Manipulation is key: on why non-mechanistic explanations in the cognitive sciences also describe relations of manipulation and control

Elber-Dorozko, Lotem

doi:10.1007/s11229-018-01901-3

Cited by 3 publications

(3 citation statements)

References 53 publications

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“…This strategy is also exemplified by the analysis of the canonical model of Class I excitability reviewed in the previous section (88). Elber-Dorozko's (98) proposal that computational models can provide non-causal yet still counterfactual explanations may also fit in this last category.…”

Section: Dynamical and Computational Explanation In Neurosciencementioning

confidence: 98%

Forms of explanation and understanding for neuroscience and artificial intelligence

Thompson

2021

Journal of Neurophysiology

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Much of the controversy evoked by the use of deep neural networks as models of biological neural systems amount to debates over what constitutes scientific progress in neuroscience. In order to discuss what constitutes scientific progress, one must have a goal in mind (progress towards what?). One such long term goal is to produce scientific explanations of intelligent capacities (e.g., object recognition, relational reasoning). I argue that the most pressing philosophical questions at the intersection of neuroscience and artificial intelligence are ultimately concerned with defining the phenomena to be explained and with what constitute valid explanations of such phenomena. I propose that a foundation in the philosophy of scientific explanation and understanding can scaffold future discussions about how an integrated science of intelligence might progress. Towards this vision, I review relevant theories of scientific explanation and discuss strategies for unifying the scientific goals of neuroscience and AI.

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Section: Dynamical and Computational Explanation In Neurosciencementioning

confidence: 98%

Forms of explanation and understanding for neuroscience and artificial intelligence

Thompson

2021

Journal of Neurophysiology

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“…Others have recently suggested that such what-if-things-had-been-different questions are also valuable in non-causal contexts (Chirimuuta 2014;Rusanen and Lappi 2016;Elber-Dorozko 2018). Input-output modeling answers relevant what-if-things-had-been-different questions.…”

Section: Computational Explanationsmentioning

confidence: 99%

The Nature of Physical Computation

Shagrir¹

2022

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Computing systems are everywhere today. Even the brain is thought to be a sort of computing system. But what does it mean to say that a given organ or system computes? What is it about laptops, smartphones, and nervous systems that they are deemed to compute, and why does it seldom occur to us to describe stomachs, hurricanes, rocks, or chairs that way? The book provides an extended argument for the semantic view of computation, which states that semantic properties are involved in the nature of computing systems. Laptops, smartphones, and nervous systems compute because they are accompanied by representations. Stomachs, hurricanes, and rocks, for instance, which do not have semantic properties, do not compute. The first part of the book argues that the linkage between the mathematical theory of computability and the notion of physical computation is weak. Theoretical notions such as algorithms, effective procedure, program, and automaton play only a minor role in identifying physical computation. The second part of the book reviews three influential accounts of physical computation and argues that while none of these accounts is satisfactory, each of them highlights certain key features of physical computation. The final part of the book develops and argues for a semantic account of physical computation and offers a characterization of computational explanations.

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“…First, landmasses and bridges are parts of a city that are spatially organized, and can be intervened upon with, e.g., explosives, bridge-building, and artificial islands. However, there is no clear way of intervening upon an Eulerian path (Elber-Dorozko, 2018). Given that intervention and causation are frequently thought to go hand in hand, the ontic theorist may claim that causal relations account for this example's directionality.…”

Section: What Is Directionality? Why Is It a Problem?mentioning

confidence: 99%

The directionality of topological explanations

Kostić

Khalifa

2021

Synthese

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Proponents of ontic conceptions of explanation require all explanations to be backed by causal, constitutive, or similar relations. Among their justifications is that only ontic conceptions can do justice to the ‘directionality’ of explanation, i.e., the requirement that if X explains Y, then not-Y does not explain not-X. Using topological explanations as an illustration, we argue that non-ontic conceptions of explanation have ample resources for securing the directionality of explanations. The different ways in which neuroscientists rely on multiplexes involving both functional and anatomical connectivity in their topological explanations vividly illustrate why ontic considerations are frequently (if not always) irrelevant to explanatory directionality. Therefore, directionality poses no problem to non-ontic conceptions of explanation.

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Manipulation is key: on why non-mechanistic explanations in the cognitive sciences also describe relations of manipulation and control

Cited by 3 publications

References 53 publications

Forms of explanation and understanding for neuroscience and artificial intelligence

Forms of explanation and understanding for neuroscience and artificial intelligence

The Nature of Physical Computation

The directionality of topological explanations

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