Semantic Memory Knowledge memory Main questions How do we gain knowledge?

1. Semantic Memory Knowledge memory Main questions How do we gain knowledge? How is our knowledge represented and organised in the mind-brain? What happens when we access information? (Note 2nd and 3rd questions are strongly related.)

2. Semantic Memory Knowledge memory Important task lexical decision task make a word-nonword judgement for a letter string

3. higgle

4. murget

5. beer

6. stout

7. Semantic Memory Knowledge memory Main questions How do we gain knowledge? Repetition memorisation of lists (Ebbinghaus) consider lexical decisions across multiple presentations

8. Lexical Decision RT for Words and Nonwords As a Function of Number of Trials 700 Nonword RT (ms) Word 400 1 2 4 6 8 10 . . . . 30 Number of Trials

9. Semantic Memory How do we gain knowledge? Repetition Drop in lexical decision RT across repetitions, especially for nonwords After many reps, nonword RT as low as word RT

10. Lexical Decision Threshold for Words and Nonwords As a Function of Number of Trials 100 Nonword Threshold (ms) Word 0.0 1 3 6 . . . . 30 Number of Trials

11. Semantic Memory How do we gain knowledge? Repetition Drop in lexical decision thresholds across repetitions, especially for nonwords After roughly 6 presentations, nonword decision threshold as low as word threshold

12. Semantic Memory Knowledge memory Main questions How is our knowledge represented and organised in the mind-brain? What happens when we access information? (These questions are strongly related.)

13. Semantic Memory Organisation Semantic network (Collins & Quillian,1969 ) hierarchical organisation categories within categories properties of items (nodes) represented once at highest category level possible— cognitive economy some nodes connected to each other properties connected to nodes

14. Node (a representation) Animal

15. properties Breathes node Animal Skin

16. p Breathes node Animal p Skin

17. p Breathes Animal p Skin is a Fish

18. p Breathes superordinate Animal p Skin is a subordinate p Gills Fish p Fins p Swims

19. p Breathes Animal p Skin is a Swims p Gills Fish p Fins is a p Pink flesh Salmon p Cold water

20. Spreading activation activation of a node spreads through the network spread of activation is automatic the strength of activation dissipates across nodes farther nodes receive less activation activation decreases with time

21. Breathes Animal Skin Swims Gills Fish Cold blooded Pink flesh Salmon Cold water

22. Breathes Animal Skin Swims Gills Fish Cold blooded Pink flesh Salmon Cold water

23. Breathes Animal Skin Swims Gills Fish Cold blooded Pink flesh Salmon Cold water

24. Breathes Animal Skin Swims Gills Fish Cold blooded Pink flesh Salmon Cold water

25. Breathes Animal Skin Swims Gills Fish Cold blooded Pink flesh Salmon Cold water

26. Evidence Sentence verification task (measure RT) A salmon is a salmon. A salmon is a fish. A salmon is an animal. Prediction: The manner in which activation spreads means that RT should be fastest for the 1st sentence, slower for the 2nd sentence, slowest for the 3rd sentence.

27. Evidence Sentence verification task (measure RT) A salmon is a salmon. (# links = 0) A salmon is a fish. (# links = 1) A salmon is an animal. (# links = 2) Prediction: The manner in which activation spreads means that RT should be fastest for the 1st sentence, slower for the 2nd sentence, slowest for the 3rd sentence.

28. Verification Time as a Function of the Number of Links from the Activated Node 1500 RT (ms) 1000 0 1 2 Number of Links

29. Evidence Sentence verification task (measure RT) use properties A salmon needs cold water. (# links = 0) A salmon has gills. (# links = 1) A salmon can breathe. (# links = 2) Prediction: The manner in which activation spreads means that RT should be fastest for the 1st sentence, slower for the 2nd sentence, slowest for the 3rd sentence.

30. Verification Time for Properties as a Function of the Number of Links from the Activated Node 1500 RT (ms) 1000 0 1 2 Number of Links

31. Evidence Sentence verification task (measure RT) Prediction: The manner in which activation spreads means that RT should be fastest for the 1st sentence, slower for the 2nd sentence, slowest for the 3rd sentence. Prediction upheld; support for the semantic network theory

32. Breathes Animal Skin Swims Gills Fish Cold blooded Salmon Eel

33. Breathes Animal Skin Swims Gills Fish Cold blooded Salmon Eel Original semantic network predicted similar RTs for all members of a category. (Prediction: A salmon is a fish = An eel is a fish)

34. Different theory Feature list model or Attribute list model (Smith, Rips, & Shoben, 1974) Idea: Each concept has a list of features or attributes

35. Different theory Feature list model or Attribute list model Idea: Each concept has a list of features or attributes FishSalmonEel breathes breathes breathes skin skin skin gills gills gills cold blooded cold blooded cold blooded swims swims swims pink flesh long and narrow cold water no pectoral fins colourful can be in warm water

36. Different theory Feature list model or Attribute list model Idea: Each concept has a list of features or attributes To make verifications: First stage: one compares the global features of the two concepts (e.g., living vs. nonliving). Get a value (score) for amount of overlap. Low value – quick rejection (“no”) High value – quick acceptance (“yes”) Middle value – not sure

37. Different theory Feature list model or Attribute list model Idea: Each concept has a list of features or attributes To make verifications: 1st stage: Compare the global features of the two concepts. Middle value – not sure Go to 2nd stage: Compare defining features of the concepts. End up with a slow response for match or mismatch. (Slow “yes” – an eel is a fish; or slow “no” – a dolphin is a fish)

38. Different theory Feature list model or Attribute list model Predicts fast RTs for typical members of a category Predicts slow RTs for atypical members of a category (e.g. A perch is a fish < A salmon is a fish < An eel is a fish)

39. Verification Time as a Function of Category Typicality RT (ms) High Medium Low (perch) (salmon) (eel) Typicality

40. Feature list model good for isa questions, but not very good with property questions Typicality is important Cognitive economy may not be so important (also Conrad, 1972)

41. Revised semantic network model (Collins & Loftus, 1975) connection between typical category members and the superordinate are shorter (closer) than the connections between atypical category members and the superordinate properties can be represented more than once (no more cognitive economy) captures idea of semantic relatedness

42. Breathes Animal Skin Swims Gills Fish Tail fin Cod Gills Trout Gills Eel Gills

43. p Breathes Animal p Skin isa Swims p p Gills Fish p Cold blooded isa Perch isa isa Gills p p Salmon Gills Eel p Gills

44. Semantic Memory Knowledge memory Main questions How do we gain knowledge? repetition (form a node?) How is our knowledge represented and organised in the mind-brain? semantic network What happens when we access information? spreading activation