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Symbolic Artificial Intelligence and Logic-based Cognitive Modeling of Formal Thought Disorder

Symbolic Artificial Intelligence and Logic-based Cognitive Modeling of Formal Thought Disorder

By Dr. Farshad Badie, Faculty of Computer Science & Informatics, Berlin School of Business & Innovation (BSBI)

Keywords: formal thought disorder; symbolic AI; semantic networking; description logic; conception language

Formal thought disorder (FTD) is a clinical mental condition that can be diagnosed by the speech production of patients. However, this diagnosis is often neither reliable nor valid, largely due to the fact that the “formal” in FTD is not (clearly) identified in the usual diagnostic methods.

In our recently published research article, Badie & Augusto (2022), we have focused on a logic-based analysis, as well as cognitive modeling, of FTD as it is manifested in the processes of association and categorization over a semantic network. In doing this, we clearly identify the “formal” in FTD. Semantic networks have been influential in applied linguistics since the late 1960s. Although they have been criticized both for lacking formal semantics and failing to provide an account of the mental representation of meaning, they have provided adequate models of human semantic memory and terminological knowledge.

Utilizing Description Logic (DL), in Badie & Augusto (2022), we have offered a logical-terminological model for the interface syntax–semantics in semantic networking for FTD patients. Description Logics (DL) are a well-known family of knowledge representation formalisms that are among the most widely used knowledge representation formalisms in semantics-based systems. DL was developed out of the attempt to represent terminological knowledge and to provide an adequate formal semantics over terminological knowledge structures, in order to establish a common ground for cognition/knowledge for humans and artificial agents. These properties make DL an adequate basis for the modeling of storage in, and activation of, semantic networks seen as terminological knowledge bases, with respect to both their normal functioning and some speech–thought pathologies.

In Badie & Augusto (2022), we have accordingly called our offered logical-terminological model for the interface syntax–semantics in semantic networking for FTD patients the dyssyntax model, because we believe that FTD is essentially a manifestation at the semantic level of faulty syntactic processes, over an individual semantic network.

It should be emphasized that our dyssyntax model does not only explain FTD, but can also diagnose it. In fact, the dyssyntax model is designed, based on the logical clarification of how faulty (and defective) logical forms in our DL-based Conception Language (CL) can affect the semantic content of linguistic productions characteristic of FTD. I have designed CL for modeling terminological knowledge and cognitive agents’ associated conceptions of the world; see Badie (2018a, 2018b, 2020a, 2020b). By assuming that concepts are distinct mental phenomena/entities that are construed by agents in a particular state of awareness, a possible interpretation is that concepts can be identified with the contents in, for example, linguistic expressions (which are basically in the form of words), formal expressions (which are basically in the form of symbols and special characters), and/or numerical expressions (which are in the form of numbers) by becoming manifested in the form of agents’ conceptions. Actually, by employing CL in Badie & Augusto (2022), we have been able to analyze conception categorization and conception association in thought processes that are recruited when agents verbalize their conceptualizations.

It should be stressed that Badie & Augusto (2022) is a wholly novel approach that focuses directly on the root of the “formal deficits” that appear at the surface as semantic in nature: we turn this semantic appearance into real formal semantics, as taken in the logical sense of the word, by coupling it to a formal syntax that can adequately formalize human thought processes recruited in language production. Badie & Augusto (2022) construes a formal analysis that is already essentially computational in the sense of computational logic-based cognitive modeling. We believe that the presented model of dyssyntax in semantic networking is computational in a more practical sense, too: it can be easily implemented computationally and executed in software systems.

In addition, it is expected that this computation will be assisted by Web Ontology Language (OWL) and/or other formal-language services in semantic technologies; this will prove useful to check individual conceptions with respect to their frequency or normalcy in large corpora that will (shortly) be available within the context of the Semantic Web.

References

  1. Badie, F. (2018a) On logical characterisation of human concept learning based on terminological systems.  Log. Philos.27, 545–566.
  2. Badie, F. (2018b) A description logic based knowledge representation model for concept understanding. In Agents and Artificial Intelligence; van den Herik, J., Rocha, A., Filipe, J., Eds.; Lecture Notes in Computer Science; Springer: Cham, Switzerland, Volume 10839.
  3. Badie, F. (2020a) A formal ontology for conception representation in terminological systems. In Reasoning: Logic, Cognition, and Games; Urbanski, M., Skura, T., Lupkowski, P., Eds.; College Publications: London, UK. pp. 137–157.
  4. Badie, F. (2020b) Logic and constructivism: A model of terminological knowledge.  Knowl. Struct. Syst.1, 23–39.
  5. Badie, F.; Augusto, L.M. (2022) The Form in Formal Thought Disorder: A Model of Dyssyntax in Semantic Networking. AI3, 353-370. https://doi.org/10.3390/ai3020022

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