The goal of this short chapter, aimed at philosophers, is to provide an overview and brief explanation of some central concepts involved in predictive processing (PP). Even those who consider themselves experts on the topic may find it helpful to see how the central terms are used in this collection. To keep things simple, we will first informally define a set of features important to predictive processing, supplemented by some short explanations and an alphabetic glossary. -/- The features described here (...) are not shared in all PP accounts. Some may not be necessary for an individual model; others may be contested. Indeed, not even all authors of this collection will accept all of them. To make this transparent, we have encouraged contributors to indicate briefly which of the features are necessary to support the arguments they provide, and which (if any) are incompatible with their account. For the sake of clarity, we provide the complete list here, very roughly ordered by how central we take them to be for “Vanilla PP” (i.e., a formulation of predictive processing that will probably be accepted by most researchers working on this topic). More detailed explanations will be given below. Note that these features do not specify individually necessary and jointly sufficient conditions for the application of the concept of “predictive processing”. All we currently have is a semantic cluster, with perhaps some overlapping sets of jointly sufficient criteria. The framework is still developing, and it is difficult, maybe impossible, to provide theory-neutral explanations of all PP ideas without already introducing strong background assumptions. (shrink)

Should models of consciousness be detailed _mechanistic_ models of particular types of systems, or should they be _minimal_ models that abstract away from the underlying mechanistic details and provide generalisations? Detailed mechanistic models may afford a complete and precise account of consciousness in human beings and other, physiologically similar mammals. But they do not provide a good model of consciousness in other animals, such as non-vertebrates, let alone artificial systems. Minimal models can be applicable to a wide range of different (...) conscious systems. But do they provide genuine explanations that are autonomous from explanations by detailed mechanistic models? This paper provides a taxonomy of minimal models and measures of consciousness, clarifies their relation to detailed mechanistic models, and highlights benefits of different minimalist approaches to consciousness. (shrink)

Might future developments in artificial intelligence (AI) lead to the creation of artificial consciousness? Some believe that large language models and other AI systems will at best be able to simulate phenomenal consciousness. These simulations are sometimes called “weak artificial consciousness”—in contrast to “strong artificial consciousness”, which refers to artificial systems that are actually conscious (Holland, 2003). What is the difference between weak and strong artificial consciousness? Here, I shall assume that the causal roles that characterise phenomenal consciousness are medium-independent (...) and can be captured in terms of computation; they are computational correlates of consciousness (Cleeremans, 2005; Reggia et al., 2016; Wiese & Friston, 2021). I propose that strong artificial consciousness requires implementing these computations in a particular way: performing these computations must have a function for the system itself, by making a contribution to the system’s goals, such as sustaining its existence. Only such computational systems give a damn (Haugeland, 2000). Hence, some systems may instantiate the computational correlates of consciousness without being conscious, if the respective computational mechanisms do not contribute to the goals of the physical system of which they are a part. Since the free energy principle (FEP) (Friston, 2019; Parr et al., 2022) provides an analysis of the concept of the existence of particular self-organising systems (Hohwy, 2021), the FEP can be used to specify what it means to contribute to the goals (such as sustaining one’s existence) of particular self-organising systems. Together with the assumptions sketched above, an account of the difference between weak and strong artificial consciousness can be developed. (shrink)

This chapter explores to what extent some core ideas of predictive processing can be applied to the phenomenology of time consciousness. The focus is on the experienced continuity of consciously perceived, temporally extended phenomena (such as enduring processes and successions of events). The main claim is that the hierarchy of representations posited by hierarchical predictive processing models can contribute to a deepened understanding of the continuity of consciousness. Computationally, such models show that sequences of events can be represented as states (...) of a hierarchy of dynamical systems. Phenomenologically, they suggest a more fine-grained analysis of the perceptual contents of the specious present, in terms of a hierarchy of temporal wholes. Visual perception of static scenes not only contains perceived objects and regions but also spatial gist; similarly, auditory perception of temporal sequences, such as melodies, involves not only perceiving individual notes but also slightly more abstract features (temporal gist), which have longer temporal durations (e.g., emotional character or rhythm). Further investigations into these elusive contents of conscious perception may be facilitated by findings regarding its neural underpinnings. Predictive processing models suggest that sensorimotor areas may influence these contents. (shrink)