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The complexity and unpredictability of a situation might contribute to how much an individual feels in control of their actions. Goal-directed behaviour tailored to different situations is enabled through a hierarchy of situated action control combining cognitive and sensorimotor control processes. We use eye-tracking to investigate the grounding of cognitive processes in the sensorimotor system. Our assumption is that different degrees of perceived control trigger cognitive states that are reflected in eye-movement behaviour. Utilizing a dynamic experimental environment, we investigate whether complexity and uncertainty of the situation are top-down processed into fixational eye movements. The distance to a reference point is affected by environmental complexity in all fixations; however environmental uncertainty is only incorporated in fixations that guide motor control. We discuss that these fixations are only executed under high sense of control when there are enough cognitive resources left to top-down process the environmental uncertainty into gaze allocation.

The sense of control (SoC) is the subjective feeling of being in control over an action, influenced by controllability, difficulty and feedback. However, it remains unclear how SoC is formed in multitasking scenarios. We conducted a study to analyze SoC and its impact on task-switching behavior in multitasking scenarios. Participants were required to perform two tasks in parallel while in control of one task at a time, requiring voluntary switching. We found that task-specific SoCs are influenced by the controllability and difficulty of each task. An overall SoC can be explained mainly by these task-specific SoCs. But, the overall SoC did not correlate with the frequency of task switches or the relative time spent on one task. Our analysis indicates that the SoC of a more control-demanding task has greater impact on the overall SoC and even affects the task-specific SoC of the other task, as well as task-switching behavior.

How humans engage in goal-directed behavior within dynamic environments is still not completely understood. Pursuing goals in an environment that is characterized by constant unpredictable changes might be possible through the interaction of multiple layers of action control. A cognitive layer exerts situational control by selecting action intentions, while a motor control layer is responsible for execution. The motor layer informs the cognitive level, about disturbances during execution of these action intentions. We present an experimental dynamic environment, combining motor control manipulation and eye-tracking to investigate visuomotor grounding of cognitive processes. Our results indicate that inefficient motor control prompts strategic shifts in eye- movement behavior, with fixations closer to a reference point under moderate motor noise and further away under increased noise. We further find fixational and smooth pursuit eye movements that can be directly mapped to pursued action intentions. These findings shed light on the changes in action selection caused by noise in the motor system and can be used in a next step to investigate moment-to-moment changes in the pursuit of action intentions under inefficient motor control.

A detailed understanding of the cognitive process underlying diagnostic reasoning in medical experts is currently lacking. While high-level theories like hypothetico-deductive reasoning were proposed long ago, the inner workings of the step-by-step dynamics within the mind remain unknown. We present a fully automated approach to elicit, monitor, and record diagnostic reasoning processes at a fine-grained level. A web-based user interface enables physicians to carry out a full diagnosis process on a simulated patient, given as a pre-defined clinical vignette. By collecting the physician's information queries and hypothesis revisions, highly detailed diagnostic reasoning trajectories are captured leading to a diagnosis and its justification. Four expert epileptologists with a mean experience of 19 years were recruited to evaluate the system and share their impressions in semi-structured interviews. We find that the recorded trajectories validate proposed theories on broader diagnostic reasoning, while also providing valuable additional details extending previous findings.

Theory of Mind (ToM) or mentalizing is the ability to infer mental states of oneself and other agents. Theory of mind plays a key role in social interactions as it allows one to predict other agents' likely future actions by inferring what they may intend or know. However, there is a wide range of ToM skills of increasing complexity. While most people are generally capable of performing complex ToM reasoning such as recursive belief inference when explicitly prompted, there is much evidence that humans do not always use ToM to their full capabilities. Instead, people often fall back to heuristics and biases, such as an egocentric bias that projects one's beliefs and perspective onto the observed agent. We explore which (internal or external) factors may influence the mentalizing processes that humans employ unsolicitedly, i.e., employ without being primed or explicitly triggered. In this paper we present an online study investigating unbalanced decision problems where one choice is significantly better than the other. Our results demonstrate that participant's are significantly less likely to exhibit an egocentric bias in such situations.

Humans develop an ability for Theory of Mind (ToM) by theage of six, which enables them to infer another agent’s men-tal state and to differentiate it from one’s own. Much evi-dence suggests that humans can do this in a presumably op-timal way and, correspondingly, a Bayesian Theory of Mind(BToM) framework has been shown to match human infer-ences and attributions. Mostly, this has been investigated withspecific, explicit mentalizing tasks. However, other researchhas shown that humans often deviate from optimal reasoningin various ways. We investigate whether typical BToM modelsreally capture human ToM reasoning in tasks that solicit moreintuitive reasoning. We present results of an empirical studywhere humans deviate from Bayesian optimal reasoning in aToM task but instead exhibit egocentric tendencies. We alsodiscuss how computational models can better account for suchsub-optimal processing.

People use speech and gesture together when describing an event or action, where both modalities have different expressiveopportunities (Kendon, 2004). One question is how the two modalities are semantically coordinated, i.e. how meaning isdistributed across speech and accompanying gestures. While this has been studied only for adult speakers so far, here, wepresent a study on how young children (4 years of age) semantically coordinate speech and gesture, and how this relatesto their cognitive and (indirectly) their verbal skills. Results indicate significant positive correlations between cognitiveskills of the children and gesture-speech coordination. In addition, high cognitive skills correlate with the number ofsemantically relevant child descriptions revealing a link between verbal and cognitive skills.

During interaction with others, we perceive and produce so-cial actions in close temporal distance or even simultaneously.It has been argued that the motor system is involved in bothprocesses, but how does it distinguish in this processing be-tween self and other? In this paper, we present a model ofself-other distinction within a hierarchical sensorimotor sys-tem that is based on principles of perception-action couplingand predictive processing. For this we draw on mechanisms as-sumed for the integration of cues to generate sense of agency,i.e., the sense that an action is self-generated. We report re-sults from simulations of different social scenarios, showingthat the model is able to solve the problem of the dual use ofthe sensorimotor system.