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Reading & Speech Perception
Connectionist Approach • E. g. , Seidenberg and Mc. Clelland (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. ’
Context Grammar pragmatics Semantics meaning Orthography print Semantic pathway Phonological pathway speech Connectionist framework for lexical processing, adapted from Seidenberg and Mc. Clelland (1989) and Plaut et al (1996).
Plaut et al. (1996) Graphemes (input) th i Orthography print ck Hidden units Phonemes (output) Phonology speech /th/ /ih/ /k/
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
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)
Simulations of Deep Dyslexia Semantics meaning Orthography print Next slide only shows this portion of model Plaut and Shallice (1993); Hinton, Plaut and Shallice (1993) Phonology speech
Structure of Model Recurrently connected clean-up units: to capture regularities among sememes Sememe units: one per feature of the meaning Hidden units to allow a non-linear mapping Grapheme units: one unit for each letter/position pair Plaut and Shallice (1993); Hinton, Plaut and Shallice (1993) Cleanup units: part of a feedback loop that adjusts the sememe output to match the meaning of words precisely
Structure of Model • • Intermediate units: learning (nonlinear) associations between letters and meaning units • Sememe (Meaning) units: representation based on semantic features • Plaut and Shallice (1993); Hinton, Plaut and Shallice (1993) Grapheme units: one unit for each letter/position pair Cleanup units: part of a feedback loop that adjusts the sememe output to match the meaning of words precisely
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/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.
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)
SPEECH PERCEPTION & CONTEXT EFFECTS
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)
Categorization categorical perception Perceptual Similarity
Some are not … Magnitude of Stimulus (e. g. Loudness) Percent responses E. g. : • Color • Pitch • Loudness • Brightness • Angle • Weight • Etc. Percent “Loud” responses Some physical continua are perceived continuously Magnitude of Stimulus
Examples • from “LAKE” to “RAKE” – http: //www. psych. ufl. edu/~white/Cate_per. htm • from /da/ to /ga/ Good /da/ 1 2 Good /ga/ 3 4 5 6 7 8
Identification: Discontinuity at Boundary % of /ga/ response 100% 50% 0% 1 2 3 4 5 Token 6 7 8
Pairwise discrimination Good /da/ 1 Good /ga/ 2 Discriminate these pairs 3 4 5 Discriminate these pairs (straddle the category boundary) 6 7 8 Discriminate these pairs
% Correct Discrimination Pairwise Discrimination (same/different)
What Happened? 1 2 3 4 5 6 Physical World 7 Perceptual Representation 1 2 3 4 5 6 7 8 8
Categorical Perception • Identification influences discrimination • This an example of how high level cognitive processes (i. e. , categorization) can influence perceptual processes
Lexical Identification Shift • 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 100 % /d/ 0 short VOT (d) long VOT (t) Ganong (1980) J. Exp. Psych: HPP 6, 110 -125
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 Legi_lature Legi*lature legislature 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.
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.
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
TRACE model • Phonemes activate word candidates. • Candidates compete with each other • Winner completes missing phoneme information
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
Effect of Word Frequency on Eye Fixations “Pick up the bench” = bench = bed = bell = lobster bench X bell bed Pictures of these objects More fixations are directed to highfrequency related distractor than lowfrequency distractor (Dahan, Magnuson, & Tanenhaus, 2001)