Uncertainty is an intrinsic part of life; most events, affairs, and questions are uncertain. A key
problem in behavioral sciences is how the mind copes with uncertain information. Quantum …

Bayesian inference offers an optimal means of processing environmental information and so
an advantage in natural selection. We consider the apparent, recent trend in increasing …

Symbolical artificial intelligence is a field of computer science that is highly related to
quantum computation. At first glance, this statement appears to be a contradiction. However …

The context-sensitivity of cognition has been demonstrated across a wide range of cognitive
functions such as perception, memory, judgement and decision making. A related …

This paper contributes to quantum-like modeling of decision making (DM) under uncertainty
through application of Heisenberg's uncertainty principle (in the form of the Robertson …

How do we make causal judgments? Many studies have demonstrated that people are
capable causal reasoners, achieving success on tasks from reasoning to categorization to …

A novel decision bias, called the evaluation bias (EB), was reported by White, Pothos, and
Busemeyer (2014). In a sequence of 2 stimuli of opposite affective valence, evaluating the …

Randomness and selection are fundamental processes rooted in the very basis of life, as
postulated by the theory of evolution. First, mutations are randomly and continuously fed in …

Empirical findings from cognitive psychology indicate that, in scenarios under high levels of
uncertainty, many people tend to make irrational decisions. To address this problem, models …

Probability judgments appear to violate basic axioms of probability theory, which seems to
contradict with the recent successes of Bayesian models of cognition. To explain these …