Category Archives: Cognitive Sciences

On the not clear distinction between fast/shallow and slow/deep cognitive processing

June 24, 2019 11:02 ,

Adrianna C. Jenkins, Rethinking Cognitive Load: A Default-Mode Network Perspective,Trends in Cognitive Sciences, Volume 23, Issue 7, 2019, Pages 531-533 DOI: 10.1016/j.tics.2019.04.008.

Typical cognitive load tasks are now known to deactivate the brain’s default-mode network (DMN). This raises the possibility that apparent effects of cognitive load could arise from disruptions of DMN processes, including social cognition. Cognitive load studies are reconsidered, with reinterpretations of past research and implications for dual-process theory.

Posted in: Cognitive sciences , Tagged:

A brief (and relatively shallow) account of computer programming as a cognitive ability

June 24, 2019 10:57 ,

Evelina Fedorenko, Anna Ivanova, Riva Dhamala, Marina Umaschi Bers, The Language of Programming: A Cognitive Perspective, Trends in Cognitive Sciences,
Volume 23, Issue 7, 2019, Pages 525-528 DOI: 10.1016/j.tics.2019.04.010.

Computer programming is becoming essential across fields. Traditionally grouped with science, technology, engineering, and mathematics (STEM) disciplines, programming also bears parallels to natural languages. These parallels may translate into overlapping processing mechanisms. Investigating the cognitive basis of programming is important for understanding the human mind and could transform education practices.

Posted in: Computer science, Psycho-physiological bases of engineering

A Survey of Knowledge Representation in Service Robotics

June 17, 2019 12:14 ,

avid Paulius, Yu Sun, A Survey of Knowledge Representation in Service Robotics,Robotics and Autonomous Systems, Volume 118, 2019, Pages 13-30 DOI: 10.1016/j.robot.2019.03.005.

Within the realm of service robotics, researchers have placed a great amount of effort into learning, understanding, and representing motions as manipulations for task execution by robots. The task of robot learning and problem-solving is very broad, as it integrates a variety of tasks such as object detection, activity recognition, task/motion planning, localization, knowledge representation and retrieval, and the intertwining of perception/vision and machine learning techniques. In this paper, we solely focus on knowledge representations and notably how knowledge is typically gathered, represented, and reproduced to solve problems as done by researchers in the past decades. In accordance with the definition of knowledge representations, we discuss the key distinction between such representations and useful learning models that have extensively been introduced and studied in recent years, such as machine learning, deep learning, probabilistic modeling, and semantic graphical structures. Along with an overview of such tools, we discuss the problems which have existed in robot learning and how they have been built and used as solutions, technologies or developments (if any) which have contributed to solving them. Finally, we discuss key principles that should be considered when designing an effective knowledge representation.

Posted in: Artificial Intelligence , Tagged: ,

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.

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

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

A survey on HTN planning

May 2, 2019 09:38 ,

Ilche Georgievski, Marco Aiello, HTN planning: Overview, comparison, and beyond, Artificial Intelligence, Volume 222, 2015, Pages 124-156 DOI: 10.1016/j.artint.2015.02.002.

Hierarchies are one of the most common structures used to understand and conceptualise the world. Within the field of Artificial Intelligence (AI) planning, which deals with the automation of world-relevant problems, Hierarchical Task Network (HTN) planning is the branch that represents and handles hierarchies. In particular, the requirement for rich domain knowledge to characterise the world enables HTN planning to be very useful, and also to perform well. However, the history of almost 40 years obfuscates the current understanding of HTN planning in terms of accomplishments, planning models, similarities and differences among hierarchical planners, and its current and objective image. On top of these issues, the ability of hierarchical planning to truly cope with the requirements of real-world applications has been often questioned. As a remedy, we propose a framework-based approach where we first provide a basis for defining different formal models of hierarchical planning, and define two models that comprise a large portion of HTN planners. Second, we provide a set of concepts that helps in interpreting HTN planners from the aspect of their search space. Then, we analyse and compare the planners based on a variety of properties organised in five segments, namely domain authoring, expressiveness, competence, computation and applicability. Furthermore, we select Web service composition as a real-world and current application, and classify and compare the approaches that employ HTN planning to solve the problem of service composition. Finally, we conclude with our findings and present directions for future work. In summary, we provide a novel and comprehensive viewpoint on a core AI planning technique.

Posted in: Artificial Intelligence , Tagged: ,

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.

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

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.

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

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.

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