PSYB57H3 Chapter Notes - Chapter 8: Connectionism

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10 Apr 2012
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Chapter 8: Associate Theories of Long-Term Memory
The Network Notion
Memory connections provide much more than retrieval paths; instead the connections are our memories
How might the network work? It is straightforward.
First we need means of representing individual ideas which will be the nodes within the network, just like knots in a
fishermans net. These nodes are then tied to each other through connections called associations or associative links
- Learning is similar to building a highway between two cities
- What it means to search through memory is to begin at one node and to travel through the connections until
the target info is reached
o Some ‘cities’ are linked by super highways and others by country roads. Other cities are not linked to
each other at all but you can get from one to the next by traveling through some intermediate cities
(this is why some memories are recalled and others are not)
Spreading Activation
A node becomes activated when it receives a strong enough input signal like a lightbulb from electricity
- What travels through the associative links is akin to energy or fuel and these links are “activation carries”
- Once a node as been activated, energy will spread out from the just-activated node through its associations and
this will activate nodes connected to the just-activated node
- Nodes receive activation from their neighbours, and as more activation arrives, the activation level increases.
The activation will reach response threshold and the node fires. This firing has several effects, including the fact
that the node will now itself be a source of activation
Activations below the response threshold are subthreshold activations have an important role to play: Activation is
assumed to accumulate, so that two subthresholds inputs may add together or summate and bring the node to
threshold.
- If a node has been activated recently, it is already “warmed up” so even a weak input will be sufficient to bring
the node to threshold
Detectors receive their activation from other detectors; they can accumulate activation from different inputs and once
activated to threshold levels, they fire
Looking at LT storage, the key idea is that activation travels from node to node via associative links. As each node
becomes activated and fires, it serves as a source for further activation spreading activation
How does one navigate through the maze of associations? Our initial proposal is that you do not “choose” at all.
instead, activation spreads out from its starting point in all directions simultaneously, flowing through whatever
connections are in place. think of fuel flowing through hoses: if two hoses radiate out from a starting point, the fuel does
not “choose” the left or the right- it will flow through both
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Evidence Favoring the Network Approach
We first need to ask whether we are even on the right track with associative nets. Is this a sensible approach at all? what
can be explained in these terms?
Hints help us remember
- One explanation: hearing ‘South Dakota” will activate nodes that represent our knowledge about this state.
Activation will then spread outward from these nodes (some weaker than others). there is a chance that there
are weak connection between South Dakota and nodes representing “Peirre”. Here, insufficient activation will
flow into the Pierre nodes, and these nodes wont reach threshold or be “found”
- Things will go differently if a hint is available. If you are told South Dakotas capital is also a mans name, this will
activate the mans name node and so activation will spread out from this source at the same time that activation
is spreading out from south Dakota nodes
Context Reinstatement
if you learn something underwater, you are more likely to remember it again while underwater. Being underwater will
activate certain thoughts, and the ndoes representing these thoughts will become connected to the nodes representing
the materials to be rememerbed
More Direct Tests of the Network Claims
Spread of Activation and Priming
Lexical-decision task participants are shown a series of letter sequences on a computer screen. Some of them spell
words and others are letter strings that aren’t words (blar, plome, tuke etc). asked to hit a “yes” button if it is a word
and “no” button if not a word. They “look up” these letter strings in their “mental dictionary” and base reponse on
whether they find the string in the dictionary or not.
- Consider a trial where they see a related pair like “bread, butter”. To choose a response, they first need to look
up the word bread in memory. Because we have no activated the bread node, some activation should naturally
send to the butter node since there is a strong association between the two and nodes spread energy to nearby
nodes. (will also take less time to activate butter)
- Therefore, if words were related, the reaction time was quicker
Sentence Verification Task participants shown sentences on a computer screen, such as “A robin is a bird” or “A robin
is an animal” ; or “cats have claws” or “cats have hearts”. Within these sentences, false ones like “a cat is a bird” were
mixed in. they were asked to hit “true” or “false” as quickly as they could.
- Participants performed this task by traveling through the network, seeing a connection between nodes. When
they find the connection for the robin node to the bird node, this confirms that there is in fact a associative path
linking these two nodes aka they are true.
- This travel will require little time if the two nodes are directly linked by an association
Also noted that there is no point in storing in memory the fact that cats have hearts and the fact that dogs have hearts.
Instead, it would be more efficient to store the fact that these various creatures are animals and the separate fact that
animals have hearts.
- According to this logic, we should expect relatively slow response to sentences like “cats have hearts”, since to
choose a response , a participant must locate the linkage from cat to animal and then a second linkage from
animal to heart.
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