1. WORD SEMANTICS 3 DAY 28 – NOV 1, 2013 Brain & Language LING 4110-4890-5110-7960 NSCI 4110-4891-6110 Harry Howard Tulane University

2. Course organization The syllabus, these slides and my recordings are available at http://www.tulane.edu/~howard/LING4110/.http://www.tulane.edu/~howard/LING4110/ If you want to learn more about EEG and neurolinguistics, you are welcome to participate in my lab. This is also a good way to get started on an honor's thesis. The grades are posted to Blackboard. 11/01/113Brain & Language - Harry Howard - Tulane University 2

3. REVIEW 11/01/113Brain & Language - Harry Howard - Tulane University 3

4. Linguistic model, Fig. 2.1 p. 37 11/01/113Brain & Language - Harry Howard - Tulane University 4 Discourse model Syntax Sentence prosody Morphology Word prosody Segmental phonology perception Segmental phonology perception Acoustic phonetics Feature extraction Segmental phonology production Segmental phonology production Articulatory phonetics Speech motor control INPUT Sentence level Word level

5. Hierarchy of categories furniture | chair | bench domain-level | basic/prototype | subordinate 11/01/113Brain & Language - Harry Howard - Tulane University 5

6. Basic is special 1. Response Times: in which queries involving a prototypical members (e.g. is a robin a bird) elicited faster response times than for non-prototypical members. 2. Priming: When primed with the higher-level (superordinate) category, subjects were faster in identifying if two words are the same. Thus, after flashing furniture, the equivalence of chair-chair is detected more rapidly than stove-stove. 3. Exemplars: When asked to name a few exemplars, the more prototypical items came up more frequently. 11/01/113Brain & Language - Harry Howard - Tulane University 6

7. Basic is really special 1. It is the highest level at which a single mental image can represent the entire category (you can’t get a mental image of vehicle or furniture). 2. It is the highest level at which category members have a similarly perceived overall shape. 3. It is the highest level at which a person uses similar motor actions for interacting with category members. 4. It is the level at which most of our knowledge is organized. 11/01/113Brain & Language - Harry Howard - Tulane University 7

8. 11/01/113Brain & Language - Harry Howard - Tulane University 8 Early and late vision early vision is beneath the surface; late vision is on it

9. 11/01/113Brain & Language - Harry Howard - Tulane University 9 Norman (2002) Constructivist approach ventral ~ identification the stimulation is inherently insufficient, necessitating an “intelligent” perceptual system that relies on inference perception is indirect/multistage process between stimulation and percept memory, stored schemata, and past experience play an important role in perception excels at analyzing the processes and mechanisms underlying perception Ecological approach dorsal ~ visual control of motor behavior the information in the ambient environment suffices and is not equivocal, and thus, no “mental processes” are needed to enable the pick-up of relevant information perception is direct/single- stage process no role for memory or related phenomena excels at the analysis of the stimulation reaching the observer affordances

10. LEXICAL SEMANTICS 3 Visual object identification 11/01/113Brain & Language - Harry Howard - Tulane University 10

11. CATEGORY-SPECIFIC DEFICITS 11/01/113Brain & Language - Harry Howard - Tulane University 11

12. 11/01/113Brain & Language - Harry Howard - Tulane University 12 Do you see any difference between (a) and (b)?

13. Category-specific semantic impairments Figure 11.1 11/01/113Brain & Language - Harry Howard - Tulane University 13 concrete (picture-able) animateinanimate animalsplants wild | lion shark domestic | cat goldfish inedible | tree flower fruit & veggie | apple banana ??toolstransport processed food | pizza cider musical instrument | piano drum hammer pencil car bike abstract belief shame 3:1

14. THE ANATOMY OF OBJECT PROCESSING: THE ROLE OF ANTEROMEDIAL TEMPORAL CORTEX Bright, Moss, Stamatakis & Tyler (2005) 11/01/113Brain & Language - Harry Howard - Tulane University 14

15. Semantic feature assignment Table 11.2 manwomanboygirlmarecolt human++++–– female–+–++– mature++––+– 11/01/113Brain & Language - Harry Howard - Tulane University 15 manwomanboygirlmarecolt man322111 woman31210 boy3202 girl311 mare31 colt3 Semantic similarity scores Table 11.3

16. Features as a network 1 excitation 11/01/113Brain & Language - Harry Howard - Tulane University 16 human female mature man woma n boy girl mare colt Activation of ‘man’ will wind up activating ‘female’, which is a contradiction.

17. Features as a network 2 excitation, inhibition 11/01/113Brain & Language - Harry Howard - Tulane University 17 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.

18. Features as a network 3 excitation, inhibition 11/01/113Brain & Language - Harry Howard - Tulane University 18 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.

19. 11/01/113Brain & Language - Harry Howard - Tulane University 19 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 the CSA proposes that category-specific deficits develop from damage to a unitary, distributed semantic system, not from damage to anatomically distinct, content-specific stores

20. Feature network for animates excitation, mutually reinforcing activation (excitation) 11/01/113Brain & Language - Harry Howard - Tulane University 20 head camel crocodile duck penguin zebra torso legs hump eyes bill stripes

21. 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 21

22. 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 22

23. Sensory/functional theory Knowledge of objects organized into: networks of sensory features: form, motion, color, taste, etc., and networks of functional features: how, when, and where the object is typically used. A CSSD arises when one of these networks is disrupted animates are mostly comprised of sensory features; inanimates are mostly comprised of functional features. (??) 11/01/113Brain & Language - Harry Howard - Tulane University 23

24. Domain-dependent hypothesis The brain has evolved dedicated neural machinery for recognizing and responding to certain categories of objects that have high survival significance: face recognition, predator detection, food identification These categories are recognized on the basis of prototypes: A given exemplar (instance) is matched to the best prototype. They are modular, but not necessary localized to a single region (i.e. they could be distributed networks). 11/01/113Brain & Language - Harry Howard - Tulane University 24

25. Example of patient FAV Read about him/her and the explanation(s) for his/her deficit, Ingram pp. 235-8. 11/01/113Brain & Language - Harry Howard - Tulane University 25

26. NEXT TIME Q8 More word semantics 11/01/113Brain & Language - Harry Howard - Tulane University 26