Արևաճաճանչաերկրափայլատակություն ηλεκτροεγκεφαλογράφημα Bundespräsidentenstichwahlwiederholungsverschiebung Morteza Ansarinia Institute for Cognitive Science Studies December 15, 2016 Word Recognition نارﺎﻤﺷراﻮﺨﻧﺎﯾاﺮﮕﯾروﺎﺑﺎﻨﺘﺴﻴﻨﻧادﺰﻳ
Model - Letters in Time and Retinotopic Space - Bayesian Reader Model - Word2vec - Megastudies - Evidence from Neuroscience ✦ Bilingual Word Recognition - Influencing Factors and Interactions - In Isolation, and in Sentence Context ✦ MEG/EEG Studies 2
that underlie the rapid and almost effortless comprehension of words in reading, how we acquire words, and respective impairments. ✦ Symbolic (Modular, or box and arrow) versus connectionist. ✦ Recognition speed is related to the type of text, attention, and reader’s skill. ✦ Perceptual span: -4 to 15 letters away from fixation point. ✦ Dual-route theory: rule-governed words and exceptions. 3
learned codes) but processes words of varying lengths and simulate masked priming. ✦ Spatial coding: order of letters is represented by an activation gradient over letter positions. ✦ Superposition matching rule is relatively insensitive to exactly where words begin in the input, and tolerates minor changes in the relative position of letters (superposition). 6 Letter order in STOP Norris (2013)
identity and order accumulates stochastically over time. ✦ Developed to account for perceptual identification and masked priming. ✦ No specific assumptions about the precise form of representations (words, bigrams, trigrams, letters). ✦ Open Bigrams: WO, WR, WD, OR, … for WORD. ✦ JU*GE primes JUDGE, but not JUDPE. 7
by assuming that readers make near-optimal decisions based on the accumulation of noisy evidence. ✦ Identifies word based on fewest number of samples. ✦ Letters are represented as vectors describing coordinates in a multidimensional space. The dimensions could be considered to correspond to letter features and positional information. ✦ Models calculate P(word|evidence) by P(word) and P(evidence|word). A word can be identified when P(w|e) exceeds some predetermined threshold. 8
as input and features vectors for words according to that corpus. ✦ Its purpose is to group vectors of similar words in a 500-dimensional vectorspace. ✦ Results: - king:queen::man:woman - Trump:Republican::Obama:Democratic - monkey:human::dinosaur:fossil - knee:leg::elbow:forearm 9
to phonology: one is via meaning, the other via hidden units. Information is represented in distributed patterns of activation over groups of processing units. ✦ CDP Model: has two non-semantic routes, a sublexical assembly route (2-layer net) and a lexical route (3-layer). ✦ LEX Model: to retrieve from semantic memory, it consists of only a single routine, and three components: letter identification, retrieval, and response generation. ✦ DRC Model: two non-semantic routes: lexical and non-lexical. Non-lexical route converts grapheme to phoneme by a set of rules. 11 Roberts et al. (2003)
word recognition IA IA PI Word-superiority effect Multiple read-out IA PI, LD Word-superiority effect SCM IA LD, MP Letter order BR Math/comp LD, MP Word frequency, letter order, RT distribution LTRS Math/comp MP, PI Letter order Overlap Math/comp PI Letter order Diffusion model Math/comp LD RT distribution, word frequency SERIOL Math/comp LD, MP Letter order Models of reading aloud CDP++ Localist/symbolic RA Reading aloud DRC IA RA, LD Reading aloud Triangle Distributed connectionist RA Reading aloud Sequence encoder Distributed connectionist RA Reading aloud Junction model Distributed connectionist RA Reading aloud Models of eye-movement control in reading E-Z reader Symbolic R Eye movements SWIFT Symbolic R Eye movements Model of morphology Amorphous discriminative learning [16] Symbolic network Self-paced reading, LD Morphology Norris (2013)
the word from perceptually similar words (lexical neighbors). ✦ Lexical Competition: perceptually similar words must compete with each other for recognition. ✦ Coltheart’s N: words of equal lengths are neighbors and distance is the number of substitutions. ✦ Levenshtein Distance: number of edits (insertion, deletion, and substitution). ✦ OLD20: average edit distance of the 20 nearest neighbors. 13
words in English, Dutch, French, and British English (e.g. naming, lexical decisions, and eye movements). ✦ Experiments: word frequency, regularity, feedforward consistency, age of acquisition, polysemy, and facilitatory effect of neighborhood density. ✦ 61% of variance can be described by frequency, letter and syllable length, neighborhood density and spelling-to-sound consistency. ✦ 0.7 correlation between megastudies and earlier small-scale human studies (Current models correlation is 0.6). ✦ Most powerful determinant of lexical decision and naming speed is logarithm of the word’s frequency of occurrence in the language. 14
methods with milliseconds resolution; reveal temporal order of neural processes and continuous measure of the intermediate events. ✦ LL N150/N170: differentiate words and pseudowords from other orthographic stimuli (e.g. symbols). - In left inferior occipitotemporal cortex (VWFA). - Response to the frequency of letter combinations, and lexical/phonological effects come into play much later. ✦ N250: modulated by orthographic similarity and lexical factors (letter identity and constant/vowel). 15 Carreiras et al. (2014)
are activated when reading in one of them. Lexical representation from both languages are activated in parallel. ✦ Cognate facilitation effect: bilinguals respond more quickly to cognates than non-cognates. ✦ L2 cognate words also activate L1 lexical representations (with similar semantic). 16
level of activation for both languages. ✦ Age of acquisition improves the strength of connections between two languages. ✦ Form and meaning overlaps increase activation. 17
the number of possible word candidates, and consequently eliminate cross-language competition and restrict parallel activation via top-down influence. ✦ Context in VWP greatly reduces fixations to between-language cognates. ✦ Semantically incongruent words enhance N400. ✦ Initially congruent, but semantically incongruent words delay N400. ✦ Bidirectional feedbacks from L1 and L2 in BIA+ model take context into consideration. 19 BLINCS Model Gaskell & Mirkovic (2017)
and meaning in L1 and L2. ✦ Phonological Similarity means similar sound of words in L1 and L2. ✦ Both ambiguities (ˢ) increase non-target language activation. ✦ Homographs and homophones share visual/auditory form, but do not share meaning (bottom-up ambiguity). ✦ Homographs cause longer reaction time in L1/L2 lexical decision tasks (bilinguals access both meanings). 20
integrated across languages and is accessed in a language non-selective way. ✦ bilingual word recognition is affected not only by cross-linguistic orthographic similarity effects, but also by cross-linguistic phonological and semantic overlap.
make words compete with their neighbors, and recognition result is distributed over a network of nodes. ✦ Frequency of a word is by far the most important factor to recognize it. ✦ In bilinguals, both languages are activated simultaneously, but proficiency and age of acquisition affects activation level. ✦ Semantic restricts L1/L2 interaction (via top-down control), but does not stop language-non-selective access. 22 Thank You :-)