The brain has a body: adaptive behavior emerges from interactions of nervous system, body and environment

Chiel, Hillel J.; Beer, Randall D.

doi:10.1016/s0166-2236(97)01149-1

Cited by 626 publications

(425 citation statements)

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“…Thus, dynamicism turns on a commitment to explanations of cognitive phenomena essentially involving (numerical) quantification of variables, a metric of time, analysis of the interdependence between variables, and focus on differential equations of change of those variables over time. In the literature, there are several features that can be emphasized or added to the picture: stability or self-organization (of behavior under certain conditions) (e.g., Kelso 1995), real time modeling (as opposed to 'ersatz' time modeling) (e.g., Van Gelder and Port 1995), continuity in state-space evolution (e.g., Calvo Garzón 2008), agent-environment coupling (e.g., Beer 1995;Chiel and Beer 1997) or quantitative character (of the variables and behavior in the system) (e.g., Van Gelder 1998). Careful delineation of all these related aspects goes clearly beyond the purposes of our discussion.…”

Section: Dynamicismmentioning

confidence: 99%

The systematicity challenge to anti-representational dynamicism

Verdejo

2014

Synthese

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After more than twenty years of representational debate in the cognitive sciences, anti-representational dynamicism may be seen as offering a rival and radically new kind of explanation of systematicity phenomena. In this paper, I argue that, on the contrary, anti-representational dynamicism must face a version of the old systematicity challenge: either it does not explain systematicity, or else, it is just an implementation of representational theories. To show this, I present a purely behavioral and representation-free account of systematicity. I then consider a case of insect sensorimotor systematic behavior: communicating behavior in honey bees. I conclude that anti-representational dynamicism fails to capture the fundamental trait of systematic behaviors qua systematic, i.e., their involving exercises of the same behavioral capacities. I suggest, finally, a collaborative strategy in pursuit of a rich and powerful account of this central phenomenon of high cognition at all levels of explanation, including the representational level.

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Section: Dynamicismmentioning

confidence: 99%

The systematicity challenge to anti-representational dynamicism

Verdejo

2014

Synthese

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“…In fact the conceptual separation between sensing, coordinating, and acting may not be tenable for such systems. Mechanical components of a robot's structure can become part of the information processing architecture [4] and thus behaviour is not reflecting the direct actions of a controller but emerges from the interaction of control, body and environment [5,6].…”

Section: The Biological Paradigmmentioning

confidence: 99%

Robot Control: From Silicon Circuitry to Cells

Tsuda

Zauner

Gunji

2006

Biologically Inspired Approaches to Advanced Information Technology

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Abstract. Life-like adaptive behaviour is so far an illusive goal in robot control. A capability to act successfully in a complex, ambiguous, and harsh environment would vastly increase the application domain of robotic devices. Established methods for robot control run up against a complexity barrier, yet living organisms amply demonstrate that this barrier is not a fundamental limitation. To gain an understanding of how the nimble behaviour of organisms can be duplicated in made-for-purpose devices we are exploring the use of biological cells in robot control. This paper describes an experimental setup that interfaces an amoeboid plasmodium of Physarum polycephalum with an omnidirectional hexapod robot to realise an interaction loop between environment and plasticity in control. Through this bio-electronic hybrid architecture the continuous negotiation process between local intracellular reconfiguration on the micro-physical scale and global behaviour of the cell in a macroscale environment can be studied in a device setting.

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“…Ideas about situated intelligence, such as those described in [23,12], have changed our views about the nature of intelligent artifacts. Natural systems exhibiting intelligent behavior are now understood as having co-evolved with their environments, rather than as isolated designs.…”

Section: Stephanie Forrestmentioning

confidence: 99%

What Is a Learning Classifier System?

Holland

Booker

Colombetti

et al. 2000

Lecture Notes in Computer Science

102

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Abstract.We asked "What is a Learning Classifier System" to some of the best-known researchers in the field. These are their answers. John H. HollandClassifier systems are intended as a framework that uses genetic algorithms to study learning in condition/action, rule-based systems. They simplify the "broadcast language" introduced in [26] by (i) eliminating the ability of rules to generate other rules, and (ii) by simplifying the specification of conditions and actions. They were introduced in [27] and were later revised to the current "standard" form in [28]. [31] gives a comprehensive description of this "standard" form, with examples. There are, however, many significant variants (e.g., Booker [9, this volume] In defining classifier systems I adopted the common view that the state of the environment is conveyed to the system via a set of detectors (e.g., rods and cones in a retina). The outputs of the detectors are treated as standardized packets of information, messages. Messages are used for internal processing as well, and some messages, by directing the system's effectors (e.g., its muscles), determine the system's actions upon its environment. There is a further environmental interaction that is critical to the learning process: the environment must, in certain situations, provide the system with some measure of its performance. Here, as earlier [26], I will use the term payoff as the general term for this measure.

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The brain has a body: adaptive behavior emerges from interactions of nervous system, body and environment

Cited by 626 publications

References 50 publications

The systematicity challenge to anti-representational dynamicism

The systematicity challenge to anti-representational dynamicism

Robot Control: From Silicon Circuitry to Cells

What Is a Learning Classifier System?

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