How Limited Systematicity Emerges

O’Reilly, Randall C.; Petrov, Alex A.; Cohen, Jonathan D.; Lebière, Christian; Herd, Seth; Kriete, Trent

doi:10.7551/mitpress/9780262027236.003.0008

The Architecture of Cognition 2014

DOI: 10.7551/mitpress/9780262027236.003.0008

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How Limited Systematicity Emerges

Randall C. O’Reilly¹,

Alex A. Petrov²,

Jonathan D. Cohen³

et al.

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Cited by 8 publications

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“…It is worth-noting that also some forms of neuro-symbolic integration currently developed in CAs like ACT-R, and belonging to the class of the neo-connectionist approaches, allows to deal with the the above mentioned problem by providing a series of mechanisms that are able to deal with limited forms of compositionality in neural networks[O'Reilly et al (2013)] and that can be integrated with additional processes allowing the compatibility with typicality effects. In this respect, such approaches play an equivalent role to that one played by the Conceptual Spaces on these issues.…”

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confidence: 99%

Conceptual Spaces for Cognitive Architectures: A lingua franca for different levels of representation

Lieto

Chella

Frixione

2017

Biologically Inspired Cognitive Architectures

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During the last decades, many Cognitive Architectures (CAs) have been realized adopting different assumptions about the organization and the representation of their knowledge level. Some of them (e.g. SOAR (Laird, 2012)) adopt a classical symbolic approach, some (e.g. LEABRA O'Reilly and Munakata (2000)) are based on a purely connectionist model, while others (e.g. CLARION (Sun, 2006)) adopt a hybrid approach combining connectionist and symbolic representational levels. Additionally, some attempts (e.g. biSOAR) trying to extend the representational capacities of CAs by integrating diagrammatical representations and reasoning are also available (Kurup & Chandrasekaran, 2007). In this paper we propose a reflection on the role that Conceptual Spaces, a framework developed by GÃ¤rdenfors (2000) more than fifteen years ago, can play in the current development of the Knowledge Level in Cognitive Systems and Architectures. In particular, we claim that Conceptual Spaces offer a lingua franca that allows to unify and generalize many aspects of the symbolic, sub-symbolic and diagrammatic approaches (by overcoming some of their typical problems) and to integrate them on a common ground. In doing so we extend and detail some of the arguments explored by GÃ¤rdenfors (1997) for defending the need of a conceptual, intermediate, representation level between the symbolic and the sub-symbolic one. In particular we focus on the advantages offered by Conceptual Spaces (with respect to symbolic and sub-symbolic approaches) in dealing with the problem of compositionality of representations based on typicality traits. Additionally, we argue that Conceptual Spaces could offer a unifying framework for interpreting many kinds of diagrammatic and analogical representations. As a consequence, their adoption could also favor the integration of diagrammatical representation and reasoning in CAs

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mentioning

confidence: 99%

Conceptual Spaces for Cognitive Architectures: A lingua franca for different levels of representation

Lieto

Chella

Frixione

2017

Biologically Inspired Cognitive Architectures

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Can Deep CNNs Avoid Infinite Regress/Circularity in Content Constitution?

Lopes

2023

Minds & Machines

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The representations of deep convolutional neural networks (CNNs) are formed from generalizing similarities and abstracting from differences in the manner of the empiricist theory of abstraction (Buckner, Synthese 195:5339–5372, 2018). The empiricist theory of abstraction is well understood to entail infinite regress and circularity in content constitution (Husserl, Logical Investigations. Routledge, 2001). This paper argues these entailments hold a fortiori for deep CNNs. Two theses result: deep CNNs require supplementation by Quine’s “apparatus of identity and quantification” in order to (1) achieve concepts, and (2) represent objects, as opposed to “half-entities” corresponding to similarity amalgams (Quine, Quintessence, Cambridge, 2004, p. 107). Similarity amalgams are also called “approximate meaning[s]” (Marcus & Davis, Rebooting AI, Pantheon, 2019, p. 132). Although Husserl inferred the “complete abandonment of the empiricist theory of abstraction” (a fortiori deep CNNs) due to the infinite regress and circularity arguments examined in this paper, I argue that the statistical learning of deep CNNs may be incorporated into a Fodorian hybrid account that supports Quine’s “sortal predicates, negation, plurals, identity, pronouns, and quantifiers” which are representationally necessary to overcome the regress/circularity in content constitution and achieve objective (as opposed to similarity-subjective) representation (Burge, Origins of Objectivity. Oxford, 2010, p. 238). I base myself initially on Yoshimi’s (New Frontiers in Psychology, 2011) attempt to explain Husserlian phenomenology with neural networks but depart from him due to the arguments and consequently propose a two-system view which converges with Weiskopf’s proposal (“Observational Concepts.” The Conceptual Mind. MIT, 2015. 223–248).

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On the psychologism of neurophenomenology

Lopes

2021

Phenom Cogn Sci

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How Limited Systematicity Emerges

Cited by 8 publications

References 0 publications

Conceptual Spaces for Cognitive Architectures: A lingua franca for different levels of representation

Conceptual Spaces for Cognitive Architectures: A lingua franca for different levels of representation

Can Deep CNNs Avoid Infinite Regress/Circularity in Content Constitution?

On the psychologism of neurophenomenology

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