The variety of information about users hidden in the details of interaction data is increasingly being utilized for recognizing complex mental processes. Digital systems can correspondingly influence mental processes of users, paving the way for new interactive systems that interface with the human mind. This thesis presents advances to such interfaces: through four papers I show how human affect and cognition can be sensed and influenced computationally.

Paper 1 presents two studies that together show that affect influences mobile interaction, which allows for binary discrimination between neutral and positive affect using sensor led machine learning classification. Paper 2 builds upon the methods presented in Paper 1 and extends the classification domain to dishonesty, also using mobile interaction data. The paper shows across three studies how dishonesty and honesty vary in interactional details, and how this difference can be utilized for estimating the veracity of user behavior based on features that are engineered by mobile interaction data.

Paper 3 presents a feasibility study of conducting virtual reality studies outside a laboratory, to increase heterogeneity and power. The paper shows through two studies how a range of VR tasks can be conducted without the use of an immediate experimenter, with participants carrying out experiments themselves. In Paper 4 I apply this methodology, and conduct a VR study with more than 200 participants to study how manipulations to avatars can influence affect responses. The paper presents evidence supporting the link between affect and avatars, and additionally discusses the interplay between positive affect and body ownership.