Grades of Explanation in Cognitive Science (original) (raw)
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Productive Explanation: A Framework for Evaluating Explanations in Psychological Science
The explanation of psychological phenomena is a central aim of psychological science. However, the nature of explanation and the processes by which we evaluate whether a theory explains a phenomenon are often unclear. Consequently, it is often unknown whether a given psychological theory indeed explains a phenomenon. We address this shortcoming by characterizing the nature of explanation in psychology, and proposing a framework in which to evaluate explanation. We present a productive account of explanation: a theory putatively explains a phenomenon if and only if a formal model of the theory produces the statistical pattern representing the phenomenon. Using this account, we outline a workable methodology of explanation: (a) explicating a verbal theory into a formal model, (b) representing phenomena as statistical patterns in data, and (c) assessing whether the formal model produces these statistical patterns. In addition, we explicate three major criteria for evaluating the goodne...
Levels of Explanation Vindicated
Review of Philosophy and Psychology, 2011
Marr's celebrated contribution to cognitive science (Marr 1982, chap. 1) was the introduction of (at least) three levels of description/explanation. However, most contemporary research has relegated the distinction between levels to a rather dispensable remark. Ignoring such an important contribution comes at a price, or so we shall argue. In the present paper, first we review Marr's main points and motivations regarding levels of explanation. Second, we examine two cases in which the distinction between levels has been neglected when considering the structure of mental representations: Cummins et al.'s distinction between structural representation and encodings ) and Fodor's account of iconic representation (Fodor 2008). These two cases illustrate the kind of problems in which researchers can find themselves if they overlook distinctions between levels and how easily these problems can be solved when levels are carefully examined. The analysis of these cases allows us to conclude that researchers in the cognitive sciences are well advised to avoid risks of confusion by respecting Marr's old lesson.
Explanations in cognitive science: unification versus pluralism
Synthese, 2021
The debate between the defenders of explanatory unification and explanatory pluralism has been ongoing from the beginning of cognitive science and is one of the central themes of its philosophy. Does cognitive science need a grand unifying theory? Should explanatory pluralism be embraced instead? Or maybe local integrative efforts are needed? What are the advantages of explanatory unification as compared to the benefits of explanatory pluralism? These questions, among others, are addressed in this Synthese’s special issue. In the introductory paper, we discuss the background of the questions, distinguishing integrative theorizing from building unified theories. On the one hand, integrative efforts involve collaboration between various disciplines, fields, approaches, or theories. These efforts could even be quite temporary, without establishing any long-term institutionalized fields or disciplines, but could also contribute to developing new interfield theories. On the other hand, unification can rely on developing complete theories of mechanisms and representations underlying all cognition, as Newell’s “unified theories of cognition”, or may appeal to grand principles, as predictive coding. Here, we also show that unification in contemporary cognitive science goes beyond reductive unity, and may involve various forms of joint efforts and division of explanatory labor. This conclusion is one of the themes present in the content of contributions constituting the special issue.
On The Explanatory Power of Explanations: Why Context Matters.
In contemporary popular-scientific explanations, what is to count as a powerful or 'better' explanation not seldomly is determined by its providing of 'lower-level' information. 1 This appeal to lower-level explanations seems indicative of a certain scientific tendency: it suggests that higher-level explanations ultimately are translatable to lower-level explanations. That is, the translation of higher-level explanations to lower-level explanations is considered a fruitful enterprise, since it is believed that ultimately all the currently separate scientific domains-including their respective higher-level explanations-can in principle be explained by unifying these in fewer lower-level explanations. This implicitly assumes that, by carrying out such a reduction, the explanatory power remains intact. The aim of this paper is to object to this assumption by stressing that, because of its dependence on contextual factors, explanatory power does not necessarily remain intact during the translation in question. Rather, it is the existence of contextual factors that seems determinative of explanatory power proper. In what follows, I will first provide a sketch of the hierarchical structure that the 'level' classification of scientific explanations represents. Second, I will assess an everyday scenario and consider what type of explanation seems better-suited to capture it in terms of explanatory power.
Toward a Theory of the Process of Explanation
Synthese, 2005
What is an explanation? An extensive but rather inconclusive discussion has been devoted to this question in the last several decades. This dis-cussion has been surveyed by Salmon (1990) and by Stegmüller (1983). Much of the early stages of this discussion dealt with ...
Explanation' appears to be ambiguous between a representational-artifact, an objective, and a doxastic sense. That the distinctions between the three are still poorly understood we regard as an impediment to progress in the philosophy of science and as a source of the field's resistance to greater integration with experimental psychology. We elucidate the overlapping contours of the three sense of 'explanation' using a variation on Powell & Horne's Semantic Integration paradigm, showing that both laypeople and scientists regard doxastic explanations as constitutive of representational-artifact, but not of objective, explanations and accuracy as closely connected to objective, but not representational-artifact, explanations.
Functions, levels, and mechanisms. Explanation in cognitive science and its problems.
In the first part of the paper we describe the philosophical debate on the expansions of cognitive science into the brain and into the environment, take sides against the “revolutionary” positions on them and in favor of a “reformist” approach, and conclude that the most appropriate model for cognitive sciences is pluralistic. This is meant in a twofold sense. On the one hand, mental phenomena require a variety of explanatory levels, whose inter-relations are of two kinds: decomposition and contextualization. On the other hand, the arguably quasi-holistic character of some cognitive tasks suggests that the mechanistic style of explanation has to be integrated in these cases with a dynamical explanatory style. This theoretical picture, however, raises two classes of problems: (a) the compatibility between the mechanistic-computationalist explanation and the dynamical one and (b) the nature of theoretical entities and relations postulated at the different levels of a pluralistic model involving computational explanations. Each point will be discussed in the second part of the paper.
Explanatory Relevance. A central issue in the Theory of Explanation
The thesis addresses a topic at the interface between Philosophy of Language, Cognitive Studies and Philosophy of Science, i.e. explanatory relevance. An explanation, I claim, is first and foremost a relational concept, that affirms the existence of asymmetrical dependence relations within our representation of the world. However, the story cannot be that short. At some level everything is related to everything, and yet not every relation is explanatory: the Big Bang is not a good explanation for this abstract, although the abstract certainly depends on the Big Bang. What does explanatory relevance depend on? Not any dependence relation is good material for an explanation, it seems. But what criteria do we use to discriminate between them? Even more strikingly, we do not make judgements of relevance based on conscious reflections, after thorough comparisons and evaluations: we just know, and quite blatantly so, if an answer is irrelevant. This behaviour deserves a closer look. To properly address it, explanatory relevance has to be contextualised within its theoretical frame. Consequently, my study takes off from an analysis of the background philosophical work on explanation. Much work has been done towards the clarification of this highly elusive notion, but a somewhat homogeneous perspective has biased the terms of the debate, at least to some extent. Moreover, the nature of its relational dimension is not straightforward, thus part of the dissertation addresses the ontology of explanations and the controversial primary role played therein by causation. Starting from these premises, I suggest that explanatory relevance is not a matter of the nature of the dependence relation involved, which is an issue for metaphysical typology. Rather, I propose a view according to which there are (at least) two dimensions to relevance. I call ontological the relevance of a given dot of the interconnected net of dependencies to the obtaining of something, that I analyse using Michael Strevens' notion of difference-making, and cognitive the relevance that our mind/brain agrees to certain inputs and tasks, judged worth attending in the nondiscrete, continuous flux of experience. These two dimensions get together within a naturalistic perspective, that addresses human explanatory practice as the result of a selective pressure towards efficiency: humans are animals who are moved by the priority of survival, and chances of survival are enhanced by a cognitive system structured as to favour the priority of relevant factors and relations.