Estimating Systemic Cognitive States from a Mixture of Physiological and Brain Signals.

As human-machine teams are being considered for a variety of mixed-initiative tasks, detecting and being responsive to human cognitive states, in particular systematic cognitive states, is among the most critical capabilities for artificial systems to ensure smooth interactions with humans and high overall team performance. Various human physiological parameters, such as heart rate, respiration rate, blood pressure, and skin conductance, as well as brain activity inferred from functional near-infrared spectroscopy or electroencephalogram, have been linked to different systemic cognitive states, such as workload, distraction, or mind-wandering among others. Whether these multimodal signals are indeed sufficient to isolate such cognitive states across individuals performing tasks or whether additional contextual information (e.

Investigating Methods for Cognitive Workload Estimation for Assistive Robots.

Robots interacting with humans in assistive contexts have to be sensitive to human cognitive states to be able to provide help when it is needed and not overburden the human when the human is busy. Yet, it is currently still unclear which sensing modality might allow robots to derive the best evidence of human workload. In this work, we analyzed and modeled data from a multi-modal simulated driving study specifically designed to evaluate different levels of cognitive workload induced by various secondary tasks such as dialogue interactions and braking events in addition to the primary driving task.

Cognitive cascades: How to model (and potentially counter) the spread of fake news.

Understanding the spread of false or dangerous beliefs-often called misinformation or disinformation-through a population has never seemed so urgent. Network science researchers have often taken a page from epidemiologists, and modeled the spread of false beliefs as similar to how a disease spreads through a social network. However, absent from those disease-inspired models is an internal model of an individual's set of current beliefs, where cognitive science has increasingly documented how the interaction between mental models and incoming messages seems to be crucially important for their adoption or rejection.

Assistive Robots for the Social Management of Health: A Framework for Robot Design and Human-Robot Interaction Research.

There is a close connection between health and the quality of one's social life. Strong social bonds are essential for health and wellbeing, but often health conditions can detrimentally affect a person's ability to interact with others. This can become a vicious cycle resulting in further decline in health.

Effects of Assistive Robot Behavior on Impressions of Patient Psychological Attributes: Vignette-Based Human-Robot Interaction Study.

Background: As robots are increasingly designed for health management applications, it is critical to not only consider the effects robots will have on patients but also consider a patient's wider social network, including the patient's caregivers and health care providers, among others.

Objective: In this paper we investigated how people evaluate robots that provide care and how they form impressions of the patient the robot cares for, based on how the robot represents the patient.

Methods: We have used a vignette-based study, showing participants hypothetical scenarios describing behaviors of assistive robots (patient-centered or task-centered) and measured their influence on people's evaluations of the robot itself (emotional intelligence [EI], trustworthiness, and acceptability) as well as people's perceptions of the patient for whom the robot provides care.

Using topic modeling to infer the emotional state of people living with Parkinson's disease.

Individuals with Parkinson's disease (PD) often exhibit facial masking (hypomimia), which causes reduced facial expressiveness. This can make it difficult for those who interact with the person to correctly read their emotional state and can lead to problematic social and therapeutic interactions. In this article, we develop a probabilistic model for an assistive device, which can automatically infer the emotional state of a person with PD using the topics that arise during the course of a conversation.

Mate choice strategies in a spatially-explicit model environment.

Decisions about the choice of a mate can greatly impact both individual fitness and selection processes. We developed a novel agent-based model to investigate two common mate choice rules that may be used by female gray treefrogs (Hyla versicolor). In this model environment, female agents using the minimum-threshold strategy found higher quality mates and traveled shorter distances on average, compared with female agents using the best-of-n strategy.

The Tactile Ethics of Soft Robotics: Designing Wisely for Human-Robot Interaction.

Soft robots promise an exciting design trajectory in the field of robotics and human-robot interaction (HRI), promising more adaptive, resilient movement within environments as well as a safer, more sensitive interface for the objects or agents the robot encounters. In particular, tactile HRI is a critical dimension for designers to consider, especially given the onrush of assistive and companion robots into our society. In this article, we propose to surface an important set of ethical challenges for the field of soft robotics to meet.

What we can and cannot (yet) do with functional near infrared spectroscopy.

Functional near infrared spectroscopy (NIRS) is a relatively new technique complimentary to EEG for the development of brain-computer interfaces (BCIs). NIRS-based systems for detecting various cognitive and affective states such as mental and emotional stress have already been demonstrated in a range of adaptive human-computer interaction (HCI) applications. However, before NIRS-BCIs can be used reliably in realistic HCI settings, substantial challenges oncerning signal processing and modeling must be addressed.

In praise of a model but not its conclusions: commentary on Cooper, Catmur, and Heyes (2012).

Cooper et al. (this issue) develop an interactive activation model of spatial and imitative compatibilities that simulates the key results from Catmur and Heyes (2011) and thus conclude that both compatibilities are mediated by the same processes since their single model can predict all the results. Although the model is impressive, the conclusions are premature because they are based on an incomplete review of the relevant literature and because the model includes some questionable assumptions.

Looking the part: social status cues shape race perception.

It is commonly believed that race is perceived through another's facial features, such as skin color. In the present research, we demonstrate that cues to social status that often surround a face systematically change the perception of its race. Participants categorized the race of faces that varied along White-Black morph continua and that were presented with high-status or low-status attire.

A mismatch in the human realism of face and voice produces an uncanny valley.

The uncanny valley has become synonymous with the uneasy feeling of viewing an animated character or robot that looks imperfectly human. Although previous uncanny valley experiments have focused on relations among a character's visual elements, the current experiment examines whether a mismatch in the human realism of a character's face and voice causes it to be evaluated as eerie. The results support this hypothesis.

Symbolic control of visual attention: semantic constraints on the spatial distribution of attention.

Humans routinely use spatial language to control the spatial distribution of attention. In so doing, spatial information may be communicated from one individual to another across opposing frames of reference, which in turn can lead to inconsistent mappings between symbols and directions (or locations). These inconsistencies may have important implications for the symbolic control of attention because they can be translated into differences in cue validity, a manipulation that is known to influence the focus of attention.

Is there more to "model" than "muddle"?

Any discussion comparing different models with respect to their quality qua models must presuppose a notion of model, that is, what it is to be a model. While Webb provides seven criteria to assess the quality of various proposed biorobotic models, she does not clarify the very notion of "model of animal behavior" itself.

Architectural mechanisms for dynamic changes of behavior selection strategies in behavior-based systems.

Behavior selection is typically a "built-in" feature of behavior-based architectures and hence, not amenable to change. There are, however, circumstances where changing behavior selection strategies is useful and can lead to better performance. In this paper, we demonstrate that such dynamic changes of behavior selection mechanisms are beneficial in several circumstances.