Applied Linguistics: Psycholinguistics, Computational Linguistics, Second Language Acquisition and Clinical Linguistics
2. Real-time language processing: production and comprehension
Speaking and understanding happen fast, incrementally and largely below awareness. In production, a speaker moves from message to grammatical structure to the sounds that realize it. Speech errors — slips of the tongue such as spoonerisms (“heft lemisphere”) — are patterned, not random: they show that sounds, morphemes and words are planned at distinct levels before articulation.
In comprehension, the listener accesses word meanings (lexical access), aided by priming (a related word is recognized faster), and builds syntactic structure word by word rather than waiting for the sentence to end. The clearest evidence is the garden-path sentence: in “The horse raced past the barn fell”, readers first take “raced” as the main verb, then must reanalyse when “fell” arrives. Reaction-time, self-paced-reading and eye-tracking methods measure this processing load as it unfolds, and event-related brain potentials add another window: a semantically odd word tends to elicit an N400 response, while a syntactic problem tends to elicit a P600.
3. Language and the brain, bilingualism and cognition
In most people language is lateralized to the left hemisphere. Damage there can cause aphasia, and the classical types map to the site of the lesion. Broca’s aphasia follows damage to the left frontal lobe (Broca’s area): speech is non-fluent, effortful and agrammatic (function words dropped), while comprehension is relatively preserved. Wernicke’s aphasia follows damage to the left posterior temporal lobe (Wernicke’s area): speech is fluent but often empty, with word-substitutions, and comprehension is seriously impaired. Conduction aphasia, classically linked to the pathway between the two areas, leaves comprehension and fluency fair but repetition markedly poor.
Bilingualism is the norm, not the exception, worldwide. A simultaneous bilingual acquires two languages from infancy; a sequential bilingual adds the second later. Bilinguals routinely code-switch (alternate languages within a conversation or sentence) in rule-governed ways, and both languages stay active, so managing them is thought to engage general control processes. A once-popular claim of a broad “bilingual advantage” in executive control is now regarded as at best small and inconsistent across studies, so it too should be stated with caution. On language and cognition, the strong “linguistic determinism” that language dictates thought is not supported; a weaker linguistic relativity — that habitual language can influence attention and memory in some domains — has modest support and is best stated cautiously.
4. Computational linguistics: modelling, parsing, corpora and LLMs
Computational linguistics models and processes language with computers. A text is first tokenized into units; a simple statistical model is the n-gram, which predicts a word from the n−1 words before it (a bigram model uses one previous word, a trigram model two). Such models need large corpora to estimate probabilities, and because many valid sequences never appear even in huge data, smoothing reserves probability for the unseen.
Parsing assigns grammatical structure to a sentence. Constituency (phrase-structure) parsing groups words into nested phrases; dependency parsing links each word to its head by grammatical relations (subject, object). Parsers are trained and tested on treebanks — corpora hand-annotated with such structures. The same pipeline supports morphological analysis, the segmenting of words into morphemes and their features, for which finite-state transducers are a standard, efficient tool. Such systems are trained and evaluated against held-out annotated data, so the quality of a corpus and its annotation sets a ceiling on what any model built from it can learn.
Large language models (LLMs) are neural networks, typically based on the transformer architecture, trained on very large text collections to predict the next token. This objective yields strikingly fluent text and strong performance on many tasks, but it is prediction, not verified knowledge: an LLM can state false things confidently (so-called hallucination), reflects biases in its training data, and does not by itself guarantee factual accuracy. Outputs therefore need checking.
- Speech recognition (ASR) maps an acoustic signal to text, combining an acoustic component with a language model; modern systems are largely neural.
- Sentiment analysis classifies the attitude expressed in a text (positive, negative, neutral), using either sentiment lexicons or machine-learning classifiers trained on labelled data.
5. Second language acquisition and teaching
A central distinction in SLA, associated with Krashen, is between acquisition (a subconscious process, like a child picking up a first language) and learning (conscious study of rules). On his influential but much-debated Monitor Model, learned knowledge mainly acts as a “monitor” that edits output, grammatical structures emerge in a fairly fixed natural order, progress depends on comprehensible input just beyond the current level (often summarized as “i + 1”), and a low affective filter (low anxiety, high motivation) lets input through.
A learner’s evolving system is an interlanguage (Selinker): a rule-governed grammar of its own, neither the first nor the target language, which may fossilize (stop developing) short of native proficiency. Teaching methods have shifted from the grammar-translation and drill-based audiolingual methods toward communicative language teaching and task-based approaches that make meaningful use the engine of learning. Learning also draws on cognitive resources such as working memory and attention; noticing a form in the input is widely held to aid its uptake.
6. Clinical linguistics
Clinical linguistics applies linguistic analysis to the assessment and treatment of communication disorders. Assessment combines standardized, norm-referenced tests with naturalistic language sampling and observation, describing a person’s difficulty at the level of sounds, words, grammar, meaning or use rather than by a label alone. Disorders are broadly developmental (present as language develops) or acquired (following injury or disease to a previously intact system).
- Developmental: Developmental Language Disorder (difficulty with language despite otherwise typical development), speech sound disorders, childhood apraxia of speech, and developmental stuttering.
- Acquired: aphasia after stroke or brain injury, dysarthria (weak or poorly controlled speech muscles), acquired apraxia of speech (impaired motor planning), and the language decline seen in some dementias.
- Evidence-based therapy integrates the best research evidence with clinical expertise and the client’s own values and goals, and measures outcomes rather than assuming them.
| Axis | When it arises | Examples |
|---|---|---|
| Developmental | As language is still developing | Developmental Language Disorder, speech sound disorder, childhood stuttering |
| Acquired | After damage to a previously intact system | Aphasia, dysarthria, acquired apraxia of speech |
Key takeaways
- First-language acquisition is rule-governed and creative: overregularization (“goed”) shows children induce rules. Nativist accounts invoke Universal Grammar and poverty of the stimulus; usage-based accounts rely on general cognition; the critical-period hypothesis says first-language capacity declines with age.
- Production and comprehension are fast and incremental: patterned speech errors reveal planning levels, priming speeds lexical access, and garden-path sentences expose word-by-word parsing with reanalysis, measured by reaction time, self-paced reading and eye-tracking.
- Language is usually left-lateralized: Broca’s aphasia (frontal) is non-fluent and agrammatic with preserved comprehension, Wernicke’s (temporal) is fluent with impaired comprehension. Bilingualism is normal, with code-switching; strong linguistic determinism is rejected, weak relativity is stated cautiously.
- Computational linguistics tokenizes and models text: an n-gram predicts a word from the previous n−1, needing corpora and smoothing. Constituency and dependency parsing use treebanks; finite-state transducers do morphology; LLMs are transformer networks predicting the next token, fluent but prone to hallucination.
- In SLA, Krashen separates subconscious acquisition from conscious learning, with the Monitor, natural order, comprehensible input (i + 1) and affective filter hypotheses; an interlanguage may fossilize. Teaching moved to communicative and task-based methods; assessment distinguishes proficiency, achievement, formative, summative and watches washback.
- Clinical linguistics assesses communication disorders through standardized tests and language sampling, and classifies them as developmental (such as Developmental Language Disorder and stuttering) or acquired (aphasia, dysarthria, apraxia of speech); therapy should be evidence-based.
Practice questions (16)
Attempt each one before opening the answer. Every explanation names the tempting wrong option as well as the right one, because that is where marks are lost.
A two-year-old who has often heard “went” starts saying “goed” instead. What does this overregularization most directly show?
Show answer
Answer: B — That the child has induced a past-tense rule and overapplied it to an irregular verb
“Goed” is a form the child has not heard, so it cannot be imitation or forgetting; it is the regular -ed rule applied to an irregular verb, showing an internalized, overextended rule. That it is a systematic error, not random noise, is exactly why it counts as evidence of a grammar.Readers slow down sharply at “fell” in “The horse raced past the barn fell.” Which claim about comprehension does this best support?
Show answer
Answer: A — Comprehension is incremental: readers commit to a structure word by word and must reanalyse when it fails
The slowdown at the disambiguating word is the signature of a garden path: the parser commits early to “raced” as the main verb and must revise when “fell” forces the reduced-relative reading, which shows incremental processing. The sentence is in fact grammatical, and readers clearly do not wait for the end, which rules out the other options.A patient speaks in halting, effortful, telegraphic phrases that drop function words, yet follows spoken instructions fairly well. This pattern is most consistent with:
Show answer
Answer: A — Broca’s aphasia, from a lesion in the left frontal lobe, with non-fluent agrammatic speech and relatively preserved comprehension
Non-fluent, effortful, agrammatic output with comprehension relatively intact is the classic Broca’s profile, tied to a left frontal lesion. Wernicke’s aphasia is the opposite — fluent speech with impaired comprehension; stuttering is a developmental fluency disorder without this agrammatism; and dysarthria is a motor-speech weakness affecting articulation, not grammar.A child raised with Tamil and English from birth, using both before age three, is best described as which kind of bilingual?
Show answer
Answer: D — A simultaneous bilingual, who acquires both languages together from infancy rather than one after the other
Acquiring two languages together from infancy is simultaneous bilingualism; adding a second language later would be sequential. Bilingualism does not require perfectly balanced ability, and code-switching is a normal, rule-governed practice, not a disorder.In a dependency parse of a sentence, what does each link represent?
Show answer
Answer: B — A grammatical relation joining a word to its head, such as subject or object
Dependency parsing links each word to its syntactic head through a labelled grammatical relation such as subject or object; constituency parsing instead groups words into nested phrases. The other options describe typing order, acoustic measurement and sound change, none of which is a dependency relation.In a bigram language model, the probability of each word is estimated from:
Show answer
Answer: C — The single word immediately preceding it in the sequence
A bigram model conditions each word on just the one word before it (an n-gram of n = 2 uses n−1 = 1 word of context). It does not use the whole document, an alphabetical rank, or cross-language averages; those would be different models or no model at all.Automatically labelling product reviews as positive, negative or neutral is an instance of which computational application?
Show answer
Answer: A — Sentiment analysis, which classifies the attitude or polarity expressed in a text
Classifying the attitude in text as positive, negative or neutral is sentiment analysis. Speech recognition turns audio into text, morphological analysis breaks words into morphemes, and dependency parsing builds syntactic structure — none of which is about attitude or polarity.An LLM produces a fluent, confident paragraph that includes a non-existent citation. What does this best illustrate about such models?
Show answer
Answer: D — That it predicts likely next tokens rather than retrieving verified facts, so outputs can be fluent yet false (hallucination)
An LLM is trained to predict plausible next tokens, so it can generate fluent text that is nonetheless false, including invented references — the phenomenon called hallucination. It has no intent to deceive, fluency does not guarantee truth, and it is not a live database of publications, so its outputs must be checked.On Krashen’s account, which situation best fits the term “acquisition” as opposed to “learning”?
Show answer
Answer: B — A migrant picking up a new language subconsciously through everyday interaction without studying rules
For Krashen, acquisition is the subconscious uptake of language through meaningful exposure, as in picking up a language by living in it. Memorizing conjugations, consciously applying a rule while editing, and drilling a paradigm are all examples of conscious learning, not acquisition.A second-language learner’s speech settles into a stable, rule-governed system of its own that stops short of native grammar and no longer changes. This is best described as:
Show answer
Answer: C — A fossilized interlanguage, an autonomous learner grammar whose development has halted before the target
An interlanguage is the learner’s own systematic grammar, and when its development halts short of the target it is said to have fossilized. A transfer error is a single first-language carry-over, a grammaticality judgement is a rating task, and the critical period is a developmental window — none names this plateaued system.Which pairing of a disorder with its category (developmental or acquired) is correct?
Show answer
Answer: A — Aphasia after a stroke is acquired, because it follows injury to a previously intact language system
Post-stroke aphasia is acquired: it damages a language system that had developed normally. It is not developmental, Developmental Language Disorder is by definition developmental rather than injury-caused, and dysarthria can certainly be acquired through later muscle or nerve damage.Which of the following statements about language, the brain and real-time processing are correct?
Show answer
Answer: A — In most people, language is lateralized predominantly to the left hemisphere; B — Broca’s aphasia is typically non-fluent with comprehension relatively preserved; C — Patterned speech errors suggest that production is planned at distinct levels before articulation
Left-lateralization is typical, Broca’s aphasia is non-fluent with comprehension relatively spared, and patterned slips reveal multi-level production planning. The last is false: strong linguistic determinism is not supported; only a weak, cautiously stated relativity has modest evidence.Which of the following statements about second language acquisition and teaching are correct?
Show answer
Answer: A — Krashen distinguishes subconscious acquisition from conscious learning of rules; B — An interlanguage is a rule-governed learner system that can fossilize short of the target; C — Communicative language teaching foregrounds meaningful use rather than isolated rule drills
The acquisition–learning distinction, the fossilizing interlanguage, and the meaning-focused nature of communicative teaching are all correct. The last is false: a proficiency test measures general ability independent of a course, while an achievement test measures what a particular course taught.Which of the following statements about clinical linguistics are correct?
Show answer
Answer: A — Assessment combines standardized tests with naturalistic language sampling and observation; B — Disorders are broadly grouped as developmental or acquired by their origin; C — Evidence-based therapy weighs research evidence together with clinical expertise and client values
Combining standardized and naturalistic assessment, grouping disorders by origin, and basing therapy on evidence plus expertise and client values are all sound. The last is wrong: the same surface symptom can be developmental or acquired, so origin matters and symptom-only classification is a known error.A tokenized sentence consists of 12 tokens. Using a sliding window, how many word bigrams (adjacent two-token sequences) can be extracted from it? Give the answer as a whole number.
Numerical answer — type the value.
Show answer
Answer: 11
A bigram is a pair of adjacent tokens, and a sequence of n tokens has n−1 adjacent pairs. With n = 12, the number of bigrams = 12 − 1 = 11. The common slip is to answer 12, forgetting that the last token begins no new pair.A bigram language model is built over a vocabulary of 6 word types. How many distinct ordered bigrams (word_i followed by word_j) are in principle possible, allowing a word to follow itself? Give the answer as a whole number.
Numerical answer — type the value.
Show answer
Answer: 36
Each ordered bigram chooses a first word and a second word independently from the 6 types, and self-pairs are allowed, so the count is 6 × 6 = 36. The trap is to compute 6 × 5 = 30 by wrongly excluding a word following itself.