Quantum active Bayesian inference and quantum Markov blankets enable robust modeling and simulation of difficult-to-render natural agent-based classical inferential paradoxes interfaced with task-specific environments. Within a non-realist cognitive completeness regime, quantum Markov blankets ensure meta-learned irrational decision making is fitted to explainable manifolds at optimal free energy, where acceptable incompatible observations or temporal Bell-inequality violations represent important verifiable real-world outcomes.

The meta-learning framework proposed by Binz et al. would gain significantly from the inclusion of affective and homeostatic elements, currently neglected in their work. These components are crucial as cognition as we know it is profoundly influenced by affective states, which arise as intricate forms of homeostatic regulation in living bodies.

We challenge Binz et al.'s claim of meta-learned model superiority over Bayesian inference for large world problems. While comparing Bayesian priors to model-training decisions, we question meta-learning feature exclusivity. We assert no special justification for rational Bayesian solutions to large world problems, advocating exploring diverse theoretical frameworks beyond rational analysis of cognition for research advancement.

Psychologists and neuroscientists extensively rely on computational models for studying and analyzing the human mind. Traditionally, such computational models have been hand-designed by expert researchers. Two prominent examples are cognitive architectures and Bayesian models of cognition. Although the former requires the specification of a fixed set of computational structures and a definition of how these structures interact with each other, the latter necessitates the commitment to a particular prior and a likelihood function that – in combination with Bayes' rule – determine the model's behavior. In recent years, a new framework has established itself as a promising tool for building models of human cognition: the framework of meta-learning. In contrast to the previously mentioned model classes, meta-learned models acquire their inductive biases from experience, that is, by repeatedly interacting with an environment. However, a coherent research program around meta-learned models of cognition is still missing to date. The purpose of this article is to synthesize previous work in this field and establish such a research program. We accomplish this by pointing out that meta-learning can be used to construct Bayes-optimal learning algorithms, allowing us to draw strong connections to the rational analysis of cognition. We then discuss several advantages of the meta-learning framework over traditional methods and reexamine prior work in the context of these new insights.

Twenty-five commentaries raise questions concerning the origins of knowledge, the interplay of iconic and propositional representations in mental life, the architecture of numerical and social cognition, the sources of uniquely human cognitive capacities, and the borders among core knowledge, perception, and thought. They also propose new methods, drawn from the vibrant, interdisciplinary cognitive sciences, for addressing these questions and deepening understanding of infant minds.

Spelke's sweeping proposal requires greater precision in specifying the place of language in early cognition. We now know by 3 months of age, infants have already begun to forge a link between language and core cognition. This precocious link, which unfolds dynamically over development, may indeed offer an entry point for acquiring higher-order, abstract conceptual and representational capacities.

Innateness of core knowledge mechanisms (in the form of “cognitive priors”) can be revealed by proper comparisons of altricial and precocial species. Cognitive priors and sensitive periods in their expression may also provide clues for the development of plausible artificial intelligence systems.

There is no room for pragmatic expectations about communicative interactions in core cognition. Spelke takes the combinatorial power of the human language faculty to overcome the limits of core cognition. The question is: Why should the combinatorial power of the human language faculty support infants' pragmatic expectations not merely about speech, but also about nonverbal communicative interactions?

Spelke posits that the concept of “social agent,” who performs object-directed actions to fulfill social goals, is the first noncore concept that infants acquire as they begin to learn their native language. We question this proposal on empirical grounds and theoretical grounds, and propose instead that the representation of object-mediated interactions may be supported by a dedicated prelinguistic mechanism.

Questions can be raised about the central status that evolutionarily ancient core knowledge systems are given in Spelke's otherwise very compelling theory. So, the existence of domain-general learning capacities has to be admitted, too, and no clear reason is provided to doubt the existence of uniquely human cognitive adaptations. All of these factors should be acknowledged when explaining human thought.

Some core knowledge may be rooted in – or even identical to – well-characterized mechanisms of mid-level visual perception and attention. In the decades since it was first proposed, this possibility has inspired (and has been supported by) several discoveries in both infant cognition and adult perception, but it also faces several challenges. To what degree does What Babies Know reflect how babies see and attend?

What Babies Know outlines a compelling case for why infancy research is fundamental for conceptualizing what it is to be human. There is another period in human development that is relatively inaccessible, yet is more important. In order to truly understand the nature of core knowledge, perception, and cognition, we must start not with the infant, but with the fetus.

Elizabeth Spelke's What Babies Know is a scholarly presentation of core knowledge theory and a masterful compendium of empirical evidence that supports it. Unfortunately, Spelke's principal theoretical assumption is that core knowledge is simply the innate product of cognitive evolution. As such, her theory fails to explicate the developmental mechanisms underlying the emergence of the cognitive systems on which that knowledge depends.

While Spelke provides powerful support for concept nativism, her focus on understanding concept nativism through six innate core knowledge systems is too confining. There is also no reason to suppose that the curse of a compositional mind constitutes a principled reason for positing less innate structure in explaining the origins of concepts. Any solution to such problems must take into account poverty of the stimulus considerations, which argue for postulating more innate structure, not less.

Core knowledge systems play an important role in theories of cognitive development. However, recent studies suggest that fundamental principles of the object and agent systems can be revised by adults and preschoolers, when given small amounts of counterevidence. We argue that not all core knowledge systems are created equal, and they may be subject to revision throughout development.

We question Spelke's key claim that the medium, in which contents from different core knowledge systems can be represented and combined, is language-based. Recalling an episodic memory, playing chess, and conducting mental rotation are tasks where core knowledge information is represented and combined. Although these tasks can be described by means of language, these tasks are not inherently language-based. Hence, language may be an important subset of an abstraction medium – not the medium as such.

Somewhat questioning Elizabeth Spelke's attempt to account for infants’ social knowledge, our commentary argues that social cognition might be divided into several specialized systems. In addition to the core system dedicated to the intersubjective dimension of close relationships, infants could be prewired to process social relationships, such as dominance, characterized by their impersonal, normative dimension.

We examine Spelke's core knowledge taxonomy and test its boundaries. We ask whether Spelke's core knowledge is a distinct type of cognition in the sense that the cognitive processes it includes and excludes are biologically and mechanically coherent.

We argue that the core social being system is unlike other core systems in that it participates in frequent, widespread learning. As a result, the social being system is less constant throughout the lifespan and less informationally encapsulated than other core systems. This learning supports the development of the precursors of bias, but also provides avenues for preempting it.

Central to What Babies Know (Spelke, 2022) is the thesis that infants' understanding is divided into independent modules of core knowledge. As a test case, we consider adding a new domain: core knowledge of substances. Experiments show that infants' understanding of substances meets some criteria of core knowledge, and they raise questions about the relations that hold between core domains.