Cognition arises from the dynamic interaction between an embodied agent and its environment, according to enactivism, while new mechanism explains cognition by identifying the organized components that underlie cognitive abilities. Although mechanistic explanations often focus on localizable, decomposable neural mechanisms that seem to conflict with enactivist views, this paper argues they are compatible because mechanistic explanations need not be narrow or reductive and can incorporate ideas like emergence and downward causation. Enactivist ideas may also guide mechanistic investigations. However, the two approaches diverge in assumptions about cognition's constitutive boundaries.
Representations and their features can be gradual rather than all-or-nothing, but this idea has been largely overlooked in philosophy of mind. This paper develops a gradual account of two representational features—structural similarity and decoupling—within the predictive processing framework of neuroscience. Structural similarity is analyzed along two dimensions: the number of preserved relations and the granularity of state space, both of which vary continuously. Decoupling is gradual in two ways: different brain areas are involved in decoupled processes to varying degrees depending on whether their activity is caused internally or externally, and the degree of decoupling can be further regulated through precision weighting of prediction error. Gradation of both features supports behavioral success.
Human experience of self and time is not just about memory; it is a continuous, prereflective structure that underpins perception, intention, and action. Drawing on Husserl's phenomenology and predictive processing, this article argues that this intrinsic temporality—the retentional-protentional dynamic—can be adapted to improve cognitive architectures for humanoid robots. By modeling this temporal continuity, robots could gain greater context-awareness and autonomy, moving beyond simple memory to a more fluid sense of self and identity over time.
Predictive processing explains perception, action, and cognition as prediction-error minimization, but it is unclear how this supports abstract reasoning. Combining predictive processing, structural representations, and grounded cognition addresses this. Structural representations are isomorphic to the world, retaining its relational patterns. Grounded cognition contributes three mechanisms: hierarchical organization abstracts from sensory qualities; language binds disparate sensory qualities into representations and acts as a social tool; metaphoric mapping uses fragments of concrete percepts to represent abstract concepts. Transplanting these into a hierarchical generative model explains higher-level cognition through detached simulations of perception and action isomorphic to actual behavior. This expands life-mind continuity by specifying how principles driving life's emergence also account for sophisticated human cognition.