Structuralism and Adaptationism: Friends? Or foes?

Historically, the empirical study of phenotypic diversification has fallen into two rough camps; (1) "structuralist approaches" focusing on developmental constraint, bias, and innovation (with evo-devo at the core); and (2) "adaptationist approaches" focusing on adaptation, and natural selection. Whilst debates, such as that surrounding the proposed "Extended" Evolutionary Synthesis, often juxtapose these two positions, this review focuses on the grey space in between. Specifically, here I present a novel analysis of structuralism which enables us to take a more nuanced look at the motivations behind the structuralist and adaptationist positions. This makes clear how the two approaches can conflict, and points of potential commensurability. The review clarifies (a) the value of the evo-devo approach to phenotypic diversity, but also (b) how it properly relates to other predominant approaches to the same issues in evolutionary biology more broadly.

A disanalogy with RCTs and its implications for second-generation causal knowledge.

We are less optimistic than Madole & Harden that family-based genome-wide association studies (GWASs) will lead to significant second-generation causal knowledge. Despite bearing some similarities, family-based GWASs and randomised controlled trials (RCTs) are not identical. Most RCTs assess a relatively homogenous causal stimulus as a treatment, whereas GWASs assess highly heterogeneous causal stimuli. Thus, GWAS results will not translate so easily into second-generation causal knowledge.

No tinkering allowed: When the end goal requires a highly specific or risky, and complex action sequence, expect ritualistic scaffolding.

On Jagiello et al.'s cultural action framework, end-goal resolvability and causal transparency make possible the transmission of complex technologies through low-fidelity cultural learning. We offer three further features of goal-directed action sequences - specificity, riskiness, and complexity - which alter the effectiveness of low-fidelity cultural learning. Incorporating these into the cultural action framework generates further novel, testable predictions for bifocal stance theory.

Unification at the cost of realism and precision.

Veissière et al. must sacrifice explanatory realism and precision in order to develop a unified formal model. Drawing on examples from cognitive archeology, we argue that this makes it difficult for them to derive the kinds of testable predictions that would allow them to resolve debates over the nature of human social cognition and cultural acquisition.

The signature-testing approach to mapping biological and artificial intelligences.

Making inferences from behaviour to cognition is problematic due to a many-to-one mapping problem, in which any one behaviour can be generated by multiple possible cognitive processes. Attempts to cross this inferential gap when comparing human intelligence to that of animals or machines can generate great debate. Here, we discuss the challenges of making comparisons using 'success-testing' approaches and call attention to an alternate experimental framework, the 'signature-testing' approach. Signature testing places the search for information-processing errors, biases, and other patterns centre stage, rather than focussing predominantly on problem-solving success. We highlight current research on both biological and artificial intelligence that fits within this framework and is creating proactive research programs that make strong inferences about the similarities and differences between the content of human, animal, and machine minds.