Lexical interface 3 Oct 27, 2017 – DAY 25 Brain & Language LING 4110-4890-5110-7960 NSCI 4110-4891-6110 Harry Howard Tulane University

Brain & Language - Harry Howard - Tulane University 27-Oct-17 Brain & Language - Harry Howard - Tulane University Course organization http://www.tulane.edu/~h0Ward/BrLg/ Fun with https://www.facebook.com/BrLg17/

Brain & Language - Harry Howard - Tulane University 27-Oct-17 Brain & Language - Harry Howard - Tulane University Quiz stats P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 tot MIN 4 5 6 AVG 7.7 8.2 8.4 9.1 MAX 10

Brain & Language - Harry Howard - Tulane University 27-Oct-17 Brain & Language - Harry Howard - Tulane University ThE lexical interface 2

Brain & Language - Harry Howard - Tulane University 27-Oct-17 Brain & Language - Harry Howard - Tulane University Dual pathways location in 'articulatory space' of spoken object location in space of auditory object location in space of visual object https://en.wikipedia.org/wiki/Two-streams_hypothesis#/media/File:Ventral-dorsal_streams.svg identity of visual object identity of spoken object identity of visual object

The canonical representation of the ventral pathway 27-Oct-17 Brain & Language - Harry Howard - Tulane University The canonical representation of the ventral pathway

The ventral pathway: comprehension in terms of linguistic notation 27-Oct-17 Brain & Language - Harry Howard - Tulane University The ventral pathway: comprehension in terms of linguistic notation [m-a-i̯-k-æ-t] [mai̯] [kæt] t11-HickokPoeppelHoward2 [NP [Poss mai̯] [N kæt]] ⟦mai̯ kæt⟧ ⟦mai̯⟧ ⟦kæt⟧ [ma̯ikæt]

Brain & Language - Harry Howard - Tulane University 27-Oct-17 Brain & Language - Harry Howard - Tulane University The process as a list ear auditory cortex STS lexical interface combinatorial net 1 combinatorial net 2 [mai̯kæt] [m][a][i̯][k][æ][t] [mai̯] [kæt] ⟦mai̯⟧ ⟦kæt⟧ ⟦⟦mai̯⟧ ⟦kæt⟧⟧ [NP [Poss mai̯] [N kæt]]

Areas ~ hubs ~ effects = sensorimotor semantics 27-Oct-17 Brain & Language - Harry Howard - Tulane University Areas ~ hubs ~ effects = sensorimotor semantics

Brain & Language - Harry Howard - Tulane University 27-Oct-17 Brain & Language - Harry Howard - Tulane University Hypotheses Hickok & Poeppel, symbolic STS phonological net p(MTG+ITS) lexical interface a(MTG+ITS) combinatorial net 1 aIFG combinatorial net 2 Pülvermüller, sensorimotor or embodied action & abstract words, iFC action words & tools, motor-somatosensory STS phonological net a(MTG+ITS) combinatorial net 1 ??? aIFG combinatorial net 2 ??? prepositions & number words, iPC

Some semantic relations 27-Oct-17 Brain & Language - Harry Howard - Tulane University Some semantic relations synonymy words share the same meaning: violin ~ fiddle antonymy words have opposite meanings: long ~ short hypernymy one word ‘contains’ the meaning of another in a taxonomy: animal ~ horse hyponymy one word is ‘contained’ in the meaning of another in a taxonomy: horse ~ animal holonymy one word is a whole for the meaning of another: hand ~ finger meronymy one word is a part for the meaning of another: finger ~ hand metonymy a part of a concept stands for the whole concept: Hollywood ~ American movie industry polysemy multiple meanings

Brain & Language - Harry Howard - Tulane University 27-Oct-17 Brain & Language - Harry Howard - Tulane University

The linkages in such a network are … 27-Oct-17 Brain & Language - Harry Howard - Tulane University The linkages in such a network are … semantic … the relationships of meaning mentioned above, such as hyponymy; these are necessary, in the sense that bacon is by definition a part of a pig. or associative … established by the fact that certain words are often used together, such as pig and farm; these are ‘accidental’, in the sense that there is nothing in the meaning of pig that requires one to be associated with farms; they are often defined in a free association test, by giving a subject the prime word and asking her to say the first word that comes mind.

Brain & Language - Harry Howard - Tulane University 27-Oct-17 Brain & Language - Harry Howard - Tulane University Semantic networks Quillian’s Teachable Language Comprehender (TLC); I could not find an image, but this illustrates the idea just as well. http://lobo.sbc.edu/Images%20for%20webpage/Semanticnetwork.jpg

Brain & Language - Harry Howard - Tulane University 27-Oct-17 Brain & Language - Harry Howard - Tulane University Lexical semantics 3 Ingram: III. Lexical semantics, §10.

Brain & Language - Harry Howard - Tulane University 27-Oct-17 Brain & Language - Harry Howard - Tulane University ‘To prime the pump’ ‘The facilitatory effect that presentation of an item can have on the response to a subsequent item’ usually measured in terms of reaction time http://explorepahistory.com/displayimage.php?imgId=6808

Category coordination 27-Oct-17 Brain & Language - Harry Howard - Tulane University Semantic + associative vs. non-associative prime-probe relations Table 10.4, Moss et al. (1995) Semantic relation Category coordination [taxonomy] Function Natural Artifact Instrumental Scripted Associated cat – dog boat – ship bow – arrow theater – play brother – sister coat – hat umbrella – rain beach – sand Non-associated aunt – nephew airplane – train knife – bread party – music pig – horse blouse – dress string – parcel zoo – penguin Increased priming with respect to control condition in which there is no relationship between prime and probe: unrelated (control, not shown) < semantic + non-associative < semantic + associative

Brain & Language - Harry Howard - Tulane University 27-Oct-17 Brain & Language - Harry Howard - Tulane University Leftovers The modality of presentation has a large influence. Auditory priming fades much more quickly than visual priming. Priming has shown that multiple word meanings are activated before a word is actually recognized. This reminds me of the TRACE model, but semantic networks work like TRACE.

Activation in a semantic network 27-Oct-17 Brain & Language - Harry Howard - Tulane University Activation in a semantic network http://lobo.sbc.edu/Images%20for%20webpage/Semanticnetwork.jpg

Semantic feature assignment Table 11.2 27-Oct-17 Brain & Language - Harry Howard - Tulane University Semantic feature assignment Table 11.2 man woman boy girl mare colt human + – female mature Semantic similarity scores Table 11.3 man woman boy girl mare colt 3 2 1

Features as a network 1 excitation 27-Oct-17 Brain & Language - Harry Howard - Tulane University Features as a network 1 excitation woman man boy human female mature colt girl Activation of ‘man’ will wind up activating ‘female’, which is a contradiction. mare

Features as a network 2 excitation, inhibition 27-Oct-17 Brain & Language - Harry Howard - Tulane University Features as a network 2 excitation, inhibition woman man boy human female mature colt girl mare Activation of ‘man’ will still wind up activating ‘female’, but inhibition will now turn it off.

Features as a network 3 excitation, inhibition 27-Oct-17 Brain & Language - Harry Howard - Tulane University In cortex, long-distance connections are excitatory, while short-distance connections are inhibitory. Features as a network 3 excitation, inhibition woman man boy human female mature colt girl mare Activation of ‘man’ will wind up activating ‘female’, but inhibition of ‘woman’ will turn it off.

Correlated feature theory 27-Oct-17 Brain & Language - Harry Howard - Tulane University Correlated feature theory The way we go from feature representation to neural organization is by hypothesizing that correlation among the features of an object leads to mutually reinforcing activation (co-activation) in the features' neural representation shared properties are inter-correlated and so become strongly activated and less susceptible to damage, distinctive properties are weakly correlated and so become weakly activated and more susceptible to damage. Performance depends on task If the task requires access to the distinctive features of an object, then a deficit for animates will emerge, due to the lesser degree of correlation among their distinctive features. So CFT proposes that category-specific deficits develop from damage to a unitary, distributed semantic system, not from damage to anatomically distinct, content-specific stores

Brain & Language - Harry Howard - Tulane University 27-Oct-17 Brain & Language - Harry Howard - Tulane University Feature network for animates excitation, mutually reinforcing activation (excitation) camel crocodile head hump eyes zebra bill torso stripes legs duck penguin

Brain & Language - Harry Howard - Tulane University 27-Oct-17 Brain & Language - Harry Howard - Tulane University Animate vs. inanimate Animate Inanimate many overlapping and inter-correlated features (legs, eyes, teeth), few distinctive features (mane, hump, pouch), and they are only weakly correlated with one another. ∴ animate concepts are easy to confuse with one another. few overlapping and inter-correlated features, relatively more distinctive features, and they tend to be more strongly correlated with one another. ∴ inanimate concepts are less easy to confuse with one another.

Brain & Language - Harry Howard - Tulane University 27-Oct-17 Brain & Language - Harry Howard - Tulane University Problem Correlated feature theory cannot account for other patterns of impairment, such as cases in which artifacts are more poorly identified than living things.

Brain & Language - Harry Howard - Tulane University 27-Oct-17 Brain & Language - Harry Howard - Tulane University Final project Improve a Wikipedia article about any of the topics mentioned in class or any other topic broadly related to neurolinguistics. Write a short essay explaining what you did and why you did it. Print the article before you improve it, highlighting any subtractions. Print the article after you improve it, highlighting your additions.

Brain & Language - Harry Howard - Tulane University 27-Oct-17 Brain & Language - Harry Howard - Tulane University NEXT TIME P6 More on the lexical interface: word semantics