“…Stated more generally, real-time neural network simulations implement a dynamical systems approach to the interpretation of behavior (cf. Galbicka, 1992). In a fully realized simulation, the simulated processes that change the strengths of connections, or ''connection weights,'' and the durations of time steps are tightly constrained by independent experimental analyses of neuroscience and behavior (e.g., Buonomano & Merzenich, 1995) and, at a minimum, are consistent with what is known about such processes.…”
The central focus of this essay is whether the effect of reinforcement is best viewed as the strengthenng of responding or the strengthening of the environmental control of responding. We make the argument that adherence to Skinner's goal of achieving a moment-to-moment analysis of behavior compels acceptance of the latter view. Moreover, a thoroughgoing commitment to a moment-to-moment analysis undermines the fundamental distinction between the conditioning process instantiated by operant and respondent contingencies while buttressing the crucially important differences in their cumulative outcomes. Computer simulations informed by experimental analyses of behavior and neuroscience are used to illustrate these points.
“…Stated more generally, real-time neural network simulations implement a dynamical systems approach to the interpretation of behavior (cf. Galbicka, 1992). In a fully realized simulation, the simulated processes that change the strengths of connections, or ''connection weights,'' and the durations of time steps are tightly constrained by independent experimental analyses of neuroscience and behavior (e.g., Buonomano & Merzenich, 1995) and, at a minimum, are consistent with what is known about such processes.…”
The central focus of this essay is whether the effect of reinforcement is best viewed as the strengthenng of responding or the strengthening of the environmental control of responding. We make the argument that adherence to Skinner's goal of achieving a moment-to-moment analysis of behavior compels acceptance of the latter view. Moreover, a thoroughgoing commitment to a moment-to-moment analysis undermines the fundamental distinction between the conditioning process instantiated by operant and respondent contingencies while buttressing the crucially important differences in their cumulative outcomes. Computer simulations informed by experimental analyses of behavior and neuroscience are used to illustrate these points.
“…If, however, we examine the dynamic relationship and pattern of response between a given behavior and the consequences that follow, then we may be able to observe and define schedules and/or patterns of reinforcement that may create the kind of nonlinear dynamics between force and flux required for self-organization (and which may not). For example (and this is purely speculative at this point), the lack of gross temporal variation in behavior governed by interval schedules of reinforcement (see Galbicka, 1992) may indicate that these schedules may not be able to create such dynamics. Further, Baum (1992) asserted that as response rates rise in variable interval schedules reinforcement rates cease to increase, leading to a feedback function that is negatively accelerated.…”
Section: Self-organization As a Response To Perturbations At The Bounmentioning
Selection by consequences is a causal mode that operates across multiple levels of analysis including in biological organisms via natural selection, and at the levels of individual (via operant contingencies) and cultural behaviors (
“…A subset of scientific verbal behavior -adaptive neural networks -has been devised specifically to capture the parallel, interacting, and dynamic character of multiple, time-varying processes (cf. Galbicka, 1992). General descriptions of neural networks have been given elsewhere (e.g., Donahoe, 1997;Donahoe, Burgos, & Palmer, 1993;cf.…”
Section: Interpretations Using Neural Network Simulationsmentioning
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