Spelke's What Babies Know masterfully describes infants’ impressive repertoire of core cognitive concepts, from which the suite of human knowledge is eventually built. The current commentary argues for the existence of a core concept that Spelke claims preverbal infants lack: social goal. Core social goal concepts, operative extremely early in human development, underlie infants’ basic abilities to interpret and evaluate entities within the moral world; such abilities support claims for a core moral domain.

What we know about what babies know – as represented by the core knowledge proposal – is perhaps missing a place for the baby itself. By studying the baby as an actor rather than an observer, we can better understand the origins of human intelligence as an interface between perception and action, and how humans think and learn about themselves in a complex world.

This commentary challenges Spelke's view on the early development of social cognition from a neuroscience perspective by presenting an overlooked body of evidence from neuroimaging research on joint attention with human infants. Indeed, evidence demonstrating adult-like, neural sensitivity to joint attention in young infants, supports alternative theoretical views concerning the origins of uniquely human forms of social cognition.

I focus here on concepts that are not part of core knowledge – the ability to treat people as social agents with shareable mental states. Spelke proposes that learning language from another might account for the development of these concepts. I suggest that homesigners, who create language rather than learn it, may be a potential counterexample to this hypothesis.

Decades of research have pushed us closer to understanding what babies know. However, a powerful approach – representational similarity analysis (RSA) – is underused in developmental research. I discuss the strengths of this approach and what it can tell us about infant conceptual knowledge. As a case study, I focus on numerosity as a domain where RSA can make unique progress.

Spelke convincingly argues that we should posit six innate modular systems beyond the periphery (i.e., beyond low-level perception and motor control). I focus on the case of spatial navigation (Ch. 3) to claim that there remain powerful considerations in favor of positing additional innate, nonperipheral modules. This opens the door to stronger forms of nativism and nonperipheral modularism than Spelke's.

What language devises, it might divide. By exploring the relations among the core geometries of the physical world, the abstract geometry of Euclid, and language, I give new insight into both the persistence of core knowledge into adulthood and our access to it through language. My extension of Spelke's language argument has implications for pedagogy, philosophy, and artificial intelligence.

What Babies Know (WBK) argues that core knowledge has a unique place in cognitive architecture, between fully perceptual and fully conceptual systems of representation. Here I argue that WBK's core knowledge is on the perception side of the perception/cognition divide. I discuss some implications of this conclusion for the roles language learning might play in transcending core knowledge.

Why have core knowledge? Standard answers typically emphasize the difficulty of learning core knowledge from experience, or the benefits it confers for learning about the world. Here, we suggest a complementary reason: Core knowledge is critical for learning not just about the external world, but about the mind itself.

Researchers must infer “what babies know” based on what babies do. Thus, to maximize information from doing, researchers should use tasks and tools that capture the richness of infants' behaviors. We clarify Gibson's views about the richness of infants' behavior and their exploration in the service of guiding action – what Gibson called “learning about affordances.”

Where does human knowledge begin? Research on human infants, children, adults, and nonhuman animals, using diverse methods from the cognitive, brain, and computational sciences, provides evidence for six early emerging, domain-specific systems of core knowledge. These automatic, unconscious systems are situated between perceptual systems and systems of explicit concepts and beliefs. They emerge early in infancy, guide children's learning, and function throughout life.

In our target article, we proposed that curiosity and creativity are both manifestations of the same novelty-seeking process. We received 29 commentaries from diverse disciplines that add insights to our initial proposal. These commentaries ultimately expanded and supplemented our model. Here we draw attention to five central practical and theoretical issues that were raised by the commentators: (1) The complex construct of novelty and associated concepts; (2) the underlying subsystems and possible mechanisms; (3) the different pathways and subtypes of curiosity and creativity; (4) creativity and curiosity “in the wild”; (5) the possible link(s) between creativity and curiosity.

Curiosity and creativity are expressions of the trade-off between leveraging that with which we are familiar or seeking out novelty. Through the computational lens of reinforcement learning, we describe how formulating the value of information seeking and generation via their complementary effects on planning horizons formally captures a range of solutions to striking this balance.

We extend the work of Ivancovsky et al. by proposing that in addition to novelty seeking, mood regulation goals – including enhancing positive mood and repairing negative mood – motivate both creativity and curiosity. Additionally, we discuss how the effects of mood on state of mind are context-dependent (not fixed), and how such flexibility may impact creativity and curiosity.

Ivancovsky et al.'s Novelty-Seeking Model suggests several mechanisms that might underlie developmental change in creativity and curiosity. We discuss how these implications both do and do not align with extant developmental findings, suggest two further elements that can provide a more complete developmental account, and discuss current methodological barriers to formulating an integrated developmental model of curiosity and creativity.

Although creativity and curiosity can be similarly construed as knowledge-building processes, their underlying motivation is fundamentally different. Specifically, curiosity drives organisms to seek information that reduces uncertainty so that they can make a better prediction about the world. On the contrary, creative processes aim to connect distant pieces of information, maximizing novelty and utility.

The novelty-seeking model suggests that curiosity and creativity originate from novelty processes. However, different types of novelty exist, each with distinctive relationships with memory, which potentially influence curiosity and creativity in distinct ways. We thus propose expanding the NSM model to consider these different novelty types and their specific involvement in creativity.

The target article covers a large amount of ground and offers a provocative perspective. This commentary focuses on (a) assumptions, namely that there are discrete stages in the creative process and that novelty and usefulness are inextricable, (b) hidden variables in the creativity–curiosity relationship, and (c) the difference between an explanation of creativity versus a description of influence on it.

Ivancovsky et al. explore the relationship between curiosity and creativity, by suggesting they align through novelty-seeking mechanisms. We argue that a general mechanism linking both capacities together is question-asking: Curiosity drives question-asking that leads to creative problem solving. Yet, current findings from our lab suggest that question complexity relates to creativity, but not necessarily to curiosity, warranting further investigation.

The target article proposes a model involving the important but not well-investigated topics of curiosity and creativity. The model, however, falls short of providing convincing explanations of the basic mechanisms underlying these phenomena. We outline the importance of mechanistic thinking in dealing with the concepts outlined in this article specifically and within psychology and cognitive neuroscience in general.