Journal

Volume 14, Issue 3 (September 30, 2013)

4 articles

• Functional Analyses, Mechanistic Explanations, and Explanatory Tradeoffs
  by Sergio Daniel Barberis
  J. CS. 2013, 14(3), 229-251;
  Abstract Recently, Piccinini and Craver have stated three theses concerning the relations between functional analysis and mechanistic explanation in cognitive sciences: No Distinctness: functional analysis and mechanistic explanation are explanations of the same kind; Integration: functional analysis is a ki... [Read more].
  Abstract Recently, Piccinini and Craver have stated three theses concerning the relations between functional analysis and mechanistic explanation in cognitive sciences: No Distinctness: functional analysis and mechanistic explanation are explanations of the same kind; Integration: functional analysis is a kind of mechanistic explanation; and Subordination: functional analyses are unsatisfactory sketches of mechanisms. In this paper, I argue, first, that functional analysis and mechanistic explanations are sub-kinds of explanation by scientific (idealized) models. From that point of view, we must take into account the tradeoff between the representational/explanatory goals of generality and precision that govern the practice of model-building. In some modeling scenarios, it is rational to maximize explanatory generality at the expense of mechanistic precision. This tradeoff allows me to put forward a problem for the mechanist position. If mechanistic modeling endorses generality as a valuable goal, then Subordination should be rejected. If mechanists reject generality as a goal, then Integration is false. I suggest that mechanists should accept that functional analysis can offer acceptable explanations of cognitive phenomena. [Collapse]
• Modal Considerations on Information Processing and Computation in the Nervous System
  by Abel Wajnerman Paz
  J. CS. 2013, 14(3), 253-286;
  Abstract We can characterize computationalism very generally as a complex thesis with two main parts: the thesis that the brain (or the nervous system) is a computational system and the thesis that neural computation explains cognition. As Piccinini and Bahar (2012) point out, over the last six decades, comp... [Read more].
  Abstract We can characterize computationalism very generally as a complex thesis with two main parts: the thesis that the brain (or the nervous system) is a computational system and the thesis that neural computation explains cognition. As Piccinini and Bahar (2012) point out, over the last six decades, computationalism has been the mainstream theory of cognition. Nevertheless, there is still substantial debate about which type of computation explains cognition, and computationalism itself still remains controversial. My aim in this paper is to make two main contributions to the debate about the first subthesis of computationalism, i.e. that the brain is a computational system. First, I want to offer an accurate elucidation of the notion relevant for understanding computationalism (the notion of computation) and clarify the relation between computation and information as well as the relations between both computation and information processing and the nervous system. Second, I want to argue against a peculiar form of computationalism: the thesis that neural processes are constitutively computational in some sense; that neural processes cannot be realized by a system that is not in some sense computational. I will call this thesis “modal computationalism.” In particular, I want to argue that neural processing can be realized by a system that is not a sui generis computer (i. e., a computing system that is neither digital nor analog) and by a system that is not a generic computer (a computer in the most general sense: one that includes digital, analog, and any other kind of computation). Actual neural processing is presumed to be computational in these two senses (Piccinini and Bahar 2012). I will argue that, even if this is true, neural processing can be realized by a computing system that is not of the same kind as those that perform actual neural processing and even by a system that is not computational at all. [Collapse]
• Overall Similarity Overrides Element Similarity when Evaluating the Quality of Analogies
  by Ricardo A. Minervino, Nicolás Oberholzer, & Máximo Trench
  J. CS. 2013, 14(3), 287-317;
  Abstract Dominant computational models of analogical reasoning (e.g., SME and LISA) consider that two facts or situations are more analogous as the similarity between corresponding propositional elements increases. We report the results of two experiments demonstrating that when people judge the quality of a... [Read more].
  Abstract Dominant computational models of analogical reasoning (e.g., SME and LISA) consider that two facts or situations are more analogous as the similarity between corresponding propositional elements increases. We report the results of two experiments demonstrating that when people judge the quality of an analogy, the similarity between matched elements is overridden by another type of similarity that implies comparing the meaning of whole propositions. In Experiment 1, participants received a base fact followed by two structurally identical target facts. Whereas in one of them propositional elements resembled their counterparts in the base, in the other they did not, but the meaning of the whole proposition resembled that of the base. Participants chose as more analogous the targets maintaining this second type of similarity. In Experiment 2, participants received a base cause followed by an effect, and were told that such effect reoccurred later as a consequence of an analogous cause. Participants had to decide which of two structurally identical facts was the cause of the target effect. Again, participants based their choices on overall similarities, passing over similarities between propositional elements, but in a more ecologically valid task that involves comparing systems of relations. We conclude with some intuitions about the mechanisms underlying how people assess the quality of an analogy, and discuss their implications for future theories of analogical thinking. [Collapse]
• Does Conceptual Metaphor Emerge from Metaphoric Language
  by Raymond W. Gibbs Jr.
  J. CS. 2013, 14(3), 319-334;
  Abstract A significant claim within contemporary metaphor scholarship is that many linguistic metaphors arise from widely-held metaphors in thought, or conceptual metaphors. People speak metaphorically to the extent that they do, because they think metaphorically about many abstract ideas and events. Moreove... [Read more].
  Abstract A significant claim within contemporary metaphor scholarship is that many linguistic metaphors arise from widely-held metaphors in thought, or conceptual metaphors. People speak metaphorically to the extent that they do, because they think metaphorically about many abstract ideas and events. Moreover, these metaphoric concepts emerge, primarily, from recurring aspects of bodily experience, such that metaphoric concepts and language is seen as embodied to a significant degree. Daniel Sanford offers a different perspective of where metaphoric concepts come from by suggesting how these emerge from tokens of linguistic metaphor. Verbal metaphors do not arise from metaphoric concepts, but metaphoric concepts may arise from repeated patterns of verbal metaphor use. My article acknowledges the possible importance of verbal metaphor in the creation of conceptual metaphors, but strongly argues that language along cannot explain the specifics of metaphoric thinking or why we talk about topics in the metaphoric ways we do. [Collapse]