Dynamical approaches to cognitive science

Beer, Randall D.

doi:10.1016/s1364-6613(99)01440-0

Cited by 511 publications

(327 citation statements)

References 37 publications

Supporting

Mentioning

298

Contrasting

Unclassified

Order By: Relevance

“…Presumably, nervous systems have evolved slow dynamics to respond to such patterns in the environment. This neuroethological perspective is formalized in a theoretical approach (35)(36)(37)(38) that proposes that phenomena, such as intentions and expectations, traditionally viewed as cognitive, arise out of a continuous dynamic interaction between the animal and the environment and serve to stabilize appropriate coordinated patterns of behavior. In this dynamical formulation, the trajectory that the internal state of the nervous system traces as the animal continuously engages its environment gives rise to the history-dependence of behavior that we interpret as intentions and expectations.…”

Section: Discussionmentioning

confidence: 99%

Dynamical basis of intentions and expectations in a simple neuronal network

Proekt

Březina

Weiss

2004

Proc. Natl. Acad. Sci. U.S.A.

166

View full text Add to dashboard Cite

Selection of behavioral responses to external stimuli is strongly influenced by internal states, such as intentions and expectations. These internal states are often attributed to higher-order brain functions. Yet here we show that even in the simple feeding network of Aplysia, external stimuli do not directly specify which motor output is expressed; instead, the motor output is specified by the state of the network at the moment of stimulation. The history-dependence of this network state manifests itself in the same way as do intentions and expectations in the behavior of higher animals. Remarkably, we find that activity-dependent plasticity of a synapse within the network itself, rather than some higher-order network, mediates one important aspect of the change in the network state. Through this mechanism, changes in the network state become an automatic consequence of the generation of behavior. Altogether, our findings suggest that intentions and expectations may emerge within behavior-generating networks themselves from the plasticity of the very processes that generate the behavior.A nimal behavior is not merely a passive response to external stimuli; rather, it expresses also the internal state of the animal. This internal state is presumably somehow embodied in the state of the nervous system. Here we study the manifestations and the neurophysiological basis of the internal state in the experimentally advantageous feeding network of the mollusk Aplysia.State-dependence of network function is not a new concept. In the neurophysiological literature, state-dependence is typically discussed as the ability of contextual cues to modify the response to a stimulus. This type of state-dependence has been demonstrated, for example, for locomotion by using stimulation of the mesencephalic locomotor region to elicit walking in the decerebrate cat. The speed of locomotion is determined by the speed of the treadmill on which the cat is placed (1). In another compelling example, stimulation of a command-like neuron in a leech immersed in water elicits swimming, whereas stimulation of the same neuron when the leech is placed on solid substrate elicits crawling (2). Similarly, stimulation of a command-like neuron in a cricket suspended in air elicits avoidance responses but it fails to do so when the cricket is placed on the ground (3). State-dependence of this type is also seen with neuromodulation. For instance, the ability of sensory stimulation to elicit stridulation in the grasshopper is critically dependent on the presence of a muscarinic agonist (4). The setting of network state by application of neuromodulators is a common phenomenon that has been well characterized in simple neuronal networks, such as the stomatogastric system of crustaceans (5-9).This type of state-dependence fails, however, to account for a fundamental feature of the state-dependence that is observed in animal behavior and human psychology. In the type of statedependence just discussed, the behavior is still unambiguously specified by the ext...

show abstract

Section: Discussionmentioning

confidence: 99%

Dynamical basis of intentions and expectations in a simple neuronal network

Proekt

Březina

Weiss

2004

Proc. Natl. Acad. Sci. U.S.A.

166

View full text Add to dashboard Cite

show abstract

“…There is growing awareness in cognitive science of the analytical and conceptual insights to be gained from applying DST to cognition (14). DST not only offers tools for analyzing systems that exhibit nonlinearities over time but also has conceptual implications for the nature of cognition itself.…”

Section: Time and The Nature Of Cognitionmentioning

confidence: 99%

Moving hand reveals dynamics of thought

Magnuson

2005

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

P erception, cognition, and action occur over time. An organism must continuously and rapidly integrate sensory data with prior knowledge and potential actions at multiple timescales. This makes time of central importance in cognitive science. Crucial questions hinge on time, from the class of systems that may underlie cognition to debates about constraints on the functional organization of the brain and theoretical disputes in specialized domains. Often, the fine-grained time-course predictions that distinguish theories exceed the temporal resolution of available measures. In this issue of PNAS, Spivey et al.(1) introduce a method (''mouse tracking'') that provides a continuous measure of underlying perception and cognition in online language processing, promising badly needed leverage for addressing theoretical impasses, narrow and broad. I will describe examples of theoretical debates that hinge on time course, the difficulties in assessing time, and how the strengths and limitations of the new method complement current techniques for estimating time course. I conclude with a discussion of the potential of the new method to extend the tools and implications of dynamical systems theory (DST) to higher-level cognition.The Time-Course Quandary Precious little of perception and cognition can be observed directly and instead must be inferred from relationships between input and behavior, often from single postperceptual responses. A typical wordrecognition paradigm is lexical decision. A subject sees or hears words and nonwords and hits keys indicating whether the stimulus was a word. The more frequently a word is used, the more quickly subjects respond yes; however, the more words that sound similar to it, the slower the response. So reaction times tell us a complex process of activation and competition underlies word recognition but indicate little beyond the approximate number of activated words and some of their characteristics. Theories make conflicting predictions about precisely which words are activated, and how strongly each competes over time as a word is heard (2-4). The temporal resolution needed to test these conflicting predictions exceeds by far that of methods like lexical decision.A similar stalemate occurred in sentence processing. Theories agree that as each word is recognized, its grammatical category constrains the assembly of syntactic structures that in turn constrain semantics. The central theoretical debate is between syntax-first (5) and constraint-based (6, 7) theories. Syntax-first parsers initially construct the simplest syntactic structure without consideration of semantics and revise it later if syntax and semantics cannot be integrated. In constraint-based theories, semantics continuously constrain syntax. Distinguishing the theories requires testing how quickly semantics of specific words influence syntactic parsing. Given the temporal resolution of conventional techniques, cases where the semantics of individual words appear to constrain syntactic parsing (supporting constraint-ba...

show abstract

“…The dynamical systems approach to cognition claims that cognitive computation is essentially the transient behavior of nonlinear dynamical systems (Crutchfield 1994;Port and van Gelder 1995;van Gelder 1998;Beer 2000;). …”

Section: Dynamical System Modelsmentioning

confidence: 99%

Towards dynamical system models of language-related brain potentials

2008

View full text Add to dashboard Cite

Event-related brain potentials (ERP) are important neural correlates of cognitive processes. In the domain of language processing, the N400 and P600 reflect lexicalsemantic integration and syntactic processing problems, respectively. We suggest an interpretation of these markers in terms of dynamical system theory and present two nonlinear dynamical models for syntactic computations where different processing strategies correspond to functionally different regions in the system's phase space.

show abstract

Dynamical approaches to cognitive science

Cited by 511 publications

References 37 publications

Dynamical basis of intentions and expectations in a simple neuronal network

Dynamical basis of intentions and expectations in a simple neuronal network

Moving hand reveals dynamics of thought

Towards dynamical system models of language-related brain potentials

Contact Info

Product

Resources

About