Category Archives: Psycho-physiological Bases Of Engineering

The concepts of agency and ownership in cognitive maps, and a nice survey of cognitive maps

May 24, 2019 11:00 ,

Shahar Arzy, Daniel L. Schacter, Self-Agency and Self-Ownership in Cognitive Mapping, Trends in Cognitive Sciences, Volume 23, Issue 6, 2019, Pages 476-487, DOI: 10.1016/j.tics.2019.04.003.

The concepts of agency of one’s actions and ownership of one’s experience have proved useful in relating body representations to bodily consciousness. Here we apply these concepts to cognitive maps. Agency is defined as ‘the sense that I am the one who is generating the experience represented on a cognitive map’, while ownership is defined as ‘the sense that I am the one who is undergoing an experience, represented on a cognitive map’. The roles of agency and ownership are examined with respect to the transformation between egocentric and allocentric representations and the underlying neurocognitive and computational mechanisms; and within the neuropsychiatric domain, including Alzheimer’s disease (AD) and other memory-related disorders, in which the senses of agency and ownership may be disrupted.

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A nice (short) survey of deep RL

May 10, 2019 11:43 ,

Matthew Botvinick, Sam Ritter, Jane X. Wang, Zeb Kurth-Nelson, Charles Blundell, Demis Hassabis, Reinforcement Learning, Fast and Slow, Trends in Cognitive Sciences, Volume 23, Issue 5, 2019, Pages 408-422 DOI: 10.1016/j.tics.2019.02.006.

Deep reinforcement learning (RL) methods have driven impressive advances in artificial intelligence in recent years, exceeding human performance in domains ranging from Atari to Go to no-limit poker. This progress has drawn the attention of cognitive scientists interested in understanding human learning. However, the concern has been raised that deep RL may be too sample-inefficient – that is, it may simply be too slow – to provide a plausible model of how humans learn. In the present review, we counter this critique by describing recently developed techniques that allow deep RL to operate more nimbly, solving problems much more quickly than previous methods. Although these techniques were developed in an AI context, we propose that they may have rich implications for psychology and neuroscience. A key insight, arising from these AI methods, concerns the fundamental connection between fast RL and slower, more incremental forms of learning.

Posted in: Psycho-physiological bases of engineering, Reinforcement learning theory , Tagged: ,

Measuring unconscious behaviour

May 10, 2019 11:39 ,

David Soto, Usman Ayub Sheikh, Clive R. Rosenthal, A Novel Framework for Unconscious Processing, Trends in Cognitive Sciences, Volume 23, Issue 5, 2019, Pages 372-376 DOI: 10.1016/j.tics.2019.03.002.

Understanding the distinction between conscious and unconscious cognition remains a priority in psychology and neuroscience. A comprehensive neurocognitive account of conscious awareness will not be possible without a sound framework to isolate and understand unconscious information processing. Here, we provide a brain-based framework that allows the identification of unconscious processes, even with null effects on behaviour.

Posted in: Cognitive sciences, Psycho-physiological bases of engineering

On how attention, modelled by bayesian inference (for category learning), can structure the way reinforcement learning works

March 20, 2019 10:18 ,

Angela Radulescu, Yael Niv, Ian Ballard, Holistic Reinforcement Learning: The Role of Structure and Attention, Trends in Cognitive Sciences, Volume 23, Issue 4, 2019, Pages 278-292, DOI: 10.1016/j.tics.2019.01.010.

Compact representations of the environment allow humans to behave efficiently in a complex world. Reinforcement learning models capture many behavioral and neural effects but do not explain recent findings showing that structure in the environment influences learning. In parallel, Bayesian cognitive models predict how humans learn structured knowledge but do not have a clear neurobiological implementation. We propose an integration of these two model classes in which structured knowledge learned via approximate Bayesian inference acts as a source of selective attention. In turn, selective attention biases reinforcement learning towards relevant dimensions of the environment. An understanding of structure learning will help to resolve the fundamental challenge in decision science: explaining why people make the decisions they do.

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On how value of actions (in the RL sense) can be coded in the brain

March 20, 2019 10:15 ,

Rory J. Bufacchi, Gian Domenico Iannetti, The Value of Actions, in Time and Space, Trends in Cognitive Sciences, Volume 23, Issue 4, 2019, Pages 270-271, DOI: 10.1016/j.tics.2019.01.011.

This value-output function can be a neural network, in which case the assumptions about the future are stored in the precise network configuration. The values that such a network outputs, or at least the intermediate steps necessary for calculating the final values, are the ‘action relevances’ we mention in our original paper (in the case of the brain, the inputs to such a value-calculating network should be state estimators, which likely include activity coming from the ventral stream, frontal areas, and limbic regions [3]). Our claim was thus that PPS-related measures reflect the instantaneous value of particular types of actions, and not that PPS measures explicitly reflect the value of any possible action at any given time (i.e., for any possible state): PPS measures reflect the instantaneous output of a function rather than the infinite array of values that the output of this function could take. We might have contributed to this misunderstanding when claiming that a field is ‘a quantity that has a magnitude for each point in space and time’. We should have clarified that the magnitude of a PPS measure can be seen as a specific sample from a field in the here and now rather than as a database containing all possible field values.

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Models of brain based on artificial neural networks

February 19, 2019 10:39 ,

James C.R. Whittington, Rafal Bogacz, Theories of Error Back-Propagation in the Brain, Trends in Cognitive Sciences, Volume 23, Issue 3, 2019, Pages 235-250 DOI: 10.1016/j.tics.2018.12.005.

This review article summarises recently proposed theories on how neural circuits in the brain could approximate the error back-propagation algorithm used by artificial neural networks. Computational models implementing these theories achieve learning as efficient as artificial neural networks, but they use simple synaptic plasticity rules based on activity of presynaptic and postsynaptic neurons. The models have similarities, such as including both feedforward and feedback connections, allowing information about error to propagate throughout the network. Furthermore, they incorporate experimental evidence on neural connectivity, responses, and plasticity. These models provide insights on how brain networks might be organised such that modification of synaptic weights on multiple levels of cortical hierarchy leads to improved performance on tasks.

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A cognitive architecture for self-development in robots that interact with humans, with a nice state-of-the-art of robot cognitive architectures

December 18, 2018 11:02 ,

C. Moulin-Frier et al., DAC-h3: A Proactive Robot Cognitive Architecture to Acquire and Express Knowledge About the World and the Self, IEEE Transactions on Cognitive and Developmental Systems, vol. 10, no. 4, pp. 1005-1022, DOI: 10.1109/TCDS.2017.2754143.

This paper introduces a cognitive architecture for a humanoid robot to engage in a proactive, mixed-initiative exploration and manipulation of its environment, where the initiative can originate from both human and robot. The framework, based on a biologically grounded theory of the brain and mind, integrates a reactive interaction engine, a number of state-of-the-art perceptual and motor learning algorithms, as well as planning abilities and an autobiographical memory. The architecture as a whole drives the robot behavior to solve the symbol grounding problem, acquire language capabilities, execute goal-oriented behavior, and express a verbal narrative of its own experience in the world. We validate our approach in human-robot interaction experiments with the iCub humanoid robot, showing that the proposed cognitive architecture can be applied in real time within a realistic scenario and that it can be used with naive users.

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

On the existence of prior knowledge, “pre-wired” in animal brains, that guides further learning

October 17, 2018 09:12 ,

Elisabetta Versace, Antone Martinho-Truswell, Alex Kacelnik, Giorgio Vallortigara, Priors in Animal and Artificial Intelligence: Where Does Learning Begin?, Trends in Cognitive Sciences, Volume 22, Issue 11, 2018, Pages 963-965, DOI: 10.1016/j.tics.2018.07.005.

A major goal for the next generation of artificial intelligence (AI) is to build machines that are able to reason and cope with novel tasks, environments, and situations in a manner that approaches the abilities of animals. Evidence from precocial species suggests that driving learning through suitable priors can help to successfully face this challenge.

Posted in: Psycho-physiological bases of engineering

A new model of reinforcement learning based on the human brain that copes with continuous spaces through continuous rewards, with a short but nice state-of-the-art of RL applied to large, continuous spaces

October 17, 2018 08:58 ,

Feifei Zhao, Yi Zeng, Guixiang Wang, Jun Bai, Bo Xu, A Brain-Inspired Decision Making Model Based on Top-Down Biasing of Prefrontal Cortex to Basal Ganglia and Its Application in Autonomous UAV Explorations, Cognitive Computation, Volume 10, Issue 2, pp 296–306, DOI: 10.1007/s12559-017-9511-3.

Decision making is a fundamental ability for intelligent agents (e.g., humanoid robots and unmanned aerial vehicles). During decision making process, agents can improve the strategy for interacting with the dynamic environment through reinforcement learning. Many state-of-the-art reinforcement learning models deal with relatively smaller number of state-action pairs, and the states are preferably discrete, such as Q-learning and Actor-Critic algorithms. While in practice, in many scenario, the states are continuous and hard to be properly discretized. Better autonomous decision making methods need to be proposed to handle these problems. Inspired by the mechanism of decision making in human brain, we propose a general computational model, named as prefrontal cortex-basal ganglia (PFC-BG) algorithm. The proposed model is inspired by the biological reinforcement learning pathway and mechanisms from the following perspectives: (1) Dopamine signals continuously update reward-relevant information for both basal ganglia and working memory in prefrontal cortex. (2) We maintain the contextual reward information in working memory. This has a top-down biasing effect on reinforcement learning in basal ganglia. The proposed model separates the continuous states into smaller distinguishable states, and introduces continuous reward function for each state to obtain reward information at different time. To verify the performance of our model, we apply it to many UAV decision making experiments, such as avoiding obstacles and flying through window and door, and the experiments support the effectiveness of the model. Compared with traditional Q-learning and Actor-Critic algorithms, the proposed model is more biologically inspired, and more accurate and faster to make decision.

Posted in: Applications of reinforcement learning to robots, Psycho-physiological bases of engineering , Tagged: ,

Z-numbers: an extension of fuzzy variables for cognitive decision making, and the concept of cognitive information

October 17, 2018 08:54 ,

Hong-gang Peng, Jian-qiang Wang, Outranking Decision-Making Method with Z-Number Cognitive Information, Cognitive Computation, Volume 10, Issue 5, pp 752–768, DOI: 10.1007/s12559-018-9556-y.

The Z-number provides an adequate and reliable description of cognitive information. The nature of Z-numbers is complex, however, and important issues in Z-number computation remain to be addressed. This study focuses on developing a computationally simple method with Z-numbers to address multicriteria decision-making (MCDM) problems. Processing Z-numbers requires the direct computation of fuzzy and probabilistic uncertainties. We used an effective method to analyze the Z-number construct. Next, we proposed some outranking relations of Z-numbers and defined the dominance degree of discrete Z-numbers. Also, after analyzing the characteristics of elimination and choice translating reality III (ELECTRE III) and qualitative flexible multiple criteria method (QUALIFLEX), we developed an improved outranking method. To demonstrate this method, we provided an illustrative example concerning job-satisfaction evaluation. We further verified the validity of the method by a criteria test and comparative analysis. The results demonstrate that the method can be successfully applied to real-world decision-making problems, and it can identify more reasonable outcomes than previous methods. This study overcomes the high computational complexity in existing Z-number computation frameworks by exploring the pairwise comparison of Z-numbers. The method inherits the merits of the classical outranking method and considers the non-compensability of criteria. Therefore, it has remarkable potential to address practical decision-making problems involving Z-information.

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