Meaning and Use in the Expression of Estimative Probability.

Words of estimative probability (WEPs), such as 'possible' and 'a good chance', provide an efficient means for expressing probability under uncertainty. Current semantic theories assume that WEPs denote crisp thresholds on the probability scale, but experimental data indicate that their use is characterised by gradience and focality. Here, we implement and compare computational models of the use of WEPs to explain novel production data. We find that, among models incorporating cognitive limitations and assumptions about goal-directed speech, a model that implements a threshold-based semantics explains the data equally well as a model that semantically encodes patterns of gradience and focality. We further validate the model by distinguishing between participants with more or fewer autistic traits, as measured with the Autism Spectrum Quotient test. These traits include communicative difficulties. We show that these difficulties are reflected in the rationality parameter of the model, which modulates the probability that the speaker selects the pragmatically optimal message.

Representing Polar Questions.

Although the linguistic properties of polar questions have been extensively studied, comparatively little is known about how polar questions are processed in real time. In this paper, we report on three eye-tracking experiments on the processing of positive and negative polar questions in English and French. Our results show that in the early stages, participants pay attention to both positive and negative states of affairs for both positive and negative questions. In the late stages, positive and certain negative polar questions were associated with a bias for the positive state, and this bias appears to be pragmatic in nature. We suggest that different biases in mental representations reflect the hearer's reasoning about the speaker's purposes of enquiry.

Probabilistic pragmatics explains gradience and focality in natural language quantification.

An influential view in philosophy and linguistics equates the meaning of a sentence to the conditions under which it is true. But it has been argued that this truth-conditional view is too rigid and that meaning is inherently gradient and revolves around prototypes. Neither of these abstract semantic theories makes direct predictions about quantitative aspects of language use. Hence, we compare these semantic theories empirically by applying probabilistic pragmatic models as a link function connecting linguistic meaning and language use. We consider the use of quantity words (e.g., "some," "all"), which are fundamental to human language and thought. Data from a large-scale production study suggest that quantity words are understood via prototypes. We formulate and compare computational models based on the two views on linguistic meaning. These models also take into account cognitive factors, such as salience and numerosity representation. Statistical and empirical model comparison show that the truth-conditional model explains the production data just as well as the prototype-based model, when the semantics are complemented by a pragmatic module that encodes probabilistic reasoning about the listener's uptake.

Strategic Deception in Adults with Autism Spectrum Disorder.

Autism Spectrum Disorder (ASD) is often associated with impaired perspective-taking skills. Deception is an important indicator of perspective-taking, and therefore may be thought to pose difficulties to people with ASD (e.g., Baron-Cohen in J Child Psychol Psychiatry 3:1141-1155, 1992). To test this hypothesis, we asked participants with and without ASD to play a computerised deception game. We found that participants with ASD were equally likely-and in complex cases of deception even more likely-to deceive and detect deception, and learned deception at a faster rate. However, participants with ASD initially deceived less frequently, and were slower at detecting deception. These results suggest that people with ASD readily engage in deception but may do so through conscious and effortful reasoning about other people's perspective.

The role of gender in the perception of autism symptom severity and future behavioral development.

Background: Increasing attention is being paid to the higher prevalence of boys with Autism Spectrum Disorder (ASD) and to the implications of this ratio discrepancy on our understanding of autism in girls. One recent avenue of research has focused on caregiver's concern, suggesting that autism might present differently in boys and girls. One unexplored factor related to concerns on child development is whether socio-cultural factors such as gender-related expectations influence the evaluation of symptom severity and predictions about future behavioral development. Methods: The latter concerns were the focus of the present study and were explored by investigating laypeople's judgment of the severity of autism symptoms using an online parent role-playing paradigm, in which participants were asked to rate vignettes depicting the behaviors of a child in different everyday life scenarios. The child's gender and the severity of ASD symptoms were manipulated to examine the effect of gender on the perception of symptom severity. Results: Results suggest that there are no gender differences in perceived symptom severity and associated degree of concern for 5-year-old boys and girls but that there is a gender difference in perceived future atypicality at 15 years old, with boys being rated as more likely to be perceived as atypical by their peers at that age than girls. Conclusions: Investigating parent's cognition about their child's future behavioral development can provide additional information regarding delayed diagnosis of autistic girls.

Processing Conversational Implicatures: Alternatives and Counterfactual Reasoning.

In a series of experiments, Bott and Noveck (2004) found that the computation of scalar inferences, a variety of conversational implicature, caused a delay in response times. In order to determine what aspect of the inferential process that underlies scalar inferences caused this delay, we extended their paradigm to three other kinds of inferences: free choice inferences, conditional perfection, and exhaustivity in "it"-clefts. In contrast to scalar inferences, the computation of these three kinds of inferences facilitated response times. Following a suggestion made by Chemla and Bott (2014), we propose that the time it takes to compute a conversational implicature depends on the structural characteristics of the required alternatives.