Lecture notes
DISCOURSE AND DIALOGUE
- Module
- NEUROPSYCHOLOGY (PSY0661)
- Institution
- Northumbria University (UNN)
LECUTRE 14 OUT OF 15
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Lecture 14 – discourse and dialogueDiscourse;
- What is discourse?
- “a connected series of utterances; a text or conversation”
- “a unit of language longer than a single sentence”
- Definition applies to both spoken words and text
- E.g., Books, speeches, conversations, films
- Key to discourse comprehension is to construct meaning beyond single sentence
comprehension, integrate information across sentences, and draw appropriate
inferences
How do we mentally represent meaning at discourse level;
- Construction Integration Model (Kintsch, 1988)
- Cyclical process as each new proposition is constructed
and integrated
-
Surface form;
- The exact words and syntax of a proposition
- Least abstract level of representation
- E.g., “John wrote Sarah a love letter.” is mentally represented as “John wrote Sarah a
love letter.”
- Short term memory of surface form decays quickly (remember Working Memory
holds ~7 chunks of information)
- New propositions quickly overwrite older propositions
- A typical novel is around 100,000 words – clearly we don’t remember them all but
we do remember “the story”!
Text base;
- Stripped down version of propositions (gist)
- Loses some aspects of the surface form but preserves semantic meaning
- Represented as a predicate (verb) + arguments (i.e., subject, object and theme)
propositions
- “John wrote Sarah a love letter.”
- = Wrote [John, Sarah, Love letter]
- “A love letter was written to Sarah by John”
- = Wrote [John, Sarah, Love letter]
Situation model;
- A mental model of the situation described by the text
- Most abstract (divorced from surface form) level of representation
- Mental representation combining text base with inferences and world knowledge
Evidence for situation model, Bransford et al., 1972
- Memory for text is organised around meaning, not surface form
- Sentences 1 &2 have the same meaning
- Sentences 3 & 4 have different meanings
- In a recognition memory task participants often mistake
sentence 1 for sentence 2 (and vice versa)
- Participants less likely to mistake sentence 3 for sentence 4
, Why do readers notice situation model anomalies?;
- Resonance model (Cook & O’Brien, 2014)
- A model of memory-based text processing
- Automatic low-level resonance – new concepts immediately activate related
concepts in long term memory and from prior context
- E.g., ‘Piano’ activates concepts such as ‘heavy’, even if this concept is irrelevant
- In previous example ‘vegetarian’ resonates with long term memory and activates
‘does not eat meat’
- ‘Cheeseburger’ resonates with long term memory and activates ‘meat’, ‘meat’
reactivates ‘Mary is a vegetarian’ and the inconsistency is noticed
- Resonance also brings relevant information back into focus e.g., if ‘Mary’ has not
been mentioned for several pages or chapters
Event indexing model;
- The Event Indexing Model is a theory of how people update (rather than create)
situation models
Five indices are tracked in the situation model;
- 1) Protagonist (Character) – Stewart, Haigh and Kidd, (2009)
- 2) Temporality (Time) – Zwaan (1996)
- 3) Intentionality (goals and motivations)
- Intentionality index keeps track of goals and plans
- “Betty decided to knit a sweater” introduces a goal
- Probe task shows that goal related information is more accessible (read faster) when
a goal is still unfulfilled and active relative to when the goal has been fulfilled (Suh &
Trabasso, 1993)
- Incomplete goals stay at forefront of reader’s mind, completed goals are
backgrounded
- 4) Spatial (Space) - Stewart, Haigh & Kidd, (2009)
- 5) Causality - Keenan et al., 1984
- E.g if a character moves to a different location, the Space index needs to be updated.
- If there is a time shift, the Temporality index needs to be updated e.g., “The next
day…”; “Twenty minutes later…”
- Anomalies are detected when incoming information mismatches one or more of the
indices
Discourse summary;
- Efficient discourse comprehension requires us to build and
update situation models
- Situation models are based on the literal linguistic input (text or
speech) translated into the gist and augmented with inferences
- Situation models help keep track of five key dimensions
- Lots of unknowns; Do we integrate information into the
situation model proposition by proposition or word by word?
- Other possible dimensions/indexes of the Event Indexing Model? How could they be
tested?
- What are the mechanisms for extracting gist (text base) from the surface form and
then integrating this into a situation model?
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