| 1. |
- Kågebäck, Mikael, 1981, et al.
(author)
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DeepColor: Reinforcement Learning optimizes information efficiency and well-formedness in color name partitioning
- 2018
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In: CogSci 2018, 40th annual Cognitive Science Society meeting, Madison Wisconsin USA, July 25-28 2018. - Oakbrook Terrace, IL, USA : Cognitive Science Society. - 9780991196784
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Conference paper (peer-reviewed)abstract
- As observed in the World Color Survey (WCS), some universal properties can be identified in color naming schemes over a large number of languages. For example, Regier, Kay, and Khetrapal (2007) and Regier, Kemp, and Kay (2015); Gibson et al. (2017) recently explained these universal patterns in terms of near optimal color partitions and information theoretic measures of efficiency of communication. Here, we introduce a computational learning framework with multi-agent systems trained by reinforcement learning to investigate these universal properties. We compare the results with Regier et al. (2007, 2015) and show that our model achieves excellent quantitative agreement. This work introduces a multi-agent reinforcement learning framework as a powerful and versatile tool to investigate such semantic universals in many domains and contribute significantly to central questions in cognitive science.
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| 2. |
- Petrovych, Veronika, 1990-, et al.
(author)
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Human Interpretation of Goal-Directed Autonomous Car Behavior
- 2018
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In: COGSCI2018 Changing / minds, 40th annual cognitive science society meeting, Madison, Wisconsin, USA, July 25-28. - Victoria, British Columbia : Cognitive Science Society. - 9780991196784 ; , s. 2235-2240
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Conference paper (peer-reviewed)abstract
- People increasingly interact with different types of autonomous robotic systems, ranging from humanoid social robots to driverless vehicles. But little is known about how people interpret the behavior of such systems, and in particular if and how they attribute cognitive capacities and mental states to them. In a study concerning people’s interpretations of autonomous car behavior, building on our previous research on human-robot interaction, participants were presented with (1) images of cars – either with or without a driver – exhibiting various goal-directed traffic behaviors, and (2) brief verbal descriptions of that behavior. They were asked to rate the extent to which these behaviors were intentional and judge the plausibility of different types of causal explanations. The results indicate that people (a) view autonomous car behavior as goal-directed, (b) discriminate between intentional and unintentional autonomous car behaviors, and (c) view the causes of autonomous and human traffic behaviors similarly, in terms of both intentionality ascriptions and behavior explanations. However, there was considerably lower agreement in participant ratings of the driverless behaviors, which might indicate an increased difficulty in interpreting goal-directed behavior of autonomous systems.
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| 3. |
- Roszko, Maximilian, et al.
(author)
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The Phenomenology of Eye Movement Intentions and their Disruption in Goal-Directed Actions
- 2018
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In: Proceedings of the 40th Annual Conference of the Cognitive Science Society. - 9780991196784 ; , s. 973-978
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Conference paper (peer-reviewed)abstract
- The role of intentions in motor planning is heavily weighted in classical psychological theories, but their role in generating eye movements, and our awareness of these oculomotor intentions, has not been investigated explicitly. In this study, the extent to which we monitor oculomotor intentions, i.e. the intentions to shift one’s gaze towards a specific location, and whether they can be expressed in conscious experience, is investigated. A forced-choice decision task was developed where a pair of faces moved systematically across a screen. In some trials, the pair of faces moved additionally as soon as the participants attempted to gaze at one of the faces, preventing them from ever viewing it. The results of the experiment suggest that humans in general do not monitor their eye movement intentions in a way that allows for mismatches between planned gaze landing target and resulting gaze landing target to be consciously experienced during decision-making.
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