Online research in philosophy

Due to the wide array of phenomena that are of interest to them, psychologists offer highly diverse and heterogeneous types of explanations. Initially, this suggests that the question "What is psychological explanation?" has no single answer. To provide appreciation of this diversity, we begin by noting some of the more common types of explanations that psychologists provide, with particular focus on classical examples of explanations advanced in three different areas of psychology: psychophysics, physiological psychology, and information-processing psychology. To analyze what is involved in these types of explanations, we consider the ways in which law-like representations of regularities and representations of mechanisms factor in psychological explanations. This consideration directs us to certain fundamental questions, e.g., "To what extent are laws necessary for psychological explanations?" and "What do psychologists have in mind when they appeal to mechanisms in explanation?" In answering such questions, it appears that laws do play important roles in psychological explanations, although most explanations in psychology appeal to accounts of mechanisms. Consequently, we provide a unifying account of what psychological explanation is.

Human behavior cannot be understood by using only models of explanation utilized in the natural sciences. Multiple models of explanation, which are not consistent with, or reducible to each other, are required and are in fact used in psychology to explain human actions. This situation, called "Multiexplanation," could cause a problem of developing a justified correspondence between psychological phenomena and multiple models of explanation. Unless this problem is solved, the explanatory capability of a psychological theory seems inconsistent and ad hoc. A solution suggesting "correspondence guidelines" between phenomena and available models of explanation and "organization guidelines" for constructing a coherent psychological theory is offered. It contributes to the development of a "multiexplanation-model theory" (or a "multimodel theory" for brevity) which employs different models of explanation needed for proposing accounts of psychological phenomena.

The central aim of this paper is to shed light on the nature of explanation in computational neuroscience. I argue that computational models in this domain possess explanatory force to the extent that they describe the mechanisms responsible for producing a given phenomenon—paralleling how other mechanistic models explain. Conceiving computational explanation as a species of mechanistic explanation affords an important distinction between computational models that play genuine explanatory roles and those that merely provide accurate descriptions or predictions of phenomena. It also serves to clarify the pattern of model refinement and elaboration undertaken by computational neuroscientists.

Explanation 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.

Until recently, the notions of function and multiple realization were supposed to save the autonomy of psychological explanations. Furthermore, the concept of supervenience presumably allows both dependence of mind on brain and non-reducibility of mind to brain, reconciling materialism with an independent explanatory role for mental and functional concepts and explanations. Eliminativism is often seen as the main or only alternative to such autonomy. It gladly accepts abandoning or thoroughly reconstructing the psychological level, and considers reduction if successful as equivalent with elimination. In comparison with the philosophy of mind, the philosophy of biology has developed more subtle and complex ideas about functions, laws, and reductive explanation than the stark dichotomy of autonomy or elimination. It has been argued that biology is a patchwork of local laws, each with different explanatory interests and more or less limited scope. This points to a pluralistic, domain-specific and multi-level view of explanations in biology. Explanatory pluralism has been proposed as an alternative to eliminativism on the one hand and methodological dualism on the other hand. It holds that theories at different levels of description, like psychology and neuroscience, can co-evolve, and mutually influence each other, without the higher-level theory being replaced by, or reduced to, the lower-level one. Such ideas seem to tally with the pluralistic character of biological explanation. In biological psychology, explanatory pluralism would lead us to expect many local and non-reductive interactions between biological, neurophysiological, psychological and evolutionary explanations of mind and behavior. This idea is illustrated by an example from behavioral genetics, where genetics, physiology and psychology constitute distinct but interrelated levels of explanation. Accounting for such a complex patchwork of related explanations seems to require a more sophisticated and precise way of looking at levels than the existing ideas on (reductive and non-reductive) explanation in the philosophy of mind.

Social cognitive neuroscience examines social phenomena and processes using cognitive neuroscience research tools such as neuroimaging and neuropsychology. This review examines four broad areas of research within social cognitive neuroscience: (a) understanding others, (b) understanding oneself, (c) controlling oneself, and (d) the processes that occur at the interface of self and others. In addition, this review highlights two core-processing distinctions that can be neurocognitively identified across all of these domains. The distinction between automatic versus controlled processes has long been important to social psychological theory and can be dissociated in the neural regions contributing to social cognition. Alternatively, the differentiation between internally-focused processes that focus on one's own or another's mental interior and externally-focused processes that focus on one's own or another's visible features and actions is a new distinction. This latter distinction emerges from social cognitive neuroscience investigations rather than from existing psychological theories demonstrating that social cognitive neuroscience can both draw on and contribute to social psychological theory.

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Psychoneural reductionists sometimes claim that sufficient amounts of lower-level explanatory achievement preclude further contributions from higher-level psychological research. Ostensibly, with nothing left to do, the effect of such preclusion on psychological explanation is extinction. Reductionist arguments for preclusion have recently involved a reorientation within the philosophical foundations of neuroscience---namely, away from the philosophical foundations and toward the neuroscience. In this chapter, I review a successful reductive explanation of an aspect of reward function in terms of dopaminergic operations of the mesocorticolimbic system in order to demonstrate why preclusion/extinction claims are dubious.

According to some philosophers, computational explanation is proprietary
to psychology—it does not belong in neuroscience. But neuroscientists routinely offer computational explanations of cognitive phenomena. In fact, computational explanation was initially imported from computability theory into the science of mind by neuroscientists, who justified this move on neurophysiological grounds. Establishing the legitimacy and importance of computational explanation in neuroscience is one thing; shedding light on it is another. I raise some philosophical questions pertaining to computational explanation and outline some promising answers that are being developed by a number of authors.

As much as assumptions about mechanisms and mechanistic explanation have deeply affected psychology, they have received disproportionately little analysis in philosophy. After a historical survey of the influences of mechanistic approaches to explanation of psychological phenomena, we specify the nature of mechanisms and mechanistic explanation. Contrary to some treatments of mechanistic explanation, we maintain that explanation is an epistemic activity that involves representing and reasoning about mechanisms. We discuss the manner in which mechanistic approaches serve to bridge levels rather than reduce them, as well as the different ways in which mechanisms are discovered. Finally, we offer a more detailed example of an important psychological phenomenon for which mechanistic explanation has provided the main source of scientific understanding.