According to the predictive coding theory of cognition (PCT), brains are predictive machines that use perception and action to minimize prediction error, i.e. the discrepancy between bottom–up, externally-generated sensory signals and top–down, internally-generated sensory predictions. Many consider PCT to have an explanatory scope that is unparalleled in contemporary cognitive science and see in it a framework that could potentially provide us with a unified account of cognition. It is also commonly assumed that PCT is a representational theory of sorts, in the sense that it postulates that our cognitive contact with the world is mediated by internal representations. However, the exact sense in which PCT is representational remains unclear; neither is it clear that it deserves such status – that is, whether it really invokes structures that are truly and nontrivially representational in nature. In the present article, I argue that the representational pretensions of PCT are completely justified. This is because the theory postulates cognitive structures – namely action-guiding, detachable, structural models that afford representational error detection – that play genuinely representational functions within the cognitive system.

Key words: cartographic maps, generative models, job description challenge, mental representation, predictive coding, structural representation.

Predictive coding is possibly one of the most influential, comprehensive, and controversial theories of neural function. While proponents praise its explanatory potential, critics object that key tenets of the theory are untested or even untestable. The present article critically examines existing evidence for predictive coding in the auditory modality. Specifically, we identify five key assumptions of the theory and evaluate each in the light of animal, human and modeling studies of auditory pattern processing. For the first two assumptions – that neural responses are shaped by expectations and that these expectations are hierarchically organized – animal and human studies provide compelling evidence. The anticipatory, predictive nature of these expectations also enjoys empirical support, especially from studies on unexpected stimulus omission. However, for the existence of separate error and prediction neurons, a key assumption of the theory, evidence is lacking. More work exists on the proposed oscillatory signatures of predictive coding, and on the relation between attention and precision. However, results on these latter two assumptions are mixed or contradictory. Looking to the future, more collaboration between human and animal studies, aided by model-based analyses will be needed to test specific assumptions and implementations of predictive coding – and, as such, help determine whether this popular grand theory can fulfill its expectations.

Key words: predictive coding, auditory, mmn, ssa, bayesian brain.

In this article, we integrate two constructionist approaches – the theory of constructed emotion and rational constructivism – to introduce several novel hypotheses for understanding emotional development. We first discuss the hypothesis that emotion categories are abstract and conceptual, whose instances share a goal-based function in a particular context but are highly variable in their affective, physical, and perceptual features. Next, we discuss the possibility that emotional development is the process of developing emotion concepts, and that emotion words may be a critical part of this process. We hypothesize that infants and children learn emotion categories the way they learn other abstract conceptual categories – by observing others use the same emotion word to label highly variable events. Finally, we hypothesize that emotional development can be understood as a concept construction problem: a child becomes capable of experiencing and perceiving emotion only when her brain develops the capacity to assemble ad hoc, situated emotion concepts for the purposes of guiding behavior and giving meaning to sensory inputs. Specifically, we offer a predictive processing account of emotional development.

Key words: prediction, construction, variation, social learning, culture

Events and event prediction are pivotal concepts across much of cognitive science, as demonstrated by the papers in this special issue. We first discuss how the study of events and the predictive processing framework may fruitfully inform each other. We then briefly point to some links to broader philosophical questions about events.

Binocular rivalry occurs when the eyes are presented with different stimuli and subjective perception alternates between them. Though recent years have seen a number of models of this phenomenon, the mechanisms behind binocular rivalry are still debated and we still lack a principled understanding of why a cognitive system such as the brain should exhibit this striking kind of behaviour. Furthermore, psychophysical and neurophysiological (single cell and imaging) studies of rivalry are not unequivocal and have proven difficult to reconcile within one framework. This review takes an epistemological approach to rivalry that considers the brain as engaged in probabilistic unconscious perceptual inference about the causes of its sensory input. We describe a simple empirical Bayesian framework, implemented with predictive coding, which seems capable of explaining binocular rivalry and reconciling many findings. The core of the explanation is that selection of one stimulus, and subsequent alternation between stimuli in rivalry occur when: (i) there is no single model or hypothesis about the causes in the environment that enjoys both high likelihood and high prior probability and (ii) when one stimulus dominates, the bottom–up, driving signal for that stimulus is explained away while, crucially, the bottom–up signal for the suppressed stimulus is not, and remains as an unexplained but explainable prediction error signal. This induces instability in perceptual dynamics that can give rise to perceptual transitions or alternations during rivalry.

An exciting theory in neuroscience is that the brain is an organ for prediction error minimization (PEM) This theory is rapidly gaining influence and is set to dominate the science of mind and brain in the years to come. PEM has extreme explanatory ambition, and profound philosophical implications. Here, I assume the theory, briefly explain it, and then I argue that PEM implies that the brain is essentially self-evidencing. This means it is imperative to identify an evidentiary boundary between the brain and its environment. This boundary defines the mind-world relation, opens the door to skepticism, and makes the mind transpire as more inferentially secluded and neurocentrically skull-bound than many would nowadays think. Therefore, PEM somewhat deflates contemporary hypotheses that cognition is extended, embodied and enactive; however, it can nevertheless accommodate the kinds of cases that fuel these hypotheses.

In this study, we challenge the deficit perspective on mathematical knowing and learning for children labeled as LD, focusing on their struggles not as a within student attribute, but rather as within teacher-learner interactions. We present two cases of fifth-grade students labeled LD as they interacted with a researcher-teacher during two constructivist-oriented teaching experiments designed to foster a concept of unit fraction. Data analysis revealed three main types of interactions, and how they changed over time, which seemed to support the students’ learning: Assess, Cause and Effect Reflection, and Comparison/Prediction Reflection. We thus argue for an intervention in interaction that occurs in the instructional process for students with LD, which should replace attempts to “fix” ‘deficiencies’ that we claim to contribute to disabling such students.

Key words: constructivism, student-teacher interaction, mathematics, intervention, learning difference

Predictive Processing accounts of Cognition, PPC, promise to forge productive alliances that will unite approaches that are otherwise at odds (see Clark, A. Surfing uncertainty: prediction, action and the embodied mind. Oxford University Press, Oxford, 2016). Can it? This paper argues that it can’t – or at least not so long as it sticks with the cognitivist rendering that Clark (2016) and others favor. In making this case the argument of this paper unfolds as follows: Sect. 1 describes the basics of PPC – its attachment to the idea that we perceive the world by guessing the world. It then details the reasons why so many find cognitivist interpretations to be inevitable. Section 2 examines how prominent proponents of cognitivist PPC have proposed dealing with a fundamental problem that troubles their accounts – the question of how the brain is able to get into the great guessing game in the first place. It is argued that on close inspection Clark’s (2016) solution, which he calls bootstrap heaven is – once we take a realistic look at the situation of the brain – in fact bootstrap hell. Section 3 argues that it is possible to avoid dwelling in bootstrap hell if one adopts a radically enactive take on PPC. A brief sketch of what this might look like is provided.

Key words: predictive processing, cognitivism, enactivism, learning, information

In this paper, we consider how certain longstanding philosophical questions about mental representation may be answered on the assumption that cognitive and perceptual systems implement hierarchical generative models, such as those discussed within the prediction error minimization (PEM) framework. We build on existing treatments of representation via structural resemblance, such as those in Gładziejewski (Synthese 193(2):559–582, 2016) and Gładziejewski and Miłkowski (Biol Philos, 2017), to argue for a representationalist interpretation of the PEM framework. We further motivate the proposed approach to content by arguing that it is consistent with approaches implicit in theories of unsupervised learning in neural networks. In the course of this discussion, we argue that the structural representation proposal, properly understood, has more in common with functional-role than with causal/informational or teleosemantic theories. In the remainder of the paper, we describe the PEM framework for approximate Bayesian inference in some detail, and discuss how structural representations might arise within the proposed Bayesian hierarchies. After explicating the notion of variational inference, we define a subjectively accessible measure of misrepresentation for hierarchical Bayesian networks by appeal to the Kullbach–Leibler divergence between posterior generative and approximate recognition densities, and discuss a related measure of objective misrepresentation in terms of correspondence with the facts.

Key words: problem of content, misrepresentation, functional role semantics, structural resemblance, prediction error minimization, generative model, recognition model, kullbach–leibler divergence, variational, bayesian inference, unsupervised learning

A recent surge of work on prediction-driven processing models – based on Bayesian inference and representation-heavy models – suggests that the material basis of conscious experience is inferentially secluded and neurocentrically brain bound. This paper develops an alternative account based on the free energy principle. It is argued that the free energy principle provides the right basic tools for understanding the anticipatory dynamics of the brain within a larger brain-body-environment dynamic, viewing the material basis of some conscious experiences as extensive – relational and thoroughly world-involving.

Key words: enactivism, experience, free energy, predictive processing