of orthography in L2 morphological processing

In an overt visual priming experiment, we investigate the role of orthography in native (L1) and non-native (L2) processing of German morphologically complex words. We compare priming effects for inflected and derived morphologically related prime-target pairs versus otherwise matched, purely orthographically related pairs. The results show morphological priming effects in both the L1 and L2 group, with no significant difference between inflection and derivation. However, L2 speakers, but not L1 speakers, also showed significant priming for orthographically related pairs. Our results support the claim that L2 speakers focus more on surface-level information such as orthography during visual word recognition. This can cause orthographic priming effects in morphologically related prime-target pairs, which may conceal L1-L2 differences in morphological processing.

Semantic Effects in Morphological Priming: The Case of Hebrew Stems

To what extent is morphological representation in different languages dependent on semantic information? Unlike Indo-European languages, the Semitic mental lexicon has been argued to be purely "morphologically driven", with complex stems represented in a decomposed format (root + vowel pattern) irrespectively of their semantic properties. We have examined this claim by comparing cross-modal root-priming effects elicited by Hebrew verbs of a productive, open-ended class (Piel) and verbs of a closed-class (Paal). Morphological priming effects were obtained for both verb types, but prime-target semantic relatedness interacted with class, and only modulated responses following Paal, but not Piel primes. We explain these results by postulating different types of morpho-lexical representation for the different classes: structured stems, in the case of Piel, and whole-stems (which lack internal morphological structure), in the case of Paal. We conclude that semantic effects in morphological priming are also obtained in Semitic languages, but they are crucially dependent on type of morpho-lexical representation.

The representation of plural inflectional affixes in English: Evidence from priming in an auditory lexical decision task

The representation of inflection is controversial: theories of morphological processing range from those that treat all inflectional morphemes as independently represented in memory to those that deny independent representation for any inflectional morphemes. Whereas identity priming for stems and derivational affixes is regularly reported, priming of inflectional affixes is understudied and has produced no clear consensus. This paper reports results from a continuous auditory lexical decision task investigating priming of plural inflectional affixes in English, in plural prime-target pairs such as crimes→trees. Our results show statistically significant priming facilitation for plural primes relative to phonological (cleanse→trees) and singular (crime→trees) controls. This finding indicates that inflectional affixes, like lexical stems, exhibit identity priming effects. We discuss implications for morphological theory and point to questions for further work addressing which representation(s) produce the priming effect.

Conceptualizing syntactic categories as semantic categories: Unifying part-of-speech identification and semantics using co-occurrence vector averaging

Co-occurrence models have been of considerable interest to psychologists because they are built on very simple functionality. This is particularly clear in the case of prediction models, such as the continuous skip-gram model introduced in Mikolov, Chen, Corrado, and Dean (2013), because these models depend on functionality closely related to the simple Rescorla-Wagner model of discriminant learning in nonhuman animals (Rescorla & Wagner, 1972), which has a rich history within psychology as a model of many animal learning processes. We replicate and extend earlier work showing that it is possible to extract accurate information about syntactic category and morphological family membership directly from patterns of word co-occurrence, and provide evidence from four experiments showing that this information predicts human reaction times and accuracy for class membership decisions.

New Perspectives on the Aging Lexicon

The field of cognitive aging has seen considerable advances in describing the linguistic and semantic changes that happen during the adult life span to uncover the structure of the mental lexicon (i.e., the mental repository of lexical and conceptual representations). Nevertheless, there is still debate concerning the sources of these changes, including the role of environmental exposure and several cognitive mechanisms associated with learning, representation, and retrieval of information. We review the current status of research in this field and outline a framework that promises to assess the contribution of both ecological and psychological aspects to the aging lexicon.

Analyzing the Time Course of Pupillometric Data

This article provides a tutorial for analyzing pupillometric data. Pupil dilation has become increasingly popular in psychological and psycholinguistic research as a measure to trace language processing. However, there is no general consensus about procedures to analyze the data, with most studies analyzing extracted features from the pupil dilation data instead of analyzing the pupil dilation trajectories directly. Recent studies have started to apply nonlinear regression and other methods to analyze the pupil dilation trajectories directly, utilizing all available information in the continuously measured signal. This article applies a nonlinear regression analysis, generalized additive mixed modeling, and illustrates how to analyze the full-time course of the pupil dilation signal. The regression analysis is particularly suited for analyzing pupil dilation in the fields of psychological and psycholinguistic research because generalized additive mixed models can include complex nonlinear interactions for investigating the effects of properties of stimuli (e.g., formant frequency) or participants (e.g., working memory score) on the pupil dilation signal. To account for the variation due to participants and items, nonlinear random effects can be included. However, one of the challenges for analyzing time series data is dealing with the autocorrelation in the residuals, which is rather extreme for the pupillary signal. On the basis of simulations, we explain potential causes of this extreme autocorrelation, and on the basis of the experimental data, we show how to reduce their adverse effects, allowing a much more coherent interpretation of pupillary data than possible with feature-based techniques.

Grounding the neurobiology of language in first principles: The necessity of non-language-centric explanations for language comprehension

Recent decades have ushered in tremendous progress in understanding the neural basis of language. Most of our current knowledge on language and the brain, however, is derived from lab-based experiments that are far removed from everyday language use, and that are inspired by questions originating in linguistic and psycholinguistic contexts. In this paper we argue that in order to make progress, the field needs to shift its focus to understanding the neurobiology of naturalistic language comprehension. We present here a new conceptual framework for understanding the neurobiological organization of language comprehension. This framework is non-language-centered in the computational/neurobiological constructs it identifies, and focuses strongly on context. Our core arguments address three general issues: (i) the difficulty in extending language-centric explanations to discourse; (ii) the necessity of taking context as a serious topic of study, modeling it formally and acknowledging the limitations on external validity when studying language comprehension outside context; and (iii) the tenuous status of the language network as an explanatory construct. We argue that adopting this framework means that neurobiological studies of language will be less focused on identifying correlations between brain activity patterns and mechanisms postulated by psycholinguistic theories. Instead, they will be less self-referential and increasingly more inclined towards integration of language with other cognitive systems, ultimately doing more justice to the neurobiological organization of language and how it supports language as it is used in everyday life.

If priming is graded rather than all-or-none, can reactivating abstract structures be the underlying mechanism?

In our commentary on Branigan & Pickering (B&P), we start by arguing that the authors implicitly adopt several assumptions, the consequence of which is to make further claims necessary and/or sufficient. Crucially, the authors assume the existence of discrete units at various levels of linguistic granularity that then must be operated upon by combinatorial mechanisms and rules (i.e., decomposition/recomposition). They further argue that structural priming provides a powerful tool to study abstract, structural representations. We provide evidence that priming effects in production are characterized better as graded than as all-or-none and that priming need not arise from a mechanism that (re)activates a shared but abstract internal structure.

Are baboons learning "orthographic" representations? Probably not

The ability of Baboons (papio papio) to distinguish between English words and nonwords has been modeled using a deep learning convolutional network model that simulates a ventral pathway in which lexical representations of different granularity develop. However, given that pigeons (columba livia), whose brain morphology is drastically different, can also be trained to distinguish between English words and nonwords, it appears that a less species-specific learning algorithm may be required to explain this behavior. Accordingly, we examined whether the learning model of Rescorla and Wagner, which has proved to be amazingly fruitful in understanding animal and human learning could account for these data. We show that a discrimination learning network using gradient orientation features as input units and word and nonword units as outputs succeeds in predicting baboon lexical decision behavior-including key lexical similarity effects and the ups and downs in accuracy as learning unfolds-with surprising precision. The models performance, in which words are not explicitly represented, is remarkable because it is usually assumed that lexicality decisions, including the decisions made by baboons and pigeons, are mediated by explicit lexical representations. By contrast, our results suggest that in learning to perform lexical decision tasks, baboons and pigeons do not construct a hierarchy of lexical units. Rather, they make optimal use of low-level information obtained through the massively parallel processing of gradient orientation features. Accordingly, we suggest that reading in humans first involves initially learning a high-level system building on letter representations acquired from explicit instruction in literacy, which is then integrated into a conventionalized oral communication system, and that like the latter, fluent reading involves the massively parallel processing of the low-level features encoding semantic contrasts.

Words from spontaneous conversational speech can be recognized with human-like accuracy by an error-driven learning algorithm that discriminates between meanings straight from smart acoustic features, bypassing the phoneme as recognition unit

Sound units play a pivotal role in cognitive models of auditory comprehension. The general consensus is that during perception listeners break down speech into auditory words and subsequently phones. Indeed, cognitive speech recognition is typically taken to be computationally intractable without phones. Here we present a computational model trained on 20 hours of conversational speech that recognizes word meanings within the range of human performance (model 25%, native speakers 20-44%), without making use of phone or word form representations. Our model also generates successfully predictions about the speed and accuracy of human auditory comprehension. At the heart of the model is a 'wide' yet sparse two-layer artificial neural network with some hundred thousand input units representing summaries of changes in acoustic frequency bands, and proxies for lexical meanings as output units. We believe that our model holds promise for resolving longstanding theoretical problems surrounding the notion of the phone in linguistic theory.