Interactive Effects of Explicit Emergent Structure: A Major Challenge for Cognitive Computational Modeling

French, Robert M.; Thomas, Elizabeth

doi:10.1111/tops.12135

Cited by 3 publications

(3 citation statements)

References 19 publications

(17 reference statements)

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Section: Discussionmentioning

confidence: 99%

“…In this sense, Marr's levels are less interactive, if not noninteractive. [ 957 ] Marr's levels were influenced by computer science. Most of the time, engineers can abstract from the circuits when designing the algorithms.…”

Section: Discussionmentioning

confidence: 99%

“…[319] Instead of being partial to one level over the others, more interactive approaches have also been suggested to strike a reasonable balance between the desire for a simplified model and the desire to incorporate as much of the known biological mechanisms as possible [958] and to productively constrain research at other levels. [23,957] Marr's viewpoint is also a reflection of the status of neuroscience research in his times that no neurophysiological study (implementation-level study) had revealed new and clear correlation to high-level cognitive behaviors. [15,959] However, over the past few decades, neuroscience has been transformed.…”

Section: Discussionmentioning

confidence: 99%

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A Marr's Three‐Level Analytical Framework for Neuromorphic Electronic Systems

Guo

Zou

et al. 2021

Advanced Intelligent Systems

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Neuromorphic electronics, an emerging field that aims for building electronic mimics of the biological brain, holds promise for reshaping the frontiers of information technology and enabling a more intelligent and efficient computing paradigm. As their biological brain counterpart, the neuromorphic electronic systems are complex, having multiple levels of organization. Inspired by David Marr's famous three‐level analytical framework developed for neuroscience, the advances in neuromorphic electronic systems are selectively surveyed and given significance to these research endeavors as appropriate from the computational level, algorithmic level, or implementation level. Under this framework, the problem of how to build a neuromorphic electronic system is defined in a tractable way. In conclusion, the development of neuromorphic electronic systems confronts a similar challenge to the one neuroscience confronts, that is, the limited constructability of the low‐level knowledge (implementations and algorithms) to achieve high‐level brain‐like (human‐level) computational functions. An opportunity arises from the communication among different levels and their codesign. Neuroscience lab‐on‐neuromorphic chip platforms offer additional opportunity for mutual benefit between the two disciplines.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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A Marr's Three‐Level Analytical Framework for Neuromorphic Electronic Systems

Guo

Zou

et al. 2021

Advanced Intelligent Systems

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Beyond Single‐Level Accounts: The Role of Cognitive Architectures in Cognitive Scientific Explanation

Cooper

Peebles

2015

Topics in Cognitive Science

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Abstract:We consider approaches to explanation within the cognitive sciences that begin with Marr's computational level (e.g., purely Bayesian accounts of cognitive phenomena) or Marr's implementational level (e.g., reductionist accounts of cognitive phenomena based only on neural level evidence) and argue that each is subject to fundamental limitations which impair their ability to provide adequate explanations of cognitive phenomena. For this reason, it is argued, explanation cannot proceed at either level without tight coupling to the algorithmic and representation level.Even at this level, however, we argue that additional constraints relating to the decomposition of the cognitive system into a set of interacting subfunctions (i.e., a cognitive architecture) are required. Integrated cognitive architectures that permit abstract specification of the functions of components and that make contact with the neural level provide a powerful bridge for linking the algorithmic and representational level to both the computational level and the implementational level. 2 Explanation within cognitive science has frequently been argued to require multiple domains or levels, and several distinct multi-level accounts of cognitive scientific explanation have been proposed (e.g., Chomsky, 1965;Cummins, 1983;Marr, 1982;Newell, 1982). The account of Marr, however, has received perhaps the most attention. A central tenet of Marr's analysis is that a complete explanation of a device's behaviour requires an account of that behaviour at what he terms the Computational Level (CL), the Algorithmic and Representational level (ARL), and the Implementation Level (IL).While Marr argued that an account at each of the three levels was required for a complete explanation, he also emphasised the primacy of the most abstract of his levels, the Computational Level.Specifically, he argued that while "algorithms and mechanisms are empirically more accessible, […] the level of computational theory […] is critically important from an information-processing point of view [… because …] the nature of the computations that underlie perception [and, by extension, cognition] depends more upon the computational problems that have to be solved than upon the particular hardware in which their solutions are implemented" (Marr, 1982, p. 27). This position has face validity, particularly with respect to naturally intelligent systems where the particular hardware in which the solutions are implemented is shaped by evolutionary factors, and so will arguably be wellsuited, or perhaps even optimised, for the computational problems that an evolved agent such as ourselves must solve (though see Jacob, 1977).Marr was critical of approaches to the understanding of cognitive systems that were not rooted in the CL. For example, in discussing the work of Newell and Simon (1972) on the application of production systems to problem-solving, he wrote that "mechanism-based approaches are genuinely dangerous.The problem is that the goal of such studies is mimicry rather tha...

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The Non‐Redundant Contributions of Marr's Three Levels of Analysis for Explaining Information‐Processing Mechanisms

Bechtel

Shagrir

2015

Topics in Cognitive Science

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Are all three of Marr's levels needed? Should they be kept distinct? We argue for the distinct contributions and methodologies of each level of analysis. It is important to maintain them because they provide three different perspectives required to understand mechanisms, especially information processing mechanisms. The computational perspective provides understanding of how a mechanism functions in broader environments that determine the computations it needs to perform (and may fail to perform). The representation and algorithmic perspective offer an understanding of how information about the environment is encoded within the mechanism and what are the patterns of organization that enable the parts of the mechanism to produce the phenomenon. The implementation perspective yields an understanding of the neural details of the mechanism and how they constrain function and algorithms. Once we adequately characterize the distinct role of each level of analysis, it is fairly straightforward to see how they relate. 1. Introduction The term level is used in a wide variety of ways. One use refers to levels of organization-a whole system is at a higher level than the parts that constitutes it and the activities the whole performs are at a higher level than those of its components (Craver & Bechtel, 2007). This is not the use Marr (1982) had in mind in distinguishing the computational, representational and algorithmic, and implementational levels. These are levels of analysis, not levels of organization and so, contrary to French et al. (this issue), Marr is not addressing topics such as the emergence of higher levels. The main question raised by the various papers in this issue is whether all three types of analysis are required and, if so, how they relate. The question of how they relate, we will argue, is fairly straightforward once we (1) characterize the distinctive role of each type of analysis and (2) show how each type of analysis makes an important, non--redundant contribution to understanding information processing mechanisms. As the explanatory relevance of the implementational level is generally not contested, we will not dwell on it. Within the context of a mechanistic framework of explanation, it specifies the parts (e.g., neurons) and operations (excitation or inhibition of other neurons) that are employed or recruited in the generation of the phenomenon of interest. Challenges to Marr's conception of levels focus on the representational and algorithmic and the computational levels of analysis. The idea of algorithms governing the transformations of representations is familiar from computer science but, as we will see, is not limited to the sorts of representations and algorithms used in digital computers. Rather, it allows for the

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Interactive Effects of Explicit Emergent Structure: A Major Challenge for Cognitive Computational Modeling

Cited by 3 publications

References 19 publications

A Marr's Three‐Level Analytical Framework for Neuromorphic Electronic Systems

A Marr's Three‐Level Analytical Framework for Neuromorphic Electronic Systems

Beyond Single‐Level Accounts: The Role of Cognitive Architectures in Cognitive Scientific Explanation

The Non‐Redundant Contributions of Marr's Three Levels of Analysis for Explaining Information‐Processing Mechanisms

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