Apperceptive patterning: Artefaction, extensional beliefs and cognitive scaffolding

In “Psychopower and Ordinary Madness” my ambition, as it relates to Bernard Stiegler’s recent literature, was twofold: 1) critiquing Stiegler’s work on exosomatization and artefactual posthumanism—or, more specifically, nonhumanism—to problematize approaches to media archaeology that rely upon technical exteriorization; 2) challenging how Stiegler engages with Giuseppe Longo and Francis Bailly’s conception of negative entropy. These efforts were directed by a prevalent techno-cultural qualifier: the rise of Synthetic Intelligence (including neural nets, deep learning, predictive processing and Bayesian models of cognition). This paper continues this project but first directs a critical analytic lens at the Derridean practice of the ontologization of grammatization from which Stiegler emerges while also distinguishing how metalanguages operate in relation to object-oriented environmental interaction by way of inferentialism. Stalking continental (Kapp, Simondon, Leroi-Gourhan, etc.) and analytic traditions (e.g., Carnap, Chalmers, Clark, Sutton, Novaes, etc.), we move from artefacts to AI and Predictive Processing so as to link theories related to technicity with philosophy of mind. Simultaneously drawing forth Robert Brandom’s conceptualization of the roles that commitments play in retrospectively reconstructing the social experiences that lead to our endorsement(s) of norms, we compliment this account with Reza Negarestani’s deprivatized account of intelligence while analyzing the equipollent role between language and media (both digital and analog)

Prolegomena to a Theory and Model of Spoken Persuasion: A Subjective-Probabilistic Interactive Model of Persuasion (SPIMP)

Various disciplines such as rhetoric, marketing, and psychology have explored persuasion as a social and argumentative phenomenon. The present thesis is predominantly based in cognitive psychology and investigates the psychological processes the persuadee undergoes when faced with a persuasive attempt. The exploration concludes with the development of a concrete model for describing persuasion processing, namely The Subjective-Probabilistic Interactive Model of Persuasion (SPIMP). In addition to cognitive psychology, the thesis relies on conceptual developments and empirical data from disciplines such as rhetoric, economics, and philosophy. The core model of the SPIMP relies on two central persuasive elements: content strength and source credibility. These elements are approached from a subjective perspective in which the persuadee estimates the probabilistic likelihood of how strong the content and how credible the source is. The elements, however, are embedded in a larger psychological framework such that the subjective estimations are contextual and social rather than solipsistic. The psychological framework relies on internal and external influences, the scope of cognition, and the framework for cognition. The SPIMP departs significantly from previous models of persuasion in a number of ways. For instance, the latter are dual-processing models whereas the SPIMP is an integrated single-process approach. Further, the normative stances differ since the previous models seemingly rely on a logicist framework whereas SPIMP relies on a probabilistic. The development of a new core model of persuasion processing constitutes a novel contribution. Further, the theoretical and psychological framework surrounding the elements of the model provides a novel framework for conceptualising persuasion processing from the perspective of the persuadee. Finally, given the multitude of disciplines connected to persuasion, the thesis provides a definition for use in future studies, which differentiates persuasion from argumentation, communicated information updating, and influence

Learning, conditionals, causation

This dissertation is on conditionals and causation. In particular, we (i) propose a method of how an agent learns conditional information, and (ii) analyse causation in terms of a new type of conditional. Our starting point is Ramsey's (1929/1990) test: accept a conditional when you can infer its consequent upon supposing its antecedent. Inspired by this test, Stalnaker (1968) developed a semantics of conditionals. In Ch. 2, we define and apply our new method of learning conditional information. It says, roughly, that you learn conditional information by updating on the corresponding Stalnaker conditional. By generalising Lewis's (1976) updating rule to Jeffrey imaging, our learning method becomes applicable to both certain and uncertain conditional information. The method generates the correct predictions for all of Douven's (2012) benchmark examples and Van Fraassen's (1981) Judy Benjamin Problem. In Ch. 3, we prefix Ramsey's test by suspending judgment on antecedent and consequent. Unlike the Ramsey Test semantics by Stalnaker (1968) and Gärdenfors (1978), our strengthened semantics requires the antecedent to be inferentially relevant for the consequent. We exploit this asymmetric relation of relevance in a semantic analysis of the natural language conjunction 'because'. In Ch. 4, we devise an analysis of actual causation in terms of production, where production is understood along the lines of our strengthened Ramsey Test. Our analysis solves the problems of overdetermination, conjunctive scenarios, early and late preemption, switches, double prevention, and spurious causation -- a set of problems that still challenges counterfactual accounts of actual causation in the tradition of Lewis (1973c). In Ch. 5, we translate our analysis of actual causation into Halpern and Pearl's (2005) framework of causal models. As a result, our analysis is considerably simplified on the cost of losing its reductiveness. The upshot is twofold: (i) Jeffrey imaging on Stalnaker conditionals emerges as an alternative to Bayesian accounts of learning conditional information; (ii) the analyses of causation in terms of our strengthened Ramsey Test conditional prove to be worthy rivals to contemporary counterfactual accounts of causation

Where causality, conditionals and epistemology meet:A logical inquiry

This dissertation is an intellectual journey along topics at the intersection of the study of conditionals, causality and epistemology. It will focus on a couple of problems at this intersection pointed out in recent research. I will demonstrate how by combining knowledge and tools from all three fields we can make substantial progress on solving these issues. I will also show that this integrated approach provides us with a better understanding of the relation between conditionals, causality and epistemology

Swahili conditional constructions in embodied Frames of Reference: Modeling semantics, pragmatics, and context-sensitivity in UML mental spaces

Studies of several languages, including Swahili [swa], suggest that realis (actual, realizable) and irrealis (unlikely, counterfactual) meanings vary along a scale (e.g., 0.0–1.0). T-values (True, False) and P-values (probability) account for this pattern. However, logic cannot describe or explain (a) epistemic stances toward beliefs, (b) deontic and dynamic stances toward states-of-being and actions, and (c) context-sensitivity in conditional interpretations. (a)–(b) are deictic properties (positions, distance) of ‘embodied’ Frames of Reference (FoRs)—space-time loci in which agents perceive and from which they contextually act (Rohrer 2007a, b). I argue that the embodied FoR describes and explains (a)–(c) better than T-values and P-values alone. In this cognitive-functional-descriptive study, I represent these embodied FoRs using Unified Modeling LanguageTM (UML) mental spaces in analyzing Swahili conditional constructions to show how necessary, sufficient, and contributing conditions obtain on the embodied FoR networks level.Swahili, conditional constructions, UML, mental spaces, Frames of Reference, epistemic stance, deontic stance, dynamic stance, context-sensitivity, non-monotonic logi

Swahili conditional constructions in embodied Frames of Reference: Modeling semantics, pragmatics, and context-sensitivity in UML mental spaces

Studies of several languages, including Swahili [swa], suggest that realis (actual, realizable) and irrealis (unlikely, counterfactual) meanings vary along a scale (e.g., 0.0–1.0). T-values (True, False) and P-values (probability) account for this pattern. However, logic cannot describe or explain (a) epistemic stances toward beliefs, (b) deontic and dynamic stances toward states-of-being and actions, and (c) context-sensitivity in conditional interpretations. (a)–(b) are deictic properties (positions, distance) of ‘embodied’ Frames of Reference (FoRs)—space-time loci in which agents perceive and from which they contextually act (Rohrer 2007a, b). I argue that the embodied FoR describes and explains (a)–(c) better than T-values and P-values alone. In this cognitive-functional-descriptive study, I represent these embodied FoRs using Unified Modeling Language (UML) mental spaces in analyzing Swahili conditional constructions to show how necessary, sufficient, and contributing conditions obtain on the embodied FoR networks level

Judgement, Responsibility and the Life-World: Perth Workshop 2011 Conference Proceedings

The workshop was part of the ARC funded project Judgement, Responsibility and the Life-world..

Learning, conditionals, causation

This dissertation is on conditionals and causation. In particular, we (i) propose a method of how an agent learns conditional information, and (ii) analyse causation in terms of a new type of conditional. Our starting point is Ramsey's (1929/1990) test: accept a conditional when you can infer its consequent upon supposing its antecedent. Inspired by this test, Stalnaker (1968) developed a semantics of conditionals. In Ch. 2, we define and apply our new method of learning conditional information. It says, roughly, that you learn conditional information by updating on the corresponding Stalnaker conditional. By generalising Lewis's (1976) updating rule to Jeffrey imaging, our learning method becomes applicable to both certain and uncertain conditional information. The method generates the correct predictions for all of Douven's (2012) benchmark examples and Van Fraassen's (1981) Judy Benjamin Problem. In Ch. 3, we prefix Ramsey's test by suspending judgment on antecedent and consequent. Unlike the Ramsey Test semantics by Stalnaker (1968) and Gärdenfors (1978), our strengthened semantics requires the antecedent to be inferentially relevant for the consequent. We exploit this asymmetric relation of relevance in a semantic analysis of the natural language conjunction 'because'. In Ch. 4, we devise an analysis of actual causation in terms of production, where production is understood along the lines of our strengthened Ramsey Test. Our analysis solves the problems of overdetermination, conjunctive scenarios, early and late preemption, switches, double prevention, and spurious causation -- a set of problems that still challenges counterfactual accounts of actual causation in the tradition of Lewis (1973c). In Ch. 5, we translate our analysis of actual causation into Halpern and Pearl's (2005) framework of causal models. As a result, our analysis is considerably simplified on the cost of losing its reductiveness. The upshot is twofold: (i) Jeffrey imaging on Stalnaker conditionals emerges as an alternative to Bayesian accounts of learning conditional information; (ii) the analyses of causation in terms of our strengthened Ramsey Test conditional prove to be worthy rivals to contemporary counterfactual accounts of causation

Irreversible Noise: The Rationalisation of Randomness and the Fetishisation of Indeterminacy

This thesis aims to elaborate the theoretical and practical significance of the concept of noise with regard to current debates concerning realism, materialism, and rationality. The scientific conception of noise follows from the developments of thermodynamics, information theory, cybernetics, and dynamic systems theory; hence its qualification as irreversible. It is argued that this conceptualization of noise is entangled in several polemics that cross the arts and sciences, and that it is crucial to an understanding of their contemporary condition. This thesis draws on contemporary scientific theories to argue that randomness is an intrinsic functional aspect at all levels of complex dynamic systems, including higher cognition and reason. However, taking randomness or noise as given, or failing to distinguish between different descriptive levels, has led to misunderstanding and ideology. After surveying the scientific and philosophical context, the practical understanding of randomness in terms of probability theory is elaborated through a history of its development in the field of economics, where its idealization has had its most pernicious effects. Moving from the suppression of noise in economics to its glorification in aesthetics, the experience of noise in the sonic sense is first given a naturalistic neuro-phenomenological explanation. Finally, the theoretical tools developed over the course of the inquiry are applied to the use of noise in music. The rational explanation of randomness in various specified contexts, and the active manipulation of probability that this enables, is opposed to the political and aesthetic tendencies to fetishize indeterminacy. This multi-level account of constrained randomness contributes to the debate by demystifying noise, showing it to be an intrinsic and functionally necessary condition of reason and consequently of freedom