Defining key concepts for mental state attribution.

The terminology used in discussions on mental state attribution is extensive and lacks consistency. In the current paper, experts from various disciplines collaborate to introduce a shared set of concepts and make recommendations regarding future use.

Mindreading quality versus quantity: A theoretically and empirically motivated two-factor structure for individual differences in adults' mindreading.

Existing methods for studying individual differences in adults' mindreading often lack good psychometric characteristics. Moreover, it remains unclear, even in theory, how mindreading varies in adults who already possess an understanding of mental states. In this pre-registered study, it was hypothesised that adults vary in their motivation for mindreading and in the degree to which their answers on mindreading tasks are appropriate (context-sensitive).

A transdiagnostic approach to neurodiversity in a representative population sample: The N+ 4 model.

Background: The concept of neurodiversity draws upon scientific research, and lessons from practice and lived experience to suggest new ways of thinking about neurodevelopmental conditions. Among the formative observations are that characteristics associated with neurodevelopmental conditions are part of a "broader phenotype" of variation across the whole population, and that there appear to be "transdiagnostic" similarities as well as differences in these characteristics. These observations raise important questions that have implications for understanding diversity in neurodevelopmental conditions and in neurocognitive phenotypes across the whole population.

Remembering visual and linguistic common ground in shared history.

Successful communication requires speakers and listeners to refer to information in their common ground. Shared history is one of the bases for common ground, as information from a communicative episode in the past can be referred to in future communication. However, to draw upon shared history, communicative partners need to have an accurate memory record that they can refer to.

Human-centred explanations for artificial intelligence systems.

As Artificial Intelligence (AI) systems increase in capability, so there are growing concerns over the ways in which the recommendations they provide can affect people's everyday life and decisions. The field of Explainable AI (XAI) aims to address such concerns but there is often a neglect of the human in this process. We present a formal definition of human-centred XAI and illustrate the application of this formalism to the design of a user interface.