This thesis develops a novel framework for explaining delusions. In Chapter 1, I introduce the two fundamental challenges posed by delusions: the evidence challenge lies in explaining the flagrant ways delusions flout evidence; and the specificity challenge lies in explaining the fact that patients’ delusions are often about a few specific themes, and patients rarely have a wide range of delusional or odd beliefs. In Chapter 2, I discuss the strengths and weaknesses of current theories of delusions, which typically appeal (...) to one or both of two factors: anomalous experience and reasoning abnormality. I argue that anomalous experience can help explain the specificity of delusions, but has difficulties in addressing the evidence challenge; reasoning abnormality can help address the evidence challenge, but has difficulties in explaining the specificity of delusions. This suggests that there may be an important factor that has not been captured by current theories of delusions. To search for this missing factor, in Chapter 3, I turn to normal believing. Inspired by the literature on Cartesian clarity and phenomenal dogmatism, I develop a dual-force framework of believing, according to which beliefs can be understood as the results of the interaction between the justificatory force and causal force of evidence and the justificatory force and causal force of clear experience, in which something clearly seems to be so to the subject. This framework suggests that the missing factor may be the clear experience with its distinctive phenomenal clarity that compels assent. In Chapter 4, I return to delusions, and argue that the dualforce framework can help us to get a better grip on some personal descriptions of delusions; make progress in addressing the evidence and specificity challenges of delusions; and shed new light on the underpinnings of delusions. In the end, I conclude with some remaining questions for future study. (shrink)

The existence of superstition and religious beliefs in most, if not all, human societies is puzzling for behavioral ecology. These phenomena bring about various fitness costs ranging from burial objects to celibacy, and these costs are not outweighed by any obvious benefits. In an attempt to resolve this problem, we present a verbal model describing how humans and other organisms learn from the observation of coincidence (associative learning). As in statistical analysis, learning organisms need rules to distinguish between real patterns (...) and randomness. These rules, which we argue are equivalent to setting the level of α for rejection of the null hypothesis in statistics, are governed by risk management as well as by comparison to previous experiences. Risk management means that the cost of a possible type I error (superstition) has to be traded off against the cost of a possible type II error (ignorance). This trade-off implies that the occurrence of superstitious beliefs is an inevitable consequence of an organism’s ability to learn from observation of coincidence. Comparison with previous experiences (as in Bayesian statistics) improves the chances of making the right decision. While this Bayesian approach is found in most learning organisms, humans have evolved a unique ability to judge from experiences whether a candidate subject has the power to mechanistically cause the observed effect. Such “strong” causal thinking evolved because it allowed humans to understand and manipulate their environment. Strong causal thinking, however, involves the generation of hypotheses about underlying mechanisms (i.e., beliefs). Assuming that natural selection has favored individuals that learn quicker and more successfully than others owing to (1) active search to detect patterns and (2) the desire to explain these patterns mechanistically, we suggest that superstition has evolved as a by-product of the first, and that belief has evolved as a by-product of the second. (shrink)