We took viewpoints to be static. But this fails to account for remarkable cases in which, say, a tree classifier moves to represent apparent motion relative to a moving viewpoint. We propose 2 possible refinements of Iconological Semantics to account for such 'traveling shots'.

In earlier work, we proposed a new framework, Iconological Semantics, to account for the interaction between logic and iconicity in sign language classifiers. The 'glue' between logic and iconicity was viewpoint variables, referring to 'camera positions'. ling.auf.net/lingbuzz/007...

We hope that this work will have relevance at the intersection of the philosophy of language, sign language semantics, and the formal analysis of iconicity.

We find that it arguably leaves some facts unaccounted for, and consider an alternative in which Role Shift involves both context shift and viewpoint shift, hence a new take on Super Monsters.

Building on the formal framework of Iconological Semantics, which has an explicit pictorial component (https://
ling.auf.net/lingbuzz/007048), we develop a minimal analysis in which Role Shift only involves overt viewpoint shift.

We argue that a key observation, familiar from informal descriptions, has been missed by formal analyses: in ASL, Role Shift usually shifts the viewpoint ('camera position') relative to which iconic elements are evaluated.

an operation in which the signer visibly adopts someone else's perspective. But Role Shift has special properties that have motivated non-standard operators, called “Super Monsters”, which forces elements within its scope to be interpreted in a maximally iconic fashion.

Such operators were correspondingly called “monsters”. In the last 20 years, cross-linguistic semantic research has uncovered multiple monsters across spoken languages. In addition, some have claimed that context shift is overtly realized in sign language by Role Shift—

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Seminar site: sites.google.com/site/philipp...
Zoom link: us06web.zoom.us/j/8153577927...
Co-organizers: Emmanuel Chemla (Earth Species Project), Lucie Ravaux (Institut Jean-Nicod, CNRS)

Pertinent aussi:
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