Much of behaviour consists of everyday activities — mundane tasks that are nevertheless essential such as dressing, grooming, commuting, and preparing meals. These activities are performed on a regular basis, in familiar environments, and in stereotyped ways, and often with limited overt attention. Yet from a cognitive perspective their production is surprisingly complex. This topic consists of eight papers that span different aspects of everyday activities, ranging from neuroscience through philosophical considerations and implications to lessons from robotics.

Routine action sequences critically rely on neural mechanisms maintaining contextual and temporal information to disambiguate similar tasks (e.g. making coffee or tea). In this study we show the involvement of areas in temporal and lateral prefrontal cortices in maintaining temporal and contextual information for the execution of hierarchically-organized action sequences.

Yanaoka and Saito offer a review of developmental evidence about the routine system and its potential developmental mechanism and driving factors. They suggest that an investigation into the interplay between routine and executive functions can form foundations for understanding learning, performing, and controlling repeated sequential actions in young children.

Taking a computational and developmental perspective, the authors trace a developmental trajectory in the understanding of natural language expressions of frequency and probability in 4- to 14-year-old children and adults. They find that the quantitative estimates of children converge to those of adults from around 9 years on.

This paper argues that a proper understanding of action slips, and thus intentional action more generally, requires an understanding of the control structure that implements an agent's guiding intentions. An account of this structure is then offered, central to which are motor representations that are active downstream of intention and attentional processes that ensure that they reliably implement the intentions they serve.

Mis and Giovannetti argue that theoretically motivated interventions for aphasia can serve as an instructive paradigm for the development of interventions to improve everyday functioning in older adults. They discuss existing aphasia interventions and hypothesized parallel targets in action based on shared characteristics between cognitive models of language and everyday action.

In a sequential reaching task, a robot arm trained using deep reinforcement learning optimizes its behavior in a similar manner to humans. This so-called centering behavior, however, only tends to develop when learning is performed in a curriculum in which the reward function becomes increasingly complex over time. Furthermore, the onset of the curriculum seems to have a sensitive period, with too early or too late onset leading to unstable or suboptimal performance.

We provide a uniform overview of a robotic cognitive control framework discussing the potential of the proposed approach in different scenarios where humans and robots collaborate for learning and executing everyday activities.

We show how the CRAM cognitive architecture allows a robot to carry out simple everyday activities and show how it can be extended by leveraging insights from the situation model perspective on the cognitive mechanisms underlying flexible context-sensitive behavior.

This article has two aims: First to present a case for the integration of cognitive tools into our cognitive systems as a process of Enculturation. Second, to present an argument against the simplifying account of cognitive tools as ‘offloading’ cognitive complexity, or ‘outsourcing’ cognitive processing to the tools themselves. We use an extensive case study of spatial navigation to demonstrate the core claims.

Characterizing the dynamics of children's conversations across development, using a Hidden Topic Markov Model to classify parents' and children's communicative acts and analyze their sequential structure in conversation.

We compare methods of aggregating repeated decisions from a single decision maker to improve medical image decision making using metacognitive processes (confidence judgments) and representational similarity from artificial neural networks for both novice and expert participants.

Traditional statistics instruction and practice emphasize a p < .05 categorical boundary. Our results suggest a categorical perception-like effect for graduate students' processing of p-values, creating a psychologically real divide between so-called statistical ‘significance’ and ‘non-significance’.