The subjective meaning of cognitive architecture: a Marrian analysis

Varma, Sashank

doi:10.3389/fpsyg.2014.00440

Cited by 9 publications

(5 citation statements)

References 59 publications

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“…Other kinds of problems are only visible through modeling at a more abstract level that enables broadly generalizable analyses. For instance, uncovering the sources of intractability in a cognitive theory requires a formulation at the computational level of analysis (Marr, 1982;Varma, 2014) which is usually not available or easily obtainable from verbal, or even algorithmic, theories (see Adolfi, Wareham, & van Rooij, 2022;Woensdregt et al, 2021). Similarly, discovering (possibly mistaken) philosophical assumptions can be harder to do from formal models alone than from theories that make their conceptual commitments explicit.…”

Section: Discoverability Of Theoretical Problemsmentioning

confidence: 99%

From empirical problem-solving to theoretical problem-finding perspectives on the cognitive sciences

Adolfi,

van de Braak,

Woensdregt

2023

Preprint

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We attempt to restore an integrative perspective on what the scientific problems of the cognitive sciences are and how they arise. As a point of departure, we draw attention to an increasingly impoverished view of science as empirical problem-solving. This frame, while occasionally useful, is typically fraught with misconceptions. We explicate some of its undesirable consequences, such as the neglect of plausibility constraints on cognitive explanations. Briefly, we challenge the implied autonomy of so-called empirical problems, the overemphasis on solving them versus finding them, and an overly narrow construal of what it means to solve them. To lift this epistemic fog, we bring theoretical problem-finding out of obscurity as a core activity in the cognitive scientist's toolbox. We illustrate various issues through hypothetical and factual case studies; among them, the inadequacies of portraying empirical problems as separate from theoretical problems; how phenomena in need of explanation depend critically on theoretical problems and plausibility constraints; and how theoretical problem-finding can be productive even when no immediate solutions are possible. Along the way, we give concrete examples of what can go wrong if we neglect the role of theoretical problems in shaping scientific questions. We arrive at an integrative account of how phenomena of interest, and cognitive-scientific problems more broadly, are carved out through theoretical problem-finding.

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Section: Discoverability Of Theoretical Problemsmentioning

confidence: 99%

From empirical problem-solving to theoretical problem-finding perspectives on the cognitive sciences

Adolfi,

van de Braak,

Woensdregt

2023

Preprint

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“…Note that (A, L A )-ABDUCTIVE INFERENCE and (F , L F )-ABDUCTIVE INFERENCE really are two distinct problems; a solution to one does not automatically yield a solution to the other. In one direction this may be obvious: a description L F of a function F need not specify an algorithm A for computing F. To see that the reverse also holds, note that a description L A of an algorithm A may concisely specify a method for computing a function F, but that in itself does not naturally give a short description of F in the explanatory language L F (Bechtel & Shagrir, 2015;Egan, 2017;Varma, 2014).…”

Section: Formalizing Scientific Abductive Inferencementioning

confidence: 99%

How hard is cognitive science?

Rich¹,

Haan²,

Wareham³

et al. 2021

Preprint

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Cognitive science is itself a cognitive activity. Yet, computational cognitive science tools are seldom used to study (limits of) cognitive scientists’ thinking. Here, we do so using computational-level modeling and complexity analysis. We present an idealized formal model of a core inference problem faced by cognitive scientists: Given observations of a system’s behaviors, infer cognitive processes that could plausibly produce the behavior. We consider variants of this problem at different levels of explanation and prove that at each level, the inference problem is intractable, or even uncomputable. We discuss the implications for cognitive science.

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“…A further interesting issue is that of the objective vs. subjective meaning of the explanatory levels. Varma ( 2014 ) discusses how Marr's approach focused on the objective meaning of each level—how it supports computational models that correspond to cognitive phenomena—and he develops a complementary analysis of the subjective meaning of each level—how it helps cognitive scientists understand cognition. With the goal of showing that different kinds of explanation arise because we have different kinds of explanatory concerns, a clear case study is proposed by Wilkinson ( 2014 ) by using contrasting theories of delusional misidentification.…”

Section: Plurality Of Levels With and Beyond Marrmentioning

confidence: 99%