We believe that a number of central philosophical debates involve phenomena that are best investigated as mind-centered aspects of cognitive agents. And so, philosophical fields such as, for example, epistemology, philosophy of mind, and philosophy of science need to address the concepts and phenomena they study with models of how the mind actually works that are available from cognitive sciences. This said, we consider it plausible that all philosophical work would benefit from knowing how the mind perceives and constructs phenomena, before detailing the phenomena themselves. So, we want to highlight that any investigation into reality is affected by the cognitive agent taking on the inquiry, since any cognitive agent’s limits and affordances will affect how such investigations are carried out and what stimuli it is possible for them to take into account.
Knowledge and reflection are defining human abilities. By adopting a naturalistic stance, we aim to investigate how they are connected to different psychological and neuroscientific models, on different levels of analysis, and how they map to each other. We will also explore how reliability is more dependably connected to reflexive processes than to reflective processes. A number of essential cognitive functions are relevant and can provide elucidating input both for traditional philosophical problems and paradoxes, as well as for artificial intelligence research.
The project will attempt to elucidate how natural phenomena (i.e., the world, reality, human nervous systems) need to be viewed as dynamical systems rather than statical systems. These phenomena should accordingly primarily be modeled with differential equations, rather than with discrete logical and language-based models. We will further inquire how analytical aspects of human System 2 processing (WM) biases philosophical thinking towards statical models, to the detriment of observational-based dynamical models. Though analytical aspects of working memory might still be used for philosophical thinking, investigators need to acquire intuitions (understood as mental models induced from observations) that account for dynamical features, not solely rule-based features. Hence, theoreticians have to interact with, and watch, attractors form and dissipate, see many examples of feedback effects, and how feedback and delay affect dynamical processes (oscillations, how they form, how they are damped) to build up such mental models, as well as watch how processes bifurcate and take on fractal-like patterns. This project will thus initiate a challenge to the status of traditional Boolean logic as a sufficient tool for doing philosophy and proposes models using coupled differential equations as an improvement.
The project will investigate how intelligence, and general intelligence, should be understood from a naturalistic perspective. By looking at the complex biological phenomena in organisms ranging from moulds, plants, animals, humans, to swarms, intelligence can be seen as consisting of adaptations that enable prediction of gradient fields by means of reflexive information processing, allowing negotiation of obstacles in approach to an attractor or in avoidance of a repeller. General intelligence can then be seen to consist of reflective networks that can inhibit and excite reflexive networks, allowing behaviour to be dialogues across contexts and environmental niches. Such an account might offer artificial intelligence research biologically plausible insights and concrete building blocks regarding general intelligence.
The project will aim to bring together the philosophical debate about eliminativism with more recent debates and developments in computational neuroscience. The project can be broken down into three major claims that stand in contrast to most existing philosophical theories. First, the project will attempt to defend a process ontology, arguing that a substance ontology results in philosophical puzzles and contradictions. Second, as a consequence of the former claim, it will be argued that ontology cannot be practiced as an independent field of research without taking epistemology and the notion of learning into account. Third, classical propositional logic is not an adequate tool for reasoning when accepting the process ontology that is presented here. Alternatively, a Bayesian probabilistic form of reasoning is proposed to be more adequate, in particular when it comes to traditional ontological puzzles.
Numerous species use different forms of communication in order to successfully interact in their respective environment. This project investigates communication as a biological natural phenomenon, found to be fruitfully grounded in an organism’s embodied structures and memory system, where specific abilities are tied to procedural, semantic, and episodic long-term memory as well as to working memory. This approach enables new perspectives of communication to emerge regarding both sender and receiver. It is further investigated how communication feature gradient properties that are plausibly divided into a reflexive and a reflective form, parallel to knowledge and reflection. This approach also offers artificial intelligence research new biologically plausible insight into both non-verbal and verbal communication.