Finding words Lecture Flashcards
Briefly describe the concept of a semantic network
Meaning is represented in nodes within an interconnected network, activating one activates surrounding, connected nodes automatically.
Describe a study which investigates the automatic activation of this network
Lexical decision task (word/ non word)- participants trained to expect building related words (ie door) after body related primes (ie body). Therefore there is explicit knowledge that body parts are not to be expected after these. Despite this, after seeing the prime BODY, “arm” evokes a quicker response than “door.”
Evidence for automatic spreading activation
What possibilities are there for which nodes are connected?
Based on association- words occur together
Overlap in meaning- share sematic traits (Tea, coke; ket, coke)
How did Rhodes and Donaldson investigate this association vs meaning of the semantic network?
It was also known that the N400 amplitude indicates the contextual fit of a word within a sentence at the meaning level (peaks at a non-fitting word.)
They used association word pairs; such as traffic-jam,
compound words in which the second word is unrelated to the first outside of the word,
association plus semantic word pairs; brother-sister, which often co-occur
Semantic word pairs; cereal- bread, which are semantically related but rarely co-occur
and unrelated word pairs; beard-tower.
The biggest N400 was expected at the unrelated condition, the least in the association + semantic, and the other two conditions could be compared
What were the results of the investigation of the association vs meaning of the semantic network by Rhodes and Donaldson?
Semantic word pairs did not actually lead to smaller N400s, only the association and association + semantic word pairs. There was no real difference in effect between these, suggesting that it was mostly an effect of association.
Lexicon contains 45,000 to 250,000 words, describe a very bottom up model which attempts to explain how we can retrieve meaning from these words as they are spoken
Logogen model
input from sensory stimulus activates logogen. If the threshold is reached, based on frequency, recency, context etc, then the information in that logogen is integrated into the sentence.
What are the limitations of the logogen model?
Its a strictly bottom-up model and its winner takes all- if the threshold is reached then the information in that logogen is activated and integrated. This suggests that none of the other “logogens” will be activated which is not really in line with what we observe in that there are usually lots of words that are at least partially activated beside each other.
name an interactive lexical retrieval mode, what makes it an interactive model?
TRACE model
It is not sequential, also allows recurrent processing- therefore not only bottom up, but top down processing can be integrated (therefore an interactive model).
How does the TRACE model explain the word superiority effect? Does this explain it better or worse than the Logogen model?
Traditional models like the logogen model just can’t explain it
Refers to the finding that individual letters are easier to process (to remember, detect etc) if they are part of a word (DRAG rather than &&D& or nonsense word GADR). Typically the stimulus is shown quite fast and the participants are asked whether they saw a certain letter.
Describe the trace model using this superiority effect
If a letter is presented on its own, it will only activate that letter but doesn’t travel further into the representational network. In It causes cascaded activation. GADR would activate the different letters but does not travel further into the representational network. But if we look at the word DRAG which actually has representation in this network level, then by activating the individual letters, activation can travel through the network and actually travel back which reinforces the activation of the individual letters.
Describe an influential lexical retrieval model which is often contrasted with the TRACE model
The Cohort model is specific to spoken language
It has three levels
Access- Initial activation of word candidates based on phonetic match with the first part of the acoustic input
Selection- one of the word-candidates is selected based on the rest of the acoustic input and context (uniqueness point, b- br- bro)
Integration- Meaning of the selected words is activated and integrated within the preceding message
Is the cohort model an interactive or sequential model? Why?
It is a sequential model- it first deals with the bottom up input and only later adds high level knowledge to the lexical retrieval process.
Why is the cohort model also described as a modular model? Contrast this against the interactive model
Lexical retrieval is initially completed based on bottom-up information (Access), and only subsequently integrated into the broader context sequence (late selection)
Interactive (TRACE model) suggests that all information, including contextual information is immediately used in lexical retrieval
What are the three possible points at which context has an effect?
> Prediction: Prior to the onset of the critical word
> During lexical access: While processing acoustic or visual input (cohort model)
> During integration: after you have already recognised the word
Describe an early 1977 study which demonstrates the role of context in lexical retrieval
Used an effect known as phoneme restauration: One phoneme in a word is replaced by meaningless noise or silence. People only “hear” the missing phoneme if there is meaningless noise (context is sufficiently restrictive).
Warren and warren used this and did a study where they gave sentences as stimuli that were identical except for the last word: It was found that the *eel was on the orange/ axle/ shoe. People ‘hear’ the word that fits the end of the sentence i.e substitute the phoneme based on context.