EEG correlates of cognitive time scales in the Necker-Zeno model for bistable perception

The Necker-Zeno model of bistable perception provides a formal relation between the average duration of meta-stable percepts (dwell times T) of ambiguous figures and two other basic time scales (t₀, ΔT) underlying cognitive processing. The model predicts that dwell times T covary with t₀, ΔT or both. We tested this prediction by exploiting that observers, in particular experienced meditators, can volitionally control dwell times T.Meditators and non-meditators observed bistable Necker cubes either passively or tried to hold their current percept. The latencies of a centro-parietal event-related potential (CPP) were recorded as a physiological correlate of t₀.Dwell times T and the CPP latencies, correlated with t₀, differed between conditions and observer groups, while ΔT remained constant in the range predicted by the model. The covariation of CPP latencies and dwell times, as well as their quadratic functional dependence extends previous psychophysical confirmation of the Necker-Zeno model to psychophysiological measures.     

Does integral affect influence intentions to use artificial intelligence for skin cancer screening? A test of the affect heuristic

Objective: This study investigated how affect influences people’s processing of messages about risks and benefits of using autonomous artificial intelligence (AI) technology to screen for skin cancer. We examined integral affect (emotion derived during decision making) separately from incidental affect (extraneous mood states).

Design: Using the affect heuristic framework, we randomly assigned 273 participants to conditions featuring risk (high, low, uncertain) or benefit (high, low, uncertain) messages about AI. Following ‘affect-as-spotlight’, we also explored whether people’s integral affect towards skin cancer moderated the relationship between risk/benefit messages and AI screening intentions.

Outcomes: Perceived risk, perceived benefit, positive and negative affect toward AI, intention to use AI screening.

Results: After controlling for incidental affect and risk perceptions, we found that compared to low risk messages, uncertain risk messages increased participants’ negative affect toward AI, decreased positive affect toward AI, increased AI risk evaluations and reduced AI benefit evaluations. Perceptual variables significantly mediated participants’ intentions to use AI for risk messages but not benefit messages. No moderation effects were found.

Conclusions: Results suggest extending the affect heuristic framework to include uncertain risk conditions. Integral AI affect influenced people’s interpretation of messages, which then impacted likelihood to use AI technology for health.     

Visual cognitive algorithms for high-dimensional data and super-intelligence challenges

In the long run the cognitive algorithms intend to make super-intelligent machines and super-intelligent humans. This paper presents a technical process to reach specific aspects of super-intelligence that are out of the current human cognitive abilities. These aspects are inabilities to discover patterns in large numeric multidimensional data with a naked eye. This is a long-standing problem in Data Science and Modeling in general. The major obstacle is in human inability to see n-D data by a naked eye and our needs in visualization means to represent n-D data in 2-D losslessly. While these means exist their number and abilities are limited. This paper expands the class of such lossless visual methods, by further developing a new concept of Generalized Shifted Paired Coordinates. It shows the advantages of proposed reversible lossless technique by representing real data and by proving mathematical properties.     

Modeling spread,interlace and interchange of information processes with variable domains

In this paper a semantic metalanguage is developed and designed to study the occurrence, spread and safe interaction of semantic processes in information modeling systems, including cognitive interference. An approach to construe a semantic network is proposed and based on a computational model in which both nodes and arcs are information processes. Concepts are represented by intensional objects within the framework of theories without types, and they, in turn, are considered as special counterparts of typed theories. Similar mixing was used in model studies for lambda calculus. To a contrast with them, in this paper, information processes correspond to parameterized metadata objects, which are variable domain constructs. Transformations of variable domains correspond to the spread of the process. Directional transformation provides the generation of metadata targets in the form of parameterized concepts. This simulates the development of the process, which corresponds to the spread of cognitive interference and allows the interpretation of a hidden time factor. The emerging model is purely process based and provides such a conceptual framework. The possibility of coding this framework with a system of interdependent lambda terms is reflected.     

WHAT MAKES A QUANTUM PHYSICS BELIEF BELIEVABLE? MANY-WORLDS AMONG SIX IMPOSSIBLE THINGS BEFORE BREAKFAST

An extraordinary, if circumscribed, positive shift has occurred since the mid-twentieth century in the perceived status of Hugh Everett III's 1956 theory of the universal wave function of quantum mechanics, now widely called the Many-Worlds Interpretation (MWI). Everett's starkly new interpretation denied the existence of a separate classical realm, contending that the experimental data can be seen as presenting a state vector for the whole universe. Since there is no state vector collapse, reality as a whole is strictly deterministic. Explained jointly by the dynamical variables and the state vector, “this reality is not the reality we customarily think of, but is a reality composed of many worlds,” wrote Everett's colleague Bryce DeWitt. In this essay, I account briefly for the change of status in conventional scientific terms, yet chiefly in extended terms of three sets of ideas that I argue can be understood to affect believability in both scientific and religious contexts, illuminating helpfully the MWI phenomenon, and its engagement with theology: orthodoxy and heresy, language and reference, and faith and agnosticism. One's orientation relative to the variable content of these dynamic, socially oriented categories helps to make belief in ideas as metaphysically challenging as Everettian Quantum Mechanics, or particular ideas about God, either more or less believable. The categories will have the same function in a theology engaging Everett's theory, and in any theology at all written in a society deeply marked by what I further argue is a subtle, powerful, and pervasive mode of quasi-scientific thinking we can call societal constructive agnosticism, of which anyone doing theology today must be aware.     

Regularization of languages by adults and children: A mathematical framework

The fascinating ability of humans to modify the linguistic input and “create” a language has been widely discussed. In the work of Newport and colleagues, it has been demonstrated that both children and adults have some ability to process inconsistent linguistic input and “improve” it by making it more consistent. In Hudson Kam and Newport (2009), artificial miniature language acquisition from an inconsistent source was studied. It was shown that (i) children are better at language regularization than adults and that (ii) adults can also regularize, depending on the structure of the input. In this paper we create a learning algorithm of the reinforcement-learning type, which exhibits patterns reported in Hudson Kam and Newport (2009) and suggests a way to explain them. It turns out that in order to capture the differences between children’s and adults’ learning patterns, we need to introduce a certain asymmetry in the learning algorithm. Namely, we have to assume that the reaction of the learners differs depending on whether or not the source’s input coincides with the learner’s internal hypothesis. We interpret this result in the context of a different reaction of children and adults to implicit, expectation-based evidence, positive or negative. We propose that a possible mechanism that contributes to the children’s ability to regularize an inconsistent input is related to their heightened sensitivity to positive evidence rather than the (implicit) negative evidence. In our model, regularization comes naturally as a consequence of a stronger reaction of the children to evidence supporting their preferred hypothesis. In adults, their ability to adequately process implicit negative evidence prevents them from regularizing the inconsistent input, resulting in a weaker degree of regularization.