Deep Teleportation: Quantum Simulation of Conscious Report in Attentional Blink
Abstract
Recent quantum models of cognition have successfully simulated several interesting effects in human experimental data, from vision to reasoning and recently even consciousness. The latter case, consciousness has been a quite challenging phenomenon to model, and most efforts have been through abstract mathematical quantum methods, mainly focused on conceptual issues. Classical (non-quantum) models of consciousness-related experiments exist, but they generally fail to align well with human data. We developed a straightforward quantum model to simulate conscious reporting of seeing or missing competing stimuli within the famous attentional blink paradigm. In an attentional blink task, a target stimulus (T2) that appears after a previous one (T1) can be consciously reported if the delay between presenting them is short enough (called lag 1), otherwise it can be rendered invisible during the so-called refractory period of attention (lags 2 to 6 and even longer). For modeling this phenomenon, we employed a three-qubit entanglement ansatz circuit in the form of a deep teleportation channel instead of the well-known EPR channel. While reporting the competing stimuli was supposed to be the classical measurement outcomes, the effect of distractor stimuli (i.e., masks, if any) was encoded simply as random angle rotations. The simulation outcome for different states was measured, and the classical outcome probabilities were further used as inputs to a simple linear neural network. The result revealed a non-linear, alternating state pattern that closely mirrors human responses in conscious stimuli reporting. The main result was a successful simulation of Lag 1 sparing, lag 7 divergence, and masking effect through probabilistic outcome of measurement in different conditions.
Summary
This paper explores the application of quantum models to simulate cognitive phenomena, specifically the attentional blink (AB). The main research question is whether a quantum model can replicate key features of the AB, such as lag-1 sparing, lag-7 divergence, and the masking effect, and whether it can provide a more integrated approach compared to classical neurodynamic models. The authors developed a quantum cognitive bio model (qCBM) using a three-qubit entanglement ansatz circuit, implementing a "deep teleportation" channel instead of the standard EPR channel. The model encodes stimuli presentation as qubit states, distractor stimuli as random angle rotations, and conscious report as measurement outcomes, which are then fed into a simple linear neural network (FFN). The key finding is that the qCBM+FFN model successfully simulates the non-linear, alternating state pattern observed in human responses during AB tasks. Specifically, the model replicates the lag-1 sparing effect (where T2 is consciously reported when presented shortly after T1), the lag-7 divergence (where the effect of masking stimuli differs based on their presence), and the general u-shaped trend in attentional blink experiments. The model's ability to capture these effects through probabilistic measurement outcomes suggests that quantum principles might play a role in modeling attentional processes. The authors argue that their quantum model provides a more integrated and comprehensive approach compared to classical neurodynamic models, potentially offering new insights into the neural mechanisms underlying attention and consciousness.
Key Insights
- •A novel "deep teleportation" channel, implemented with a three-qubit entangled ansatz circuit, is used instead of the conventional EPR channel, providing a more complex quantum system for modeling cognitive processes.
- •The model successfully simulates key features of the attentional blink, including lag-1 sparing, lag-7 divergence, and the overall U-shaped trend, mirroring human experimental data.
- •Distractor stimuli (masks) are effectively encoded as random angle rotations applied to the qubits, demonstrating a simple yet effective method for incorporating external factors into the quantum model.
- •A simple feed-forward neural network (FFN) is used to map the quantum measurement outcomes to probabilities of conscious report, suggesting that a hybrid quantum-classical approach can be effective.
- •The paper highlights an inconsistency in classical neurodynamical models, where they often fail to show a drop from lag 2 to 3 in the attentional blink effect, an effect that *is* captured by the proposed quantum model.
- •The authors acknowledge that the model simulates conscious reports, not necessarily conscious experience, separating the simulation from the more philosophical debate surrounding the "collapse of the wave function" and consciousness.
- •The study reveals that the probabilities of "seen" vs "unseen" are reversed around depth/lag 3, and the difference oscillates and vanishes by deeper lags, which is a key observation in the simulation results.
Practical Implications
- •This research opens new avenues for exploring quantum models in cognitive science, potentially leading to more accurate and comprehensive simulations of complex cognitive processes like attention and consciousness.
- •Neuroscientists and cognitive psychologists could benefit from this research by using quantum models to complement classical approaches, potentially gaining new insights into the underlying neural mechanisms of attention and consciousness.
- •The model provides a framework for simulating the effects of different experimental conditions (e.g., presence or absence of masks) on attentional blink, which could be used to design more effective experimental paradigms.
- •The "deep teleportation" technique could be further explored and adapted for other quantum cognitive models, potentially improving their ability to simulate complex cognitive phenomena.
- •Future research directions include investigating the relationship between quantum depth and neuronal layers, as well as exploring the potential role of subcortical brain areas in attentional blink phenomena.