Ambivalence in decision making: An eye tracking study

A Quantum Model of Trust Calibration in Human-AI Interactions

This exploratory study investigates a human agent's evolving judgements of reliability when interacting with an AI system. Two aims drove this investigation: (1) compare the predictive performance of quantum vs. Markov random walk models regarding human reliability judgements of an AI system and (2) identify a neural correlate of the perturbation of a human agent's judgement of the AI's reliability. As AI becomes more prevalent, it is important to understand how humans trust these technologies and how trust evolves when interacting with them. A mixed-methods experiment was developed for exploring reliability calibration in human-AI interactions. The behavioural data collected were used as a baseline to assess the predictive performance of the quantum and Markov models. We found the quantum model to better predict the evolving reliability ratings than the Markov model. This may be due to the quantum model being more amenable to represent the sometimes pronounced within-subject variability of reliability ratings. Additionally, a clear event-related potential response was found in the electroencephalographic (EEG) data, which is attributed to the expectations of reliability being perturbed. The identification of a trust-related EEG-based measure opens the door to explore how it could be used to adapt the parameters of the quantum model in real time.

Eye tracking and eye expression decoding based on transparent, flexible and ultra-persistent electrostatic interface

Eye tracking provides valuable insight for analyzing visual attention and underlying thinking progress through the observation of eye movements. Here, a transparent, flexible and ultra-persistent electrostatic sensing interface is proposed for realizing active eye tracking (AET) system based on the electrostatic induction effect. Through a triple-layer structure combined with a dielectric bilayer and a rough-surface Ag nanowire (Ag NW) electrode layer, the inherent capacitance and interfacial trapping density of the electrostatic interface has been strongly enhanced, contributing to an unprecedented charge storage capability. The electrostatic charge density of the interface reached 1671.10 μC·m^-2 with a charge-keeping rate of 96.91% after 1000 non-contact operation cycles, which can finally realize oculogyric detection with an angular resolution of 5°. Thus, the AET system enables real-time decoding eye movements for customer preference recording and eye-controlled human-computer interaction, supporting its limitless potentiality in commercial purpose, virtual reality, human computer interactions and medical monitoring.

Open Systems, Quantum Probability, and Logic for Quantum-like Modeling in Biology, Cognition, and Decision-Making

The aim of this review is to highlight the possibility of applying the mathematical formalism and methodology of quantum theory to model behavior of complex biosystems, from genomes and proteins to animals, humans, and ecological and social systems. Such models are known as quantum-like, and they should be distinguished from genuine quantum physical modeling of biological phenomena. One of the distinguishing features of quantum-like models is their applicability to macroscopic biosystems or, to be more precise, to information processing in them. Quantum-like modeling has its basis in quantum information theory, and it can be considered one of the fruits of the quantum information revolution. Since any isolated biosystem is dead, modeling of biological as well as mental processes should be based on the theory of open systems in its most general form-the theory of open quantum systems. In this review, we explain its applications to biology and cognition, especially theory of quantum instruments and the quantum master equation. We mention the possible interpretations of the basic entities of quantum-like models with special interest given to QBism, as it may be the most useful interpretation.