A Computational Model of Bistable Perception- Attention Dynamics with Long Range Correlations

Fürstenau, Norbert

doi:10.1007/978-3-540-74565-5_20

Cited by 5 publications

(7 citation statements)

References 35 publications

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“…Of course, this also depends on the detailed experimental conditions. For the present theoretical model, these observations were reproduced in previous publications (Fürstenau 2004(Fürstenau , 2006(Fürstenau , 2007. As a new result Table 1 lists the data of a series of simulations with varying memory time constant τ M .…”

Section: Relative Frequency Of Perceptual Dwell Timessupporting

confidence: 52%

“…Although it has been shown in previous publications (Fürstenau 2006(Fürstenau , 2007 that this minimum version of the model correctly predicts a number of experimental results (e.g., -distribution of percept dwell times), it clearly has certain limitations, such as the asymmetry of the pitchfork bifurcations on the P1 and P2 levels of the v * (G)-topology (see Fig. 3b below) which results in different oscillation amplitudes superimposed to the stationary P1, P2 states.…”

Section: The Phase Oscillator Modelmentioning

confidence: 86%

“…Without explicitly considering early feedforward processing, in the present approach, selective attention (i.e., preference of one of the two percepts) may be modeled by the dynamic bias parameter v b (t) which determines relative percept duration times and long range correlations via the v b -memory time constant τ M (see below ;Fürstenau 2007). Because the model architecture is suggested to basically represent the ventral ("what") V4-InferoTemporal (IT)-PraeFrontal (PF)-V4 loop and the thalamocortical hippocampal (memory) loop as representative target structures it consequently favors a top-down view assuming the usage of prior knowledge (memory) according to Hamker (2004).…”

Section: The Phase Oscillator Modelmentioning

confidence: 99%

“…The numerical solution for τ = 1T S , T = 2T S , with simulation time steps T S = T /2 = 20 ms (Fürstenau 2006(Fürstenau , 2007 yields for the lowest frequencies f /Hz = 9.1, 20.2, 32.2, and 44.5. For i < 10, and dependent on τ/T , the spectrum lies well within the range of typical EEG frequency bands as well as those of microsaccades (3-4 Hz) and tremor (30-150 Hz) of fixational eye movements.…”

Section: Stationary Solutions Bifurcations and Oscillationsmentioning

confidence: 99%

“…As a new result Table 1 lists the data of a series of simulations with varying memory time constant τ M . Initial results were provided already in Fürstenau (2007). Average shape parameters α and average perceptual duration times m are listed of 100 simulation runs with N = 5000 time steps T S each (=100 s for T S = T /2 = 20 ms) and parameters as those used for Fig.…”

Section: Relative Frequency Of Perceptual Dwell Timesmentioning

confidence: 99%

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

Fürstenau

2010

Biol Cybern

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Simulation results of bistable perception due to ambiguous visual stimuli are presented which are obtained with a behavioral nonlinear dynamics model using perception-attention-memory coupling. This model provides an explanation of recent experimental results of Gao et al. (Cogn Process 7:105-112, 2006a) and it supports their speculation that the fractal character of perceptual dominance time series may be understood in terms of nonlinear and reentrant dynamics of brain processing. Percept reversals are induced by attention fatigue and noise, with an attention bias which balances the relative percept duration. Dynamical coupling of the attention bias to the perception state introduces memory effects leading to significant long range correlations of perceptual duration times as quantified by the Hurst parameter H > 0.5 (Mandelbrot, The fractal geometry of nature, 1991), in agreement with Gao et al. (Cogn Process 7:105-112, 2006a).

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Section: Relative Frequency Of Perceptual Dwell Timessupporting

confidence: 52%

Section: The Phase Oscillator Modelmentioning

confidence: 86%

Section: The Phase Oscillator Modelmentioning

confidence: 99%

Section: Stationary Solutions Bifurcations and Oscillationsmentioning

confidence: 99%

Section: Relative Frequency Of Perceptual Dwell Timesmentioning

confidence: 99%

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

Fürstenau

2010

Biol Cybern

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Simulating Visual Qualia in the CERA-CRANIUM Cognitive Architecture

Arrabales

Ledezma

Sanchís

2011

Advances in Experimental Medicine and Biology

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The concept of qualia poses a central problem in the framework of consciousness studies. Despite it being a controversial issue even in the study of human consciousness, we argue that qualia can be complementarily studied using artificial cognitive architectures. In this work we address the problem of defining qualia in the domain of artificial systems, providing a model of "artificial qualia". Furthermore, we partially apply the proposed model to the generation of visual qualia using the cognitive architecture CERA-CRANIUM, which is modeled after the global workspace theory of consciousness. It is our aim to define, characterize and identify artificial qualia as direct products of a simulated conscious perception process. Simple forms of the apparent motion effect are used as the basis for a preliminary experimental setting focused on the simulation and analysis of synthetic visual experience. In contrast with the study of biological brains, the inspection of the dynamics and transient inner states of the artificial cognitive architecture can be performed effectively, thus enabling the detailed analysis of covert and overt percepts generated by the system when it is confronted with specific visual stimuli. The observed states in the artificial cognitive architecture during the simulation of apparent motion effects are used to discuss the existence of possible analogous mechanisms in human cognition processes.

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Computational Nonlinear Dynamics Model of Percept Switching with Ambiguous Stimuli

Fürstenau

2009

Digital Human Modeling

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A Computational Model of Bistable Perception- Attention Dynamics with Long Range Correlations

Cited by 5 publications

References 35 publications

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

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

Simulating Visual Qualia in the CERA-CRANIUM Cognitive Architecture

Computational Nonlinear Dynamics Model of Percept Switching with Ambiguous Stimuli

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