2. Reading & Speech Perception

3. Connectionist Approach E.g., Seidenberg and McClelland (1989) and Plaut (1996). Central to these models is the absence of any lexicon. Instead, rely on distributed representations The model has no stored information about words and ‘… knowledge of words is encoded in the connections in the network.’

4. Context Grammar pragmatics Semantics meaning Orthography print Phonology speech Phonological pathway Semantic pathway Connectionist framework for lexical processing, adapted from Seidenberg and McClelland (1989) and Plaut et al (1996).

5. Plaut et al. (1996) Graphemes (input) Hidden units Phonemes (output) /th/ /ih/ /k/ th i ck Orthography print Phonology speech

6. Plaut et al. (1996) Simulations Network learned from 3000 written-spoken word pairs by backpropagation. Performance of the network closely resembled that of adult readers Predictions: –Irregular slower than regular: RT( Pint ) > RT( Pond ) –Frequency effect: RT( Cottage ) > RT( House ) –Consistentency effects for nonwords: RT( MAVE ) > RT( NUST ) –Lesions led to decreases in performance on irregular words, especially low frequency words

7. Deep Dyslexia: example patient Semantic Errors canoe  kayak onion  orange window  shade paper  pencil nail  fingernail ache  Alka Seltzer Visual Errors fear  flag rage  race Nonwords: no response substitution of visually similar word (fank -> bank)

8. Simulations of Deep Dyslexia Semantics meaning Orthography print Phonology speech Plaut and Shallice (1993); Hinton, Plaut and Shallice (1993) Next slide only shows this portion of model

9. Structure of Model Grapheme units: one unit for each letter/position pair Hidden units to allow a non-linear mapping Sememe units: one per feature of the meaning Recurrently connected clean-up units: to capture regularities among sememes Cleanup units: part of a feedback loop that adjusts the sememe output to match the meaning of words precisely Plaut and Shallice (1993); Hinton, Plaut and Shallice (1993)

10. Structure of Model Grapheme units: one unit for each letter/position pair Intermediate units: learning (nonlinear) associations between letters and meaning units Sememe (Meaning) units: representation based on semantic features Cleanup units: part of a feedback loop that adjusts the sememe output to match the meaning of words precisely Plaut and Shallice (1993); Hinton, Plaut and Shallice (1993)

11. What the network learns Learning was done with back-propagation The network created semantic attractors: each word meaning is a point in semantic space and has its own basin of attraction. For a demonstration of attractor networks with visual patterns: http://www.cbu.edu/~pong/ai/hopfield/hopfieldapplet.html http://www.cbu.edu/~pong/ai/hopfield/hopfieldapplet.html Damage to the sememe or clean-up units can change the boundaries of the attractors. This explains semantic errors. Meanings fall into a neighboring attractor.

12. Semantic Space and Effects of Network Damage Activations of meaning units can be represented in high-dimensional semantic space With network damage, regions of attraction change Semantic Errors: “BED”  “COT” Visual Errors: “CAT”  “COT” Plaut and Shallice (1993); Hinton, Plaut and Shallice (1993)

13. SPEECH PERCEPTION & CONTEXT EFFECTS

14. Differences among items that fall into different categories are exaggerated, and differences among items that fall into the same category are minimized. (from Rob Goldstone, Indiana University)

15. Categorization Perceptual Similarity categorical perception

16. Some physical continua are perceived continuously E.g.: Color Pitch Loudness Brightness Angle Weight Etc. Percent “Loud” responses Magnitude of Stimulus (e.g. Loudness) Some are not … Percent responses Magnitude of Stimulus

17. Examples from “LAKE” to “RAKE” –http://www.psych.ufl.edu/~white/Cate_per.htmhttp://www.psych.ufl.edu/~white/Cate_per.htm from /da/ to /ga/ Good /ga/Good /da/ 1 2 3 4 5 6 7 8

18. Identification: Discontinuity at Boundary % of /ga/ response 100% 0% 50% Token 1 2 3 4 5 6 7 8

19. Pairwise discrimination Good /ga/Good /da/ 1 2 3 4 5 6 7 8 Discriminate these pairs (straddle the category boundary) Discriminate these pairs

20. Pairwise Discrimination (same/different) % Correct Discrimination

21. What Happened? 1 2 3 4 5 6 7 8 Physical World Perceptual Representation

22. Categorical Perception Identification influences discrimination This an example of how high level cognitive processes (i.e., categorization) can influence perceptual processes

23. Lexical Identification Shift Ganong (1980) J. Exp. Psych: HPP 6, 110-125 Identification experiment VOT continuum word at one end, non-word at the other  Bias to interpret sounds as words nonword-word: dask-task word-nonword: dash-tash short VOT (d) long VOT (t) % /d/ 100 0

24. Phonemic restoration If a speech sound is replaced by a noise (a cough or a buzz), then listeners think they have heard the speech sound anyway. Furthermore, they cannot tell exactly where the noise was in the utterance. For instance: Auditory presentation Perception Legislature legislature Legi_laturelegi lature Legi*lature legisture It was found that the *eel was on the axle. wheel It was found that the *eel was on the shoe. heel It was found that the *eel was on the orange. peel It was found that the *eel was on the table. meal Warren, R. M. (1970). Perceptual restorations of missing speech sounds. Science, 167, 392-393.

25. Phoneme monitoring (PM) Subjects hear words, and have to press a button as soon as they hear a pre-specified target phoneme. Easy form: the target phoneme is always in the same position; Difficult form: the target phoneme can occur anywhere in the words. Phoneme monitoring is faster in high frequency words than in low frequency words or in nonwords in the easy form. This suggests that there is top-down influence.  there are two ways in which we identify phonemes, either via top-down information or via bottom-up information.

26. TRACE model Similar to interactive activation model but applied to speech recognition Connections between levels are bi-directional and excitatory  top-down effects Connections within levels are inhibitory producing competition between alternatives

27. TRACE model Phonemes activate word candidates. Candidates compete with each other Winner completes missing phoneme information

28. TRACE model Phonemes are processed one at a time System activates candidate words that are consistent with current information Candidates compete with each other Winner is selected and competitors are inhibited

29. Effect of Word Frequency on Eye Fixations X bench bed bell lobster “Pick up the bench” (Dahan, Magnuson, & Tanenhaus, 2001) More fixations are directed to high- frequency related distractor than low- frequency distractor Pictures of these objects = bench = bed = bell = lobster