Chater and Christiansen propose that cultural complexity emerges primarily through processes of “social tinkering,” rather than from individual cognitive sophistication. This perspective aligns with a substantial body of research demonstrating that cumulative culture arises through repeated cycles of social transmission and refinement (Caldwell, Renner, & Atkinson, Reference Caldwell, Renner and Atkinson2018; Derex & Boyd, Reference Derex and Boyd2016). These findings support the view that complex cultural systems can emerge even when individual cognitive capacities are limited, shifting the explanatory focus from individual intelligence to collective dynamics.

A key strength of the target article is its emphasis on distributed processes. By framing cultural evolution as an emergent property of population-level interactions, the authors challenge accounts that locate complexity within individual cognition. Experimental work shows that iterative transmission can generate increasingly structured cultural forms (Caldwell et al., Reference Caldwell, Renner and Atkinson2018), while partially connected networks can optimize the balance between innovation and retention (Derex & Boyd, Reference Derex and Boyd2016). Together, these findings suggest that cultural complexity depends less on individual brilliance than on the structure and dynamics of social interaction.

The concept of “social tinkering” also effectively captures the incremental and path-dependent nature of cultural change. Cultural traits are rarely engineered de novo; instead, they are modified, recombined, and repurposed over time. This framing resonates with broader evolutionary principles, emphasizing gradual adaptation through variation and selective retention. As a descriptive metaphor, it provides an intuitively appealing account of how complex systems can emerge from simple, local adjustments.

However, the concept remains theoretically under-specified. As presented, “social tinkering” functions more as a heuristic than a formal explanatory framework. Existing cultural evolution theory offers more explicit accounts of variation, selection, and retention (Acerbi & Mesoudi, Reference Acerbi and Mesoudi2015; Wilson, Reference Wilson2016), whereas the present framework lacks comparable mechanistic detail. It does not clearly specify when tinkering leads to cumulative complexity versus stagnation or loss, nor how different sources of variation – such as innovation, error, or recombination – interact with selective pressures. Without such specification, it is difficult to derive testable predictions or evaluate the theory’s explanatory scope.

A second limitation concerns the role of individual cognition. Although the authors intentionally downplay cognitive sophistication, individuals do not passively transmit information; they actively transform it through biases in memory, inference, and learning. These transformations introduce systematic structure into cultural transmission, shaping both its direction and stability. Empirical studies show that cognitive constraints influence which cultural variants are generated and retained (Gutchess & Rajaram, Reference Gutchess and Rajaram2023). For example, memory biases can favor certain patterns, while inferential processes may regularize noisy inputs. Cultural complexity, therefore, reflects not only social processes but also the structured ways in which individuals process and reproduce information.

Third, cultural dynamics are strongly shaped by social network structure. Transmission occurs within networks that constrain who interacts with whom and how information flows. Network properties – such as clustering, modularity, and centralization – can significantly influence cultural accumulation (Cantor et al., Reference Cantor, Maldonado-Chaparro, Beck, Brandl, Carter, He, Hillemann, Klarevas-Irby, Ogino, Papageorgiou, Prox and Farine2021). Highly clustered networks may stabilize local traditions, whereas more diffuse networks may accelerate diffusion. Central individuals may disproportionately shape outcomes by acting as hubs of transmission. These findings suggest that social tinkering is not uniform but depends on underlying interaction structures, which should be incorporated into the framework.

Fourth, the framework does not sufficiently account for environmental and ecological influences. Cultural evolution is increasingly understood as an adaptive process shaped by interactions between social learning and environmental pressures. Cultural traits often persist because they solve context-specific problems, and their transmission depends on ecological relevance. Empirical evidence shows that resource constraints and environmental conditions shape cultural practices (Meehan et al., Reference Meehan, Grunseit, Condie, Hagani and Merom2023), while recent work highlights how cultural evolution frameworks can address real-world challenges such as sustainability (Richerson, Boyd, & Efferson, Reference Richerson, Boyd and Efferson2024). Ignoring these factors risks overlooking a key driver of cultural complexity: adaptation to changing environments.

Taken together, these limitations indicate that social tinkering should be embedded within a broader multilevel framework integrating transmission dynamics, cognitive constraints, social structure, and environmental pressures. Crucially, these components interact. Cognitive biases may shape how individuals respond to network structure, while environmental demands may alter both transmission pathways and selection criteria. An integrative approach would align the framework with contemporary cultural evolution research and enable more precise predictions.

Future work should proceed in three directions. First, formal modeling is needed to specify the mechanisms underlying social tinkering and generate quantitative predictions. Agent-based models are particularly well suited to linking individual behavior with population-level outcomes. Second, experimental paradigms – such as iterated learning and network-based diffusion – should be used to test how cognitive and social factors jointly shape cultural dynamics. Third, large-scale empirical data should be leveraged to examine how cultural complexity varies across ecological and institutional contexts, taking advantage of recent advances in computational social science.

In sum, Chater and Christiansen offer a compelling account of cultural complexity as an emergent social phenomenon. Their emphasis on collective processes represents an important conceptual advance. However, to fully realize its explanatory potential, social tinkering must be developed into a mechanistically explicit and empirically testable framework. Integrating insights from recent theoretical and empirical work provides a clear path forward toward a more comprehensive account of cultural complexity.