1. Lexical interface 3 Oct 27, 2017 – DAY 25
Brain & Language
LING
NSCI
Harry Howard
Tulane University

2. Brain & Language - Harry Howard - Tulane University
27-Oct-17
Brain & Language - Harry Howard - Tulane University
Course organization
Fun with

3. 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

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

5. 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
identity
of visual object
identity
of spoken object
identity
of visual object

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

7. 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]

8. 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]]

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

10. 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

11. 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

12. Brain & Language - Harry Howard - Tulane University
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Brain & Language - Harry Howard - Tulane University

13. The linkages in such a network are …
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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.

14. 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.

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

16. 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

17. 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

18. 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.

19. Activation in a semantic network
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Brain & Language - Harry Howard - Tulane University
Activation in a semantic network

20. Semantic feature assignment Table 11.2
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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

21. 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

22. 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.

23. 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.

24. 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

25. 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

26. 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.

27. 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.

28. Brain & Language - Harry Howard - Tulane University
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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.

29. 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