A causal network in narrative comprehension is a representation connecting story events and states through evidence-supported causal or enabling relationships. Unlike a simple chronology, the network asks which earlier events help explain later outcomes and why some actions become central to the story. Research by Tom Trabasso, Linda Sperry and Paul van den Broek in 1985 linked causal chain membership and direct connections with importance judgements, memory and summarisation.
Why is this useful for vocabulary? Because before, because, enabled, triggered and therefore do not mean the same thing. A pupil can recognise every action word yet mistakenly treat all consecutive events as causes. A causal-network approach turns vocabulary into a tool for explaining which relationships the text supports and which are merely plausible guesses.
This guide explains causal event nodes, directed links, counterfactual tests, chain membership and direct connectivity, with evidence from Trabasso and Sperry’s 1985 study, Trabasso and van den Broek’s accompanying reanalysis and van den Broek’s 1990 work on causal inference. A complete fictional library example and applications in English, Science, Mathematics, History, General Paper and translation make the research practical.
Editorial note: All classroom story events and diagrams are constructed for teaching. Published study results are attributed to the actual research, and a causal diagram is never represented as automatic proof of real-world causation.
Contents: why some story events explain others
Open 49 chapters and evidence checks
- 1. A story can be understood through its causal links
- 2. Why vocabulary becomes more than a sequence of verbs
- 3. Trabasso and Sperry’s 1985 study
- 4. Trabasso and van den Broek’s 1985 reanalysis
- 5. Nodes can represent events
- 6. Arrows represent a specified direction
- 7. Chronology is necessary for many causes, not sufficient
- 8. A simple original story shows the structure
- 9. One event can have more than one cause
- 10. One cause can produce several consequences
- 11. Necessary and sufficient conditions differ
- 12. Counterfactual questions test causal links
- 13. The counterfactual should preserve context
- 14. Causal chains link important events across a story
- 15. Not every detail belongs on the main chain
- 16. Direct causal connectivity counts immediate links
- 17. A hub event can connect several subplots
- 18. A causal edge is different from semantic association
- 19. Causal connectivity is not narrative truth
- 20. Causal coherence helps a reader anticipate
- 21. The model relates to intentionality
- 22. A superordinate goal can organise subgoals
- 23. Van den Broek’s 1990 review of causal inference
- 24. Readers may infer unstated links
- 25. Inferences can be wrong even when plausible
- 26. Memory for causes can exceed memory for wording
- 27. The 1985 reanalysis examined multiple outcomes
- 28. Story grammar and causal networks are related but different
- 29. Event indexing covers more than causality
- 30. Event segmentation asks a different question
- 31. Causal networks need clear node boundaries
- 32. Do not add arrows for every ‘and’
- 33. ‘Because’ and ‘therefore’ carry distinct logic
- 34. A goal is not automatically a cause of success
- 35. A causal explanation can include failed attempts
- 36. The same event can enable different goals
- 37. Primary English: build a because-chain
- 38. Primary English: choose the central action
- 39. Secondary English: explain a turning point
- 40. Secondary English: examine alternative motives
- 41. Science: causal diagrams require real mechanisms
- 42. Mathematics: distinguish dependence from chronology
- 43. History: multiple causes need source criticism
- 44. General Paper: a claim needs a causal mechanism
- 45. Translation: preserve the strength of a causal claim
- 46. Workplace incident reports require traceable links
- 47. A practical five-column diagram audit
- 48. A complete retelling should preserve the chain
- 49. The final lesson: explanatory links make events meaningful
Quick Test: sequence, association and cause are different
| Original example | Possible relation | Important boundary |
|---|---|---|
| Rain began, then the clock struck noon | Temporal sequence | No proven causal link |
| Rain began; Mira opened her umbrella to stay dry | Goal-directed response to rain | Supported by the stated purpose |
| The roof leaked; the books became wet | Possible physical causal link | Verify the story’s circumstances |
| Mira wanted to protect the books and moved them | Intention motivating action | Wanting protection does not guarantee success |
| Mira moved books; the caretaker repaired the roof | Two responses to a common problem | One action need not cause the other |
A causal network needs a reason for each arrow. An arrow can be based on stated events or a clearly identified inference; simple adjacency is not enough.
1. A story can be understood through its causal links
A causal network in narrative comprehension is a representation of events or states and the causal or enabling relationships among them. Instead of keeping only a chronological list, readers can identify which earlier occurrences made later actions possible or helped explain them.
The network is an analytical model, not a claim that every narrative event has one single provable cause. Storytelling can omit information, provide several contributing conditions or describe events that happen together without one causing the other. The learner must keep the network consistent with what the text actually supports.
2. Why vocabulary becomes more than a sequence of verbs
A pupil may know slipped, fell, broke and repaired but misunderstand why the last action occurred. The verbs name activities; causal reasoning connects the events into a coherent explanation.
That is vocabulary as a technology of reasoning. Connectives such as because, therefore, although and consequently become useful only when the actual relationship warrants them. A good reader can distinguish a cause from a mere earlier event.
3. Trabasso and Sperry’s 1985 study
Tom Trabasso and Linda Sperry published Causal Relatedness and Importance of Story Events in 1985. They analysed causal relationships among the events of six folktales and considered the role of those relationships in judgements of event importance.
They found that direct causal connections and membership in a story-spanning causal chain helped predict which events were judged important. That is an empirical result for the studied narratives and method. It does not prove that a writer should always maximise the number of causes to produce a good story.
4. Trabasso and van den Broek’s 1985 reanalysis
Trabasso and Paul van den Broek published Causal Thinking and the Representation of Narrative Events in the same year. They compared causal-network representations with prior story-grammar analyses of narratives and memory results.
Their reanalysis found that whether an event belonged to a causal chain and how many causal connections it had were useful predictors of recall, summarisation and perceived importance. Some story-grammar factors also contributed. The result supports examining causal links rather than treating chronological position as sufficient.
5. Nodes can represent events
A causal network commonly uses nodes to represent events, states or goals and links to represent supported causal relationships. In a simple school diagram, each node may contain a concise event description rather than an isolated verb.
For example, ‘Mira dropped the glass’ is clearer than merely ‘dropped’ because it identifies the actor and object. A precise node reduces the chance of joining the wrong events. The model is only as accurate as its underlying event descriptions and evidence.
6. Arrows represent a specified direction
A directed link from event A to event B means that A contributes to explaining or enabling B under the analysis rule. The arrow does not simply mean that A appeared earlier in the paragraph.
This distinction is central. A character may laugh after a phone rings without the ring necessarily causing the laughter. The researcher must justify why a particular edge exists. A classroom should require a sentence explaining each arrow, not reward a dense decorative diagram.
7. Chronology is necessary for many causes, not sufficient
For ordinary physical causation, a cause generally precedes or overlaps its effect in a relevant way. But temporal priority alone does not establish that an earlier event produced the later outcome.
A student who writes ‘The storm happened before the exam, therefore the storm caused the failure’ needs additional evidence. The causal-network approach invites explicit testing of links instead of assuming every earlier node causes the next.
8. A simple original story shows the structure
Imagine this invented narrative: the library roof leaked; rainwater reached a shelf; several books became wet; Mira moved the books; a caretaker arranged a repair.
The story supports an explanatory chain with several events, but the exact relationships must be stated carefully. The leak may help explain water reaching the shelf, while moving the books is a response to the damage risk. The repair is an attempted remedy rather than evidence that the earlier water event never happened.
9. One event can have more than one cause
A book becoming wet might depend on both rain entering through a damaged roof and its placement on an exposed shelf. One condition alone may not have been sufficient in the particular fictional scenario.
A causal network can represent multiple contributing events. The diagram should not force a single parent for every outcome. This is useful for Science and History, where mechanisms frequently involve several conditions rather than one convenient headline.
10. One cause can produce several consequences
A roof leak may lead to damaged books, a temporary closure of a section and a maintenance inspection. Those consequences can branch from one initiating event.
A branching diagram captures this structure more faithfully than a single unbroken list. However, all branches need textual or documentary support. A pupil who adds a financial loss absent from the story is making an unsupported inference, even if the idea sounds plausible.
11. Necessary and sufficient conditions differ
A necessary condition must be present for a specified event to occur under the stated circumstances, while a sufficient condition guarantees the result within an appropriate model. These are distinct logical concepts.
Trabasso and Sperry’s research used context-dependent logical and counterfactual criteria to identify causal relatedness. Their method did not imply every folk-story event can be assigned one context-free mathematical cause. A school exercise should identify the conditions and the limits of the model.
12. Counterfactual questions test causal links
A useful counterfactual asks what would have happened if an earlier event had not occurred. For example, if the roof had not leaked, would the shelf’s books still have been wet under the circumstances described?
This can help assess whether the proposed link is necessary within the story’s world. It remains a reasoning exercise, not a substitute for experiments where real-world causation is disputed. Alternative causes may change the answer, so the circumstances must be specified.
13. The counterfactual should preserve context
If the student removes the roof leak but invents an unrelated flood to keep the books wet, the original causal test has changed its assumptions. A controlled question holds other relevant circumstances fixed as far as the fictional scenario allows.
The purpose is to isolate the proposed relation, not to create an entirely different story. In factual research, counterfactual causality can be much harder because the alternative history cannot be observed directly. The classroom should distinguish simple story logic from proof about real events.
14. Causal chains link important events across a story
A causal chain is a sequence of connected events that helps explain how an opening situation led to a later outcome. Some narratives contain a central chain from initiating problem through attempts to resolution.
Trabasso and colleagues found that membership in such a chain helped predict judged importance or memory measures in their studied folktales. That pattern suggests why a good summary often includes the initiating problem, decisive actions and consequence rather than every decorative detail.
15. Not every detail belongs on the main chain
A story can mention the colour of a wall or the weather without making those details causally necessary for the central action. Such information can support atmosphere or characterisation while being less connected to the main causal explanation.
A student asked for a plot summary may omit some atmospheric details without losing the central event sequence. A literature analysis might still care deeply about them for imagery or theme. Causal centrality and literary value are not identical concepts.
16. Direct causal connectivity counts immediate links
A node’s direct causal connectivity concerns how many immediately supported links it has with other event nodes under the chosen coding rule. An event may enable multiple later outcomes or depend on several earlier conditions.
Trabasso and Sperry reported that this connectivity contributed to judgements of event importance. It is a descriptive analytic variable, not a numerical moral value of the character. A highly connected mistake may be central to a story without being admirable.
17. A hub event can connect several subplots
A sudden power failure might interrupt a school experiment, cancel a presentation and prompt an emergency response. If the text supports these outcomes, the failure is a hub within the causal network.
The central event has high explanatory relevance because several branches depend on it. A pupil can use that insight to identify the turning point. However, a hub’s existence must be established from the narrative, not presumed from dramatic language.
18. A causal edge is different from semantic association
The words rain and umbrella are associated, but a particular umbrella may have been purchased before the rain. Their semantic relatedness alone does not establish a causal arrow between two specific events.
A causal network is an event-level structure, not a general word-association list. To add an edge, the learner needs a claim such as ‘The rain led Mira to open the umbrella’ supported by the story. This makes the graph precise rather than merely thematic.
19. Causal connectivity is not narrative truth
A fictional story may contain a clear causal chain involving invented technology. The narrative can be internally coherent without describing a mechanism that works in reality.
A reader should distinguish causal explanation within a story world from scientifically or historically verified causation. In Science, evidence and established mechanisms control what claims are acceptable. A network diagram cannot transform fictional logic into physical fact.
20. Causal coherence helps a reader anticipate
If an early sentence establishes a character’s goal, a reader may expect actions that advance it. A causally connected event can feel more coherent than a disconnected scene with no explanation.
This use of expectation can support comprehension but also lead to false inferences. The author may deliberately introduce an unexpected setback. The learner should update the network when new evidence arrives, not reject the text because it differs from a familiar plot.
21. The model relates to intentionality
A protagonist’s goal can explain a sequence of planned actions. If Mira wants to protect wet books, moving them to a dry room makes sense as a goal-directed response.
But the intention and the achieved consequence are distinct nodes. A character can attempt a repair without succeeding. The causal network should record what happened and what merely motivated the action. This protects accurate summaries of unfulfilled plans.
22. A superordinate goal can organise subgoals
A protagonist seeking to save a library collection may first identify the leak, then move books and finally arrange a roof inspection. Those are subordinate actions serving a larger goal.
A hierarchical goal representation can help explain why distant events belong to one plot. Trabasso and later researchers examined connections among goals and actions. A student should not assume every subordinate action succeeded or was necessary unless the narrative warrants that claim.
23. Van den Broek’s 1990 review of causal inference
Paul van den Broek’s 1990 chapter examined the properties and conditions of causal relations used in understanding narrative texts. It discussed temporal priority, operativity and necessity or sufficiency within the described circumstances.
The review helps distinguish a meaningful causal account from a chronological outline. Researchers consider the evidence that a relation is active in the story. A classroom adaptation can use simple ‘because’ questions while maintaining this distinction.
24. Readers may infer unstated links
A story can omit a familiar step. If a character drops a glass and the next sentence says it shattered, readers may infer that the fall caused the break even without the writer using because.
Such a bridging inference can support coherence. But it should be labelled as inferred rather than explicitly stated if the question requires literal evidence. A good causal network may contain justified inferences with a different evidence status from directly narrated causes.
25. Inferences can be wrong even when plausible
If a reader sees rain and a power outage, they may infer that rain caused the outage. The story might later reveal a scheduled maintenance shutdown. The earlier causal link must then be revised.
That is why a graph should be updateable. Readers should not defend a causal edge solely because it seemed plausible at first. A reliable vocabulary system treats evidence and revision as part of understanding.
26. Memory for causes can exceed memory for wording
A person might remember that Mira moved the books to prevent damage without recalling exactly which verbs the author used. The situation-level connection can survive even when precise sentence wording fades.
This illustrates the difference between a story’s propositional relations and the surface text. Yet a quotation or official incident record still requires exact words. The kind of memory needed depends on the task; a causal summary and a verbatim transcript are not interchangeable.
27. The 1985 reanalysis examined multiple outcomes
Trabasso and van den Broek compared causal-network properties with immediate and delayed recall, summarisation and importance judgements. Causal chain membership and number of connections helped account for multiple aspects of the data.
A student should not read this as proof that causality alone explains every memory result. The researchers also examined story-grammar categories and other variables. A research model can provide a strong partial explanation while leaving other influences meaningful.
28. Story grammar and causal networks are related but different
Story grammar labels narrative functions such as setting, initiating event, attempt and resolution. A causal network instead emphasises explicit enabling and explanatory links among events.
The two can be used together. A setting event may enable the problem, and an attempt can produce an outcome. But the label ‘attempt’ alone does not establish a causal arrow to success. A careful diagram uses both story function and evidence-supported connections.
29. Event indexing covers more than causality
The event-indexing model tracks time, space, protagonist, causality and intentionality. A causal network isolates one especially important kind of relationship and studies connectivity, chain membership and explanatory coherence.
A story can have a major time or location shift with little causal effect. Conversely, a cause may connect events across different places or days. Distinguishing the theories avoids rewriting the same lesson under two titles and gives each tool its proper purpose.
30. Event segmentation asks a different question
Event segmentation asks where ongoing activity is divided into meaningful episodes. A causal network asks which episodes or states explain others.
A clear boundary can occur between an initiating event and a later consequence, and the link can remain central despite the boundary. A student can mark both without confusing separation with lack of connection. Good narrative analysis balances the two.
31. Causal networks need clear node boundaries
If one node combines the roof leaking, the books becoming wet and Mira moving them, it becomes difficult to analyse which event caused which. Separating the actions into understandable nodes makes the causal direction visible.
A node should be neither so vague that it hides the process nor so tiny that it becomes useless. The scale depends on the research question or school task. A graph’s quality begins with event descriptions precise enough to test.
32. Do not add arrows for every ‘and’
The connective and can join two events without asserting causation. ‘Mira washed the dishes and Ben read a book’ need not imply one activity caused the other.
A pupil drawing arrows between every pair of consecutive sentences would create a false network. Ask what evidence establishes the cause. Sometimes the honest representation is two separate branches that share only a time or setting.
33. ‘Because’ and ‘therefore’ carry distinct logic
Because commonly introduces an explanation of an event or claim, while therefore signals a conclusion or consequence from preceding information. Neither word makes an unsupported relationship true.
In school writing, a student should be able to justify the connection without relying solely on the connective. An argument can be grammatically fluent and still have a missing premise. Vocabulary is effective when the logical relation is defensible.
34. A goal is not automatically a cause of success
Mira may want to rescue every wet book and work diligently toward that objective. The goal helps explain her actions but does not establish that all books were saved.
This distinction is important in History, Science and public policy. An intention can motivate attempts while a physical or institutional constraint determines the outcome. A network diagram should represent the effort and result separately.
35. A causal explanation can include failed attempts
A protagonist may try a key that does not open a door and later find the correct one. The first attempt belongs to the goal-directed chain even though it did not directly produce the final opening.
A student who summarises only successful actions may conceal why the character changed strategy. The network can include a failed branch that helps explain the later choice. Causal centrality and successful outcome should not be conflated.
36. The same event can enable different goals
A new bridge may allow a character to reach school and another to deliver medicine. One enabling event supports different goal chains.
The diagram should distinguish what the bridge made possible from what each person actually intended or completed. A causal network can therefore be many-to-many rather than a simple ladder. The relationship is powerful when its direction and evidence are explicit.
37. Primary English: build a because-chain
Give a Primary learner three original sentences: ‘It rained. The path became wet. Mira walked more slowly.’ Ask how each event might relate to the next and which links are stated versus inferred.
The teacher should not insist that rain is the only imaginable reason for a wet path. The activity teaches causal connectives and cautious explanation. A good pupil can say why they think a link is supported and identify a possible alternative.
38. Primary English: choose the central action
A short story might contain an initiating problem, an attempt to solve it and a result. Ask the student which action most directly changed what happened next and which sentence merely adds setting details.
The exercise teaches purposeful summarising. The reader can include important atmosphere when the question concerns style, but a plot explanation should preserve the causal sequence. A network helps prevent disconnected retelling.
39. Secondary English: explain a turning point
In literature, one decision may connect several earlier conflicts to later consequences. A Secondary student can represent that decision as a central node and identify the events supporting each arrow.
Then ask whether the decision was truly sufficient for the outcome or whether other conditions mattered. This produces a richer analysis than calling the character’s action ‘important’ without evidence. Vocabulary like enabled, triggered and contributed sharpens the claim.
40. Secondary English: examine alternative motives
A character can perform an action for several plausible reasons. A network that includes one motive should distinguish whether the narrator states it, another character alleges it or the reader infers it.
Literary analysis gains accuracy when claims about intention are properly qualified. An unsupported motive can distort the interpretation of a turning point. The diagram should remain open to correction as the story reveals more evidence.
41. Science: causal diagrams require real mechanisms
A Science passage may describe heating water, evaporation and changes in humidity. A cause-and-effect diagram can organise the events, but scientific validity depends on actual physical mechanisms and measurement.
A teacher should ask which variable was changed, what observation followed and what alternative explanation was controlled. A fluent narrative of sequence is not a scientific demonstration. The graph becomes useful only when the edges reflect supported causal relations.
42. Mathematics: distinguish dependence from chronology
A word problem may describe money being added and removed. A causal or state-transition diagram can help represent why the later total differs from the original amount.
The equation must use the correct quantities and operations. A transaction occurring second is not automatically the cause of a different unrelated balance. Mathematical modelling requires precise membership, timing and conservation rules, not just arrows between events.
43. History: multiple causes need source criticism
A historical transformation may involve economic pressures, political decisions and external events. A causal network can help distinguish triggering incidents from deeper enabling conditions.
However, historical causation is contested and evidence-dependent. The student must cite primary documents and scholarly arguments rather than present every diagram arrow as a settled fact. A plausible network is a hypothesis until supported by reliable records.
44. General Paper: a claim needs a causal mechanism
An essay can assert that a policy caused an improvement simply because the two occurred near each other. A causal-network approach asks which intermediate events link the policy to the outcome.
Students should identify the mechanism and check alternative explanations, delays and confounding variables. Vocabulary terms such as correlation, contribution and causation need careful use. The strongest argument is the one whose arrows can be defended.
45. Translation: preserve the strength of a causal claim
The source text may say that one event contributed to an outcome, not that it guaranteed the result. A translator who strengthens or weakens the causal verb changes the author’s proposition.
Make a source-side network of actual relationships and compare the target sentence. Preserve uncertainty, negation and the identity of each actor. An elegant translation is inadequate if it converts a possible enabling condition into a sole proven cause.
46. Workplace incident reports require traceable links
An incident report can include a failed alarm, delayed response and equipment damage. Investigators must distinguish what was observed from what is hypothesised to have caused the final loss.
Use timestamps, records and authorised evidence. A causal-network diagram may assist organisation but does not replace an investigation. The value of precise vocabulary is avoiding premature blame and ensuring every asserted connection has support.
47. A practical five-column diagram audit
Record each event node, its direct predecessor, proposed causal link, textual evidence and degree of certainty. Where a link is inferred, mark it as such and list plausible alternatives if necessary.
This turns a visual network into a checkable explanation. A pupil can revise the diagram when a new sentence changes the evidence. The network is most useful when it remains an accountable model rather than decorative arrows that imply more than the story says.
48. A complete retelling should preserve the chain
Ask the learner to retell the initiating problem, the key causes and responses, and the final outcome without copying the story word for word. Then check that all decisive relationships remain accurate.
A strong summary is selective but faithful: it omits irrelevant details when appropriate and retains the causal connections that make the plot understandable. This is a practical application of the 1985 research programme, not a normed memory test.
49. The final lesson: explanatory links make events meaningful
Causal-network research helps explain why some narrative events feel central to memory and summarisation: they connect important causes, goals and consequences across the story.
Its educational value is precision. A learner must know when one event caused, enabled, merely preceded or was only associated with another. Vocabulary becomes technology when those relationships are expressed accurately and supported by evidence.
Full worked case: the library leak
Consider a constructed narrative: during heavy rain, a damaged roof leaks above a library shelf. Water reaches the shelf and some books become wet. Mira moves the remaining books to a dry room, and the caretaker arranges a roof inspection. The rain, leak, wet books and protective actions belong to a meaningful sequence.
| Node | Event or state | Connection worth checking |
|---|---|---|
| A | Rain falls while roof is damaged | Contributes to leak under the described circumstances |
| B | Water passes through the damaged roof | May explain water on shelf |
| C | Books become wet | Result linked to water reaching them |
| D | Mira moves remaining books | Response to risk of damage |
| E | Caretaker arranges inspection | Another response to the detected problem |
The network should distinguish physical links A–B–C from decisions D and E, which depend on what characters observed and intended. Neither response proves that the leak was already repaired. Students should explain which arrows were stated or inferred rather than merely drawing a single chain through every node.
Eight-stage causal-network workbook
- List discrete events as clear statements naming actors and affected objects.
- Arrange the events chronologically without assuming every adjacent pair is causal.
- Identify explicit causal language and any implied enabling conditions.
- For a proposed arrow, ask what evidence makes the link defensible.
- Use a counterfactual carefully: what might differ without the proposed earlier event?
- Mark the central causal chain, branches and failed attempts separately.
- Distinguish the character’s intention from actual outcomes and verified facts.
- Write an independent summary retaining important relationships without inventing causes.
Frequently asked questions
1. What is a causal network in reading?
A representation of story events linked through supported causal or enabling relationships.
2. Is a causal network just a timeline?
No. A timeline records order; a causal network asks why one event contributed to another.
3. What is a causal node?
An event, state or goal represented as a unit in the network.
4. What is a causal edge?
A directed link expressing a proposed causal or enabling relation under stated rules.
5. What is causal chain membership?
Whether an event belongs to a linked explanatory sequence connecting the story’s important events.
6. What is direct causal connectivity?
The number of immediate supported causal links involving a node under the graph’s definition.
7. What did the 1985 studies find?
Causal chain membership and connections helped predict judged event importance and recall in the studied narratives.
8. Does occurring first prove causing the later event?
No. Temporal priority alone is not sufficient.
9. Can a causal diagram prove a real historical claim?
No. Real-world explanations require independent reliable evidence.
10. What is the learning goal?
Summarise and explain narrative events with accurate, justified causal relations.
Original research and connected vocabulary guides
- Trabasso and Sperry (1985): Causal Relatedness and Importance of Story Events — folktale causal links and importance judgements.
- Trabasso and van den Broek (1985): Causal Thinking and the Representation of Narrative Events — recall, summarisation, story grammar and causal connectivity.
- ERIC Research Record: Causal Thinking and the Representation of Narrative Events — publication metadata and reanalysis summary.
- Van den Broek (1990): Causal Inferences and the Comprehension of Narrative Texts — causal-relation criteria and reading.
- Suh and Trabasso (1993): Inferences During Reading — goal and action inferences in narrative causal networks.
- Trabasso and Suh (1993): Understanding Text — Achieving Explanatory Coherence — multiple inference methods and causal explanations.
- Vocabulary | What Is the Event-Indexing Model? — five dimensions beyond causality alone.
- Vocabulary | What Is Event Segmentation? — dividing actions into meaningful episodes.
- Vocabulary | What Is Situation-Model Updating? — revising event states as the narrative progresses.
- Vocabulary Learning Hub — accurate words, inferences and explanatory writing.
Causal-network research shows that readers often remember and judge the importance of events partly through their explanatory links. The strongest educational use is not drawing the most arrows; it is defending each relation. Vocabulary becomes practical when a student can distinguish what happened first, what caused the outcome and what remains only an inference.