I suggest that, first, the broad framework of subjective selection proposed by Singh is not as different from existing research in cultural evolution as is made out, and second, that an under-discussed but important cause of cross-cultural regularities (the “cultural manifold”) is payoff-biased diffusion or migration between societies.

Singh emphasizes how individual-level processes, including motivation, shape cultural evolution. I suggest this approach can be enriched by considering cultural affordances – the goal-relevant opportunities and threats that a cultural variant might provide to an individual. This framework might be useful for explaining not only cultural universals but also nuances in variation between and within cultures.

Reviving Gould and Lewontin’s critique of adaptationism, Singh’s subjective selection favors truth over useful simplification. Nonetheless, while psychologically plausible, its explanatory power remains limited. Moreover, by treating evolved psychology as fixed, it neglects gene-culture coevolution. The result is a vision of cultural evolution whose expansiveness risks outpacing its scientific utility.

I read Singh’s stimulating proposal as a comeback to methodological individualism: social and cultural phenomena are driven by individual motivations, given by individuals’ subjective reasons for action. Singh’s version of this classic doctrine is useful when it leads him to question cultural group selection. But contrary to methodological individualism, the cognitive mechanisms shaping culture are not necessarily conscious, coherent, or causal.

I contrast Singh’s subjective functionalism with structuralist-related approaches in anthropology, where “attractors” are not isolated packages of beliefs and practices but robust relationships between such packages within cultural systems. While impressive in its synthesis, Singh’s account falls short of explaining a defining feature of human culture: its holistic nature. I use shamanism as an illustrative example.

The concept of super-attracters downplays the impact of basic emotions upon decision-making. The complexity of emotions belies the instinctual nature of such emotional systems, which are ultimately established by genetics and evolutionary processes. Examples of super-attractants may perhaps be better framed as evolutionary adaptiveness versus vestigial/byproduct behaviors.

In this commentary, we argue that aesthetic chills provide empirical evidence for Singh’s subjective functionalism. These pleasurable and measurable somatic events mark dopaminergic-mediated validation when cultural artifacts resonate with cognitive architecture. Research demonstrates chills can be predicted, induced, and therapeutically deployed for depression. This transforms aesthetics from ornamental to functional, revealing how feeling drives cultural transmission while enabling interventions.

I praise the synthesis and expansion of attractor theory to explain statistical universals, but critique its failure to explain absolute universals. I agree that lullabies, hero stories, and shamanism are good candidates for primarily cultural evolutionary explanations, but argue that the larger, most universal categories they are sub-phenomena of – music, language, religion – require gene-culture coevolutionary explanations.

We propose an extension in which core knowledge – innate, skeletal, and abstract cognitive representations – shape “super attractors.” They shape our cognition and our communication with others. People choose actions based not only on what they feel is right but also on whether they think others will agree. We propose that universal priors shape the space of shared intuitions.

Our commentary aims to enrich Singh’s subjective selection framework in two fundamental ways. First, language is necessary for coordinating on shared instrumental goals. Without it, the explanatory burden rests largely on evolved psychology. Second, goals themselves are culturally constructed, socially transmitted, and cumulatively evolving over time. This co-evolutionary dynamic between problems and solutions exceeds what fixed psychological hierarchies can explain.

This commentary enriches Singh’s subjective selection framework by focusing on the nature of goals. It considers their origins (biology, ecology, culture) and varying salience, allowing the model to account for both highly instrumental actions and seemingly goal-demoted behaviors like ritual, thus broadening its applicability.

Singh explains how cultural convergence is driven by super-attractors through subjective selection, but this account underestimates how social norms shape which psychological goals are maintained or suppressed. We clarify that social norms filter subjective selection by guiding what individuals regard as effective or desirable behaviors and by redefining their goals, thereby embedding subjective selection within the multilevel normative ecosystem.

The commentators mostly endorsed the value of attending to goals and evaluations in explanations of cultural patterns, including super-attractors. They also voiced concerns about subjective selection, enumerated other factors influencing goals and evaluation, proposed additional super-attractors and processes that might shape them, and demanded greater engagement with power and influence. Many issues signal larger disagreements in the naturalistic study of culture. Here, I respond.

As artificial intelligence (AI) continues to advance, it is natural to ask whether AI systems can be not only intelligent but also conscious. I consider why people might think AI could develop consciousness, identifying some biases that lead us astray. I ask what it would take for conscious AI to be a realistic prospect, challenging the assumption that computation provides a sufficient basis for consciousness. I’ll instead make the case that consciousness depends on our nature as living organisms – a form of biological naturalism. I lay out a range of scenarios for conscious AI, concluding that real artificial consciousness is unlikely along current trajectories but becomes more plausible as AI becomes more brain-like and/or life-like. I finish by exploring ethical considerations arising from AI that either is or convincingly appears to be conscious. If we sell our minds too cheaply to our machine creations, we not only overestimate them – we underestimate ourselves.

No one is in a position to say what really matters for consciousness, or whether only living systems can instantiate the complexity needed for conscious feelings. Seth’s attempt to connect life to consciousness through the mathematics of active inference and free energy minimization plays one hunch, but it is too soon to say whether it is the best hunch.

We agree that consciousness, as we know it, is inextricably biological – but not every biological detail is essential. Identifying which features are necessary, across scales, is vital for assessing the limits of computationalism. This process will reveal a richer, more biologically informed computationalism – one that future machines could, in principle, instantiate, but only after much deeper biological understanding.

Seth’s biological naturalism builds on ideas from predictive processing, the free energy principle, and active inference to (1) argue that only (some) biological systems are in the consciousness business and to (2) refute the possibility of artificial consciousness, i.e., consciousness in AI and other artificial systems. We find this perspective problematic for a few different reasons.

Seth argues that anthropocentrism, anthropomorphism, and human exceptionalism bias us towards an overly optimistic view of the prospects of conscious AI. I agree, but argue that these phenomena might also bias us towards an overly pessimistic view of the prospects for artificial consciousness. Furthermore, it is not always clear when these factors are functioning as “biases.”

Seth’s case for biological naturalism rests on a worryingly speculative foundation: the free energy principle. Arguments for computational functionalism are likewise speculative. Yet I am optimistic that the clash can be resolved empirically. Alleged dependencies of consciousness markers on biological properties can be tested comparatively, and a pattern of failed predictions would tilt the odds towards computational functionalism.