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Semantic memory Flashcards

(45 cards)

1
Q

What is semantic memory?

A

General knowledge (no specific event).

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2
Q

Anatomy of semantic dementia

A

Anterior parts of the temporal lobe.

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3
Q

2 reasons for categorisation of objects and events (unit of semantic categories)

A
  1. Economy (encoding of general structure).
  2. Generalisation (apply old knowledge to new situations).
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4
Q

Economy in categorisation

A

Pick up general pattern from individual instances. Common info is stored once, making it more efficient. Then use specific info (outstanding details).

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5
Q

Generalisation in categorisation

A

Applying prior knowledge in new contexts and predict properties about a new object.

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6
Q

Why are concepts so important?

A

They are like ‘ the glue that holds our mental world together’. Ties past experiences to present interactions with the world.

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7
Q

What type of hierarchical structure does semantic knowledge have?

A

Taxonomic (superordinate/basic level/subordinate).

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8
Q

Collins and Quillian (1969) ‘a canary…?’ true or false

A

Evidence of hierarchical storage. RTs are faster for descriptions that are specific to the concept (eg. ‘can sing’) and increase as the descriptions move further up the hierarchy. Different properties are stored at different hierarchical levels so ‘can sing’ immediately activates concept of canary while ‘has feathers’ searching until the ‘bird’ node is reached.

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9
Q

Mandler and McDonough (1993) semantic development in infants habituation

A

Children learn general categories first, before making finer distinctions.

Infants habituate to toys that are different animals. 9 month-olds showed no dishabituation to the rabbit but there was a significant increase in playing time with the bus. There was no significant difference in playing time for the 11 month=olds. 9 month olds did not distinguish between different types of animals. (Same with 4x dog habituation and then probed with a dog or a fish. Significant difference only for 0;11).

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10
Q

Keil (1979) predicability trees

A

Investigating which predicates could go with which concepts according to 5, 7 and 11 year-olds.

As age increases, there is a finer differentiation as age increases (a rabbit can no longer be sorry). A progressive differentiation about the features of concepts (not simply concepts themselves).

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11
Q

Development of loss of concepts in semantic dementia

A

Loss of specific concepts first. More general concepts are kept.

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12
Q

Acquisition and loss of knowledge progression

A

Development: acquire superordinate-level concepts first.
Semantic dementia: lose superordinate-level concepts last.

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13
Q

Is there a single hierarchy of knowledge?

A

No, concepts can be flexible (dogs are both canines and pets). Concepts are context-dependent (a disease that affects dogs is more likely to affect wolves; a shop that sells dogs is more likely to sell cats).

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14
Q

Rosche (1973) on within-category differences

A

Concepts cluster around prototypes. An apple is a fruitier fruit than an olive.

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15
Q

What are the 4 effects of prototypes?

A
  1. Faster categorisation and verification.
  2. More frequently listed in a category.
  3. Learnt first and spared in SD.
  4. Recognised well even if never seen.
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16
Q

Lupyan (2013) on how ‘all-or-none’ concepts are structured

A

Fuzzy concepts. Even for triangles, which has a strict rule on group membership, some triangles are more triangular than others.

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17
Q

Rosch et al. (1976) on how basic level is different

A

Basic level has an advantage in naming (picture verification, language learning, SD). Psychologically privileged as they strike a good balance of being inclusive but informative.

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18
Q

Empirical challenges in semantic knowledge

A
  1. Complex knowledge structure - why are some levels/concepts more important? Hierarchical structure, typicality, basic level.
  2. Developmental challenges - how is structure learnt and how is flexibility preserved?
  3. Conceptual challenges - how is structure coded neurally?
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19
Q

What is a connectionist model?

A

A network of simple interacting units with modifiable connections (based on experience). Input units -> hidden units -> output units.

20
Q

Function of units

A

Integration and activation.

21
Q

What is learning in a connectionist network?

A

The changes in the strengths of connections.

22
Q

5 principles of connectionist models.

A
  1. Neurons integrate information (input from many other neurons).
  2. Neural activity reflects level of input (more input = more activity).
  3. Layered brain structure.
  4. Influence via connections.
  5. Learning alters connection strengths.
23
Q

What does the processing unit do? 3 steps.

A
  1. Integrate input from the previous layer (input from multiple other units).
  2. Transform the net input to an activity level (ai).
  3. Transmits activity level to units in next layer.
24
Q

How is input in unit(i) calculated?

A

Input(i) = activity level of previous unit(j) * the strength of connection between the units(ij).

Netinput(i) = the sum of inputs.

25
How is unit(i) output calculated?
The function is sigmoidal (x = netinput(i); y=activity level(i).
26
Equation for unit output
a(i) = 1/(1+e^-netinput(i)). When net input is 0, activity is 0.5. Activity is bound between 0 and 1.
27
How are the weights of the connections learnt?
Error-driven learning. The required output is specified.
28
What sort of learning rule is used in connectionist models?
Backpropagation.
29
What is the Delta rule?
The equation for the change in connection strength. Blame is a(j), the activity level from the previous unit. Change in weight = (correct activity - actual activity) * blame * learning rate. = error term * blame * learning rate.
30
What sort of learning process happens in PDP models?
Gradual and iterated learning process.
31
What is the backpropagation of error?
Activate 2 inputs -> correct output? If no -> change weights.
32
What sort of input did Rumelhart (1990) use?
Unstructured input. 'Epochs' of input-output learning (item + relation -> features).
33
What were the internal representations of PDP in Rumelhart (1990)?
Structure was learnt from unstructured input (emergent structure). The pattern of activity between similar items was similar. The network learnt by itself how to represent items based on overlapping structures of input.
34
How are concepts represented in PDP models?
Hidden units show that the network learnt different concepts as distributed patterns of activity (no single unit represented a single input/output).
35
Rogers and McClelland (2004) explaining the progressive loss in SD
Noise in the hidden layer. By adding noise in the hidden layer, a specific object is pushed out of its specific spot but remains in the broader area (so broader features are retained while specific features are lost).
36
Rogers and McClelland (2004) explaining typicality effects
The network represents items based on similarity. Within a concept, there is a graded pattern of similarity (some items are really close and some are further away). Distance affects categorisation.
37
Rogers and McClelland (2004) on why the basic level seems special
There is nothing special about the basic level. Instead, it is due to the effects of item frequency (parents use the basic level more often). Frequency affects connection strength. Learnt quickly and resistant to noise/damage. (Model can't address why the basic level is more frequent).
38
Strengths of PDP
1. Simple. 2. Economy and generalisation. 3. Graceful degradation (due to distributed processing). 4. Represents both rules and exceptions.
39
3 ideas of theory of semantic knowledge
1. Coherent covariation. Learn to associate objects with properties that tend to occur together. Statistical regularity help learns categories. 2. Concept similarity in a multi-dimensional space. Similarity = how alike activation patterns are. 3. Knowledge structure via learning. Iterative process of error-driven statistical learning.
40
PDP efficiency
Capacity to store more information if needed because n units = 2^n items stored.
41
PDP generalisation
Once network learns that 'sparrow is a bird', the network is able to generalise knowledge based on prior knowledge of other bird types.
42
PDP fault tolerance
Graceful degradation. Distribution = no single unit is crucial. Complex representation = noise isn't catastrophic.
43
PDP rules and exceptions
Can be simultaneously represented. Representations are 'graded' (contrast with dual-route theories).
44
3 criticisms of semantic PDP
1. Model has built-in features and is told the right answer. 2. We know more than similarity - we also understand causality. 3. Does not explain how knowledge is actually used (combine concepts/draw inferences).
45
3 general criticisms of PDP
1. Learning methods aren't plausible. Backpropagation isn't biologically realistic. 2. Fragile new learning overrides old connections. 3. They are not really cognitive models (just simple statistics/too underconstrained and no rules).