Category Archives: Cognitive Sciences

Interesting hypothesis about how cognitive abilities can be modelled with closed control loops that run in parallel -using hierarchies of abstraction and prediction-, traditionally used just for low-level behaviours

June 8, 2017 08:48 ,

Giovanni Pezzulo, Paul Cisek, Navigating the Affordance Landscape: Feedback Control as a Process Model of Behavior and Cognition, Trends in Cognitive Sciences, Volume 20, Issue 6, June 2016, Pages 414-424, ISSN 1364-6613, DOI: 10.1016/j.tics.2016.03.013.

We discuss how cybernetic principles of feedback control, used to explain sensorimotor behavior, can be extended to provide a foundation for understanding cognition. In particular, we describe behavior as parallel processes of competition and selection among potential action opportunities (‘affordances’) expressed at multiple levels of abstraction. Adaptive selection among currently available affordances is biased not only by predictions of their immediate outcomes and payoffs but also by predictions of what new affordances they will make available. This allows animals to purposively create new affordances that they can later exploit to achieve high-level goals, resulting in intentional action that links across multiple levels of control. Finally, we discuss how such a ‘hierarchical affordance competition’ process can be mapped to brain structure.

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

Physiological evidences that visual attention is based on predictions

June 8, 2017 08:37 ,

Martin Rolfs, Martin Szinte, Remapping Attention Pointers: Linking Physiology and Behavior, Trends in Cognitive Sciences, Volume 20, Issue 6, 2016, Pages 399-401, ISSN 1364-6613, DOI: 10.1016/j.tics.2016.04.003.

Our eyes rapidly scan visual scenes, displacing the projection on the retina with every move. Yet these frequent retinal image shifts do not appear to hamper vision. Two recent physiological studies shed new light on the role of attention in visual processing across saccadic eye movements.

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“Nexting” (predicting events that occur next, possibly at different time scales) implemented in a robot through temporal difference learning and with a large number of learners

June 7, 2017 11:27 ,

Joseph Modayil, Adam White, Richard S. Sutton (2011), Multi-timescale Nexting in a Reinforcement Learning Robot, arXiv:1112.1133 [cs.LG]. ARXIV, (this version to appear in the Proceedings of the Conference on the Simulation of Adaptive Behavior, 2012).

The term “nexting” has been used by psychologists to refer to the propensity of people and many other animals to continually predict what will happen next in an immediate, local, and personal sense. The ability to “next” constitutes a basic kind of awareness and knowledge of one’s environment. In this paper we present results with a robot that learns to next in real time, predicting thousands of features of the world’s state, including all sensory inputs, at timescales from 0.1 to 8 seconds. This was achieved by treating each state feature as a reward-like target and applying temporal-difference methods to learn a corresponding value function with a discount rate corresponding to the timescale. We show that two thousand predictions, each dependent on six thousand state features, can be learned and updated online at better than 10Hz on a laptop computer, using the standard TD(lambda) algorithm with linear function approximation. We show that this approach is efficient enough to be practical, with most of the learning complete within 30 minutes. We also show that a single tile-coded feature representation suffices to accurately predict many different signals at a significant range of timescales. Finally, we show that the accuracy of our learned predictions compares favorably with the optimal off-line solution.

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

Theoretical models for explaining the human (quick) decicion-making process

April 20, 2016 16:42 ,

Roger Ratcliff, Philip L. Smith, Scott D. Brown, Gail McKoon, Diffusion Decision Model: Current Issues and History, Trends in Cognitive Sciences, Volume 20, Issue 4, April 2016, Pages 260-281, ISSN 1364-6613, DOI: 10.1016/j.tics.2016.01.007.

There is growing interest in diffusion models to represent the cognitive and neural processes of speeded decision making. Sequential-sampling models like the diffusion model have a long history in psychology. They view decision making as a process of noisy accumulation of evidence from a stimulus. The standard model assumes that evidence accumulates at a constant rate during the second or two it takes to make a decision. This process can be linked to the behaviors of populations of neurons and to theories of optimality. Diffusion models have been used successfully in a range of cognitive tasks and as psychometric tools in clinical research to examine individual differences. In this review, we relate the models to both earlier and more recent research in psychology.

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Cognitive Models as Bridge between Brain and Behavior

April 20, 2016 16:40 ,

Bradley C. Love, Cognitive Models as Bridge between Brain and Behavior, Trends in Cognitive Sciences, Volume 20, Issue 4, April 2016, Pages 247-248, ISSN 1364-6613, DOI: 10.1016/j.tics.2016.02.006.

How can disparate neural and behavioral measures be integrated? Turner and colleagues propose joint modeling as a solution. Joint modeling mutually constrains the interpretation of brain and behavioral measures by exploiting their covariation structure. Simultaneous estimation allows for more accurate prediction than would be possible by considering these measures in isolation.

Posted in: Cognitive sciences , Tagged:

The diverse roles of the hippocampus

April 20, 2016 16:16 ,

Daniel Bendor, Hugo J. Spiers, Does the Hippocampus Map Out the Future?, Trends in Cognitive Sciences, Volume 20, Issue 3, March 2016, Pages 167-169, ISSN 1364-6613, DOI: 10.1016/j.tics.2016.01.003.

Decades of research have established two central roles of the hippocampus – memory consolidation and spatial navigation. Recently, a third function of the hippocampus has been proposed: simulating future events. However, claims that the neural patterns underlying simulation occur without prior experience have come under fire in light of newly published data.

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How to make that a symbol becomes related to things on which it is not grounded, and a nice introduction to the symbolist/subsymbolist dilemma

April 20, 2016 15:22 ,

Veale, Tony and Al-Najjar, Khalid (2016). Grounded for life: creative symbol-grounding for lexical invention. Connection Science 28(2). DOI: 10.1080/09540091.2015.1130025

One of the challenges of linguistic creativity is to use words in a way that is novel and striking and even whimsical, to convey meanings that remain stubbornly grounded in the very same world of familiar experiences as serves to anchor the most literal and unimaginative language. The challenge remains unmet by systems that merely shuttle or arrange words to achieve novel arrangements without concern as to how those arrangements are to spur the processes of meaning construction in a listener. In this paper we explore a problem of lexical invention that cannot be solved without a model ? explicit or implicit ? of the perceptual grounding of language: the invention of apt new names for colours. To solve this problem here we shall call upon the notion of a linguistic readymade, a phrase that is wrenched from its original context of use to be given new meaning and new resonance in new settings. To ensure that our linguistic readymades ? which owe a great deal to Marcel Duchamp’s notion of found art ? are anchored in a consensus model of perception, we introduce the notion of a lexicalised colour stereotype.

Posted in: Artificial Intelligence , Tagged: , ,

Limitations of the simulation of physical systems when used in AI reasoning processes for prediction

February 24, 2016 19:09 ,

Ernest Davis, Gary Marcus, The scope and limits of simulation in automated reasoning, Artificial Intelligence, Volume 233, April 2016, Pages 60-72, ISSN 0004-3702, DOI: 10.1016/j.artint.2015.12.003.

In scientific computing and in realistic graphic animation, simulation – that is, step-by-step calculation of the complete trajectory of a physical system – is one of the most common and important modes of calculation. In this article, we address the scope and limits of the use of simulation, with respect to AI tasks that involve high-level physical reasoning. We argue that, in many cases, simulation can play at most a limited role. Simulation is most effective when the task is prediction, when complete information is available, when a reasonably high quality theory is available, and when the range of scales involved, both temporal and spatial, is not extreme. When these conditions do not hold, simulation is less effective or entirely inappropriate. We discuss twelve features of physical reasoning problems that pose challenges for simulation-based reasoning. We briefly survey alternative techniques for physical reasoning that do not rely on simulation.

Posted in: Artificial Intelligence , Tagged: ,

It seems that the human motor cortex not only contains a map of motions but a map of basic behaviors (compositions of motions)

February 24, 2016 19:04 ,

Michael S.A. Graziano, Ethological Action Maps: A Paradigm Shift for the Motor Cortex, Trends in Cognitive Sciences, Volume 20, Issue 2, February 2016, Pages 121-132, ISSN 1364-6613, DOI: 10.1016/j.tics.2015.10.008.

The map of the body in the motor cortex is one of the most iconic images in neuroscience. The map, however, is not perfect. It contains overlaps, reversals, and fractures. The complex pattern suggests that a body plan is not the only organizing principle. Recently a second organizing principle was discovered: an action map. The motor cortex appears to contain functional zones, each of which emphasizes an ethologically relevant category of behavior. Some of these complex actions can be evoked by cortical stimulation. Although the findings were initially controversial, interest in the ethological action map has grown. Experiments on primates, mice, and rats have now confirmed and extended the earlier findings with a range of new methods.

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How mood influcences learning, concretely perception of rewards in the context of reinforcement learning

February 4, 2016 16:34 ,

Eran Eldar, Robb B. Rutledge, Raymond J. Dolan, Yael Niv, Mood as Representation of Momentum, Trends in Cognitive Sciences, Volume 20, Issue 1, January 2016, Pages 15-24, ISSN 1364-6613, DOI: j.tics.2015.07.010.

Experiences affect mood, which in turn affects subsequent experiences. Recent studies suggest two specific principles. First, mood depends on how recent reward outcomes differ from expectations. Second, mood biases the way we perceive outcomes (e.g., rewards), and this bias affects learning about those outcomes. We propose that this two-way interaction serves to mitigate inefficiencies in the application of reinforcement learning to real-world problems. Specifically, we propose that mood represents the overall momentum of recent outcomes, and its biasing influence on the perception of outcomes ‘corrects’ learning to account for environmental dependencies. We describe potential dysfunctions of this adaptive mechanism that might contribute to the symptoms of mood disorders.

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