The dynamical foundations of motion pattern formation: Stability, selective adaptation, and perceptual continuity

Abstract

A dynamical model is used to show that global motion pattern formation for several different apparent motion stimuli can be embodied in the stable distribution of activation over a population of concurrently activated, directionally selective motion detectors. The model, which is based on motion detectors being interactive, noisy, and self-stabilizing, accounts for such phenomena as bistability, spontaneous switching, hysteresis, and selective adaptation. Simulations show that dynamical solutions to the motion correspondence problem for a bistable stimulus (two qualitativelydifferent patterns are formed) apply as well to the solution for a monostable stimulus (only one pattern is formed) and highlight the role of interactions among sequentially stimulated detectors in establishing the state dependence and, thereby, the temporal persistence of percepts.

A nonlinear dynamics model for simulating long range correlations of cognitive bistability

“…An example simulation is presented in Fürstenau (2009). This is comparable to Ditzinger and Haken (1989) and similar models such as proposed recently by Wilson (1999), Hock et al (2003), and Noest et al (2007). With the vector model the perception state P1 (v * ≈ 1) of the present reduced model corresponds to the perception ground state P0 of each percept Pi (= upper level of the hysteresis curves v * i (G i ), like that one of Fig.…”

“…It would be of interest to compare predictions of related dynamical modeling approaches (Ditzinger and Haken 1989;Noest et al 2007;Hock et al 2003) with the present ones with regard to the experimentally observed long range correlations. An interesting experimental task for testing the cognitive self-oscillator hypothesis and for quantifying the relative importance of deterministic versus stochastic dynamics would be the isolation of significant peaks around 0.1-0.5 Hz in the frequency spectrum of the dwell-time time series.…”

“…That is, the system state (e.g., phase difference) at time t + dt depends nonlinearly (sine function) on the state at time t. A sine map was also derived by Haken (2002) for pulse coupled neural ensembles. Aspects of the nonlinear system dynamics on the level of neural populations were taken into account by several authors (Hock et al 2003;Noest et al 2007;Deco and Marti 2007). The latter authors formalized the interaction between two populations of neurons by Wilson-Cowen type first-order nonlinear stochastic differential equations including additive Gaussian noise, however without explicitly regarding attention fatigue.…”

“…Multiplicative stimulus strength and adaptive feedback gain (attention parameter) also contrast with the dynamical model of Hock et al (2003). These authors use an additive stimulus and nonlinearity only present in the coupling between the alternative percepts.…”

The interaction between competition, learning, and habituation dynamics in speech perception

“…The perspective of dynamical systems has provided novel insights into numerous domains of human behavior, including movement coordination (e.g., Hock, Schöner, and Giese 2003), interpersonal coordination (e.g., Schmidt, Bienvenu, Fitzpatrick, and Amazeen 1998), learning (e.g., Zanone and Kelso 1992;Vallabha and McClelland 2007;Tuller, Jantzen, and Jirsa 2008), visual perception (e.g., Kawamoto and Anderson 1985), and speech perception (e.g., Grossberg and Myers 2000;Grossberg and Kazerounian 2011). With dynamical systems theory comes a commitment to look at the continuous aspects of perceptual and cognitive phenomena rather than purely looking at stable categories and discrete changes (Spivey 2007).…”