Category Archives: Psycho-physiological Bases Of Engineering

A model of the psychomotor behaviour of humans intended to be useful for integration with robots

September 3, 2021 11:14 ,

Stephen Fox, Adrian Kotelba, Ilari Marstio, Jari Montonen, Aligning human psychomotor characteristics with robots, exoskeletons and augmented reality, Robotics and Computer-Integrated Manufacturing, Volume 63, 2020, DOI: 10.1016/j.rcim.2019.101922.

In previous production literature, the uncertainty of human behaviour has been recognized as a source of productivity, quality, and safety problems. However, fundamental reasons for the uncertainty of human behavior have received little analysis in the production literature. Furthermore, potential for these fundamental reasons to be aligned with production technologies in order to improve production performance has not been addressed. By contrast, in this paper, fundamental reasons for the uncertainty of human behaviour are explained through a model of psychomotor characteristics that encompasses physiology, past experiences, personality, gender, culture, emotion, reasoning, and biocybernetics. Through reference to 10 action research cases, the formal model is applied to provide guidelines for planning production work that includes robots, exoskeletons, and augmented reality.

Posted in: Human-robot interaction, Psycho-physiological bases of engineering , Tagged:

Symbol grounding through neural networks

September 3, 2021 11:06 ,

Shridhar M, Mittal D, Hsu D., INGRESS: Interactive visual grounding of referring expressions, The International Journal of Robotics Research. January 2020, DOI: 10.1177/0278364919897133.

This article presents INGRESS, a robot system that follows human natural language instructions to pick and place everyday objects. The key question here is to ground referring expressions: understand expressions about objects and their relationships from image and natural language inputs. INGRESS allows unconstrained object categories and rich language expressions. Further, it asks questions to clarify ambiguous referring expressions interactively. To achieve these, we take the approach of grounding by generation and propose a two-stage neural-network model for grounding. The first stage uses a neural network to generate visual descriptions of objects, compares them with the input language expressions, and identifies a set of candidate objects. The second stage uses another neural network to examine all pairwise relations between the candidates and infers the most likely referred objects. The same neural networks are used for both grounding and question generation for disambiguation. Experiments show that INGRESS outperformed a state-of-the-art method on the RefCOCO dataset and in robot experiments with humans. The INGRESS source code is available at https://github.com/MohitShridhar/ingress.

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Do we prefer that our predictions fit observations -to validate our expectations- or that they surprise us -to acquire new knowledge-?

October 23, 2020 16:59 ,

Clare Press, Peter Kok, Daniel Yon, The Perceptual Prediction Paradox, Trends in Cognitive Sciences, Volume 24, Issue 1, January 2020, Pages 4-6, DOI: 10.1016/j.tics.2019.11.003.

From the noisy information bombarding our senses, our brains must construct percepts that are veridical – reflecting the true state of the world – and informative – conveying what we did not already know. Influential theories suggest that both challenges are met through mechanisms that use expectations about the likely state of the world to shape perception. However, current models explaining how expectations render perception either veridical or informative are mutually incompatible. While the former propose that perceptual experiences are dominated by events we expect, the latter propose that perception of expected events is suppressed. To solve this paradox we propose a two-process model in which probabilistic knowledge initially biases perception towards what is likely and subsequently upweights events that are particularly surprising.

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Similarities between motor control and cognitive control

October 23, 2020 16:56 ,

Harrison Ritz, Romy Frömer, Amitai Shenhav, Bridging Motor and Cognitive Control: It’s About Time!, Trends in Cognitive Sciences, Volume 24, Issue 1, January 2020, Pages 4-6, DOI: 10.1016/j.tics.2019.11.005.

Is how we control our thoughts similar to how we control our movements? Egger et al. show that the neural dynamics underlying the control of internal states exhibit similar algorithmic properties as those that control movements. This experiment reveals a promising connection between how we control our brain and our body.

Posted in: Psycho-physiological bases of engineering

Interesting alternative to the classical “maximize expected utility” rule for decision making

October 23, 2020 16:54 ,

EtienneKoechlin, Human Decision-Making beyond the Rational Decision Theory, Trends in Cognitive Sciences, Volume 24, Issue 1, January 2020, Pages 4-6, DOI: 10.1016/j.tics.2019.11.001.

Two recent studies (Farashahi et al. and Rouault et al.) provide compelling evidence refuting the Subjective Expected Utility (SEU) hypothesis as a ground model describing human decision-making. Together, these studies pave the way towards a new model that subsumes the notion of decision-making and adaptive behavior into a single account.

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On the importance of dynamics and diversity in (cognitive) symbol systems

October 23, 2020 15:55 ,

Tadahiro Taniguchi; Emre Ugur; Matej Hoffmann; Lorenzo Jamone; Takayuki Nagai; Benjamin Rosman, Symbol Emergence in Cognitive Developmental Systems: A Survey, IEEE Transactions on Cognitive and Developmental Systems ( Volume: 11, Issue: 4, Dec. 2019), DOI: 10.1109/TCDS.2018.2867772.

Humans use signs, e.g., sentences in a spoken language, for communication and thought. Hence, symbol systems like language are crucial for our communication with other agents and adaptation to our real-world environment. The symbol systems we use in our human society adaptively and dynamically change over time. In the context of artificial intelligence (AI) and cognitive systems, the symbol grounding problem has been regarded as one of the central problems related to symbols. However, the symbol grounding problem was originally posed to connect symbolic AI and sensorimotor information and did not consider many interdisciplinary phenomena in human communication and dynamic symbol systems in our society, which semiotics considered. In this paper, we focus on the symbol emergence problem, addressing not only cognitive dynamics but also the dynamics of symbol systems in society, rather than the symbol grounding problem. We first introduce the notion of a symbol in semiotics from the humanities, to leave the very narrow idea of symbols in symbolic AI. Furthermore, over the years, it became more and more clear that symbol emergence has to be regarded as a multifaceted problem. Therefore, second, we review the history of the symbol emergence problem in different fields, including both biological and artificial systems, showing their mutual relations. We summarize the discussion and provide an integrative viewpoint and comprehensive overview of symbol emergence in cognitive systems. Additionally, we describe the challenges facing the creation of cognitive systems that can be part of symbol emergence systems.

Posted in: Developmental robotics, Psycho-physiological bases of engineering , Tagged: ,

Interesting related work on internal models for action prediction and on the exploration/exploitation trade-off

October 23, 2020 15:48 ,

Simón C. Smith; J. Michael Herrmann, Evaluation of Internal Models in Autonomous Learning, IEEE Transactions on Cognitive and Developmental Systems ( Volume: 11, Issue: 4, Dec. 2019), DOI: 10.1109/TCDS.2018.2865999.

Internal models (IMs) can represent relations between sensors and actuators in natural and artificial agents. In autonomous robots, the adaptation of IMs and the adaptation of the behavior are interdependent processes which have been studied under paradigms for self-organization of behavior such as homeokinesis. We compare the effect of various types of IMs on the generation of behavior in order to evaluate model quality across different behaviors. The considered IMs differ in the degree of flexibility and expressivity related to, respectively, learning speed and structural complexity of the model. We show that the different IMs generate different error characteristics which in turn lead to variations of the self-generated behavior of the robot. Due to the tradeoff between error minimization and complexity of the explored environment, we compare the models in the sense of Pareto optimality. Among the linear and nonlinear models that we analyze, echo-state networks achieve a particularly high performance which we explain as a result of the combination of fast learning and complex internal dynamics. More generally, we provide evidence that Pareto optimization is preferable in autonomous learning as it allows that a special solution can be negotiated in any particular environment.

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On rewards and values when the RL theory is applied to human brain

September 16, 2019 16:04 ,

Keno Juechems, Christopher Summerfield, Where Does Value Come From?. Trends in Cognitive Sciences, Volume 23, Issue 10, 2019, Pages 836-850, DOI: 10.1016/j.tics.2019.07.012.

The computational framework of reinforcement learning (RL) has allowed us to both understand biological brains and build successful artificial agents. However, in this opinion, we highlight open challenges for RL as a model of animal behaviour in natural environments. We ask how the external reward function is designed for biological systems, and how we can account for the context sensitivity of valuation. We summarise both old and new theories proposing that animals track current and desired internal states and seek to minimise the distance to a goal across multiple value dimensions. We suggest that this framework readily accounts for canonical phenomena observed in the fields of psychology, behavioural ecology, and economics, and recent findings from brain-imaging studies of value-guided decision-making.

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On the integer numbers in the brain

September 16, 2019 16:01 ,

Susan Carey, David Barner, Ontogenetic Origins of Human Integer Representations. Trends in Cognitive Sciences, Volume 23, Issue 10, 2019, Pages 823-835, DOI: 10.1016/j.tics.2019.07.004.

Do children learn number words by associating them with perceptual magnitudes? Recent studies argue that approximate numerical magnitudes play a foundational role in the development of integer concepts. Against this, we argue that approximate number representations fail both empirically and in principle to provide the content required of integer concepts. Instead, we suggest that children\u2019s understanding of integer concepts proceeds in two phases. In the first phase, children learn small exact number word meanings by associating words with small sets. In the second phase, children learn the meanings of larger number words by mastering the logic of exact counting algorithms, which implement the successor function and Hume\u2019s principle (that one-to-one correspondence guarantees exact equality). In neither phase do approximate number representations play a foundational role.

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On the role and limitations of motor internal simulation as a way of predicting the effects of a future action in the brain

September 16, 2019 15:46 ,

Myrthel Dogge, Ruud Custers, Henk Aarts, Moving Forward: On the Limits of Motor-Based Forward Models. Trends in Cognitive Sciences, Volume 23, Issue 9, 2019, Pages 743-753, DOI: 10.1016/j.tics.2019.06.008.

The human ability to anticipate the consequences that result from action is an essential building block for cognitive, emotional, and social functioning. A dominant view is that this faculty is based on motor predictions, in which a forward model uses a copy of the motor command to predict imminent sensory action-consequences. Although this account was originally conceived to explain the processing of action-outcomes that are tightly coupled to bodily movements, it has been increasingly extrapolated to effects beyond the body. Here, we critically evaluate this generalization and argue that, although there is ample evidence for the role of predictions in the processing of environment-related action-outcomes, there is hitherto little reason to assume that these predictions result from motor-based forward models.

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