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Autistic individuals are commonly said-and also consider themselves-to be excessively literalist, in the sense that they tend to prefer literal interpretations of words and utterances. This literalist bias seems to be fairly specific to autism and still lacks a convincing explanation. In this paper we explore a novel hypothesis that has the potential to account for the literalist bias in autism. We argue that literalism results from an atypical functioning of the predictive system: specifically, an atypical balance between predictions and error signals in language processing may make individuals more uncertain about their own predictions. Such uncertainty is then often resolved by resorting to the safest interpretation, that is, the literal one. We start by reviewing existing explanations of other autistic traits that appeal to predictive processing. We then apply these insights to language, by showing that predictions play a key role in everyday comprehension and that a low level of confidence in one's own predictions is likely to escalate comprehension difficulties. Finally, we take a deeper look at non-literal uses of language by discussing the case of metaphors, to illustrate how a predictive processing account offers a promising explanation of the literalist bias in autism.
Review of Philosophy and Psychology
1 3
Literalism inAutistic People: aPredictive Processing
AgustínVicente1 · ChristianMichel2· ValentinaPetrolini3
Accepted: 28 August 2023
© The Author(s) 2023
Autistic individuals are commonly said – and also consider themselves – to be exces-
sively literalist, in the sense that they tend to prefer literal interpretations of words
and utterances. This literalist bias seems to be fairly specific to autism and still lacks
a convincing explanation. In this paper we explore a novel hypothesis that has the
potential to account for the literalist bias in autism. We argue that literalism results
from an atypical functioning of the predictive system: specifically, an atypical bal-
ance between predictions and error signals in language processing may make indi-
viduals more uncertain about their own predictions. Such uncertainty is then often
resolved by resorting to the safest interpretation, that is, the literal one. We start by
reviewing existing explanations of other autistic traits that appeal to predictive pro-
cessing. We then apply these insights to language, by showing that predictions play
a key role in everyday comprehension and that a low level of confidence in one’s
own predictions is likely to escalate comprehension difficulties. Finally, we take a
deeper look at non-literal uses of language by discussing the case of metaphors, to
illustrate how a predictive processing account offers a promising explanation of the
literalist bias in autism.
* Agustín Vicente
Christian Michel
Valentina Petrolini
1 Ikerbasque/University oftheBasque Country (UPV/EHU), Vitoria-Gasteiz, Spain
2 University ofEdinburgh, Edinburgh, UK
3 University oftheBasque Country (UPV/EHU), Vitoria-Gasteiz, Spain
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A.Vicente et al.
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1 Introduction: Literalism inAutism
What speakers mean by an utterance typically goes beyond the literal meanings of
the words and sentences they use. Inferring what speakers mean in such usual cases
involves pragmatic skills that bridge the gap between linguistic meaning and what is
communicated.1 Difficulties in the domain of such pragmatic skills are considered
a hallmark of autism spectrum conditions (Tager-Flusberg etal. 2005). In particu-
lar, autistic individuals are commonly said to be excessively literalist, in the sense
that they tend to prefer literal interpretations of words and utterances, even when
speakers intend to be understood non-literally. Such literalism applies in principle
to all kinds of implicit meaning, from indirect speech to figurative language, irony,
and sarcasm. That said, it seems easier for autistic individuals not to experience
such literalist bias as strongly in some areas (e.g., conventional indirect speech acts)
compared to others (e.g., irony and sarcasm). While literalism is experienced as an
issue for many autistic people (see this Wrong Planet thread for some first-person
accounts; and Morra 2016 for a more systematic study), results obtained in labora-
tory settings fail to offer a clear-cut picture. For instance, a recent set of meta-reviews
on figurative language in autism confirms the existence of mixed results (Kaland-
adze etal. 2018, 2019). While laboratory results suggest that autistic individuals are
more likely to encounter difficulties in understanding non-literal language than neu-
rotypicals, some argue that such difficulties appear to be related to general linguistic
difficulties, rather than to autistic traits themselves (Gernsbacher and Pripas-Kapit
2012). In particular, since Norbury’s seminal work (Norbury 2005), several authors
relate difficulties with non-literal uses of language in autism to structural language
delays that are also quite common across the spectrum. Andrés-Roqueta and Katsos
(2017), for instance, distinguish between linguistic and social pragmatics. Accord-
ing to such a distinction, autistic people may experience two different kinds of diffi-
culties when comprehending non-literal uses of language: difficulties that arise from
structural language issues, and difficulties related to theory of mind (ToM) issues,
which would mostly impact irony and sarcasm.
Some of us have criticized this view in previous work. The bulk of our criticism
hinges on the fact that many studies test whether autistic individuals understand
some non-literal uses of language tout court (e.g., metaphors), rather than investigat-
ing whether they interpret such non-literal uses literally. When literalism is directly
tested, results are still not uniform, but: (a) the balance tips towards literalism; (b)
the specific literalist difficulties in most non-literal uses of language do not appear
to be related to more general issues with structural language (see Chahboun etal.
2016, 2017; Vulchanova etal. 2012; Walenski and Love 2017 for “literalist” results
on metaphors and idioms; Kasirer and Mashal 2014, 2016, on literalist tenden-
cies remaining after having controlled for language impairment). We discuss these
1 We ignore here complications – long-debated especially in the philosophy of language – about how
exactly to characterize the notion of literal meaning. For present purposes, we take it as uncontroversial
what is a literal and a nonliteral meaning (what is literally/linguistically/ conventionally expressed and
what is meant) in an utterance such as: “Juliet is the sun“.
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Literalism inAutistic People: aPredictive Processing…
results in more detail in Section5.2. For now, our aim is to point out that literalism
appears to be a characteristic feature of autism, as it seems to be exhibited by many
people on the spectrum, in a fairly specific way, and irrespectively of their gram-
matical abilities and breadth of vocabulary.
Several explanations have been proposed to account for the literalist bias in
autism. We have also criticized such explanations in previous publications (Vicente
and Martín-González 2021; Vicente and Falkum 2023). According to our criticism,
the two most widespread explanations – i.e., the ones appealing to executive dys-
functions and to ToM difficulties, respectively – only work if one already assumes
a prior literalist bias. The executive dysfunction explanation (e.g., Mashal and
Kasirer 2011) aims to explain literalism through difficulties in literal meaning inhi-
bition. The ToM explanation (Happé 1993), instead, relates literalism to the argua-
bly diminished mentalizing skills in the autistic population, also assuming that such
skills have to be put to use to derive speakers’ meanings. Both accounts presuppose
literalism because they seem to work only for individuals who have to overcome
a particularly strong activation of literal meanings in the first place. For what we
know, in typical individuals, interpreting a metaphor or an indirect speech act does
not involve inhibiting literal meanings, or reasoning about intentions on the basis of
a literal interpretation. By and large, the process of interpreting a piece of non literal
language is relatively swift and does not involve figuring out the mental states of
speakers (see e.g., Bendtz etal. 2022 for indirect speech; Abbot-Smith etal. 2022,
for implicatures; Wilson and Carston 2006 for metaphors and other figurative uses
of language).
Besides the executive dysfunction and the ToM accounts, two other theories of
literalism have been influential: the local processing view (Happé and Frith 2006)
and the structural language account already mentioned above (Norbury 2005). The
local processing view explains literalism as an effect of global processing issues,
which would make it difficult to integrate the contextual information required
to properly understand non literal uses of language. Yet, while local processing
undoubtedly affects comprehension of narratives, it is unclear whether it should also
be taken to affect the processing of an individual metaphorical sentence, such as:
‘that boy is a turtle’. That is, the interpretation of units of the size of a sentence
do not seem to require much global processing. The structural language hypothesis,
on the other hand, can explain why some autistic individuals experience difficulties
understanding non literal language, but it fails to explain why they would interpret
non literal language literally. Individuals with Developmental Language Disorder
(DLD) have also been found to experience difficulties in the domain of the non lit-
eral, but they do not exhibit the literalist bias observed in autistic individuals (Büh-
ler etal. 2018). More importantly, while DLD children exhibit the difficulties with
non-literal language that are also characteristic of typically developing children of
a similar verbal mental age, such correspondence between non-literal language dif-
ficulties and verbal mental age seems to be lacking in the case of autism (Chahboun
etal. 2017).
In sum, there is reason to believe that the literalist bias typical of autism still lacks
a proper explanation. In our previous work we suggested that literalism could be
seen as an expression of rigidity or inflexibility, a cluster of patterns of behavior
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A.Vicente et al.
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that are typified as characteristic of autism (APA 2022; Petrolini etal. 2023). In this
paper we explore another hypothesis that has the potential to account for the general
rigidity trait just mentioned, as well as for literalism in particular. We explain literal-
ism as one effect of an atypical functioning of the predictive system. We hold that
an atypical balance between predictions and error signals in language processing
may make individuals (more) uncertain about their own predictions, and that such
uncertainty may be resolved by selecting the safest interpretation, i.e., the literal one
(at least in most cases).
The structure of the paper is as follows. We first offer an overview of existing
explanations of autistic traits that appeal to predictive processing (Sections2 and3).
We then move on to applying these insights to language, by showing that predic-
tions play a key role in everyday comprehension and that a low level of confidence
in one’s own predictions is bound to escalate comprehension difficulties (Section4).
Finally, we take a deeper look at non literal uses of language by discussing the cases
of metaphors and implicatures, to illustrate how a predictive processing account
offers a promising explanation of the literalist bias in autism (Sections5 and 6).
2 The Predictive Processing Account ofAutism
Predictive processing holds that the brain, in its effort at building a model of the
environment, constantly makes predictions about what the subject is going to expe-
rience, updating the model according to the input received. Depending on how reli-
able the model has proven to be in the past, predictions are assigned prior probabili-
ties. Imagine driving down a familiar road on a foggy day, with prior experiences
and expectations effectively shaping what you see. Predictions are then organized
in a hierarchical system of layers, such that priors of a specific prediction depend
on the priors of related predictions. Representations higher up in the hierarchy are
general and abstract: they correspond to a larger spatiotemporal scale, and include
beliefs and knowledge about the external world, social norms, cultural background
beliefs, or the awareness of certain stable contextual elements in a conversation.
Representations in the lowest levels, by contrast, are typically modality-specific and
tend to change quite quickly, such as edge patterns in the visual pathway (see, Löhr
and Michel 2023, pp. 5–6 for a graphical representation).
The system also contains an error weighting mechanism, which estimates the
precision of the signals to tune error signals up or down. Such a mechanism can
suppress error signals generated by unreliable input. In the foggy road scenario, for
instance, we tend to rely less on our visual input and to give greater consideration to
prior experiences and expectations. The error weighting mechanism therefore regu-
lates how we should balance expectations and sensory evidence depending on the
Predictive processing (PP henceforth) approaches to autism hold that the basic
difference between a neurotypical and an autistic brain is the weight assigned to
incoming sensory information or experiences with respect to top-down predictions
or priors. The neurotypical brain relies more on its priors, compared to the autis-
tic brain; correspondingly, the autistic brain assigns more weight to sensory data
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Literalism inAutistic People: aPredictive Processing…
compared to the neurotypical brain. As a result, the autistic brain modulates sen-
sory data less than the neurotypical brain, with sensory signals being experienced
more intensely. Pellicano and Burr (2012), probably the first attempt at accounting
for autistic symptoms from a PP approach, used this kind of explanation to account
for hypersensitivity, one of the characteristic features of autism. On their view, such
hypersensitivity is caused by “hypo-priors” – that is, a diminished confidence in
the brain’s own predictions as compared to the neurotypical brain. Following this
intuition, several accounts have been proposed to further identify PP mechanisms
in autism (see Arthur etal. 2022). Van Cruys etal. (2014), for instance, suggest that
autistic behavior is driven by inflexible overweighting of prediction errors. Lawson
etal. (2017) propose that the observed impairments are a consequence of the over-
estimation of the environmental volatility. Despite their differences in emphasis and
details, all these accounts maintain that autistic individuals experience issues with
the stable representation of higher-level priors, which in turn derives from some sort
of imbalance in the weighting of top-down and bottom-up information. This has
recently been dubbed “the imbalance hypothesis” (Chrysaitis and Seriès 2022). In
the next section, we delve deeper into other PP explanations that have been devel-
oped to explain a wider range of autistic features.
3 PP Explanations ofOther Autistic Features
Since Pellicano and Burr’s seminal proposal (2012), PP accounts have offered
explanations of most autistic symptoms, from insistence on sameness to social dif-
ficulties. Palmer etal. (2017) provide a good summary of existing research. As we
mention above, all these strands of research are committed to the view that autistic
brains tend to assign greater weight to incoming sensory information at the expense
of higher-level priors. This insight is then applied to a wide variety of traits that are
regarded as typical in autism. One key case is hypersensitivity to sensory stimu-
lation, a trait that should be expected if prediction errors – i.e., expectation viola-
tions – are weighted more highly (see also Van de Cruys et al. 2019). The same
applies to local processing and detail-oriented processing styles, which are under-
stood as the result of increased attention towards lower-level prediction error sig-
nals (Palmer etal. 2017; Mottron et al. 2006). Cognitive and social autistic traits
– such as restricted and repetitive behaviors, theory of mind difficulties, and many
others – are also amenable to PP explanations (Palmer etal. 2015; Van de Cruys
etal. 2014). In these cases, overestimating the importance of prediction errors argu-
ably prevents autistic individuals from forming higher-level expectations about other
people’s beliefs and behavior, as well as about the environment. Rigid thinking and
behavior, including the reliance on routines and the strict adherence to rules, would
emerge as a response that provides some reassurance in the face of a world filled
with error and uncertainty (Van de Cruys etal. 2014; Lawson etal. 2017).
PP approaches are thus particularly well-suited to account for the need and pref-
erence that autistic people have for a structured, predictable, environment. Such a
preference has been widely reported by studies exploring the challenges faced by
autistic students and teachers in educational settings (McDougal etal. 2020; Wood
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A.Vicente et al.
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and Happé 2021), as well as by first-person accounts and lived experiences of autis-
tic individuals (see for instance Mol 2020). Such a preference towards structure and
stability can also be related to a diminished confidence in priors. If some version
of the imbalance hypothesis is correct, the world would easily get too chaotic for
autistic individuals. Repetition and routines would thus serve the function of simpli-
fying things considerably, either by arranging things so that predictions are success-
ful (i.e., you know more confidently what is coming next), or by having an external
agent or tool – e.g., a planner – boosting confidence in your predictions, thereby
lowering uncertainty. While in some cases the environment gets to be structured by
individuals themselves – e.g., through self-imposed routines – in other cases exter-
nal agents are driving the process – e.g., in educational or intervention settings.
In this respect, the construct of Intolerance of uncertainty (IU) appears to be par-
ticularly interesting to develop a PP account of autism. Intolerance of uncertainty
has been introduced in the context of autism research fairly recently (Boulter etal.
2014; South and Rodgers 2017; Hodgson etal. 2017; Vasa etal. 2018), while it
has been previously investigated as a psychological construct in anxiety, depression,
and obsessive-compulsive disorder (Carleton etal. 2012; Dugas etal. 2001). Studies
investigating IU in non-autistic populations characterize it in terms of “decreased
thresholds for the perception of ambiguity and enhanced discomfort with ambigu-
ity” (Dugas etal. 2001), “negative beliefs about uncertainty and its implications”
(Carleton et al. 2012), or as an “increased tendency to become overwhelmed by
the unexpected and the unknown” (Jenkinson etal. 2020). Although the associa-
tion between IU and anxiety has proven robust in non-autistic populations, it is still
unclear whether these results have been successfully replicated in autistic samples.
This is mostly due to the fact that existing studies are overwhelmingly cross-sec-
tional in nature, and that questionnaire measures of IU – such as Dugas etal. (2004)
– have not yet been validated with autistic individuals (see Jenkinson et al. 2020
for a review). Moreover, although some PP accounts have employed IU with autis-
tic populations (Neil etal. 2016), other proponents have recently cast doubt on its
applicability (Bervoets etal. 2021). For our purposes, it is interesting to briefly dis-
cuss this construct because it has been related to implicit meaning comprehension
in autism (Wilson and Bishop 2021), and also because it has the potential to explain
several challenges that autistic people experience with respect to non-explicit com-
munication in general.
From a PP perspective, we propose that autistic people should display higher
uncertainty instead of being more intolerant of uncertainty than neurotypicals (see
Bervoets etal. 2021 for a similar suggestion). We may distinguish between higher
uncertainty and intolerance of uncertainty as follows. Intolerance of uncertainty
would imply that two individuals A and B hold the same probability assignments,
but display different psychological or emotional reactions. In other words, A toler-
ates the same degree of uncertainty to a lesser degree than B. For example, neither
A nor B know whether they will be able to catch the next flight, but they both know
that it’s highly likely. Yet, A gets more anxious than B. Higher uncertainty, by con-
trast, would imply different probability assignments that generate different psycho-
logical or emotional reactions. On this reading, A and B’s degrees of uncertainty are
different to begin with: for instance, A might assign lower probability to (or be less
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Literalism inAutistic People: aPredictive Processing…
confident about) each of the relevant predictions that compose the belief: “We are
catching the next flight”. If confronted with each of these relevant predictions piece
by piece, A would thus be more likely to say “I don’t know” more often than B.2
In what follows we set out to apply predictive processing frameworks to issues
related to language in autism, by focusing on non-literal uses of language. First, we
draw on some recent proposals that emphasize the role played by prediction in typi-
cal language comprehension (Section4). Then, we flesh out in more detail what we
take this to imply for autistic communication (Section5).
4 Prediction inLanguage
Until recently, classic models of language comprehension have been incremental:
hearers would build a representation of the speaker’s utterance on the basis of the
pieces they were hearing, as they were hearing them. Yet, according to some rela-
tively novel accounts, predictions play a key role in linguistic comprehension. In
a series of publications, Pickering and collaborators (Pickering and Garrod 2013,
2021; Gambi etal. 2015; Pickering and Gambi 2018) have proposed a model of lan-
guage understanding where hearers are constantly issuing predictions about prosody,
syntax, content, and intentions of the speaker. Predictions at these different levels
are constantly and dynamically updated depending on what the speaker is actually
producing. For instance, the hearer may predict a noun coming next, but the speaker
produces a verb, which forces the hearer to revise the syntax she had initially pre-
dicted. Generally speaking, there is robust evidence that hearers anticipate what is
coming next at various levels, even if predictions may sometimes be just about the
word class or about semantic features of the upcoming word.
Although Pickering and Garrod’s account is not explicitly couched within a PP
framework,3 it emphasizes the central role of predictions as well as the idea that
comprehension and production should not be regarded as different systems. Rather,
comprehension occurs through the production system: linguistic predictions are
2 To better understand this point, it is helpful to consider the results of a recent visual search experi-
ment conducted by Allenmark etal. (2021). Two groups of individuals – autistic and neurotypical – were
instructed to look for a target object appearing on a screen at different locations, while a distractor was
appearing with higher or lower probability in different parts of the display. Specifically, the distractor
appeared with 90% probability in one half of the display, and with 10% probability in the other half. Eye-
tracking data show that, upon habituation, both autistic and neurotypical individuals were able to avoid
being distracted by the object appearing in the “frequent region”. Yet, autistic individuals were slower
to identify the target object when this would appear after the distractor was in the “rare region”. In these
situations, autistic individuals were more likely to look at the target, then move on to search elsewhere
to finally return to the target and identify it correctly. This suggests that they initially misidentified the
target as a distractor, and then went on to double-check and make sure before answering, thereby dis-
playing greater uncertainty. By contrast, neurotypical individuals were more likely to gloss over the rare
distractor event and directly go for the target, thereby employing a rough-and-ready heuristics that turned
out to be correct in most circumstances. These results suggest that autistic individuals might overreact to
prediction errors, and take them seriously whenever they occur.
3 Generally speaking, PP accounts adhere to a broader set of commitments beyond the merely predictive
nature of language, such as hierarchical generative models, precision weighting, and so on.
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A.Vicente et al.
1 3
based on covert imitation of the speaker’s production, so that hearers predict what
the speaker is going to say and mean by engaging their own productive system.4 As
it happens in other areas, predictions are deemed to be challenged by errors. Errors
force (or at least encourage) hearers to update their predictions, although many
errors are simply ignored. For example, people understand ‘The mother gave the
candle the daughter’ as meaning the daughter receiving the candle because it is the
content they have predicted (Cai etal. 2022). A substantial body of work on “good
enough” linguistic processing (e.g., Ferreira etal. 2002; Ferreira and Lowder 2016;
Karimi and Ferreira 2016) rejects the picture of careful bottom-up and composi-
tional sentence processing. Rather, as many examples of semantic and grammatical
illusions show, processing tends to be biased towards jumping to overall situational
conclusions. This is the case for so-called “Moses sentences” (“How many animals
took Moses on the arch?”) as well as for semantic illusions (“This book fills a much-
needed gap”).
As these examples show, good-enough processing sits quite comfortably with a
PP apparatus. A successful predictive system for conversation requires a delicate
balance of weight assignments between predictions and incoming signals. Moreover,
predicting what an interlocutor is going to say engages all kinds of levels in the hier-
archy, as such predictions involve the cultural, social and institutional environment,
as well as more specific knowledge about the interlocutor and the situation at hand.
When conversations are not one-on-one, but in a group, difficulties escalate even
further. This suggests that autistic people may encounter general difficulties when
it comes to understanding other people in conversation. To the extent that under-
standing implies predicting, a low level of confidence in one’s own higher-level
predictions will make understanding harder. Figuring out what the speaker wants to
say may be incredibly hard, especially because most neurotypical conversations are
highly open-ended and imprecise.
A linguistic processing style that assigns a lower level of confidence to the pre-
dictions about the interlocutor’s output is likely to engender several consequences
for autistic individuals, especially in terms of language comprehension, and in par-
ticular, when non-literal or non-explicit language is involved. As a consequence,
linguistic comprehension in autistic individuals may be characterized by higher
uncertainty about their own predictions compared to neurotypicals. Given the higher
weight assigned to incoming signals, linguistic input ends up being processed in the
here-and-now, thereby making any violation or imprecision appear more puzzling
and salient (similarly to what happens perceptually, see Allenmark etal. 2021).5 In
4 This is in turn an articulation of the idea of “analysis by synthesis” (Bever and Poeppel 2010) taken up
by PP in the form of the generative model. Recently, some proposals regarding sentence processing have
also been advanced within the PP paradigm (Michel 2019; Rappe 2019).
5 If this is correct, autistic individuals should display a preference for maximal precision in conversa-
tion. Some phenomena that have been discussed in the literature point in this direction. Examples include
the search for neologisms and perfectionism – e.g., intolerance of other people’s linguistic errors, the use
of overly specific and idiosyncratic terms – as well as the violation of Gricean maxims of informative-
ness by offering too many details or by being too specific (Roberts etal. 2007; Paul etal. 2009; Volden
and Phillips 2010).
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Literalism inAutistic People: aPredictive Processing…
the following section we discuss the case of metaphors to flesh out how the hypoth-
esis would work in more detail.
5 Literal Comprehension: theCase ofMetaphors
In this section we focus on a possible PP explanation behind the literalist bias
observed in many autistic individuals in the case of metaphor comprehension.
There is sufficiently robust evidence that autistic individuals experience difficulties
in understanding metaphors (Vulchanova etal. 2015; Kalandadze etal. 2018), with
a documented tendency towards literal interpretation (Vicente and Falkum 2023;
Chahboun etal. 2016; Morra 2016). As further evidence of such difficulties, sev-
eral intervention programs are specifically designed to support autistic individuals in
the process of recognizing metaphors and facilitating their comprehension (Melogno
and Pinto 2022; Melogno etal. 2017). That said, such difficulties are at times not
directly apparent in the context of empirical studies, possibly as a consequence of
specific experimental designs and type of metaphors selected (see Section 5.2).
Although some researchers have suggested that issues with figurative language in
autism may be rather indicative of issues with language tout court (Norbury 2005;
Gernsbacher and Pripas-Kapit 2012), more recent evidence indicates that autistic
individuals with intact language abilities show a delay in metaphorical processing
(Chahboun etal. 2017) as well as a tendency to interpret figurative language literally
(Walenski and Love 2017).
From a PP perspective, difficulties with figurative language may derive from its
paradigmatically open-ended character. As we mention above, difficulties with figu-
rative language (and with metaphors in particular) usually take two different forms:
(a) comprehension issues simpliciter – i.e., having a hard time understanding a met-
aphor or idiom; (b) literal interpretation – i.e., interpreting a metaphor or idiom liter-
ally as opposed to figuratively. Here we suggest that (a) and (b) may arise from dif-
ferent processes, and we offer an explanation as to why they are typically observed
in different populations – i.e., individuals with developmental language disorders or
clinical as well as nonclinical conditions affecting structural language (e.g., being a
L2 learner) and autistic individuals, respectively. These two processes may be sum-
marized as follows.
Comprehension issues may arise because, upon hearing a non literal expression,
the hearer expects a certain feature (or kind of feature) that does not appear. For
instance, upon encountering the metaphor “that boy is a turtle”, the hearer expects
certain features to appear in the predicate (e.g., features that apply to persons), and is
surprised by a predicate whose more salient features are not the expected ones. She
may then try to retrieve said features, but lack of world knowledge, issues with lin-
guistic development and/or abilities, or issues with analogical thinking may hinder
her efforts. In such cases, the outcome will probably be that she cannot understand
the metaphor altogether. In these cases, not understanding a figurative expression
requires the hearer to stick to her prior prediction – e.g., that a sentence such as
“That boy is…” would be followed by a predicate that applies to persons. In liter-
alism cases, literal interpretation may instead arise from individuals overweighting
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A.Vicente et al.
1 3
incoming signals at the expense of prior experiences. Literal interpretation may then
be the safest bet after detecting an unexpected semantic clash. Neurotypicals would
usually have higher confidence in their predictions, which in turn would make it
easier to select features that are applicable to the subject of the utterance while sup-
pressing the rest (e.g. slow, in the case of “that boy is a turtle”). By contrast, autistic
individuals would display lower confidence in their higher-level predictions, thereby
experiencing uncertainty concerning the features they had predicted the predicate
would have. In that case, they may end up not suppressing any feature of the predi-
cate, trying to make sense of the whole utterance assuming a literal interpretation of
the metaphorical vehicle.6
The PP explanation that we are sketching may thus allow us to disambiguate the
difference between not understanding a metaphorical expression and interpreting it
literally. The former usually implies abiding by the prediction concerning features
that are nonetheless not found in the linguistic stimulus. The individual holds onto
their expectation of a certain semantic feature in the predicate; then, not being able
to select an interpretation that has such a feature, they give up the attempt at mak-
ing sense of the utterance. Thus, the interpretation of an utterance such as ‘the boy
is a turtle’ ends up being something like: “the boy is something that turtles also
are”. The literalist interpretation, in contrast, revises the prediction concerning the
semantic features of the predicate. In the “turtle” example, the prediction that ends
up being revised is that the predicate’s semantic features are features that apply to
agents. As a result of such a revision, the hearer will entertain the possibility that the
boy literally is a turtle. To further illustrate such a difference, consider the case of
a L2 speaker who first encounters the non-literal use of the verb ‘to sit’, as in: “two
whales sat underneath our boat the whole time we were anchored”, or in “Nicole
sat in Bruttig for weeks” (Fraser 2022). It is reasonable to assume that they might
not fully understand what ‘sit’ means in such cases, though most likely they will
be aware that it does not refer to being in a sitting position, since literal uses of ‘sit’
are not compatible neither with whales sitting nor with people sitting for weeks.
Although the L2 speaker may not be able to guess the non-literal meaning con-
veyed by ‘sit’ in either example, they will not try to force a literal interpretation. The
reason, we suggest, is that they would operate under a set of (stable) higher-level
assumptions that make it possible to understand that the relevant sentence requires a
non-literal interpretation.
Notably, the ability to represent higher level priors per se is not sufficient: in fact,
autistic individuals surely possess some higher-level priors such as the assumption
that some sentences can be interpreted metaphorically (see Morra 2016, for some
first-person reports). As we will see in the next section, autistic individuals do not
seem to experience difficulties in producing metaphors, which means that metaphors
6 Different kinds of metaphors may engender different experiences in this respect. For instance, meta-
phors in subject position (referential metaphors), or any metaphorical expression that is uttered as the
beginning of a phrase (e.g., ‘the autumn of life’) requires that the interpreter readjust her predictions
differently. The hearer expects a continuation involving certain semantic features that do not appear. Low
confidence in such expectation might be beneficial, but only provided that higher-order predictions about
general intelligibility of what speakers say are sufficiently stable.
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1 3
Literalism inAutistic People: aPredictive Processing…
in themselves are not alien to them. However, priors also need to be stably repre-
sented (i.e., trusted) in the face of unexpected events: e.g., the hearer should not
question whether the speaker really has the intention to express a metaphor.
Further, to pick out the proper non-literal interpretation among many possibili-
ties, other stable priors – such as the ones about general conversational context
– need to be present. As we suggest, those priors might also be unstable for an autis-
tic individual, especially when having a conversation with neurotypical people. Con-
sider again the example: “The boy is a turtle”. In this case, it is the overall context
that determines whether this sentence should be interpreted literally. For instance,
the sentence could be interpreted literally in the context of a fictional story where a
boy turns into a turtle and a girl into a rabbit. In another context, such as when we
are talking about certain traits of a real-world boy, the sentence is better interpreted
metaphorically. Now in this latter case, the context also determines how exactly to
interpret the metaphor: it could be interpreted as the boy being slow, or as living
an isolated and overly sheltered life. Overall, there is a good number of predictions
involved in interpreting any utterance that is not literal and/or explicit, such that a
diminished confidence in any of said predictions may result in retreating to the lit-
eral interpretation.
In what follows, we argue that appealing to uncertainty concerning predictions
about speaker’s production can explain two puzzling facts: the comprehension-pro-
duction asymmetry observed in autistic individuals, and the mixed results obtained
in the lab versus the experience of many autistic individuals in real life situations.
5.1 The Comprehension‑Production Asymmetry
A puzzling general asymmetry between comprehension and production has been
repeatedly observed in autism. Kanner (1943) already noted that otherwise hyper-
sensitive children seemed to be unconcerned with loud sounds as long as they were
the ones producing them: “the child himself can happily make as great a noise as
any that he dreads and move objects about to his heart’s content” (p. 245). This pas-
sage suggests that sounds produced by the individuals themselves would be easier
to process as opposed to the ones coming from the environment. In the domain of
language, another dominant idea has been that autistic individuals would experience
fewer issues in production than in comprehension, thereby reversing a pattern usu-
ally observed in neurotypicals (Davidson and Weismer 2017).
With respect to metaphor production, autistic individuals have been found to
perform comparably to – and in some cases better than – neurotypicals. Indeed,
a series of studies by Kasirer and colleagues show that autistic individuals, while
having difficulties with metaphor comprehension, are able to produce novel and
creative metaphors (Kasirer and Mashal 2016; Kasirer etal. 2020). First-person
accounts also substantiate this point: as reported by Morra’s analysis of a series
of threads in the Wrong Planet forum (Morra 2016), 63% of respondents report
using metaphors often, whereas only 37% of them perceive their metaphor com-
prehension as “unproblematic”. See the following for some examples: “[…] I am
perfectly capable of cocking my brain and throwing [metaphors and analogies]
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A.Vicente et al.
1 3
out, not always so good at catching them”; “I’ve had trouble understanding com-
mon metaphors (like kill two birds with one stone), but I’m really good at figur-
ing out literary metaphors and creating metaphors”; “i can construct metaphors,
but i can not understand many metaphors that i did not create” (pp. 135–137).
How can we explain this puzzling asymmetry between comprehension and pro-
duction? Drawing on the PP framework outlined above, it is reasonable to think
that in cases of linguistic production the overall degree of uncertainty will be
reduced. Barring other structural forms of linguistic disability, linguistic produc-
tion is more controlled and guided in nature than linguistic comprehension: one
may plan what to say next, which kind of words and grammatical construction
to use, how far to depart from literal meaning, etc. All this does not involve the
processing of external sensory information, which may inhibit the development
of stable and precise higher-level priors if they are given excessive weight. In this
sense, the degree of uncertainty involved in linguistic production is less over-
whelming compared to comprehension.
Notably, increased certainty about knowing what to say or what is coming next
might even trump uncertainty in being understood by others. Indeed, there seem
to be several situations in which autistic individuals seemingly disregard context
and/or their interlocutor. For example, they may break Gricean maxims of rel-
evance or quantity (Paul etal. 2009), deliberately speak in a different language or
heavily employ neologisms and overly specific terms (Llorente etal. 2022), which
violates the requirement of looking for common lexicons (Clark 1996). The pro-
duction of creative metaphors reported by Kasirer andMashal (2014,2016)and
Kasirer etal. (2020) appears to be consistent with this idea. Once a fitting meta-
phor has been identified, the autistic speaker seems to be more focused on repro-
ducing it as is than on determining whether such an expression would belong to
the common ground with their interlocutor. Some first-person accounts reported
by Morra (2016) also stress the difficulty in being understood by others when
using figurative language. See for instance the following exchange:
Speaker A: “I understand metaphors just fine and use them quite often […]”;
Speaker B: “Same here, but sometimes they aren’t caught by others”;
Speaker C: “I try to use metaphors to help them understand. Unfortunately,
using motorcycle metaphors is lost on most people”.
This linguistic behavior becomes less puzzling if we assume that attention in
production may be more focused on knowing what to say and how to say it – e.g.,
on carefully choosing one’s words – as opposed to other aspects of linguistic
and social communication – e.g., being understood by one’s conversational part-
ners. As we mention above, focusing on these other aspects would require the
mobilization of massive background knowledge as well as a stable representa-
tion and selection of the contextually appropriate high-level priors, which might
be particularly challenging for autistic individuals. In the next section we tackle
yet another gap that has been observed in the studies on figurative language and
autism, namely the discrepancy between results obtained in laboratory settings
and daily life experiences.
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Literalism inAutistic People: aPredictive Processing…
5.2 Mixed Results intheLab andDaily Life Experience
As mentioned, most laboratory studies on non-literal uses of language in autism
yield mixed results. Here we want to explain at least part of such variability. We
begin by summarizing some of these mixed results and then we move on to high-
light one factor that we think is relevant to account for the gap between laboratory
and everyday settings. In line with the rest of the section, this factor concerns poten-
tially relevant differences in the degree of predictability and control displayed by
these different contexts.
Kissine etal. (2015) and Marocchini etal. (2022), find that autistic children can
perform just as neurotypicals in understanding conventional indirect speech acts
(i.e., knowing that “Can you pass me the salt?” counts as a request even if it has
the form of a yes/no question). Yet, previous work by Paul and Cohen (1985), and
Ozonoff and Miller (1996) found that autistic participants exhibited difficulties in
grasping indirect speech acts appropriately. Even the derivation of scalar implica-
tures (e.g., understanding ‘some’ as expressing ‘some but not all’) appears to be dif-
ficult according to some studies (Pastor-Cerezuela etal. 2018; Schaeken etal. 2018;
Mazzaggio etal. 2021), but not in several others (Chevallier etal. 2010; Su and Su
2015; Hochstein etal. 2018; Pijnacker etal. 2009). Studies concerning metaphor
comprehension yield similarly mixed results. Norbury (2005), in her seminal study,
suggested that, if matched on structural language, autistic and typically developing
children performed just as well in a forced choice metaphor task. Her view, as men-
tioned above, has given rise to the idea that metaphor understanding is related to
structural language development, which means that autistic children may experience
a delay in metaphor understanding that corresponds to the more general delay they
experience in language acquisition. However, Vulchanova and colleagues find a lit-
eralist bias in metaphor comprehension in a series of studies (see references above).
In turn, Kasirer andMashal (2014, 2016) and Kasirer etal. (2020) report difficulties
with conventional metaphors, but not with novel metaphors.
There seems to be more agreement concerning irony and sarcasm. Many studies
have found strong difficulties in irony comprehension (Deliens et al. 2018; Happé
1993; Kaland et al. 2002; MacKay and Shaw 2004; Martin and McDonald 2004;
Saban-Bezalel etal. 2019). Some studies have found delays rather than difficulties,
thereby suggesting a compensatory picture, as shown by implicit measures such as
response times and eye gaze (Pexman etal. 2011) or brain imaging techniques (Col-
ich etal. 2012; Wang etal. 2006; Williams et al. 2013). Still, if the task reduces
demands, some autistic individuals seem to experience fewer difficulties (Glen-
wright and Agbayewa 2012).
To some extent, these mixed results can be explained by appealing to autism’s
heterogeneity and the fact that experimental groups can be relatively small. Thus, it
may be that responses to the same task may vary considerably from one experimen-
tal group to another. However, experimental design also has an obvious influence. In
one of the most comprehensive reviews on the topic, Kalandadze etal. (2019) iden-
tifies several dimensions that may affect performance in metaphor comprehension
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A.Vicente et al.
1 3
tasks, such as experimental design and setting.7 An interesting dimension that we
want to highlight here relates to the extent to which some tasks are more struc-
tured – and, as a consequence, more predictable than others. For instance, while
Kasirer and Mashal (2014, 2016) use a forced choice task with possible explica-
tions of a metaphorical Noun + Adj expression, Chahboun etal. (2017) use a lexical
priming task. These two ways of testing metaphor comprehension and the literalist
bias exhibit many differences. To begin with, the former is about selecting para-
phrases and guessing which one is better, with little time constraints, while the latter
is about making quick decisions on the basis of associations. The task employed by
Kasirer and Marshal is a quite structured, multiple choice task that offers the pos-
sibility to ponder possible responses. A lexical priming task, in contrast, is much
more demanding and unstructured: it is a “game” about reacting quickly to a word
or non-word stimulus. Similarly – as emphasized by Kalandadze etal. (2019) – mul-
tiple-choice and non-verbal enactment tasks (i.e., acting out a metaphor with toys
after having heard a story containing such metaphor) are more structured – and thus
arguably less demanding – than tasks centered around verbal explanation, such as
answering open questions about metaphors heard in a story (Rundblad and Annaz
The study by Paul and Cohen (1985) is also a good example of how experimen-
tal designs can have a strong influence on results. Paul and Cohen evaluated com-
prehension of indirect speech acts in two different cases. In both cases, participants
had to color circles either in blue or red following indirect requests such as: “I’ll
be happy if you color this circle blue”. However, in the first case, participants were
explicitly informed that they would be presented with requests, while in the second,
requests were made as part of a conversation held between experimenter and partici-
pant while participants were drawing. In both cases, autistic participants performed
worse than controls matched by verbal mental age, but they performed clearly better
in the first case than in the second, arguably because the task demands were more
explicit and left less room for uncertainty.
This suggests – in line with the PP approach outlined above – that more struc-
tured and predictable tasks may be more amicable to autistic participants. Now,
think about daily life conversations, which are paradigmatically more unpredicta-
ble and involve a greater number of variables. Sometimes uncertainty about how a
metaphorical utterance should be understood is due to the metaphor not being suf-
ficiently embedded in a context. For instance, a sentence such as: “My sister was
a rock” could be understood in any of the following senses: my sister was solid,
reliable, or she was indestructible, or even in some situations she was stupid or stub-
born (Pouscoulous 2014, p. 246). A clearly structured conversation would facilitate
comprehension in such cases: for instance, contextual elements may be highlighted
and reinforced, so that hearers can be more confident about the relevant properties
7 Notably, it has been already observed that experimental design and setting affect performance in meta-
phor comprehension both in neurotypical and schizophrenic populations (see Pouscoulous 2011 and
2014 and Rossetti etal. 2018 respectively).
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Literalism inAutistic People: aPredictive Processing…
of the abovementioned metaphor – e.g., reliability or strength. Yet, typical conversa-
tions lack such a structured form.
More generally, it is virtually impossible to predict whether a given conversation
would involve non-literal language, be it in the form of direct or indirect requests,
metaphors or idioms, implicatures, or irony and sarcasm. Some of these linguistic
forms do not display a specifically recognizable structure, thereby making it harder
to identify them in a given stretch of conversation. By contrast, some of the studies
discussed above – e.g., forced choice tasks – structure the environment in a way that
makes it apparent that some specific linguistic forms will appear, or at least that one
correct answer will be presented along with some distractors. This option is hardly
ever present in everyday conversations, where participants are left on their own to
figure out how to interpret their interlocutor’s utterances. A similar pattern may
be detected in studies that explore cognitive flexibility and task switching, where
observed behavioral difficulties rarely reflect inflexibility measures collected in the
lab (Geurts et al. 2009). Also in this case, many laboratory tasks tend to be quite
compartmentalized and structured (e.g., Wisconsin Sorting Cards Task), making it
difficult to replicate the complexity and degree of uncertainty that people encounter
in everyday situations. Geurts etal. (2009) made an important claim with respect
to a lack of correspondence between laboratory results concerning cognitive flex-
ibility and observed cognitive flexibility difficulties in the autistic population. We
suggest that there may be a similar lack of correspondence between what we observe
in structured tasks in the laboratory settings and difficulties that arise in much more
unstructured situations in daily life conversations.
In the next section we briefly explore literalism in other forms of figurative lan-
guage beyond metaphors, also trying to corroborate a PP-style explanation that
would accommodate the complex and often unpredictable character of everyday
6 Literalism Beyond Metaphors
A PP approach can also be endorsed to explain the difficulties of autistic individu-
als related to the comprehension of other non-literal uses of language. We already
mentioned some of these uses, such as indirect speech acts. Ideally, a PP approach
should offer a unified treatment of the literalist bias in autism observed in all non-lit-
eral uses, including those that are currently indisputably related to ToM issues, or to
“social pragmatics” more generally, namely, typically, irony and sarcasm (Andrés-
Roqueta and Katsos, 2017, 2020). In this section, we focus on implicatures, since
recent work by Wilson and Bishop (2019, 2021, 2022) has already related difficul-
ties with implicatures in the autistic population to uncertainty. We then conclude
with some speculations concerning irony.
Research into implicature processing by autistic individuals is still limited. While
there is work on some-but-not-all scalar implicature derivation, significantly less
work has been conducted on other forms of scalar reasoning (e.g., ad hoc implica-
tures), and particularly scarce work exists about conversational implicatures. Con-
cerning ad hoc implicatures, Mazzaggio et al. (2021) found that autistic children
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A.Vicente et al.
1 3
have difficulties with both scalar and ad hoc implicatures. In the case of ad hoc
implicatures, autistic children experienced more difficulties than their typically
developing peers inferring from: “My bed is the one with a teddy bear” that the
speaker refers to the bed with only a teddy bear and not to the bed with a teddy
bear and a doll. The authors suggest that the difficulties with those types of impli-
catures in autistic children is determined by an impaired capacity to determine what
the relevant alternatives are in each context, which would also be predicted by the
PP account of autism. In this case, however, there would be no impairment as such;
rather, autistic children would be unsure about whether the speaker is being maxi-
mally informative. Concerning conversational implicatures, we find the pioneering
work conducted by Wilson & Bishop particularly interesting. In a series of papers,
Wilson and Bishop (2019, 2021, 2022) develop a 7-task battery of tests to investi-
gate whether core language skills and pragmatic abilities can be teased apart and,
thus, whether they can be said to result from different cognitive underpinnings.
Focusing on their Implicature Comprehension Test (ICT), featuring particularized
conversational implicatures, they observe that autistic individuals are twice as likely
to choose a “non-normative” interpretation of an implied meaning, and five times
as likely to select “I don’t know” as an answer when asked about the presence of an
implicated meaning (Wilson and Bishop 2021).
In Wilson & Bishop’s ICT, participants watch short animated cartoons featuring
two characters, Tom and Sally, who participate in a dialogue. After each dialogue, a
robot appears on a different screen and asks whether the implicature can be derived.
In their 2021 study, the participant can reply “yes”, “no” or “don’t know”. In their
design, the task directly inquires about the implicature, which makes the generation
of the implicature an explicit act. In 36 items, the participant needs to process the
implicature to answer the question correctly. “Yes” is the “normative” answer in half
of the items, and “No” in the other half. This is one of the examples:
Sally: Can the two of us sit here?
Tom: The children just went to find the toilet.
Robot: Do you think Tom and Sally can sit there?
7 Conclusion
In this paper we show how a PP-style explanation may be productively applied to
a range of issues concerning pragmatic difficulties exhibited by many autistic indi-
viduals. Prominent PP explanations have already been proposed to account for sev-
eral autistic traits, including hypersensitivity (Van de Cruys etal. 2019), repetitions,
adherence to routines, and preference for structured environments (Lawson et al.
2017). Most of these explanations rely on the idea that the autistic brain assigns
more weight to incoming signals at the expense of prior predictions. As a conse-
quence, a lower degree of confidence ends up being assigned to each prediction,
thereby generating higher uncertainty. Here we show how a similar hypothesis
may shed some light on the literalist tendency frequently observed in the autistic
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Literalism inAutistic People: aPredictive Processing…
population. We start by drawing on some recent accounts (Pickering and Gambi
2018, Ferreira and Chantavarin 2018) to establish that predictions play a key role
in everyday comprehension, where errors and violations are systematically ignored
in favor of predictions. If understanding implies predicting, higher uncertainty with
respect to one’s own predictions is likely to make comprehension significantly
harder. We then illustrate how such clashes may occur by discussing the case of met-
aphor comprehension, which tends to showcase the literalist bias observed in many
autistic individuals. According to our hypothesis, when it comes to understanding
metaphors – e.g., “that boy is a turtle” – autistic individuals experience more uncer-
tainty concerning the features they had predicted the predicate would have – e.g.,
features related to human beings. As a consequence, they may end up not suppress-
ing any feature of the predicate, trying to make sense of the whole utterance through
a literal interpretation – i.e., the boy literally is a turtle.
Our hypothesis has two main advantages. First, it allows us to make sense of a
puzzling set of data concerning the apparent asymmetry between metaphor compre-
hension and production in the autistic population. Indeed, empirical studies (Kasirer
and Mashal 2016; Kasirer etal. 2020) and first-person accounts (Morra 2016) con-
verge on the idea that autistic individuals are able to produce novel and creative met-
aphors. In our view, this depends on the fact that the overall degree of uncertainty is
reduced in linguistic production, given that the speaker is in control of what comes
next. The comprehension difficulties highlighted by the literature may therefore not
be due to metaphors per se, but to a higher degree of uncertainty with respect to
one’s own predictions. This is likely to generalize to other aspects of linguistic com-
munication beyond the scope of this paper – such as irony, indirect speech acts, etc.
One may wonder whether a similarly high degree of uncertainty could also apply
to linguistic production. In our view, less uncertainty in production results from a
higher value being placed on “knowing what to say” (e.g., to minimize anxiety),
combined with a diminished focus on being understood by others. Some linguistic
behaviors observed in autism – such as the violation of Gricean maxims or the fre-
quent use of neologisms – suggest that an imbalance of this sort might occur (see
Second, our hypothesis offers a sensible explanation of the gap we observe
between empirical results – which often struggle to identify specific difficulties
– and everyday situations, where literalism is widely reported as an issue that autis-
tic people experience. Generally speaking, studies conducted in laboratory settings
tend to be more structured and predictable, thereby drastically reducing uncertainty.
By contrast, daily life conversations are significantly more unpredictable and involve
a greater number of variables. The observed gap between laboratory results and
daily life experiences may therefore reflect the different degree of structure and pre-
dictability of these two environments. This point also applies more generally, given
that the gap between laboratory results and daily life experiences extends to several
areas of autism research. The very nature of the phenomena of study – e.g., conver-
sational abilities, social and language skills – often makes these traits resistant to
being captured by studies conducted exclusively in laboratory settings. A more com-
plete picture could be obtained by combining results obtained in the lab with more
observational measures – such as recorded conversations – and with the qualitative
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A.Vicente et al.
1 3
analyses of first-person reports, in order to detect possible discrepancies across con-
texts and conditions. This suggests, in line with several recommendations (see for
instance Eigsti and Schuh 2016) that research should orient itself towards ecologi-
cally more valid paradigms.
Given the results obtained, and especially the fact that autistic individuals are five
times as likely to select “I don’t know” as an answer when asked about the pres-
ence of an implicated meaning, Wilson and Bishop (2021) speculate that the “I don’t
know” answer may suggest a cognitive preference for certainty and explicit commu-
nication.8 We find Wilson and Bishop’s work particularly interesting because of the
role they assign to intolerance of uncertainty. According to them, difficulties with
implicature derivation may relate to how uncomfortable the individual feels when
communication is not explicit. However, as we have explained above, discomfort
with uncertainty may be an epiphenomenon generated by a higher level of experi-
enced uncertainty. Choosing “I don’t know” responses, as well as failing to derive
an implicature for lack of confidence, seem to be a result of higher uncertainty, not
of intolerance of uncertainty. That is, the choice of ‘I don’t know’ responses may
reveal that autistic individuals are unsure about whether their expectation (i.e., the
response has to be “yes” or “no”) has been satisfied.
What about irony? In the case of irony, the hearer often needs to interpret a sen-
tence as expressing the opposite of what is literally expressed. Importantly, depend-
ing on the context, the sentence can also be unproblematically interpreted and com-
prehended literally (unlike what happens with most metaphors).9 For instance: “This
is a great friend” can in principle be interpreted both literally and ironically in differ-
ent contexts. Due to its features, irony therefore places a higher demand on context
comprehension (especially social context comprehension, see Fabry 2021). We sug-
gest that irony may be a particularly difficult case, given that the uncertainty related
to higher order priors probably plays an even larger role in comprehension. Indeed,
while one can perfectly recognize a metaphor without understanding what exactly it
means – precisely because its literal meaning is in most cases hard to work out – one
might get stuck more easily with the literal meaning of an ironic sentence. What
also increases uncertainty in the case of irony is that, while “easy” metaphors tend
to have a more or less recognizable structure (“X is Y”), potentially every utterance
may be interpreted ironically. This makes it particularly difficult to identify possible
patterns and to successfully predict when irony is going to occur within a conversa-
tion. The lack of a proper context representation, possibly due to the overweighting
of incoming stimuli at the expense of higher-level priors, may lead to not recogniz-
ing the irony or to being highly uncertain about whether irony is being employed.
Although there is evidence that irony comprehension is, generally speaking, chal-
lenging for autistic individuals (Barzy etal. 2022), some results are intriguing. For
8 Note also that the kind of implicatures tested by Wilson and Bishop belong to what Jary (2013)
labeled ‘material implicatures’, whose derivation does not require reflecting about the interlocutors’ men-
tal states (see Abbot-Smith etal. 2022 for empirical support).
9 Note that some metaphors can also be interpreted literally. For instance: “This surgeon is a butcher”
may be legitimately interpreted as: “The person, who is a butcher, is (also) a surgeon [= she has two pro-
fessions]”. Yet, literally interpretable metaphors are the exception rather than the rule.
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1 3
Literalism inAutistic People: aPredictive Processing…
instance, Panzeri et al. (2022) found an interesting bimodal distribution of results
when testing three groups of children – one autistic group and two typically devel-
oping groups of different ages – for irony comprehension. While the majority of the
autistic group performed worse than controls, a minority performed at ceiling. More
research is therefore needed to determine whether a PP explanation may also be suc-
cessfully applied to irony comprehension, and we aim to develop such an explana-
tion in future work.
Acknowledgements The authors want to express their gratitude to all Lindy Lab members for their com-
ments, and in particular to Elena Castroviejo and Isabel Martín González for their valuable feedback.
Versions of this paper were presented at the 2022 PLM conference in Warsaw, the 2023 Logos 30th Anni-
versary Conference in Barcelona, and the 2023 IPrA conference in Brussels. We are grateful to audiences
in these events for their feedback. Special thanks are due to Nicholas Allott and Ingrid Lossius Falkum.
Thanks also to three reviewers from RoPP and to editor Derek Ball for helping us shape up the paper and
clarify our views.
Funding Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.
Research for this paper was funded by the following grants: (1) BBVA Foundation Grant for Scientific
Research Projects 2021 (RILITEA) funded AV and VP (The Foundation takes no responsibility for the
opinions, statements and contents of this project, which are entirely the responsibility of its authors).
(2) Agencia Estatal de Investigación (AEI) and Ministry of Science and Innovation, grant number
PID2021-122233OB-I00 (AV and VP). (3) The Basque Government, grant number IT1537-22 (AV and
VP). (4) Agencia Estatal de Investigación (AEI) and Ministry of Science and Innovation, grant numbers
PID2021-128950OB-I00 and IJC2020-043408-I (VP).
Data Availability Data sharing is not applicable to this article as no new datawere created or analyzed in
this study.
Conflict of Interest The authors declare that they have no conflict of interest.
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Characterizations of autism include multiple references to rigid or inflexible features, but the notion of rigidity itself has received little systematic discussion. In this paper we shed some light on the notion of rigidity in autism by identifying different facets of this phenomenon as discussed in the literature, such as fixed interests, insistence on sameness, inflexible adherence to routines, black-and-white mentality, intolerance of uncertainty, ritualized patterns of verbal and non-verbal behavior, literalism, and discomfort with change. Rigidity is typically approached in a disjointed fashion (i.e., facet by facet), although there are recent attempts at providing unifying explanations. Some of these attempts assume that the rigidity facets mainly relate to executive functioning: although such an approach is intuitively persuasive, we argue that there are equally plausible alternative explanations. We conclude by calling for more research on the different facets of rigidity and on how they cluster together in the autistic population, while suggesting some ways in which intervention could benefit from a finer-grained view of rigidity.
Full-text available
This article reviews the literature reporting on the trainings implemented with children with Autism Spectrum Disorder (ASD) without intellectual disability to enhance their capability to cope with metaphor comprehension. The studies in this review can be classified into two main strands of thought, behavioral-analytic and psycholinguistic, respectively. Beyond some basic similarities all these studies share in their attempt at training children to consider the semantic features of metaphors, the mental pathways activated by those trainings are based on different cognitive and linguistic processes. The trainings based on the behavioral-analytic perspective teach the meaning of metaphors by making an extensive use of prompts: iconic, echoic, and textual. In the trainings based on the psycholinguistic perspective, instead, a wide range of activities are devised to stimulate children's analytical abilities to cope with semantic relations in metaphors. A significant part of these activities are jointly conducted between adult and children, and aimed at promoting the child's autonomy. Among the most interesting theoretical challenges stemming from the abovementioned studies, this review considers the spontaneous creation of original metaphors in children with ASD when solicited to understand metaphorical expressions. This unexpected reaction highlights the complexity of the relationships between metaphor comprehension and production in children with ASD.
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Nonliteral language understanding has always been recognized as problematic in autistic individuals. We ran a study on 26 autistic children (mean age = 7.3 years) and 2 comparison groups of typically developing children, 1 matched for chronological age, and 1 of younger peers (mean age = 6.11 years) matched for linguistic abilities, aiming at assessing their understanding of ironic criticisms and compliments, and identifying the cognitive and linguistic factors that may underpin this ability. Autistic participants lagged behind the comparison groups in the comprehension of both types of irony, and their performance was related to mindreading and linguistic abilities. Significant correlations were found between first-order Theory of Mind (ToM) and both types of irony, between second-order ToM and ironic compliments, and between linguistic abilities and ironic criticisms. The autistic group displayed an interesting, and previously unattested in the literature, bimodal distribution: the great majority of them (n = 18) displayed a very poor performance in irony understanding, whereas some (n = 6) were at ceiling. We discuss these results in terms of two different profiles of autistic children.
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Social communication difficulties are a diagnostic feature in autism. These difficulties are sometimes attributed, at least in part, to impaired ability in making inferences about what other people mean. In this registered report, we tested a competing hypothesis that the communication profile of adults on the autism spectrum can be more strongly characterised by reduced confidence in making inferences in the face of uncertain information. We tested this hypothesis by comparing the performance of 102 autistic and 109 non-autistic adults on a test of implied meaning, using a test of grammaticality judgements as a control task. We hypothesised that autistic adults would report substantially lower confidence, allowing for differences in accuracy, than non-autistic adults on the test of implied meaning compared to the grammaticality test. However, our results did not suggest this. Instead, we found that accuracy and confidence were both reduced to a similar extent on the test of implied meaning in the autistic group compared to the control group, although these were only subtle differences. This pattern of results was specific to inference-making, as the autistic and non-autistic groups did not differ on the grammar test. This supports the idea that specific differences in pragmatic language processing can exist in autism in the absence of core language problems. Importantly, this pattern of results (differences on the test of implied meaning and no differences on the grammar test) was reversed in a group with self-reported reading difficulties, indicating that the differences in inference-making were specific to the autistic group. Lastly, we found relationships between Intolerance of Uncertainty, performance on the test of implied meaning, and self-reported social communication challenges. This supports the idea that discomfort with uncertainty plays a role in the pragmatic language and communication challenges in autism.
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Face-to-face communication requires skills that go beyond core language abilities. In dialog, we routinely make inferences beyond the literal meaning of utterances and distinguish between different speech acts based on e.g. contextual cues. It is however not known whether such communicative skills potentially overlap with core language skills or other capacities, such as Theory of Mind (ToM). In this fMRI study we investigate these questions by capitalizing on individual variation in pragmatic skills in the general population. Based on behavioral data from 201 participants, we selected participants with higher vs lower pragmatic skills for the fMRI-study (N = 57). In the scanner, participants listened to dialogs including a direct or an indirect target utterance. The paradigm allowed participants at the whole group level to (passively) distinguish indirect from direct speech acts, as evidenced by a robust activity difference between these speech acts in an extended language network including ToM areas. Individual differences in pragmatic skills modulated activation in two additional regions outside the core language regions (one cluster in the left lateral parietal cortex and intraparietal sulcus and one in the precuneus). The behavioral results indicate segregation of pragmatic skill from core language and ToM. In conclusion, contextualized and multimodal communication requires a set of inter-related pragmatic processes that are neurocognitively segregated: (1) from core language and (2) partly from ToM.
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We applaud the efforts by Stark and colleagues [1] to chart how a predictive processing account of autism may lead to autistic anxiety. We wholeheartedly agree that this is a productive route to shed light on a real problem in autism and that this kind of dialogue is much needed in a field that has been plagued by dogged misconceptions, with sometimes harmful consequences, for autistic people. Stark and colleagues provide an example of how lived autistic experiences of, for example anxiety, can be scientifically validated by sound theories about a different cognitive (predictive) processing profile. At the same time, it illustrates how new misconceptions could take hold if old concepts like 'intolerance of uncertainty' aren't sufficiently scrutinized with state-of-the-art theoretical tools (c.q. predictive processing in autism). To preempt future misconceptions, we clarify the concept of 'intolerance of uncertainty' here and show that it does not fit well within a predictive processing framework.
According to predictive processing, an increasingly influential paradigm in cognitive science, the function of the brain is to minimize the prediction error of its sensory input. Conceptual engineering is the practice of assessing and changing concepts or word meanings. We contribute to both strands of research by proposing the first cognitive account of conceptual engineering, using the predictive processing framework. Our model reveals a new kind of implementation problem as prediction errors are only minimized if enough agents embrace conceptual changes. This problem can be overcome by emphasizing the importance of social norms and conceptual pluralism.
Several competing neuro-computational theories of autism have emerged from predictive coding models of the brain. These accounts have a common focus on the relationship between prior beliefs and sensory inputs as a mechanism for explaining key features of autism, yet they differ in exactly how they characterise atypicalities in perception and action. We tested these competing predictions using computational modelling of two datasets that allowed us to probe both visual and motor aspects of active inference: manual gripping forces during object lifting and anticipatory eye movements during a naturalistic interception task. We compared estimated belief trajectories between autistic and neurotypical individuals to determine the underlying differences in active inference. We found no evidence of chronic deficits in the use of priors or weighting of sensory information during object lifting. Differences in prior beliefs, rates of belief updating, and the precision weighting of prediction errors were, however, observed for anticipatory eye movements. Notably, we observed autism-related difficulties in flexibly adapting learning rates in response to environmental change (i.e., volatility). These findings suggest that aberrant encoding of precision and context-sensitive adjustments provide a better explanation of autistic perception than generic attenuation of priors or persistently high precision prediction errors.
Ten years ago, Pellicano and Burr published one of the most influential articles in the study of autism spectrum disorders, linking them to aberrant Bayesian inference processes in the brain. In particular, they proposed that autistic individuals are less influenced by their brains' prior beliefs about the environment. In this systematic review, we investigate if this theory is supported by the experimental evidence. To that end, we collect all studies which included comparisons across diagnostic groups or autistic traits and categorise them based on the investigated priors. Our results are highly mixed, with a slight majority of studies finding no difference in the integration of Bayesian priors. We find that priors developed during the experiments exhibited reduced influences more frequently than priors acquired previously, with various studies providing evidence for learning differences between participant groups. Finally, we focus on the methodological and computational aspects of the included studies, showing low statistical power and often inconsistent approaches. Based on our findings, we propose guidelines for future research.
People sometimes interpret implausible sentences nonliterally, for example treating The mother gave the candle the daughter as meaning the daughter receiving the candle. But how do they do so? We contrasted a nonliteral syntactic analysis account, according to which people compute a syntactic analysis appropriate for this nonliteral meaning, with a nonliteral semantic interpretation account, according to which they arrive at this meaning via purely semantic processing. The former but not the latter account postulates that people consider not only a literal-but-implausible double-object (DO) analysis in comprehending The mother gave the candle the daughter, but also a nonliteral-but-plausible prepositional-object (PO) analysis (i.e., including to before the daughter). In three structural priming experiments, participants heard a plausible or implausible DO or PO prime sentence. They then answered a comprehension question first or described a picture of a dative event first. In accord with the nonliteral syntactic analysis account, priming was reduced following implausible sentences than following plausible sentences and following nonliterally interpreted implausible sentences than literally interpreted implausible sentences. The results suggest that comprehenders constructed a nonliteral syntactic analysis, which we argue was predicted early in the sentence.