PSYB57H3 Chapter Notes - Chapter 8: Ellipse, International Securities Identification Number

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16 Apr 2012
Chapter 8 - Associative Theories of Long-Term Memory
- Memory search is aided by connections b/w material to be learned and the things one already knows?
- What are these connections, and what does the travelling?
The Network Notion
- Memory connections provide more than retrieval paths connection are our memories
- Memory is represented by a memory connections a connection b/w some memory content representing things
and some memory content representing the sound pattern of “_____
How Might the Network Work?
- Essence of memory network is straightforward
- Need some means of representing individual ideas nodes w/I the network, just like the knots w/I a fisherman’s
net (Latin-knot, nodus), are tied to each other via connections called association/associative links (like nodes
cities on map, association-highways that link the cities)
- Not all association are of equal length
- How do these memory connections get established? Ideas become linked only if during the learning episode the
learner pays attention to the items to be remembered some active intellectual engagements is needed to create
the connections, the nature of this engagement is crucial
Spreading Activation
- A node becomes activated when it has receive a strong enough input signal (like energy or fuel) and the
associative links are like activation carriers
- Nodes receive activation form their neighbours, and as more and more activation arrives at a particular node, the
activation level
- The activation level will reach the node’s
response threshold the node fires (as several
- Activation levels below the response threshold, so-
called subthreshold activation, have an important
role to play: Activation is assumed to accumulate, so
that 2 subthreshold inputs may add
together/summate and bring the node to threshold
- If a node has been partially activated recently, it is
already “warmed up” so that even as weak input
will be sufficient to bring the node to threshold
- Almost like neurons
- They key idea is activation travels from node to
node via the associative links, as each node
becomes activated and fires, it serves as a source
for further activation, spreading onward through the network
- Spreading activation allows us to deal w/ a key issue
- If you start at 1 node, how do you decide where to go from there? You do not choose at all, activation spreads out
form its starting point in all direction simultaneously, flowing through whatever connections are in place
Evidence Favouring the Network Approach
- This sketch leaves a great deal unspecified, but that is deliberate: associative nets can be implemented in various
- Why do hints help us to remember?
i.e. what is the capital of South Dakota? And given the hint Is is perhaps a man’s name
South Dakota will activate the nodes in memory that represent your knowledge about this state
Activation will spread outward from these nodes to the “capital city”
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- Is it possible that there is only a weak connection b/w SOUTH DAKOTA nodes and the nodes representing
If you are told, “South Dakota’s capital is also a man’s name”, this will activate MAN’S NAME node, and os activation
will spread out from the source at the same tie that activation is spreading out form the SOUTH DAKOTA nodes
Nodes for “Pierre” will now receive activation form 2 sources simultaneously
Context Reinstatement
- The logic is the same as it was in our discussion
of hints
- Imagine a list of words, including “pointer”
while underwater. If asked later on, “What words
were on the list?” activation will flow outward
From the nodes representing your general
thoughts about the list. Enough of this
activation will reach the POINTER nodes to
activate them, but perhaps not. If you are
underwater at the time of the rest, then this
will trigger certain thoughts, and we just suggested that the nodes representing these thoughts may be linked
to the nodes representing the learned material including the POINTER nodes As a result, the POINTER nodes
will be receiving a double input: They’ll receive activation form the nods representing through about the list
and also form the nodes representing the underwater thoughts
DOUBLE INPUT = More likely that POINTER nodes will be activated, leading to the memory advantage that we
associate w/ context reinstatement
More Direct Tests of the Network Claim
- Network proposal provides a natural way to bring together the evidence presented importance of connections
and the proposal now before us simply develops this idea by being a bit more precise about what connections
and how they work
Spread of Activation and Priming
- Associative network is subthreshold activation can accumulate, so that insufficient activation received from 1
source can add to the insufficient activation received form another source heart of proposal for why hints work
and why context reinstatement helps memory
- More direct evidence is lexical-decision task research participants are shown a series of letter sequences on a
computer screen, some of the sequence spell words; other sequences are letter strings that are words (i.e. blar,
plome, tuke). Participants hit a “yes” button if the sequence spells a word and a “no” button otherwise. They
perform this task by “looking up” these letter strings in their “mental discovery: and they base their response on
whether they find the string in the dictionary or not. We use participant’s speed of response in this task as an index
of how quickly they can locate the word in their memories
i.e. participants see related pair (i.e. bread and butter). To choose a response, they need to look up the word
bread in memory to activate the relevant nodes and repeat for another word. The nearby words will includes
BUTTER since association is strong. When a participant turns to the second word in the pair, the participants
find this word and know that this string is a word = hit “yes” button
Prediction is correct participants’’ lexical-decision responses were faster by 100 ms if the stimulus words
were related
Sentence Verification
- When you search through the network, activation spread form node to node like travel and so farther one must
travel and be longer
i.e. Collins and Quillian (1969) used sentence verification task participants shown sentences on a computer
screen (“A robin is a bird” or “A robin is an animal”). Mixed together are true and fake sentences (i.e. “A cat is a
bird”) Participants hit “true” or “false” button as quick as they could. People perform this task by travelling through
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