We argue that computation via quantum mechanical processes is irrelevant to explaining how brains produce thought, contrary to the ongoing speculations of many theorists. First, quantum effects do not have the temporal properties required for neural information processing. Second, there are substantial physical obstacles to any organic instantiation of quantum computation. Third, there is no psychological evidence that such mental phenomena as consciousness and mathematical thinking require explanation via quantum theory. We conclude that understanding brain function is unlikely to require quantum computation or similar mechanisms.

To become eligible for support services at the postsecondary level, students with learning disabilities (LD) must submit documentation to verify the existence of a specific LD. The documentation submitted over a 5-year period by 415 students to a large public research university that offers a comprehensive program for students with LD was examined. An analysis of the documentation indicates that serious problems exist in the type and quality of evaluation reports. Problems include flaws in the comprehensiveness of the assessments, and the use of questionable instruments for this population. Recommendations for establishing guidelines for acceptable documentation are offered.

We present a theory and neurocomputational model of how specific brain operations produce complex decision and preference phenomena, including those explored in prospect theory and decision affect theory. We propose that valuation and decision making are emotional processes, involving interacting brain areas that include two expectation-discrepancy subsystems: a dopamine-encoded system for positive events and a serotonin-encoded system for negative ones.The model provides a rigorous account of loss aversion and the shape of the value function from prospect theory. It also suggests multiple distinct neurological mechanisms by which information framing may affect choices, including ones involving anticipated pleasure. It further offers a neural basis for the interactions among affect, prior expectations and counterfactual comparisons explored in decision affect theory. Along with predicting the effects of particular brain disturbances and damage, the model suggests specific neurological explanations for individual differences observed in choice and valuation behaviors.Keywords: computational neuroscience; decision making; emotion; framing; prospect theory. what kind of career to pursue? In traditional economics, the standard answer is that people decide by maximizing expected utility, but psychologists have found many problems with this kind of decision theory as a description of human behavior (e.g., Camerer, 2000;Kahneman & Tversky, 2000;Koehler, Brenner, & Tversky, 1997;Rottenstreich & Hsee, 2001;Tversky & Kahneman, 1991). Economists commonly take preferences as given, but from a psychological point of view it should be possible to explain how preferences arise from cognitive and affective processes.Work in this spirit has made tremendous progress in revealing key features and dynamics missed by theories disconnected from the study of cognitive, emotional and socially motivated phenomena, such as a common hypersensitivity to losses over equivalent gains (Kahneman & Tversky, 1979) and the affective influence of prior expectations and counterfactual comparisons on preference judgments (Mellers, 2000). Moreover, with the rise of cognitive and affective neuroscience, it should be possible to identify precise neural mechanisms underlying these behavioral-level explanations of why people make the choices that they do.We propose neural affective decision theory as a psychologically and neurologically realistic account of specific brain mechanisms underlying human preference and decision. The theory consists of four principles, which we shall list here and describe in detail later:1. Affect. Decision making is a cognitive-affective process, crucially dependent on emotional evaluation of potential actions. Brain.Decision making is a neural process driven by coordinated dynamic interactions among multiple brain areas, including parts of prefrontal cortex as well as major subcortical systems.3. Valuation. The brain forms preferences via interacting but distinct mechanisms for positive and negative outcomes, encode...

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Thagard, P., & Litt, A. (forthcoming). Models of scientific explanation. In R. Sun (Ed.), The Cambridge handbook of computational cognitive modeling. Cambridge: Cambridge University Press. EXPLANATIONExplanation of why things happen is one of humans' most important cognitive operations. In everyday life, people are continually generating explanations of why other people behave the way they do, why they get sick, why computers or cars are not working properly, and of many other puzzling occurrences.More systematically, scientists develop theories to provide general explanations of physical phenomena such as why objects fall to earth, chemical phenomena such as why elements combine, biological phenomena such as why species evolve, medical phenomena such as why organisms develop diseases, and psychological phenomena such as why people sometimes make mental errors.This chapter reviews computational models of the cognitive processes that underlie these kinds of explanations of why events happen. It is not concerned with another sense of explanation that just means clarification, as when someone explains the U. S. constitution. The focus will be on scientific explanations, but more mundane examples will occasionally be used, on the grounds that the cognitive processes for explaining why events happen are much the same in everyday life and in science, although scientific explanations tend tobe more systematic and rigorous than everyday ones. In addition to providing a concise review of previous computational models of explanation, this chapter describes a new neural network model that shows how explanations can be performed by multimodal distributed representations. 2Before proceeding with accounts of particular computational models of explanation, let us characterize more generally the three major processes involved in explanation and the four major theoretical approaches that have been taken in computational models of it. The three major processes are: providing an explanation from available information, generating new hypotheses that provide explanations, and evaluating competing explanations. The four major theoretical approaches are:deductive, using logic or rule-based systems; schematic, using explanation patterns or analogies; probabilistic, using Bayesian networks; and neural, using networks of artificial neurons. For each of these theoretical approaches, it is possible to characterize the different ways in which the provision, generation, and evaluation of explanations are understood computationally.The processes of providing, generating and evaluating explanations can be illustrated with a simple medical example. Suppose you arrive at your doctor's office with a high fever, headache, extreme fatigue, a bad cough, and major muscle aches.Your doctor will probably tell you that you have been infected by the influenza virus, with an explanation like:People infected by the flu virus often have the symptoms you describe.You have been exposed to and infected by the flu virus.So, you have these symptoms.If influenza ...