Comparative Analysis of Think-Aloud Methods for Everyday Activities in the Context of Cognitive Robotics

Meier, Moritz; Mason, Celeste; Putze, Felix; Schultz, Tanja

doi:10.21437/interspeech.2019-3072

Cited by 5 publications

(4 citation statements)

References 12 publications

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“…EASE Table Setting data set (EASE‐TSD) (Meier et al., 2018) consists of table setting instances that have been collected in a subproject of the Everyday Science and Engineering (EASE) collaborative research center. Participants were instructed to set the table under varying conditions (e.g., for a different number of people, different meals, and adhering to different degrees of formalism).…”

Section: Model Simulation and Evaluationmentioning

confidence: 99%

“…While several approaches exist that specifically consider ill‐defined tasks (Firby, 1987; Jiménez, De La Rosa, Fernández, Fernández, & Borrajo, 2012), they are ill‐suited to explain the cognitive processes involved in human planning and action selection as they are either infeasible or inefficient in real‐world settings (Georgievski & Aiello, 2015). There are several data sets available that consider everyday activities, but without studying human action selection behavior, instead focusing on motion segmentation and action recognition (Damen, D. et al., 2018; Rohrbach et al., 2016; Rybok, Friedberger, Hanebeck, & Stiefelhagen, 2011; Tenorth, Bandouch, & Beetz, 2009), collecting biosignals of everyday activities (Meier, Mason, Porzel, Putze, & Schultz, 2018), and understanding causal dependencies of actions (Uhde, Berberich, Ramirez‐Amaro, & Cheng, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Action Selection in Everyday Activities: The Opportunistic Planning Model

Wenzl,

Schultheis

2024

Cognitive Science

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While action selection strategies in well‐defined domains have received considerable attention, little is yet known about how people choose what to do next in ill‐defined tasks. In this contribution, we shed light on this issue by considering everyday tasks, which in many cases have a multitude of possible solutions (e.g., it does not matter in which order the items are brought to the table when setting a table) and are thus categorized as ill‐defined problems. Even if there are no hard constraints on the ordering of subtasks in everyday activities, our research shows that people exhibit specific preferences. We propose that these preferences arise from bounded rationality, that is, people only have limited knowledge and processing power available, which results in a preference to minimize the overall physical and cognitive effort. In the context of everyday activities, this can be achieved by (a) taking properties of the spatial environment into account to use them to one's advantage, and (b) employing a stepwise‐optimal action selection strategy. We present the Opportunistic Planning Model as an explanatory cognitive model, which instantiates these assumptions, and show that the model is able to generalize to new everyday tasks, outperforming machine learning models such as neural networks during generalization.

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Section: Model Simulation and Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Action Selection in Everyday Activities: The Opportunistic Planning Model

Wenzl,

Schultheis

2024

Cognitive Science

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“…Videos were annotated with EASELAN [39], a modification of the ELAN [36] software by the Max Planck Institute for Psycholinguistics. The process was carried out according to a predefined subset of SOMA events for human activity description developed in collaboration between the human activity and ontology subgroups of EASE CRC.…”

Section: Brain Patterns Of Naturalistic Viewingmentioning

confidence: 99%

Towards a Neuronally Consistent Ontology for Robotic Agents

Ahrens,

Pomarlan,

Beßler

et al. 2023

Frontiers in Artificial Intelligence and Applications

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The Collaborative Research Center for Everyday Activity Science & Engineering (CRC EASE) aims to enable robots to perform environmental interaction tasks with close to human capacity. It therefore employs a shared ontology to model the activity of both kinds of agents, empowering robots to learn from human experiences. To properly describe these human experiences, the ontology will strongly benefit from incorporating characteristics of neuronal information processing which are not accessible from a behavioral perspective alone. We, therefore, propose the analysis of human neuroimaging data for evaluation and validation of concepts and events defined in the ontology model underlying most of the CRC projects. In an exploratory analysis, we employed an Independent Component Analysis (ICA) on functional Magnetic Resonance Imaging (fMRI) data from participants who were presented with the same complex video stimuli of activities as robotic and human agents in different environments and contexts. We then correlated the activity patterns of brain networks represented by derived components with timings of annotated event categories as defined by the ontology model. The present results demonstrate a subset of common networks with stable correlations and specificity towards particular event classes and groups, associated with environmental and contextual factors. These neuronal characteristics will open up avenues for adapting the ontology model to be more consistent with human information processing.

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“…The following study would not have been conducted without LabLinking, since it builds on the complementary expertise and equipment of two laboratories: the Medical Assistance Systems Group (MAS) at Bielefeld University with its rich expertise in social robotics based on robots such as Pepper, Nao, or Flobi [19][20][21], and the Cognitive Systems Lab (CSL) at University of Bremen with vast experience in biosignal-adaptive cognitive systems [22] based on multimodal biosignal acquisition [23] and processing using machine learning methods [24], including the recording and interpretation of spoken communication [25] and high-density EEG in the context of intelligent robots and systems [26].…”

Section: Introductionmentioning

confidence: 99%

EEG Correlates of Distractions and Hesitations in Human–Robot Interaction: A LabLinking Pilot Study

Richter

Putze

Ivucic

et al. 2023

MTI

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In this paper, we investigate the effect of distractions and hesitations as a scaffolding strategy. Recent research points to the potential beneficial effects of a speaker’s hesitations on the listeners’ comprehension of utterances, although results from studies on this issue indicate that humans do not make strategic use of them. The role of hesitations and their communicative function in human-human interaction is a much-discussed topic in current research. To better understand the underlying cognitive processes, we developed a human–robot interaction (HRI) setup that allows the measurement of the electroencephalogram (EEG) signals of a human participant while interacting with a robot. We thereby address the research question of whether we find effects on single-trial EEG based on the distraction and the corresponding robot’s hesitation scaffolding strategy. To carry out the experiments, we leverage our LabLinking method, which enables interdisciplinary joint research between remote labs. This study could not have been conducted without LabLinking, as the two involved labs needed to combine their individual expertise and equipment to achieve the goal together. The results of our study indicate that the EEG correlates in the distracted condition are different from the baseline condition without distractions. Furthermore, we could differentiate the EEG correlates of distraction with and without a hesitation scaffolding strategy. This proof-of-concept study shows that LabLinking makes it possible to conduct collaborative HRI studies in remote laboratories and lays the first foundation for more in-depth research into robotic scaffolding strategies.

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Comparative Analysis of Think-Aloud Methods for Everyday Activities in the Context of Cognitive Robotics

Cited by 5 publications

References 12 publications

Action Selection in Everyday Activities: The Opportunistic Planning Model

Action Selection in Everyday Activities: The Opportunistic Planning Model

Towards a Neuronally Consistent Ontology for Robotic Agents

EEG Correlates of Distractions and Hesitations in Human–Robot Interaction: A LabLinking Pilot Study

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