LEXICAL INTERFACE 3 OCT 28, 2015 – DAY 26 Brain & Language LING NSCI Fall 2015

Course organization Schedule: topics topics Today's chapter: Fun with 10/26/15Brain & Language - Harry Howard - Tulane University 2

Grades Q1Q2Q3Q4Q5Q6 MIN AVG MAX 10 10/26/15Brain & Language - Harry Howard - Tulane University 3

THE LEXICAL INTERFACE 2 10/26/15Brain & Language - Harry Howard - Tulane University 4

The lexical interface 10/26/15Brain & Language - Harry Howard - Tulane University 5

Areas ~ hubs ~ effects = sensorimotor semantics 10/26/15Brain & Language - Harry Howard - Tulane University 6

Hypotheses 10/26/15Brain & Language - Harry Howard - Tulane University 7 STS phonological net p(MTG+ITS) lexical interface a(MTG+ITS) combinatorial net 1 aIFG combinatorial net 2 STS phonological net action words, tools motor + somato cortex a(MTG+ITS) combinatorial net 1 ??? aIFG combinatorial net 2 ??? imageable words medial temporal gyrus imageable words medial temporal gyrus Hickok & Poeppel, symbolic? Pülvermüller, sensorimotor or embodied

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 10/26/15Brain & Language - Harry Howard - Tulane University 8

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Semantic networks 10/26/15Brain & Language - Harry Howard - Tulane University 10 Ingram goes into great detail on Quillian’s Teachable Language Comprehender (TLC); I could not find an image, but this illustrates the idea just as well.

The linkages in such a network are … semantic … the relationships of meaning mentioned above, such as hyponymy; these are necessary, in the sense that a robin is by definition a kind of bird. 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; 10/26/15Brain & Language - Harry Howard - Tulane University 11

LEXICAL SEMANTICS 3 Ingram: III. Lexical semantics, §10. 10/26/15Brain & Language - Harry Howard - Tulane University 12

‘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 10/26/15Brain & Language - Harry Howard - Tulane University 13

Semantic + associative vs. non-associative prime-probe relations Table 10.4, Moss et al. (1995) Semantic relation Category coordination [taxonomy] Function NaturalArtifactInstrumentalScripted Associated cat – dogboat – shipbow – arrowtheater – play brother – sistercoat – hatumbrella – rainbeach – sand Non- associated aunt – nephewairplane – trainknife – breadparty – music pig – horseblouse – dressstring – parcelzoo – penguin 10/26/15Brain & Language - Harry Howard - Tulane University 14 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

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. 10/26/15Brain & Language - Harry Howard - Tulane University 15

Activation in a semantic network 10/26/15Brain & Language - Harry Howard - Tulane University 16

Semantic feature assignment Table 11.2 manwomanboygirlmarecolt human++++–– female–+–++– mature++––+– 11/01/113Brain & Language - Harry Howard - Tulane University 17 manwomanboygirlmarecolt man woman31210 boy3202 girl311 mare31 colt3 Semantic similarity scores Table 11.3

Features as a network 1 excitation 11/01/113Brain & Language - Harry Howard - Tulane University 18 human female mature man woma n boy girl mare colt Activation of ‘man’ will wind up activating ‘female’, which is a contradiction.

Features as a network 2 excitation, inhibition 11/01/113Brain & Language - Harry Howard - Tulane University 19 human female mature man woma n boy girl mare colt Activation of ‘man’ will still wind up activating ‘female’, but inhibition will now turn it off.

Features as a network 3 excitation, inhibition 11/01/113Brain & Language - Harry Howard - Tulane University 20 human female mature man woma n boy girl mare colt In cortex, long-distance connections are excitatory, while short-distance connections are inhibitory. Activation of ‘man’ will wind up activating ‘female’, but inhibition of ‘woman’ will turn it off.

11/01/113Brain & Language - Harry Howard - Tulane University 21 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

Feature network for animates excitation, mutually reinforcing activation (excitation) 11/01/113Brain & Language - Harry Howard - Tulane University 22 head camel crocodile duck penguin zebra torso legs hump eyes bill stripes

Inanimate vs. animate, side by side Inanimate 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. Animate 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. 11/01/113Brain & Language - Harry Howard - Tulane University 23

Problem Correlated feature theory cannot account for other patterns of impairment, such as cases in which artifacts are more poorly identified than living things. 11/01/113Brain & Language - Harry Howard - Tulane University 24

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. 10/26/15Brain & Language - Harry Howard - Tulane University 25

NEXT TIME More on the lexical interface: word semantics 10/26/15Brain & Language - Harry Howard - Tulane University 26