Epistemic Ambiguity Across
Languages
Lavi Wolf
Ben-Gurion University of the Negev
International Congress of Linguists (ICL19), July 2013,
Geneva
Outline
• A. Epistemic ambiguity.
• B. Ambiguity data in English.
• C. Cross-linguistic data.
• D. Formal account.
• E. Cognitive significance.
2
Epistemic Modality (EM)
• Epistemic auxiliary verbs: can, could, may, might, must.
• Epistemic modality conveys knowledge or belief (usually
the speaker’s but can be different), i.e. epistemic
statements are made in light of what is known.
• (1) Max might be lonely.
• Paraphrase: in light of what is known there is a possibility
that Max is lonely.
3
Theories of EM
• Truth-conditional (Kratzer, 1981; Lewis, 1986): EM are
quantifiers over possible worlds.
• Force-modification (Halliday 1970; Palmer 1986): EM
modify the force by which the speech act is performed.
• Ambiguity (Lyons, 1977): EM are ambiguous between
objective and subjective readings.
4
The subjective-objective
distinction, Lyons 1977
• (2) It might rain tomorrow.
• Two possible readings:
• A. Subjective: The speaker (e.g. a layman) suggests
with a low degree of certainty that it will rain.
• B. Objective: The speaker (e.g. a professional
weatherman) asserts, with certainty, that there is a
possibility that it will rain.
5
Metaphysical modality
• (3) It might rain tomorrow.
• Metaphysical modality (cf. Condoravdi, 2002 ; Kaufmann et
al. 2006) : In light how the world is like, it is metaphysically
possible that it will rain tomorrow.
• Claim: Objective EM are actually metaphysical (Tancredi,
2007; Iatridou, 1990)
Metaphysical vs. Epistemic
Modality
• Metaphysical modal assertions do not depend on any
knowledge but rather on how the world is like. Thus, the
following utterance should be felicitous, but it isn’t:
• (4) a. #It is well known that it will not rain tomorrow but it
might (still) rain.
• This suggests that might tends to be epistemic rather than
metaphysical. Contrast with the modal adjective possible:
• (5) It is well known that it will not rain tomorrow but it’s
(still) possible that it will.
Modifying the subjective-objective
distinction
• EM can be used either expressively or descriptively.
• The descriptive use: EM are part of the propositional
content, i.e. the speaker reports that there is a mutual
belief that something is the case.
• The expressive use: EM modify the speech act, i.e. the
speaker expresses her degree of belief with regards to the
asserted proposition.
8
The expressive connection
• There is a basic linguistic distinction between
descriptive content and expressive content (Kaplan
1999; Potts 2007).
• The class of expressives includes emotive morphemes,
words, and constructions such as the epithets the jerk,
expressive attributive adjectives like damn, honorifics,
some discourse particles, and some uses of diminutive
suffixes.
• Expressive EM pattern with this class.
9
Perspective-dependence
• Expressives:
• (6) a. #That bastard Kresge is famous, but I personally
think that he's a good guy. (Potts 2007)
• Expressive EM:
• b. #Max might be lonely, but I personally think that he
isn't.
• Descriptive EM:
• c. It might rain tomorrow, but I personally think that it
won't.
10
Immediacy
• Expressives:
• (7) a. That bastard Kresge was late for work yesterday. (Potts
2007)
• (= the speaker believes today that Kreske is a bastard)
• Expressive EM:
• b. John might have been lonely yesterday.
• (= the speaker believes today that John was possibly lonely)
• Descriptive EM:
• c. There might have been ice cream in the freezer yesterday.
(von Fintel & Gillies, 2008)
• (= the speaker reports that according to what was known
yesterday there was a possibility for ice cream in the freezer)
11
Epistemic Ambiguity - Data
• Descriptive EM are embeddable, expressive EM are not
(Papafragou, 2006):
• (8) a. ?If Max might be lonely, his wife will be worried.
• b. If it might rain tomorrow people should bring umbrellas.
• (9) a. ?It’s surprising that Superman might be jealous of Lois.
• b. It’s surprising that it might rain tomorrow.
12
Epistemic Ambiguity - Data
• Agreements and disagreements about expressive EM
assertions target the prejacent (Papafragou, 2006) :
• (10) A: The professor must be smart.
• B: That’s not true/I agree
• ≠ It’s not true/the hearer agrees that the professor must
be smart.
• = It’s not true/the hearer agrees that the professor is
smart.
13
Epistemic Ambiguity - Data
• Agreements and disagreements about expressive EM
assertions target the prejacent:
• (11) A: This might be the best movie ever.
• B: That’s not true/I agree.
• ≠ It’s not true/the hearer agrees that the movie might be
good.
• = It’s not true/the hearer agrees that the movie is good.
14
Epistemic Ambiguity - Data
• Agreements and disagreements about descriptive EM
assertions target the whole modalized utterance
(Papafragou, 2006):
• (12) A: (According to the weather forecast) it might rain
tomorrow.
• B: That’s not true/I agree
• = It is not true/the hearer agrees that it might rain.
• ≠ It’s not true/the hearer agrees that it will rain.
15
Epistemic Ambiguity – Scope data
• EM take wide scope over various quantifiers (von Fintel &
Iatridou, 2003):
• (13) #Every candidate might win.
#  >> >> win
• (14) #Every student might be the tallest person in the
department.
#  >> >> tallest
16
Epistemic Ambiguity – Scope data
• EM utterances can have narrow scope when the utterances
are properly modified (Tancredi, 2007):
• (15) Objectively speaking, every candidate might win.
  >>  >> win
• (16) (As is widely known) every person in our fund-raising
events might be the richest person in the country.
  >>  >> richest
17
Cross-linguistic
• English EM are subjective by default (termed doxastic in
Tancredi, 2007), hence expressive.
• In default contexts English EM are subject to the ECP Epistemic Containment Principle (von Fintel & Iatridou,
2003) and take wide scope over various quantifiers:
• (17) #Every student may have left but not every one of
them has.
• Forced (and infelicitous) reading:  >> 
18
Cross-linguistic
• Dutch EM are objective by default (Huitink, 2008), hence
descriptive.
• Dutch EM are not subject to the ECP:
• (18) Iedere student kan vertrokken zijn, maar niet iedere student is vertrokken.
•
Every student may left
be but not every student be left
• Every student may have left but not every one of them has.
• Possible (and felicitous) reading:  >> 
19
Cross-linguistic
• Chinese EM are marked for scope by a special particle
DOU (Lin, 2012):
• (19) a. Mei-ge xuesheng keneng dou likai-le
•
Every student may
DOU leave.  >> 
• b. Mei-ge xuesheng dou keneng likai-le.
• Every student DOU may leave.  >> 
• Wide scope Chinese EM are subjective and narrow
scope Chinese EM are objective (Lin, p.c).
20
Cross-linguistic
• Tancredi (2007): Japanese EM kamoshirenai is ambiguous
while moshikashitara is subjective:
•
•
•
•
•
•
•
•
(20) a. #Subete-no gakusei-ga moshikashitara Jonesde aru
Every-GEN student-NOM perhaps JonesCOP
Every student is perhaps Jones
 >> 
b. (Kyakkanteki-nimite) Subete-no gakusei-ga Jones de aru kamoshirenai
Objectively looking Every-GEN student-NOM Jones COP may
(Objectively speaking) Every student may be Jones
 >> 
21
Cross-linguistic
• Hebrew EM asuy is ambiguous while ulay is expressive:
• (21) a. #Kol student ulay ya’azov aval lo kulam ya’azvoo
•
Every student may leave but not all students leave
• infelicitous
• b. Kol student asuy la’azov aval lo kulam ya’azvoo
•
Every student may leave but not all students leave
•  >> 
22
Formal account
• An assertion is composed of propositional content and
sincerity condition, i.e. the degree of belief by which
the assertion is made (cf. Vanderveken, 1990).
• Assertion operator: A(C,S)
• C: propositional content of the asserted utterance.
• S: degree of strength by which the act of assertion is
made.
23
Formal account
• Halpern’s (1990) logic of probability.
• Probability structure <D,W,π,f> : D is a domain, W is a set of
possible worlds, π is a valuation function and f is a discrete
probability function on W.
• Distinguished propositional function P(): the probability of 
• For any proposition , a set of worlds W , model M, world w
and assignment function g:
• (22) [|P()|]M,w,g = f ( {wW | (M,w,g) |= )
24
Non-modalized assertion
• (23) John is lonely.
• a. A[ lonely(j), P(lonely(j)) ≥ high]
• Propositional content: John is lonely.
• Degree of belief: equal to or greater than ‘high’ (by a
standard norm of assertion), when ‘high’ corresponds
to some contextually-determined numerical value .
25
Descriptive modality
• (24) Max might be a thief.
• A[ P(thief(m))>0, P ( P(thief(m))>0) ≥high]
• Propositional content: There is a greater than 0 chance
(i.e. it is possible) that Max is a thief.
• Degree of belief: equal to or greater than ‘high’.
• The descriptive use of the modal does not modify the
speech act, but rather the propositional content.
26
Expressive modality
• (25) John might be lonely.
• A[ lonely(j), P(lonely(j)) > 0]
• Propositional content: John is lonely.
• Degree of belief: greater than 0 (i.e. presented with a low
degree of certainty).
• The modal is not part of the propositional content.
• Note that the expressive and descriptive EM are lexically
the same, i.e. the same probability degree. The difference
is in the scope of the modal.
27
Explaining embeddability
• Expressive EM are hard to embed because they are not part
of the propositional content but rather modify the speech
act, and speech act modifiers are hard (albeit not
impossible – cf. Krifka, 2011 ; Cohen & Krifka, 2011, ) to
embed.
28
Explaining agreements and
disagreements
• Since descriptive EM are propositional, agreements and
disagreements about utterances containing them, contain
them.
• Since expressive EM are non-propositional, agreements and
disagreements about utterances containing them, do not
contain them.
29
Explaining scope
• Expressive EM take scope over the whole speech act and
are syntactically located in the left periphery (cf. Wolf &
Spector-Shirtz, 2012) at ForceP, therefore have only wide
scope when interacting with other quantifiers.
• Descriptive EM take scope within the proposition and are
syntactically located above TP (cf. Hacquard, 2010; Wolf &
Spector-Shirtz, 2012), therefore can have narrow scope
when interacting with other quantifiers.
30
Cognitive significance
• When a speaker performs an assertion, she asserts some
propositional content with some degree of strength.
• The hearer takes this degree of strength into
consideration, together with an estimate of how reliable
she considers the speaker to be.
• These factor combine with other sources of evidence
(direct, inferential, hearsay) that pertain to the
proposition.
31
Cognitive significance
• Each source of evidence 1in is assigned a weight, wi,
indicating how reliable this source is (sum of all weights is 1).
• A mixture model takes all of these considerations and
produces a final probability value:
• (26)
• If the final probability value surpasses some threshold of
acceptance, the hearer will accept .
32
Cognitive significance
• Speakers may use EM to convey descriptive information
about the state of knowledge in the world, i.e. in light of
what is commonly known/believed, it is possible that .
• Speakers may also use EM to expressively convey the
degree by which they participate in the hearer’s mixture
model, via subjective belief, as sources of evidence for
the truth of .
• Thus, when a speaker performs a descriptive EM
assertion she makes a claim about possibilities, while
expressive EM assertions mark the speaker as a source of
evidence for a claim about realities.
33
Thank you
References
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