Developmental changes in brain activation during novel grammar learning in 8-25-year-olds.

While it is well established that grammar learning success varies with age, the cause of this developmental change is largely unknown. This study examined functional MRI activation across a broad developmental sample of 165 Dutch-speaking individuals (8-25 years) as they were implicitly learning a new grammatical system. This approach allowed us to assess the direct effects of age on grammar learning ability while exploring its neural correlates. In contrast to the alleged advantage of children language learners over adults, we found that adults outperformed children. Moreover, our behavioral data showed a sharp discontinuity in the relationship between age and grammar learning performance: there was a strong positive linear correlation between 8 and 15.4 years of age, after which age had no further effect. Neurally, our data indicate two important findings: (i) during grammar learning, adults and children activate similar brain regions, suggesting continuity in the neural networks that support initial grammar learning; and (ii) activation level is age-dependent, with children showing less activation than older participants. We suggest that these age-dependent processes may constrain developmental effects in grammar learning. The present study provides new insights into the neural basis of age-related differences in grammar learning in second language acquisition.

Study protocol: a comprehensive multi-method neuroimaging approach to disentangle developmental effects and individual differences in second language learning.

BACKGROUND: While it is well established that second language (L2) learning success changes with age and across individuals, the underlying neural mechanisms responsible for this developmental shift and these individual differences are largely unknown. We will study the behavioral and neural factors that subserve new grammar and word learning in a large cross-sectional developmental sample. This study falls under the NWO (Nederlandse Organisatie voor Wetenschappelijk Onderzoek [Dutch Research Council]) Language in Interaction consortium (website: https://www.languageininteraction.nl/ ). METHODS: We will sample 360 healthy individuals across a broad age range between 8 and 25 years. In this paper, we describe the study design and protocol, which involves multiple study visits covering a comprehensive behavioral battery and extensive magnetic resonance imaging (MRI) protocols. On the basis of these measures, we will create behavioral and neural fingerprints that capture age-based and individual variability in new language learning. The behavioral fingerprint will be based on first and second language proficiency, memory systems, and executive functioning. We will map the neural fingerprint for each participant using the following MRI modalities: T1-weighted, diffusion-weighted, resting-state functional MRI, and multiple functional-MRI paradigms. With respect to the functional MRI measures, half of the sample will learn grammatical features and half will learn words of a new language. Combining all individual fingerprints allows us to explore the neural maturation effects on grammar and word learning. DISCUSSION: This will be one of the largest neuroimaging studies to date that investigates the developmental shift in L2 learning covering preadolescence to adulthood. Our comprehensive approach of combining behavioral and neuroimaging data will contribute to the understanding of the mechanisms influencing this developmental shift and individual differences in new language learning. We aim to answer: (I) do these fingerprints differ according to age and can these explain the age-related differences observed in new language learning? And (II) which aspects of the behavioral and neural fingerprints explain individual differences (across and within ages) in grammar and word learning? The results of this study provide a unique opportunity to understand how the development of brain structure and function influence new language learning success.

The time-limited influence of sentential context on function word identification.

Sentential context effects on the identification of the Dutch function words te (to) and de (the) were examined. In Experiment 1, listeners labeled words on a [t inverted e]-[d inverted e] continuum more often as te when the context waste biased (Ik probeer [? inverted e] schieten [I try to/the shoot]) than when it was de biased (Ik probeer [? inverted e] schoenen [I try to/the shoes]). The effect was weaker in slower responses. In Experiment 2, disambiguation began later, in the second word after [? inverted e]. There was a weak context effect only in the slower responses. In Experiments 3 and 4, disambiguation occurred on the word before [? inverted e]: There was no context effect when one set of sentences was used, but there was an effect (larger in the faster responses) when more sentences were used. Syntactic processing affects word identification only within a limited time frame. It appears to do so not by influencing lexical access processes through feedback but, instead, by biasing decision making.

Merging information in speech recognition: feedback is never necessary.

Top-down feedback does not benefit speech recognition; on the contrary, it can hinder it. No experimental data imply that feedback loops are required for speech recognition. Feedback is accordingly unnecessary and spoken word recognition is modular. To defend this thesis, we analyse lexical involvement in phonemic decision making. TRACE (McClelland & Elman 1986), a model with feedback from the lexicon to prelexical processes, is unable to account for all the available data on phonemic decision making. The modular Race model (Cutler & Norris 1979) is likewise challenged by some recent results, however. We therefore present a new modular model of phonemic decision making, the Merge model. In Merge, information flows from prelexical processes to the lexicon without feedback. Because phonemic decisions are based on the merging of prelexical and lexical information, Merge correctly predicts lexical involvement in phonemic decisions in both words and nonwords. Computer simulations show how Merge is able to account for the data through a process of competition between lexical hypotheses. We discuss the issue of feedback in other areas of language processing and conclude that modular models are particularly well suited to the problems and constraints of speech recognition.

The possible-word constraint in the segmentation of continuous speech.

We propose that word recognition in continuous speech is subject to constraints on what may constitute a viable word of the language. This Possible-Word Constraint (PWC) reduces activation of candidate words if their recognition would imply word status for adjacent input which could not be a word--for instance, a single consonant. In two word-spotting experiments, listeners found it much harder to detect apple, for example, in fapple (where [f] alone would be an impossible word), than in vuffapple (where vuff could be a word of English). We demonstrate that the PWC can readily be implemented in a competition-based model of continuous speech recognition, as a constraint on the process of competition between candidate words; where a stretch of speech between a candidate word and a (known or likely) word boundary is not a possible word, activation of the candidate word is reduced. This implementation accurately simulates both the present results and data from a range of earlier studies of speech segmentation.

Competition and segmentation in spoken-word recognition.

Spoken utterances contain few reliable cues to word boundaries, but listeners nonetheless experience little difficulty identifying words in continuous speech. The authors present data and simulations that suggest that this ability is best accounted for by a model of spoken-word recognition combining competition between alternative lexical candidates, and sensitivity to prosodic structure. In a word-spotting experiment, stress pattern effects emerged most clearly when there were many competing lexical candidates for part of the input. Thus, competition between simultaneously active word candidates can modulate the size of prosodic effects, which suggests that spoken-word recognition must be sensitive both to prosodic structure and to the effects of competition. A version of the Shortlist model (D. G. Norris, 1994b) incorporating the Metrical Segmentation Strategy (A. Cutler & D. Norris, 1988) accurately simulates the results using a lexicon of more than 25,000 words.

Rhyme decisions to spoken words and nonwords.

Lexical effects in auditory rhyme-decision performance were examined in three experiments. Experiment 1 showed reliable lexical involvement: rhyme-monitoring responses to words were faster than rhyme-monitoring responses to nonwords; and decisions were faster in response to high-frequency as opposed to low-frequency words. Experiments 2 and 3 tested for lexical influences in the rejection of three types of nonrhyming item: words, nonwords with rhyming lexical neighbor (e.G., jop after the cue rob), and nonwords with no rhyming lexical neighbor (e.g., vop after rob). Words were rejected more rapidly than nonwords, and there were reliable differences in the speed and accuracy of rejection of the two types of nonword. The advantage for words over nonwords was replicated for positive rhyme decisions. However, there were no differences in the speed of acceptance, as rhymes, of the two types of nonword. The implications of these results for interactive and autonomous models of spoken word recognition are discussed. It is concluded that the differences in rejection of nonrhyming nonwords are due to the operation of a guessing strategy.

The influence of the lexicon on phonetic categorization: stimulus quality in word-final ambiguity.

The categorization of word-final phonemes provides a test to distinguish between an interactive and an autonomous model of speech recognition. Word-final lexical effects ought to be stronger than word-initial lexical effects, and the models make different reaction time (RT) predictions only for word-final decisions. A first experiment found no lexical shifts between the categorization functions of word-final fricatives in pairs such as fish-fiss and kish-kiss. In a second experiment, with stimuli degraded by low-pass filtering, reliable lexical shifts did emerge. Both models need revision to account for this stimulus-quality effect. Stimulus quality rather than stimulus ambiguity per se determines the extent of lexical involvement in phonetic categorization. Furthermore, the lexical shifts were limited to fast RT ranges, contrary to the interactive model's predictions. These data therefore favor an autonomous bottom-up model of speech recognition.