Shafer’s theory of evidential reasoning has recently received much attention as a possible model for probabilistic reasoning in expert system applications. This paper discusses the particular difficulties of implementing Shafer’s belief functions in the context of the most common form of expert system, rule-based systems. The two most important problems are: the representation of the expert’s s...

Studies of how evidence affects beliefs sometimes show belief polarization in response to mixed evidence. However, the nature of the mental processes leading to change in opinion is up for debate. Different accounts of how people process evidence and then update their beliefs make different predictions, especially about one-sided evidence, which is rarely examined. We presented subjects with mu...

Reasoning often occurs under emotionally charged, opinion-laden circumstances. The belief-bias effect indexes the extent to which reasoning is based upon beliefs rather than logical structure. We hypothesized that emotional content will increase this effect, particularly for individuals with higher emotional reactivity. The serotonin transporter genotype is associated with emotional reactivity ...

Hyperspectral data is not linearly separable, and it has a high characteristic dimension. This paper proposes a new algorithm that combines a deep belief network based on the Boltzmann machine with a self-organizing neural network. The primary features of the hyperspectral image are extracted with a deep belief network. The weights of the network are fine-tuned using the labeled sample. Feature...

The development of associative memory during childhood may be influenced by metacognitive factors. Here, one aspect of metamemory function--belief in strategy efficacy-was tested for a role in the effective use of encoding strategies. A sample of 61 children and adults (8-25 years of age) completed an associative recognition memory test and were assessed on belief in the efficacy of encoding st...

Researchers in decision analysis and artificial intelligence (AI) have used Bayesian belief networks to build probabilistic expert systems. Using standard methods drawn from the theory of computational complexity, workers in the field have shown that the problem of probabilistic inference in belief networks is difficult and almost certainly intractable. We have developed a randomized approximat...

This paper explores the notion that people who believe in psychic ability possess various psychological attributes that increase the likelihood of them misattributing paranormal causation to experiences that have a normal explanation. The paper discusses the structure and measurement of belief in psychic ability, then reviews the considerable body of work exploring the relationship between beli...

Qualitative probabilistic networks (QPNs) [13] are an abstraction of in uence diagrams and Bayesian belief networks replacing numerical relations by qualitative in uences and synergies. To reason in a QPN is to nd the e ect of decision or new evidence on a variable of interest in terms of the sign of the change in belief (increase or decrease). We review our work on qualitative belief propagati...

The elementary school teachers' view of mathematics is important because it will influence the way they will teach mathematics. Based on a survey study in three Finnish universities we explored the structure of student teachers view of mathematics and also the different belief profiles that they had. The core of student teachers' view consisted of three correlated beliefs: belief of one's own t...

This paper provides a theoretical framework for pricing assets in a multiperiod economy with heterogeneous beliefs. The stock price dynamics follow a binomial lattice structure. Agents are allowed to differ in their beliefs of the probability and asset return in each state of nature. By constructing a consensus belief, we examine the impact of heterogeneous beliefs on market equilibrium. Static...