1. Abstract Neuron w2w2 wnwn w1w1 w0w0 i 0 =1 o u t p u t y i2i2 inin i1i1... i n p u t i 1 if net > 0 0 otherwise {

2. Computing with Abstract Neurons McCollough-Pitts Neurons were initially used to model  pattern classification size = small AND shape = round AND color = green AND Location = on_tree => Unripe_fruit  linking classified patterns to behavior size = large OR motion = approaching => move_away size = small AND location = above => move_above McCollough-Pitts Neurons can compute logical functions.  AND, NOT, OR

3. Computing logical functions: the OR function Assume a binary threshold activation function. What should you set w 01, w 02 and w 0b to be so that you can get the right answers for y 0 ? i1i1 i2i2 y0y0 000 011 101 111 x0x0 f i1i1 w 01 y0y0 i2i2 b=1 w 02 w 0b

4. Many answers would work y = f (w 01 i 1 + w 02 i 2 + w 0b b) recall the threshold function the separation happens when w 01 i 1 + w 02 i 2 + w 0b b = 0 move things around and you get i 2 = - (w 01/ w 02 )i 1 - (w 0b b/w 02 ) i2i2 i1i1

5. Decision Hyperplane The two classes are therefore separated by the `decision' line which is defined by putting the activation equal to the threshold. It turns out that it is possible to generalise this result to Threshold Units with n inputs. In 3-D the two classes are separated by a decision-plane. In n-D this becomes a decision- hyperplane.

6. Linearly separable patterns Linearly Separable Patterns PERCEPTRON is an architecture which can solve this type of decision boundary problem. An "on" response in the output node represents one class, and an "off" response represents the other.

7. The XOR function i1i1 i2i2 y 000 011 101 110

8. The Input Pattern Space

9. The Decision planes

10. Multiple Layers I1I1 I2I2 1.50.5 1 11 1 1 y

11. Multiple Layers I1I1 I2I2 1.50.5 1 11 1 1 01 y

12. Multiple Layers I1I1 I2I2 1.50.5 1 11 1 1 11 y

13. Computing other relations The 2/3 node is a useful function that activates its outputs (3) if any (2) of its 3 inputs are active Such a node is also called a triangle node and will be useful for lots of representations.

14. Triangle nodes and McCullough-Pitts Neurons? Object (B)Value (C) Relation (A) ABC

15. “They all rose” triangle nodes: when two of the abstract neurons fire, the third also fires model of spreading activation

16. Basic Ideas behind the model Parallel activation streams. Top down and bottom up activation combine to determine the best matching structure. Triangle nodes bind features of objects to values Mutual inhibition and competition between structures Mental connections are active neural connections

17. 5 levels of Neural Theory of Language Cognition and Language Computation Structured Connectionism Computational Neurobiology Biology MidtermQuiz Finals Neural Development Triangle Nodes Neural Net and learning abstraction Pyscholinguistic experiments

18. Psychological Studies Eva Mok CS182/CogSci110/Ling109 Spring 2006

19. Read the list ORANGE BROWN GREEN YELLOW BLUE RED

20. Name the print color XXXXX

21. Name the print color RED GREEN BLUE BROWN ORANGE YELLOW

22. The Stroop Test Form and meaning interact in comprehension, production and learning

23. Top down and bottom up information Bottom-up: stimulus driving processing Top-down: knowledge and context driving processing When are these information integrated?  Modular view: Staged serial processing  Interaction view: Information is used as soon as available

24. Tanenhaus et al. (1979) [also Swinney, 1979] Word / non-word forced choice

25. Modeling the task with triangle nodes

26. Reaction times in milliseconds after: “They all rose” flower685659 stood677623 desk (control) 711652 0 delay 200ms. delay (facilitation) (no facilitation)

27. When is context integrated? Prime: spoken sentences ending in homophones They all rose vs. They bought a rose Probe: stood and flower No offset: primes both stood and flower 200 ms offset: only primes appropriate sense Modularity? Or weak contextual constraints?

28. Eye tracking computer Eye camera Scene camera Allopenna, Magnuson & Tanenhaus (1998) “Pick up the beaker” Adapted from Jim Magnuson, “Interaction in language processing: Pragmatic constraints on lexical access”

29. Do rhymes compete? Cohort (Marlsen-Wilson): onset similarity is primary because of the incremental (serial) nature of speech  Cat activates cap, cast, cattle, camera, etc.  Rhymes won’t compete NAM (Neighborhood Activation Model; Luce): global similarity is primary  Cat activates bat, rat, cot, cast, etc.  Rhymes among set of strong competitors TRACE (McClelland & Elman): global similarity constrained by incremental nature of speech  Cohorts and rhymes compete, but with different time course Adapted from Jim Magnuson, “Interaction in language processing: Pragmatic constraints on lexical access”

30. TRACE predicts different time course for cohorts and rhymes Adapted from Jim Magnuson, “Interaction in language processing: Pragmatic constraints on lexical access”

31. TRACE predictions match eye-tracking data Adapted from Jim Magnuson, “Interaction in language processing: Pragmatic constraints on lexical access”

32. Natural contexts are used continuously Conclusion from this and other eye-tracking studies: When constraints from natural contexts are extremely predictive, they are integrated as quickly as we can measure Suggests rapid, continuous interaction among  Linguistic levels  Nonlinguistic context Even for processes assumed to be low-level and automatic Constrains processing theories, also has implications for, e.g., learnability Adapted from Jim Magnuson, “Interaction in language processing: Pragmatic constraints on lexical access”

33. Eye movement paradigm More sensitive than conventional paradigms More naturalistic Simultaneous measures of multiple items Transparently linkable to computational model Adapted from Jim Magnuson, “Interaction in language processing: Pragmatic constraints on lexical access”

34. Eye-tracker without headsets http://www.bcs.rochester.edu/infanteyetrack/eyetrack.html

35. Recap: Goals of psycholinguistic studies Direct goal: finding out what affect sentence processing Indirect goal: getting at how words, syntax, concepts are represented in the brain Modeling: testing out these hypotheses with computational models

36. Areas studied in psycholinguistics Lexical access / lexical structure Syntactic structure Referent selection The role of working memory Disfluencies

37. Disfluencies and new information Disfluencies: pause, repetition, restart Often just seen as production / comprehension difficulties Arnold, Fagnano, and Tanenhaus (2003)  How are disfluent references interpreted?  Componenets to referent selection lexical meaning discourse constraints

38. Candle, camel, grapes, salt shaker a.DISCOURSE-OLD CONTEXT: Put the grapes below the candle. DISCOURSE-NEW CONTEXT: Put the grapes below the camel. b. FLUENT: Now put the candle below the salt shaker. DISFLUENT: Now put theee, uh, candle below the salt shaker.

39. Predictions on 4 conditions: (Target = candle) Disfluent/New, Fluent/Given: Target  Put the grapes below the camel. Now put theee, uh, candle below the salt shaker.  Put the grapes below the candle. Now put the candle below the salt shaker. Disfluent/Given, Fluent/New: Competitor  Put the grapes below the candle. Now put theee, uh, candle below the salt shaker.  Put the grapes below the camel. Now put the candle below the salt shaker.

40. Disfluencies affect what we look at Percentage of fixations on all new objects from 200 to 500 ms after the onset of “the”/“theee uh” (i.e. before the onset of the head noun)

41. Target is preferred in two conditions Percentage of target fixations minus percentage competitor fixations in each condition. Fixations cover 200–500 ms after the onset of the head noun.

42. A lot of information is integrated in sentence processing! Stroop test [i.e. color words]: form, meaning Tanenhaus et al (1997) [i.e. “they all rose”]: phonology, meaning, syntactic category Allopena et al (1998) [i.e. cohorts & rhymes]: phonology, visual context Arnold et al (2003) [i.e. “theee, uh, candle”]: discourse information, visual context

43. Producing words from pictures or from other words A comparison of aphasic lexical access from two different input modalities Gary Dell with Myrna Schwartz, Dan Foygel, Nadine Martin, Eleanor Saffran, Deborah Gagnon, Rick Hanley, Janice Kay, Susanne Gahl, Rachel Baron, Stefanie Abel, Walter Huber

44. A 2-step Interactive Model of Lexical Access in Production FOGDOGCATRATMAT frdkmaeotg OnsetsVowels Codas Semantic Features Adapted from Gary Dell, “ Producing words from pictures or from other words”

45. 1. Lemma Access: Activate semantic features of CAT FOGDOGCATRATMAT frdkmaeotg OnsetsVowels Codas Semantic Features Adapted from Gary Dell, “ Producing words from pictures or from other words”

46. 1. Lemma Access: Activation spreads through network FOGDOGCATRATMAT frdkmaeotg OnsetsVowels Codas Adapted from Gary Dell, “ Producing words from pictures or from other words”

47. Activation after 8 steps FOGDOGCATRATMAT frdkmaeotg OnsetsVowels Codas Adapted from Gary Dell, “ Producing words from pictures or from other words”

48. 1. Lemma Access: Most active word from proper category is selected and linked to syntactic frame FOGDOGCATRATMAT frdkmaeotg OnsetsVowels Codas Adapted from Gary Dell, “ Producing words from pictures or from other words” NP N

49. 2. Phonological Access: Jolt of activation is sent to selected word FOGDOGCATRATMAT frdkmaeotg OnsetsVowels Codas Adapted from Gary Dell, “ Producing words from pictures or from other words” NP N

50. 2. Phonological Access: Activation spreads through network FOGDOGCATRATMAT frdkmaeotg OnsetsVowels Codas Adapted from Gary Dell, “ Producing words from pictures or from other words” NP N

51. 2. Phonological Access: Most activated phonemes are selected FOGDOGCATRATMAT frdkmaeotg OnsetsVowels Codas Adapted from Gary Dell, “ Producing words from pictures or from other words” Syl On Vo Co

52. Modeling lexical access errors Semantic error Formal error (i.e. errors related by form) Mixed error (semantic + formal) Phonological access error

53. Semantic error: Shared features activate semantic neighbors FOGDOGCATRATMAT frdkmaeotg OnsetsVowels Codas Adapted from Gary Dell, “ Producing words from pictures or from other words” NP N

54. Formal error: Phoneme-word feedback activates formal neighbors FOGDOGCATRATMAT frdkmaeotg OnsetsVowels Codas Adapted from Gary Dell, “ Producing words from pictures or from other words” NP N

55. Mixed error: neighbors activated by both top-down & bottom-up sources FOGDOGCATRATMAT frdkmaeotg OnsetsVowels Codas Adapted from Gary Dell, “ Producing words from pictures or from other words” NP N

56. Phonological access error: Selection of incorrect phonemes FOGDOGCATRATMAT frdkmaeotg OnsetsVowels Codas Adapted from Gary Dell, “ Producing words from pictures or from other words” Syl On Vo Co

57. I’ve shown you... Behavioral experiments, and A connectionist model with the goal of understanding how language is represented and processed in the brain Next time: Lisa will talk about imaging experiments