Abstract Neuron

1. o u t p u t y { 1 if net > 0 0 otherwise w0 i0=1 w1 w2 wn . . . i1 i2 in i n p u t i Abstract Neuron

2. Link to Vision: The Necker Cube

4. Constrained Best Fit in Nature inanimate animate

5. Triangle Nodes: Encoding relational information with abstract neurons • The triangle node (aka 2/3 node) is a useful function that activates its outputs (3) if any (2) of its 3 inputs are active • Such a node will be useful for lots of representations.

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

7. Basic Ideas • 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

8. 5levels of Neural Theory of Language Pyscholinguistic experiments Spatial Relation Motor Control Metaphor Grammar Cognition and Language Computation Structured Connectionism abstraction Neural Net SHRUTI Computational Neurobiology Triangle Nodes Biology Neural Development Quiz Midterm Finals

9. Behavioral Experiments • Identity – Mental activity is Structured Neural Activity • Spreading Activation— Psychological model/theory behind priming and interference experiments • Simulation — Necessary for meaningfulness and contextual inference • Parameters — Govern simulation, strict inference, link to language

10. Bottom-up vs. Top-down Processes • Bottom-up: When processing is driven by the stimulus • Top-down: When knowledge and context are used to assist and drive processing • Interaction: The stimulus is the basis of processing but almost immediately top-down processes are initiated

11. Stroop Effect • Interference between form and meaning

12. Name the words BookCarTableBoxTrashManBed CornSitPaperCoin Glass HouseJar KeyRugCatDoll Letter BabyTomato CheckPhone Soda DishLampWoman

13. Name the print color of the words BlueGreenRed YellowOrangeBlackRed PurpleGreenRedBlueYellowBlackRed GreenWhiteBlueYellow Red BlackBlue WhiteRed Yellow GreenBlackPurple

14. Body-Specificity Hypothesis If concepts and word meanings are constituted, in part, by mental simulations our own perceptions and actions… …then their neurocognitive representations should differ for people with different kinds of bodies, who perceive and act upon the environment in systematically different ways. (Casasanto, in review, link on course page)

15. Testing body-specificity pinch chew Manual Action Non-Manual Action

16. Testing body-specificity Left handed movements for one block of trials Right handed movements for the other block of trials

17. Design • 96 Words (48 manual verbs, 48 non-manual verbs) • 2 blocks (LH movements, RH movements) • 2 groups of Ss (Left handers, Right handers) • Dependent measures: RT & Surprise recognition (Old/New)

18. Move marbles by wordcolor caress jab fling grip pound tap yank erase dial sigh cough growl watch crawl peek tumble dance say Manual Action Non-Manual Action

19. Predictions If action word meanings are constituted, in part, by mental simulation of perceptuo-motor experiences, then: Both online and offline effects of congruity should be found between manual motor actions and the meanings of manual action verbs (but not non-manual action verbs). Right and left handed participants should show opposite effects of using their right and left hands to move marbles during incidental encoding of manual action verbs. (Casasanto, in review)

20. ns ns Non-Manual Action Verb ReactionTime Results *** *** Marble movement time in ms Manual Action Verb (Casasanto, in review)

21. Congruity effect in ms ABS Laterality Quotient Handedness-ness predicts congruity effect

22. ns ns Non-Manual Action Verb Recognition Memory Results LeftHand 0.80 RightHand 0.75 *** *** 0.70 Proportion correct recognition 0.65 0.60 0.55 0.50 LEFTIES RIGHTIES Manual Action Verb (Casasanto, in review)

23. Procedure for experiment that demonstrates the word-superiority effect. First the word is presented, then the mask XXXX’s, then the letters.

24. Word-Superiority Effect Reicher (1969) • Which condition resulted in faster & more accurate recognition of the letter? • The word condition • Letters are recognized faster when they are part of a word then when they are alone • This rejects the completely bottom-up feature model • Also a challenge for serial processing

25. Connectionist ModelMcClelland & Rumelhart (1981) • Knowledge is distributed and processing occurs in parallel, with both bottom-up and top-down influences • This model can explain the Word-Superiority Effect because it can account for context effects

26. Connectionist Model of Word Recognition

27. Basic Ideas • 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

28. Interaction in language processing: Pragmatic constraints on lexical access Jim Magnuson Columbia University

29. Information integration • A central issue in psycholinguistics and cognitive science: • When/how are such sources integrated? • Two views • Interaction • Use information as soon as it is available • Free flow between levels of representation • Modularity • Protect and optimize levels by encapsulation • Staged serial processing • Reanalyze / appeal to top-down information only when needed

31. Reaction Times in Milliseconds after: “They all rose” 0 delay 200ms. delay

32. Example: Modularity and word recognition • Tanenhaus et al. (1979) [also Swinney, 1979] • Given a homophone likerose, and a context biased towards one sense, when is context integrated? • Spoken sentence primes ending in homophones: • They all rose vs. They bought a rose • Secondary task: name a displayed orthographic word • Probe at offset of ambiguous word: priming for both“stood” and “flower” • 200 ms later: only priming for appropriate sense • Suggests encapsulation followed by rapid integration • But the constraint here is weak -- overestimates modularity? • How could we examine strong constraints in natural contexts?

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

35. Allopenna, Magnuson & Tanenhaus (1998) Eye Eye camera tracking computer Scene camera ‘Pick up the beaker’

36. TRACE predictions Do rhymes compete? • Cohort (Marlsen-Wilson): onset similarity is primary because of the incremental nature of speech (serial/staged; Shortlist/Merge) • Cat activates cap, cast, cattle, camera, etc. • Rhymes won’t compete • NAM (Neighborhood Activation Model; Luce): global similarity is primary • Cat activatesbat, 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

37. Allopenna et al. Results

38. Study 1 Conclusions • As predicted by interactive models, cohorts and rhymes are activated, with different time courses • Eye movement paradigm • More sensitive than conventional paradigms • More naturalistic • Simultaneous measures of multiple items • Transparently linkable to computational model • Time locked to speech at a fine grain

39. Theoretical conclusions • Natural contexts provide strong constraints that are used • When those constraints 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

40. 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

41. Boxes and arrows in the linguistic system Semantics Syntax Lexicon Output Phonology Input Phonology

42. Picture Naming Task Semantics Say: “cat” Syntax Lexicon Output Phonology Input Phonology

43. A 2-step Interactive Model of Lexical Access in Production Semantic Features FOG DOG CAT RAT MAT f r d k m ae o t g Onsets Vowels Codas

44. Step 1 – Lemma Access Activate semantic features of CAT FOG DOG CAT RAT MAT f r d k m ae o t g Onsets Vowels Codas

45. Step 1 – Lemma Access Activation spreads through network FOG DOG CAT RAT MAT f r d k m ae o t g Onsets Vowels Codas

46. Step 1 – Lemma Access Most active word from proper category is selected and linked to syntactic frame NP N FOG DOG CAT RAT MAT f r d k m ae o t g Onsets Vowels Codas

47. Step 2 – Phonological Access Jolt of activation is sent to selected word NP N FOG DOG CAT RAT MAT f r d k m ae o t g Onsets Vowels Codas

48. Step 2 – Phonological Access Activation spreads through network NP N FOG DOG CAT RAT MAT f r d k m ae o t g Onsets Vowels Codas

49. Step 2 – Phonological Access Most activated phonemes are selected FOG DOG CAT RAT MAT Syl On Vo Co f r d k m ae o t g Onsets Vowels Codas

50. Semantic Error – “dog” Shared features activate semantic neighbors NP N FOG DOG CAT RAT MAT f r d k m ae o t g Onsets Vowels Codas