Intelligent systems have traditionally been designed as tools rather than collaborators, often lacking critical characteristics that collaboration partnerships require. Recent advances in large language model (LLM) agents open new opportunities for human-LLM-agent collaboration by enabling natural communication and various social and cognitive behaviors. Yet it remains unclear whether principles of computer-mediated collaboration established in HCI and CSCW persist, change, or fail when humans collaborate with LLM agents. To support systematic investigations of these questions, we introduce an open and configurable research platform for HCI researchers. The platform's modular design allows seamless adaptation of classic CSCW experiments and manipulation of theory-grounded interaction controls. We demonstrate the platform's effectiveness and usability through two case studies: (1) re-implementing the classic human-human-collaboration task Shape Factory as a between-subject human-agent-collaboration experiment with 16 participants, and (2) a participatory cognitive walkthrough with five HCI researchers to refine workflows and interfaces for experiment setup and analysis.

Reinforcement Learning (RL) agents often exhibit learning behaviors that are not intuitively interpretable by human observers, which can result in suboptimal feedback in collaborative teaching settings. Yet, how humans perceive and interpret RL agent's learning behavior is largely unknown. In a bottom-up approach with two experiments, this work provides a data-driven understanding of the factors of human observers' understanding of the agent's learning process. A novel, observation-based paradigm to directly assess human inferences about agent learning was developed. In an exploratory interview study (\textit{N}=9), we identify four core themes in human interpretations: Agent Goals, Knowledge, Decision Making, and Learning Mechanisms. A second confirmatory study (\textit{N}=34) applied an expanded version of the paradigm across two tasks (navigation/manipulation) and two RL algorithms (tabular/function approximation). Analyses of 816 responses confirmed the reliability of the paradigm and refined the thematic framework, revealing how these themes evolve over time and interrelate. Our findings provide a human-centered understanding of how people make sense of agent learning, offering actionable insights for designing interpretable RL systems and improving transparency in Human-Robot Interaction.

EEG-based eye tracking (ET) is emerging as a promising application of brain-computer interfaces (BCIs) (Dietrich et al., 2017; Fuhl et al., 2023; Kastrati et al., 2021; Sun et al., 2023). While EEG is typically used to record the electrical activity of the brain, it also captures eye movement artifacts due to the inherent electrical charge of the eyes. Although these signals are usually considered noise in other BCI applications and are often removed (Croft and Barry, 2000), they can be effectively used to track eye movements. These signals are also easier to decode than brain activity, as they are not complicated by the complexity and noise associated with brain signal interpretation. In addition, achieving reliable and accurate eye tracking using EEG technology could significantly enhance existing consumer BCIs, opening up a wide range of new applications. Apart from the potential for BCI applications, EEG-based eye tracking is an interesting alternative to eye tracking in its own right, offering several advantages over camera-based eye tracking, which is the predominant method used for eye tracking today.

This article summarizes a systematic review of the electroencephalography (EEG)-based cognitive workload (CWL) estimation. The focus of the article is twofold: identify the disparate experimental paradigms used for reliably eliciting discreet and quantifiable levels of cognitive load and the specific nature and representational structure of the commonly used input formulations in deep neural networks (DNNs) used for signal classification. The analysis revealed a number of studies using EEG signals in its native representation of a two-dimensional matrix for offline classification of CWL. However, only a few studies adopted an online or pseudo-online classification strategy for real-time CWL estimation. Further, only a couple of interpretable DNNs and a single generative model were employed for cognitive load detection till date during this review. More often than not, researchers were using DNNs as black-box type models. In conclusion, DNNs prove to be valuable tools for classifying EEG signals, primarily due to the substantial modeling power provided by the depth of their network architecture. It is further suggested that interpretable and explainable DNN models must be employed for cognitive workload estimation since existing methods are limited in the face of the non-stationary nature of the signal.

Last year, I was in Paris for the International Convention of Psychological Science, one of the most prestigious gatherings in cognitive science. I listened to talks from my field, human reasoning, but I also enjoyed those on ethology, because I find studies on non-human animals, from turtles to parrots, fascinating. Despite their typically small sample sizes, I found the scientific reasoning in the animal-studies talks sounder, and their explanations richer, than the work I heard on human reasoning. The reason is simple: Ethologists evaluate their experimental paradigm, or set-up, in light of its ecological validity, or how well it matches natural surroundings. An animal's true habitat, and its evolutionary history, have always centered the discussion.

In March, I was in Paris for the International Convention of Psychological Science, one of the most prestigious gatherings in cognitive science. I listened to talks from my field, human reasoning, but I also enjoyed those on ethology, because I find studies on non-human animals, from turtles to parrots, fascinating. Despite their typically small sample sizes, I found the scientific reasoning in the animal-studies talks sounder, and their explanations richer, than the work I heard on human reasoning. The reason is simple: Ethologists evaluate their experimental paradigm, or set-up, in light of its ecological validity, or how well it matches natural surroundings. An animal's true habitat, and its evolutionary history, have always centered the discussion.

On October 8-10, 2009 an interdisciplinary group met at the Wylie Center in Beverley, Massachusetts to evaluate the state of the art in the computational modeling of narrative. Three important findings emerged: (1) current work in computational modeling is described by three different levels of representation; (2) there is a paucity of studies at the highest, most abstract level aimed at inferring the meaning or message of the narrative; and (3) there is a need to establish a standard data bank of annotated narratives, analogous to the Penn Treebank.

EEG connectivity measures could provide a new type of feature space for inferring a subject's intention in Brain-Computer Interfaces (BCIs). However, very little is known on EEG connectivity patterns for BCIs. In this study, EEG connectivity during motor imagery (MI) of the left and right is investigated in a broad frequency range across the whole scalp by combining Beamforming with Transfer Entropy and taking into account possible volume conduction effects. Observed connectivity patterns indicate that modulation intentionally induced by MI is strongest in the gamma-band, i.e., above 35 Hz. Furthermore, modulation between MI and rest is found to be more pronounced than between MI of different hands. This is in contrast to results on MI obtained with bandpower features, and might provide an explanation for the so far only moderate success of connectivity features in BCIs. It is concluded that future studies on connectivity based BCIs should focus on high frequency bands and consider experimental paradigms that maximally vary cognitive demands between conditions.