Our commentary suggests that different materialities (fragile, enduring, and mixed) may influence cognitive evolution. Building on Stibbard-Hawkes, we propose that predictive brains minimise errors and seek information, actively structuring environments for epistemic benefits. This perspective complements Stibbard-Hawkes' view.

This introduction sets out the context for the special feature on gender and deindustrialization. It briefly outlines the development of research in this field and the contribution made by the articles included in this issue, before pointing to some directions for future research.

Memory has become increasingly important in the study of deindustrialization over the last decade. The ways in which those who witnessed drastic socio-economic change reflect on their experiences decades later are crucial in understanding the ramifications. In this paper, I am concerned with the relationships between individual and popular/public memory for women manufacturing workers who participated in militant industrial action to oppose closure. Over a fourteen-month period in 1981 and 1982, three Scottish workforces refused to accept the relocation of their factories and launched occupations in resistance. The workers at the multinational factories of Lee Jeans (Greenock), Lovable Bra (Cumbernauld), and Plessey Capacitors (Bathgate) launched action to oppose shutdowns, which were announced during a period of accelerated closure in Britain. This aspect makes these workers unique in the history of factory closings; as has been demonstrated extensively, militant resistance was very much the exception. The vast majority of industrial workers reluctantly accepted management decisions, with most energy from the labor movement spent on securing enhanced redundancy packages.1 These workers are therefore exceptional among those who experienced the brutality of deindustrialization. They are additionally unique as the workers involved were predominantly women, whose experiences have not been sufficiently incorporated in previous studies of manufacturing closure.2 The disputes were widely reported on at the time; the story of Scottish women fighting against multinational corporations’ “unfair” decisions during a period of rapidly increasing unemployment captured the attention of the labor movement, journalists, and politicians. And, whilst they were not part of a coordinated response to closure, there were clear links between the actions, and significant overlap among the workers involved.

Neural organization raises, in an especially clear way, a major problem confronting contemporary cognitive science. The problem (the “big issue” of my title) is: What is the relation between the strategies used to solve basic problems of perception and action and those used to solve more abstract or “cognitive” problems? Is there a smooth, incremental route from what Arbib et al. call “instinctual schemas” to higher-level kinds of cognitive prowess? I argue that, despite some suggestive comments, Arbib et al. do not resolve this issue.

Firestone & Scholl (F&S) bracket many attentional effects as “peripheral,” altering the inputs to a cognitive process without altering the processing itself. By way of contrast, I highlight an emerging class of neurocomputational models that imply profound, pervasive, nonperipheral influences of attention on perception. This transforms the landscape for empirical debates concerning possible top-down effects on perception.

Brains, it has recently been argued, are essentially prediction machines. They are bundles of cells that support perception and action by constantly attempting to match incoming sensory inputs with top-down expectations or predictions. This is achieved using a hierarchical generative model that aims to minimize prediction error within a bidirectional cascade of cortical processing. Such accounts offer a unifying model of perception and action, illuminate the functional role of attention, and may neatly capture the special contribution of cortical processing to adaptive success. This target article critically examines this “hierarchical prediction machine” approach, concluding that it offers the best clue yet to the shape of a unified science of mind and action. Sections 1 and 2 lay out the key elements and implications of the approach. Section 3 explores a variety of pitfalls and challenges, spanning the evidential, the methodological, and the more properly conceptual. The paper ends (sections 4 and 5) by asking how such approaches might impact our more general vision of mind, experience, and agency.

The target article sketched and explored a mechanism (action-oriented predictive processing) most plausibly associated with core forms of cortical processing. In assessing the attractions and pitfalls of the proposal we should keep that element distinct from larger, though interlocking, issues concerning the nature of adaptive organization in general.

A talking brain corrects a few preconceptions.

After offering a brief account of how we understand the shared circuits model (SCM), we divide our response into four sections. First, in section R1, we assess to what extent SCM is committed to an account of the ontogeny and phylogeny of shared circuits. In section R2, we examine doubts raised by several commentators as to whether SCM might be expanded so as to accommodate the mirroring of emotions, sensations, and intransitive actions more generally. Section R3 responds to various criticisms that relate to the account of social-learning Hurley proposes in the target article. We conclude in section R4 by responding to a number of commentators who argued for the limitation of control theory as a framework for studying social cognition.