Neurolinguistics studies how the brain makes language possible: understanding an utterance, finding words, building sentences, learning a language and connecting language with action. The gold standard is an explanation that connects a precise language question to a suitable brain measurement, a fair comparison and a conclusion that survives alternative explanations.
A coloured brain image or a named electrical response is useful evidence only when we know what people did and what was compared. This guide shows how to read that evidence, calculate simple contrasts, recognise misleading claims and design better questions. Its numerical examples and miniature experiments are original teaching cases, with invented data explicitly identified; they are not measurements of any reader or clinical assessments.
Start with The Gold Standard for the wider learning framework. The Education parent asks whether understanding transfers into life. Here that transfer means being able to inspect an unfamiliar brain-and-language claim without accepting either its headline or its picture uncritically.
Choose your neurolinguistics route
Chapters 1–3: Connect language, networks and imaging
Chapters 4–6: Interpret timing, components and disruption
Chapters 7–9: Design comparisons and test generalisation
Chapters 10–12: Audit decoding and transfer the reasoning
Chapter index
Connect language, networks and imaging · Chapters 1–3
1. Begin with the language operation
2. Read a network claim without turning it into a map of boxes
Interpret timing, components and disruption · Chapters 4–6
4. Use electrical timing without inventing a universal stopwatch
Design comparisons and test generalisation · Chapters 7–9
7. Design contrasts that can distinguish explanations
Audit decoding and transfer the reasoning · Chapters 10–12
10. Evaluate decoding beyond a spectacular headline
Answer questions and continue learning · Chapters 13–14
CHAPTER 1 OF 14 · Connect language, networks and imaging
1. Begin with the language operation
Imagine hearing, “Put the small blue folder beside the printer.” Several questions immediately become separable. Did the listener hear the sounds? Recognise the words? Identify which folder was intended? Understand the spatial relationship? Remember the instruction long enough to act? Move the folder successfully? An unsuccessful action alone does not identify which operation failed.
Neurolinguistics connects these questions with the nervous system. The Max Planck Institute’s Neurobiology of Language description explicitly connects accounts of speaking, reading and listening with computational models and neural architecture. Its research scope includes language production, comprehension and acquisition. This is a useful definition of the field’s ambition: explanations must connect levels rather than merely rename behaviour with anatomical vocabulary.
Three descriptions of one event
A linguistic description identifies the words and relationships: “beside” specifies a spatial relation, and “small blue” restricts the referent. A behavioural description records the answer, movement or response time. A neural description concerns the measured activity or structure associated with performance. These descriptions answer different questions; none automatically replaces the others.
For an original classroom thought experiment, suppose a listener moves the correct folder to the wrong side of the printer. Before proposing a brain explanation, check the wording, the available objects and what “beside” meant in that setting. The task may have allowed several correct positions. An ambiguous scoring rule can create an apparent processing failure without any unusual language mechanism.
The neighbouring Psycholinguistics root develops the mental processes and behavioural experiments involved in language. Neurolinguistics adds the question of neural implementation. Their overlap is productive: a convincing brain study still needs a coherent account of what the participant understood, decided or produced.
The first practical rule is therefore to write the operation in an ordinary sentence before naming a brain region. “Selecting a noun for a pictured object” is already more testable than “using the language centre”.
CHAPTER 2 OF 14 · Connect language, networks and imaging
2. Read a network claim without turning it into a map of boxes
A brain region is an anatomical location. A network is a set of regions considered in relation to one another. A computation is an operation proposed to explain a transformation, such as relating a word to its context. These terms should not be treated as interchangeable labels.
Malik-Moraleda and colleagues’ 2022 investigation across 45 languages and 12 language families reported common properties of a language network, including its broad spatial organisation, left lateralisation, integration among regions and selectivity for language processing. The result supports shared organisational features across the languages studied. It does not establish identical activity in every speaker, represent every language, or imply that every task involving words uses only that network.
What “involved” leaves unanswered
Consider a hypothetical experiment in which a region responds more during sentence listening than during a comparison condition. That observation leaves several possibilities open. It might reflect a process involved in understanding sentences, a difference in the comparison material, or a task demand accompanying the sentences. A location supplies a constraint; it does not by itself specify an algorithm.
Similarly, two signals that vary together do not establish a direction of communication. In an invented example, two classroom microphones become louder whenever a bell rings. Their correlation does not mean that one microphone causes the other to respond. A shared input is sufficient. Brain-network explanations require the same caution, with additional biological complexity.
A useful reading exercise is to underline the verb in the claim. “Responds during”, “predicts”, “is connected with”, “is required for” and “implements” are increasingly different statements. Ask what observation licenses that particular verb. If the study measured association, a causal verb needs additional evidence.
This does not make localisation unimportant. It makes localisation a starting constraint on a mechanism. Good explanations connect where a response occurs with what changes it, when it changes, what behaviour accompanies it and which alternative account would predict something different.
CHAPTER 3 OF 14 · Connect language, networks and imaging
3. Understand what an fMRI contrast actually compares
The NIH’s MRI overview distinguishes anatomical imaging from functional MRI used to investigate activity associated with tasks. A common functional measure is the blood-oxygen-level-dependent, or BOLD, signal. It is a physiological measurement related to neural activity, not a direct transcript of words or a count of thoughts.
Logothetis and colleagues’ 2001 experiment compared electrical recordings and BOLD responses in monkey visual cortex. The results linked BOLD more closely to local field potentials than to multi-unit spiking in their analysis. This foundational measurement study was not a human language experiment. Its relevance is that the relationship between a scanner signal and neural activity must be investigated rather than assumed to be one-to-one.
Worked example: two baselines, two questions
Assume a fictional region has mean signal 1,012 arbitrary units during sentence listening, 1,008 during acoustically complex unintelligible material and 1,000 during a resting comparison. Ignore uncertainty temporarily so we can isolate the arithmetic.
Relative to rest, the sentence change is 100 × (1,012 − 1,000) / 1,000 = 1.2%. Relative to the complex-sound condition, it is 100 × (1,012 − 1,008) / 1,008, approximately 0.397%. Both calculations are correct. They answer different questions because their reference conditions differ.
The first comparison includes everything that differs between sentence listening and rest. The second attempts to subtract some processes shared with complex listening, but its adequacy depends on the actual control. Neither number means “1.2% more comprehension” or “0.397% more intelligence”. Arbitrary scanner units have not become a psychological scale merely because we calculated a percentage.
Now suppose the control also demands sustained effort to identify distorted sounds. That effort may change the contrast. A smaller difference would not automatically show weaker language processing. The repair is to explain the control’s demands, inspect performance and choose a comparison aligned with the question. Always read the subtraction before interpreting the coloured map.
CHAPTER 4 OF 14 · Interpret timing, components and disruption
4. Use electrical timing without inventing a universal stopwatch
Electroencephalography, or EEG, records voltage differences at electrodes. An event-related potential, or ERP, is an analysis of activity aligned to events, often involving averages over repeated trials. The event definition matters: the appearance of a printed word, the onset of a spoken word and the moment enough sound becomes available to identify it are not necessarily equivalent reference points.
In their 1980 sentence-reading study, Kutas and Hillyard found different responses to semantically inappropriate endings and physically enlarged words. The semantic manipulation produced a negative-going response known as the N400, while the physical manipulation produced a late positive response. This is evidence that experimentally distinguishable changes can have different electrical signatures; it is not a claim that all meaning is completed at exactly 400 milliseconds.
Worked example: sampling and amplitude
Suppose a fictional recording samples at 500 measurements per second. The interval is 1 / 500 second = 0.002 second, or 2 milliseconds. A 100-millisecond analysis window contains 50 sample intervals. That arithmetic describes the recording grid, not the accuracy with which a psychological process has been located in time.
Assume the mean voltage in a preselected window is −2 microvolts in condition A and −5 microvolts in B. The contrast B minus A is −3 microvolts. Reporting “B is three microvolts more negative” is clearer than “B is bigger”, which can confuse numerical value with the magnitude of negativity. The reference, baseline and chosen electrodes must accompany any real interpretation.
The specialist N400 guide explores contextual support in more detail. Use it to distinguish word expectancy from factual truth: an unfamiliar true statement can be unexpected, while a familiar false statement can be easy to anticipate.
An ERP component is a reproducible pattern under specified conditions, not a little object travelling through the brain. To evaluate a timing claim, first identify the event, comparison, window and measurement convention. Then ask whether the proposed psychological interpretation fits the experimental manipulation.
CHAPTER 5 OF 14 · Interpret timing, components and disruption
5. Separate a component’s name from its explanation
Names make research manageable, but they can also turn observations into premature explanations. If a response is called the P600, the label does not by itself settle what computation produced it. Naming a waveform and explaining its functional role are different achievements.
Van Herten, Kolk and Chwilla’s 2005 experiment studied semantic reversal anomalies in Dutch sentences and challenged a purely syntactic interpretation of P600 effects. The important lesson is methodological: a response associated with one class of manipulation can appear under conditions that force researchers to reconsider a narrow explanation. The existing P600 specialist article provides a route into sentence reanalysis and its interpretive limits.
An original contrast to reason about
Compare “The courier thanked the customer” with “The customer thanked the courier”. Both are plausible and grammatical, but their roles differ. Now imagine a story in which only the customer received help. The first sentence may fit the story less well. A response difference could concern the relation between the sentence and that story, even though the grammatical forms are similar.
These invented sentences have not been tested with EEG. They illustrate why a researcher should state the manipulation before predicting a component. A strong design would separate role assignment, contextual plausibility, word frequency and the participant’s explicit task as far as the question requires.
Write two competing predictions. One account might predict a difference whenever role relationships conflict with context. Another might predict a difference mainly when participants must judge whether the sentence is acceptable. Comparing tasks could help separate these accounts. Simply observing a named component in one task would leave both possibilities alive.
The transferable skill is to replace “this waveform means X” with “under these conditions, this contrast is consistent with X, while Y remains to be tested”. That sentence is less dramatic but gives the next experiment a clear purpose.
CHAPTER 6 OF 14 · Interpret timing, components and disruption
6. Learn from disruption without oversimplifying people
Studies of impaired language can constrain theories of how language works. The NIDCD explanation of aphasia describes language difficulties arising from brain damage, commonly stroke or traumatic injury, that can affect expression, understanding, reading and writing. Aphasia can coexist with motor-speech difficulties. These are related clinical concerns, not interchangeable names for the same operation.
For understanding research, the key question is what an observed difficulty distinguishes. Poor spoken naming could involve recognising the object, retrieving a word, assembling its sounds or producing speech. A single test score cannot select among all those explanations.
Worked thought experiment: a dissociation
Consider two entirely fictional performance profiles on well-matched tasks. Person A identifies 18 of 20 pictured objects correctly by pointing but names only 8 aloud. Person B identifies 9 by pointing and names 17 aloud. These numbers create a crossed pattern worth investigating. They do not by themselves establish two isolated brain modules.
Before interpreting the pattern, check whether both tasks used comparable objects, whether pointing choices offered clues, whether hearing or vision affected instructions and whether the scores are reliable. A four-choice recognition task and an unrestricted naming task have different opportunities for success. Accuracy percentages alone do not equalise difficulty.
The strongest next step is to specify which alternative account each additional task addresses. Matching an object to its use may test conceptual access differently from recognising its printed name. Changing the response format can help reveal whether an output requirement is masking understanding.
These examples are about research reasoning, not diagnosing anyone. A person’s communication deserves more than an inference from one error or an anatomical label. In research, disruption offers evidence about dependencies; interpreting those dependencies still requires attention to task design, the extent of injury and the person’s broader performance. The goal is a better explanation of language, not reducing a person to a deficit.
CHAPTER 7 OF 14 · Design comparisons and test generalisation
7. Design contrasts that can distinguish explanations
The most expensive instrument cannot repair a comparison that confounds the variables of interest. Suppose an imagined experiment compares long, rare words in difficult sentences with short, common words in easy sentences. A neural difference could reflect several changes at once. Calling it “the brain response to syntax” would outrun the design.
Begin instead with the claim: does sentence context alter the effect of word familiarity? Construct a small factorial plan with two context levels and two familiarity levels. This creates four conditions and allows the effect of one factor to be compared across levels of the other.
Worked example: an interaction
Use invented behavioural response times to make the logic visible before considering brain signals. In supportive context, familiar targets average 500 milliseconds and unfamiliar targets 560 milliseconds. In weak context, familiar targets average 540 milliseconds and unfamiliar targets 660 milliseconds.
The familiarity cost is 560 − 500 = 60 milliseconds with supportive context and 660 − 540 = 120 milliseconds with weak context. The difference of those differences is 120 − 60 = 60 milliseconds. In this invented dataset, the familiarity cost depends on context. That is the descriptive interaction; it is not yet a statistical conclusion about a population.
For a neural measure, the same comparison logic can apply, but its units and interpretation must be stated separately. A voltage interaction and a response-time interaction need not be numerically comparable. A correspondence between them is a hypothesis to examine, not something guaranteed by using the same condition names.
Repair the materials before collecting data
Ask independent readers whether the contexts support the intended interpretation. Keep response instructions consistent. Balance which version of an item each participant sees so that repeated exposure does not reveal the manipulation. Record exclusions and the intended analysis before inspecting the outcome.
Finally, add a comprehension check that answers the relevant question. A participant can press a button quickly without understanding who did what. If the theory concerns role assignment, the check should test role assignment. Measurement quality begins with making the intended mental operation necessary for successful performance.
CHAPTER 8 OF 14 · Design comparisons and test generalisation
8. Keep selection separate from confirmation
Brain datasets offer many possible regions, time windows and comparisons. This flexibility creates opportunities for discovery, but selecting the most striking result and then treating it as an independently predicted finding can mislead.
Kriegeskorte and colleagues’ methodological demonstrations showed how non-independent selection and analysis can produce distorted results, including apparent effects in noise data. The relevant issue is whether the analysis statistic is independent of the selection criterion under the null hypothesis. Separating data can help, but the separation must include earlier choices such as feature selection, not only the final model fit.
An original selection example
Imagine ten hypothetical brain regions whose true condition difference is zero. Random measurement variation gives one region the largest positive observed difference. If we select that region because it won and report its same observed value as an unbiased estimate, we have rewarded the noise that helped it win.
The problem does not disappear because the region has an interesting anatomical name. The selection procedure is part of the evidence. A useful repair is to define the region using a genuinely separate localiser or an independently specified anatomical criterion, then estimate the target contrast without using its own favourable fluctuations to choose the region.
Keep an explicit boundary between exploration and confirmation. An exploratory analysis can reveal an unexpected pattern and motivate another study. Its value increases when the researcher explains how it was found. Concealing the search makes it difficult for readers to judge how surprising the result really is.
For a practical notebook exercise, write two columns before analysing an imaginary dataset: “choices fixed in advance” and “choices made after inspection”. Put the region, time window, exclusion rule and outcome measure in the appropriate column. This simple record often reveals why two analyses that look similar deserve different levels of confidence.
The question is not whether researchers may explore. They should. The question is whether the final claim accurately represents the path from data to result.
CHAPTER 9 OF 14 · Design comparisons and test generalisation
9. Make cross-language comparisons genuinely comparable
Testing more languages expands the scope of neurolinguistics, but translation alone does not guarantee an equivalent experiment. An instruction, sentence length or unusual word may place different demands on different participant groups. Shared organisational features and language-specific processing questions can both matter.
The 45-language investigation introduced earlier provides evidence across a substantially wider linguistic range than a single-language experiment. Its scope should remain visible when interpreting the result. A finding about broad network properties does not settle every question about word order, literacy, multilingual experience or individual development.
Original case: the translated sentence task
Suppose a researcher translates twenty English sentences into another language and compares response times. The second set averages more syllables and introduces words unfamiliar to several participants. A slower response cannot cleanly identify a different neural mechanism for grammar because the materials and familiarity changed together.
A better plan specifies the function that must be comparable. If the question concerns identifying the agent of an action, both sets should require that operation, even if the languages express it differently. Match relevant difficulty using local norms rather than insisting on identical surface word counts. Keep a record of participants’ experience with the language and writing system used.
Distinguish two goals. One is to ask whether an effect generalises across languages. Another is to explain a difference between languages. The second generally requires especially careful control of alternative differences in participants, materials and procedures. Observing similar results can also require care: an insensitive measure may miss meaningful distinctions.
For everyday reading of research, ask who was studied, in which language, using which modality and task. “Multilingual participants” is too broad if the interpretation depends on age of acquisition, proficiency or daily use. These details define the population and situation to which the inference can reasonably travel.
The practical outcome is respectful precision. A language should not be labelled neurologically easier, richer or more advanced because one small comparison produced a larger signal. First establish what the task and measurement actually differed on.
CHAPTER 10 OF 14 · Audit decoding and transfer the reasoning
10. Evaluate decoding beyond a spectacular headline
A decoder uses measured features to predict a target under a specified procedure. Success can be scientifically important while remaining narrower than unrestricted access to a person’s thoughts.
Tang and colleagues’ 2023 study demonstrated reconstruction of continuous language from fMRI recordings using a semantic decoder. The authors reported meaningful generated sequences across several tasks and found that participant cooperation was required both for training and applying their decoder. Those conditions belong in the description of the achievement. The result should not be retold as effortless, exact reading of any person’s private inner speech.
Worked example: accuracy against a real baseline
Consider an invented classification task with 100 test trials: 80 belong to category A and 20 to category B. A rule that always predicts A achieves 80% accuracy without distinguishing the categories at all.
Now suppose a decoder correctly identifies 72 A trials and 12 B trials. Overall accuracy is (72 + 12) / 100 = 84%. Its recall for A is 72 / 80 = 90%; its recall for B is 12 / 20 = 60%. Balanced accuracy, the mean of these two recalls, is 75%. The always-A rule has balanced accuracy of 50%.
These calculations answer different evaluation questions. The model improves overall accuracy by four percentage points over the simple baseline, while balanced accuracy exposes its unequal success across categories. Neither score establishes performance on another participant, another language or freely generated conversation.
Ask what was genuinely unseen
An evaluation may withhold trials while retaining the same people, stimuli or recording sessions in training. That can be appropriate for a particular goal, but the claim must match it. Predicting new trials from a known participant is different from predicting data from a new participant. Write the intended use first, then define the held-out unit accordingly.
For generated text, grammatical fluency is not enough. A sentence can sound excellent while assigning the wrong action, object or speaker. Evaluation should test the information the system claims to recover and compare it with suitable controls. Read the errors as carefully as the most impressive example.
CHAPTER 11 OF 14 · Audit decoding and transfer the reasoning
11. Diagnose and repair a brain-and-language claim
When a claim sounds impressive but remains unclear, repair it in a fixed order: identify the task, name the measurement, state the comparison, describe the result and then examine the inference. This prevents the conclusion from silently changing the meaning of the observation.
Case A: “More activation means better learning”
Imagine a report in which one group shows a larger signal during vocabulary practice. Before calling the method better, ask whether participants learned more words, retained them and used them in a new context. If those outcomes were not measured, the educational claim is untested. A brain difference can motivate a hypothesis about learning without supplying the missing learning evidence.
The repair is concrete: define the educational outcome, compare appropriate groups or conditions, assess retention and test transfer. The Education parent is useful here because it keeps attention on what a learner can subsequently do.
Case B: “No significant difference means the systems are identical”
A fictional study with highly variable measurements reports no clear condition effect. The absence of convincing evidence for a difference does not establish exact equality. Inspect the estimated effect and uncertainty. A broad uncertainty interval may accommodate several practically important differences.
The repair is to specify what difference would matter, collect sufficiently informative evidence and use an analysis appropriate to that question. Avoid turning an inconclusive result into a positive claim of identity.
Case C: “This region proves a unique process”
If a region responds during the target task, ask what other tasks were tested and how the region was selected. A location observed in one contrast cannot establish that only one process ever involves it. The repair is a set of discriminating comparisons and a narrower conclusion until those comparisons exist.
In all three cases, the repair improves the question rather than dismissing neuroscience. Good scepticism is productive: it identifies the missing observation that would change confidence. A useful audit ends with a proposed test, not merely the word “correlation”.
CHAPTER 12 OF 14 · Audit decoding and transfer the reasoning
12. Transfer check: move from printed sentences to speech
You have learned to inspect an experiment with printed words. Now change the context. An imagined team wants to compare responses to expected and unexpected words in spoken announcements at a railway station. The team proposes reusing the printed sentences, replacing the screen with recordings and keeping the analysis unchanged.
Before reading the answer, identify three things that might now differ, one behavioural check and one conclusion the team should avoid. The purpose is to transfer the reasoning, not remember a component’s name.
Answer: preserve the question, reconsider the measurement
First, speech unfolds over time. A printed target can appear as a complete visual item at a known screen onset, whereas a spoken word supplies information progressively. The team must state the alignment event and explain why it suits the question. Keeping the same numerical time window without considering the new event definition may change what is being compared.
Second, recordings introduce acoustic differences. Target duration, loudness, speaker delivery and surrounding sounds can covary with the intended context manipulation. The team should inspect and control relevant differences instead of assuming that a written match guarantees an auditory match.
Third, a railway announcement involves an action goal. Listeners may need to identify a platform or change a route. The comprehension check should test the information required for that decision, rather than merely whether a sound occurred.
The conclusion to avoid is that a changed waveform automatically reveals a changed language faculty. The stimulus, timing reference, environment and task have changed. An informative result would connect those changes to explicit predictions and report what remains uncertain.
A second transfer step
Now replace the announcement with a written message on a phone. Readers can revisit text, but small screens and competing notifications alter the situation. Design the comparison around the decision the user must make. If the practical aim is fewer navigation errors, measure navigation errors as well as any laboratory signal.
This is the bridge to the How X Works hub: follow the input, transformation, output and failure point. Neurolinguistic reasoning becomes useful when it helps locate which link in that chain needs a better explanation.
CHAPTER 13 OF 14 · Answer questions and continue learning
13. Questions that deserve precise answers
Is neurolinguistics the same as psycholinguistics?
They overlap substantially. Psycholinguistics investigates processes involved in understanding, producing and acquiring language; neurolinguistics specifically connects language with the brain and nervous system. A behavioural experiment can constrain a neural account, and a neural result still needs an explanation of the language task. Choosing between the labels matters less than specifying the research question.
Does a brain image show where a particular word lives?
A task-related image normally shows a measured contrast under a particular analysis. It should not be interpreted as a literal storage map for a word merely because that word appeared during the task. Ask whether the analysis distinguishes the target from relevant alternatives and what evidence supports the proposed representation.
Is a larger N400 a sign of a worse reader?
No individual ranking follows from the label. The meaningful question concerns a defined comparison and how context, materials and task affect it. A condition effect in an experiment is not automatically an individual diagnostic measure. The specialist N400 article provides examples of why unexpectedness and misunderstanding must be separated.
Can a decoder be accurate but practically unhelpful?
Yes. In the invented example in Chapter 10, a majority-class rule reaches 80% overall accuracy while never identifying category B. Practical usefulness depends on which errors matter, how the evaluation represents real use and whether the system improves on an appropriate baseline.
Do these findings tell a teacher which lesson to use?
They can suggest questions and mechanisms, but a classroom decision needs evidence about learning outcomes in the relevant setting. If the aim is remembering and using vocabulary, evaluate remembering and use. A neural measure can contribute to an explanation without replacing those outcomes.
What should I do when two methods appear to disagree?
Check whether they measured the same comparison, people, task and time scale. Then ask whether the theoretical account actually predicted matching results. Different measurements can constrain different parts of an explanation. Treating every difference as a contradiction can be as misleading as declaring all findings compatible without examining them.
CHAPTER 14 OF 14 · Answer questions and continue learning
14. Build a practical learning and research route
Start with one language operation you can describe clearly. Choose understanding a spatial instruction, assigning roles in a sentence or selecting a word for an object. Write the proposed mechanism and one plausible alternative. This prevents the instrument from deciding the question simply because it is available.
Next, build a paper experiment. Write example materials, identify the comparison and explain what a correct answer requires. Ask another reader to find ambiguities in the scoring. This stage needs no scanner and often reveals the most important design problem.
Then choose a measurement because it addresses a remaining uncertainty. If timing matters, explain the event and interval of interest. If location matters, explain the contrast and region-selection procedure. If prediction matters, define the unseen data and baseline. If the claim concerns a necessary contribution, explain what evidence would distinguish necessity from association.
Finally, write a results paragraph for each possible outcome before collecting data. Include a clear effect, an uncertain effect and a result favouring the alternative explanation. A design becomes more informative when you know what each outcome would change. Keep the language question visible throughout.
Read the evidence in a useful order
For the field’s scope, begin with the Max Planck neurobiology programme. For measurement foundations, pair the NIH MRI overview with Logothetis and colleagues’ original physiological study. They answer different questions about the tool and its interpretation.
For language responses, compare Kutas and Hillyard’s original N400 experiment with van Herten and colleagues’ semantic-anomaly experiment. For generalisation, inspect the population and tasks in the 45-language study. For analysis integrity, read the demonstrations of circular selection. For decoding, inspect the achievement and conditions in Tang and colleagues’ study.
Continue through Psycholinguistics when the mental operation or behavioural task needs more explanation, and through the How X Works hub when you want to apply the same mechanism-and-evidence discipline to another subject. The standard to carry forward is simple: explain what changed, what was measured and why the evidence supports the particular claim you are making.