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.g., about the task state or the task environment) is required for making appropriate inferences remains an important open problem. In this paper, we introduce an experimental and machine learning framework for investigating these questions and focus specifically on using physiological and neurophysiological measurements to learn classifiers associated with systemic cognitive states like cognitive load, distraction, sense of urgency, mind wandering, and interference. Specifically, we describe a multitasking interactive experimental setting used to obtain a comprehensive multimodal data set which provided the foundation for a first evaluation of various standard state-of-the-art machine learning techniques with respect to their effectiveness in inferring systemic cognitive states. While the classification success of these standard methods based on just the physiological and neurophysiological signals across subjects was modest, which is to be expected given the complexity of the classification problem and the possibility that higher accuracy rates might not in general be achievable, the results nevertheless can serve as a baseline for evaluating future efforts to improve classification, especially methods that take contextual aspects such as task and environmental states into account.

Repair: The Interface Between Interaction and Cognition.

Conversational repair is the process people use to detect and resolve problems of speaking, hearing, and understanding. Through repair, participants in social interaction display how they establish and maintain communication and mutual understanding. We argue that repair provides a crucial theoretical interface for research between diverse approaches to studying human interaction. We provide an overview of conversation analytic findings about repair in order to encourage further cross-disciplinary research involving both detailed inductive inquiry and more theory-driven experimental approaches. We outline CA's main typologies of repair and its methodological rationale, and we provide transcripts and examples that readers can explore for themselves using open data from online corpora. Since participants in interaction use repair to deal with problems as they emerge at the surface level of talk, we conclude that repair can be a point of convergence for studying mis/communication from multiple methodological perspectives.

Prediction of turn-ends based on anticipation of upcoming words.

During conversation listeners have to perform several tasks simultaneously. They have to comprehend their interlocutor's turn, while also having to prepare their own next turn. Moreover, a careful analysis of the timing of natural conversation reveals that next speakers also time their turns very precisely. This is possible only if listeners can predict accurately when the speaker's turn is going to end. But how are people able to predict when a turn-ends? We propose that people know when a turn-ends, because they know how it ends. We conducted a gating study to examine if better turn-end predictions coincide with more accurate anticipation of the last words of a turn. We used turns from an earlier button-press experiment where people had to press a button exactly when a turn-ended. We show that the proportion of correct guesses in our experiment is higher when a turn's end was estimated better in time in the button-press experiment. When people were too late in their anticipation in the button-press experiment, they also anticipated more words in our gating study. We conclude that people made predictions in advance about the upcoming content of a turn and used this prediction to estimate the duration of the turn. We suggest an economical model of turn-end anticipation that is based on anticipation of words and syntactic frames in comprehension.

Transcript-based computer animation of movement: evaluating a new tool for nonverbal behavior research.

A new approach for the use of computer animation in experimental nonverbal research is introduced. The method was evaluated in a pilot study comparing video recordings of movement in dyadic interactions with computer animations based on transcripts of the behavior, to determine whether similar impression effects could be obtained. At the core of our development is a software tool allowing for the conversion of so-called position time-series protocols of movement into animation scripts for a professional computer animation platform. Our software combines computer-assisted movement transcription and editing with state-of-the-art 3-D animation technology. We present empirical evidence indicating remarkable overall correspondence between video recordings and computer animations. Due to the lack of facial activity in the computer animations, a decline in visual attention for the face area could be observed, which did not, however, affect the impression ratings.

Morphometric and dendritic analysis of fascia dentata granule cells in human aging and senile dementia.

In this study the cellular morphology in the human fascia dentata of 5 very old demented cases (4 Alzheimer's disease and 1 multi-infarct dementia patients) was compared with 5 (very) old controls cases. The postmortem delay in fixation was for all cases within 3.5 h. In the demented group, a significant reduction in thickness of the molecular layer, density of dendritic spines in the middle third of the molecular layer and total dendritic length (+/- 30%) was found. The number of dendritic segments, indicative of the branching frequency showed no difference. In both the control and the demented group, three-quarters of all dendritic bifurcations of granule cells occurred in the inner third of the molecular layer in which the commissural and associational fibers terminate. The size of the dendrites in the demented group could be the result of at least 3 independent processes: a regressive change due to partial denervation of the outer two-thirds of the molecular layer as axons from the perforant pathway are lost; a dendritic regrowth in response to sprouting of the commissural-associational fiber systems and septal afferents, which is presumed to occur in response to degeneration of perforant path axons; a dendritic regrowth in response to the loss of the dendrites of neighboring cells which have died. Analysis of our material suggests that dendritic degeneration is the predominant factor in the demented group.

The influence of post-mortem fixation delay on the reliability of the Golgi silver impregnation.

Brains of guinea-pigs were studied using the rapid Golgi technique after formol fixation. The delay between death and fixation varied from 5 min to 24 h. A considerable drop in density of spines on the apical, basal and oblique dendrites of pyramidal V cells was found from 1.5 h onwards. The total length of the basal dendrite arborization showed a reduction after 4 h. It was concluded that, particularly for quantitative methods, a fixation delay of more than 4 h makes results progressively less reliable.