We present a visual world eye-tracking study on Tseltal (a Mayan language) and investigate whether verbal information can be used to anticipate an upcoming referent. Basic word order in transitive sentences in Tseltal is Verb–Object–Subject (VOS). The verb is usually encountered first, making argument structure and syntactic information available at the outset, which should facilitate anticipation of the post-verbal arguments. Tseltal speakers listened to verb-initial sentences with either an object-predictive verb (e.g., “eat”) or a general verb (e.g., “look for”) (e.g., (...) “Ya slo’/sle ta stukel on te kereme,” Is eating/is looking (for) by himself the avocado the boy/ “The boy is eating/is looking (for) an avocado by himself”) while seeing a visual display showing one potential referent (e.g., avocado) and three distractors (e.g., bag, toy car, coffee grinder). We manipulated verb type (predictive vs. general) and recorded participants' eye movements while they listened and inspected the visual scene. Participants’ fixations to the target referent were analyzed using multilevel logistic regression models. Shortly after hearing the predictive verb, participants fixated the target object before it was mentioned. In contrast, when the verb was general, fixations to the target only started to increase once the object was heard. Our results suggest that Tseltal hearers pre-activate semantic features of the grammatical object prior to its linguistic expression. This provides evidence from a verb-initial language for online incremental semantic interpretation and anticipatory processing during language comprehension. These processes are comparable to the ones identified in subject-initial languages, which is consistent with the notion that different languages follow similar universal processing principles. (shrink)

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. (shrink)