Baumard's new explanation of the Industrial Revolution shows that Life History Theory holds great potential. Here, I suggest two related hypotheses for examination. One is that there are long-term roots of slow life traits and preferences. The other is that Life History Theory can explain other aspects of economic modernity such as the Scientific Revolution and bureaucratic states. If so, then Life History Theory offers a way to reconcile several bodies of evidence and lines of explanation into a coherent general account of economic modernity.

The idea, based on Life History Theory, that the Industrial Revolution was a positive feedback process wherein prosperity induced prosperity-promoting preference shifts is just an intriguing speculation. The evidence does not distinguish this explanation from simple alternatives. For example, increased prosperity may have freed up time for individuals to engage in innovative activity and increased the benefits from doing so.

We suggest in this commentary an emotional origin of the Industrial Revolution. Specifically, increased living standards directly preceding the Industrial Revolution produced increased happiness and subjective well-being that, in turn, fueled the explosion of innovation and economic growth experienced in industrial England.

Life History Theory (LHT) predicts a monotonous relationship between affluence and the rate of innovations and strong correlations within a cluster of behavioral features. Although both predictions can be true in specific cases, they are incorrect in general. Therefore, the author's explanations may be right, but they do not prove LHT and cannot be generalized to other apparently similar processes.

We present data from 122 nations showing that Baumard's argument on the ecological predictors of life history strategies and innovation is incomplete. Our analyses indicate that wealth, parasite stress, and cold climate impose orthogonal effects on life histories, innovation, and industrialization. Baumard also overlooks the historical exploitation of other nations which significantly enlarged the “pooled energy budget” available to England.

Recent studies have shown that population-level time perspectives can be approximated using “big data” on search engine queries, and that these indices, in turn, predict the per-capita Gross Domestic Product of countries. Although these findings seem to support Baumard's suggestion that affluence makes people more future-oriented, they also reveal a more complex relationship between time perspectives and economic outputs.

This comment focuses on difficulties in establishing causality among various phenomena present in early modern Europe at the beginning of the Industrial Revolution. It concludes that, rather than focus on a single cause out of many candidates, we should consider the possibility of a set of mutually reinforcing causes, among which those suggested by Life History Theory may be included.

Baumard's thesis that the English Industrial Revolution can be explained by Life History Theory's predictions for psychological development is a progression of much literature in economic and social history. However, the theory suffers from its reliance on increasingly fragile data indicators for “wealth” and its focus on “innovation” as new research begins to explore sectoral dynamics in long run growth.

We question Baumard's underlying assumption that humans have a propensity to innovate. Affordable transportation and energy underpinned the Industrial Revolution, making mass production/consumption possible. Although we cannot accept Baumard's thesis on the Industrial Revolution, it may help explain why complexity and innovation increase rapidly in the context of abundant energy.

Baumard's application of Life History Theory to explain the origins of economic growth is a needed amendment to incentive-based explanations of modern economics. However, even though it is grounded in evolution, the theory does not do enough to specify the relevant evolutionary mechanisms. As such, it accommodates too many alternative historic scenarios, yet remains unable to explain divergent regional patterns of economic growth.

Cognition played a pivotal role in the acceleration of technological innovation during the Industrial Revolution. Growing affluence may have provided favourable environmental conditions for a boost in cognition, enabling individuals to tackle more complex (industrial) problems. Dynamical systems thinking may provide useful tools to describe sudden transitions like the Industrial Revolution, by modelling the recursive feedback between psychology and environment.

Baumard proposes a model to explain the dramatic rise in innovation that occurred during the Industrial Revolution, whereby rising living standards led to slower life history strategies, which, in turn, fostered innovation. We test his model explicitly using time series data, finding limited support for these proposed linkages. Instead, we find evidence that rising living standards appear to have a time-lagged bidirectional relationship with increasing innovation.

Baumard proposes that life history slowing in populations over time is the principal driver of innovation rates. We show that this is only true of micro-innovation rates, which reflect cognitive and economic specialization as an adaptation to high population density, and not macro-innovation rates, which relate more to a population's level of general intelligence.

I am grateful to have received so many stimulating commentaries from interested colleagues regarding the psychological origins of the Industrial Revolution and the role of evolutionary theory in understanding historical phenomena. Commentators criticized, extended, and explored the implications of the perspective I presented, and I wholeheartedly agree with many commentaries that more work is needed. In this response, I thus focus on what is needed to further test the psychological origins of the Industrial Revolution. Specifically, I argue, in agreement with many commentators, that we need: (1) better data about standards of living, psychological preferences, and innovation rates (sect. R1); (2) better models to understand the role of resources (and not just mortality) in driving cultural evolution and the multiple aspects of the behavioral constellation of affluence (sect. R2); and (3) better predictions and better statistical instruments to disentangle the possible mechanisms behind the rise of innovativeness (genetic selection, rational choice, and phenotypic plasticity) (sect. R3).

“Neural coding” is a popular metaphor in neuroscience, where objective properties of the world are communicated to the brain in the form of spikes. Here I argue that this metaphor is often inappropriate and misleading. First, when neurons are said to encode experimental parameters, the neural code depends on experimental details that are not carried by the coding variable (e.g., the spike count). Thus, the representational power of neural codes is much more limited than generally implied. Second, neural codes carry information only by reference to things with known meaning. In contrast, perceptual systems must build information from relations between sensory signals and actions, forming an internal model. Neural codes are inadequate for this purpose because they are unstructured and therefore unable to represent relations. Third, coding variables are observables tied to the temporality of experiments, whereas spikes are timed actions that mediate coupling in a distributed dynamical system. The coding metaphor tries to fit the dynamic, circular, and distributed causal structure of the brain into a linear chain of transformations between observables, but the two causal structures are incongruent. I conclude that the neural coding metaphor cannot provide a valid basis for theories of brain function, because it is incompatible with both the causal structure of the brain and the representational requirements of cognition.

First, I argue that there is no agreement within non-classical cognitive science as to whether one should eliminate representations, hence, it is not clear that Brette's appeal to it is going to solve the problems with coding. Second, I argue that Brette's criticism of predictive coding as being intellectualistic is not justified, as predictive coding is compatible with embodied cognition.

Pointing to similarities between challenges encountered in today's neural coding and twentieth-century behaviorism, we draw attention to lessons learned from resolving the latter. In particular, Perceptual Control Theory posits behavior as a closed-loop control process with immediate and teleological causes. With two examples, we illustrate how these ideas may also address challenges facing current neural coding paradigms.

The Bayesian brain hypothesis, predictive processing, and variational free energy minimisation are typically used to describe perceptual processes based on accurate generative models of the world. However, generative models need not be veridical representations of the environment. We suggest that they can (and should) be used to describe sensorimotor relationships relevant for behaviour rather than precise accounts of the world.

Brette argues that coding as a concept is inappropriate for explanations of neurocognitive phenomena. Here, we argue that Brette's conceptual analysis mischaracterizes the structure of causal claims in coding and other forms of analysis-by-decomposition. We argue that analyses of this form are permissible and conceptually coherent and offer essential tools for building and developing models of neurocognitive systems like the brain.

Brette highlights a conceptual problem in contemporary neuroscience: Loose talk of “coding” sometimes leads to a conflation of the distinction between representing and merely detecting a property. The solution is to replace casual talk of “coding” with an explicit, demanding set of conditions for neural representation. Various theories of this general type can be found in the philosophical literature.