We sympathize with many of the points Burt makes in challenging the value of genetics to advance our understanding of social science. Here, we discuss how recent reflections on epistemic validity in the behavioral sciences can further contribute to a reappraisal of the role of sociogenomics to explain and predict human traits, aptitudes, and achievement.

Burt's critique of using polygenic scores in social science conflates the “scientific costs” of sociogenomics with “sociopolitical and ethical” concerns. Furthermore, she paradoxically enlists recent advances in controlling for environmental confounding to argue such confounding is scientifically “intractable.” Disinterested social scientists should support ongoing efforts to improve this technology rather than obstructing progress and excusing genetically confounded research.

Genetic studies in the social sciences could be augmented through the additional consideration of functional (transcriptome, methylome, metabolome) and/or multimodal genetic data when attempting to understand the genetics of social phenomena. Understanding the biological pathways linking genetics and the environment will allow scientists to better evaluate the functional importance of polygenic scores.

The critique of the genetics of complex social outcomes is partly well-founded, insofar as social outcomes sometimes have unreliable relations with cognitive traits. But the correct conclusion is not to dismiss the entire field altogether. Rather, the implication is to redirect geneticists' attention to the stable cognitive phenotypes that are natural candidates for genetic analysis.

This commentary emphasizes two problem areas mentioned by Burt. First, that within-family designs do not eradicate stratification confounds. Second, that the linear/additive model of genetic causes of form and variation is not supported by recent progress in molecular biology. It concludes with an appeal for a (biologically and psychologically) more realistic model of such causes.

Burt formulates her critique at a general level of abstraction that highlights the methodological deficiencies of sociogenomics without also calling attention to precisely the same deficiencies in the social science model she seeks to defend against its encroachments. What might have been a methodological bulwark against the excesses of sociogenomics is instead a one-sided critique that merely renews its charter.

Polygenic score (PGS) computations assume an additive model of gene action because associations between phenotypes and alleles at different loci are compounded, ignoring interactions between alleles or loci let alone between genotype and environment. Consequently, PGSs are subject to the same objections that invalidated traditional heritability analyses in the 1970s. Thus, PGSs should not be used in the social sciences.

Although Burt provides a valuable critique of the scientific value of integrating genetic data into social science research, she reinforces rather than disrupts the age-old horserace between genetic effects and environmental effects. We must move past this false dichotomy to create a new ontology that recognizes the ways in which genetic and environmental processes are inextricably intertwined.

It is a hotly contested issue whether polygenic scores should play a major role in the social sciences. Here, we defend a methodologically pluralist stance in which sociogenomics should abandon its hype and recognize that it suffers from all the methodological difficulties of the social sciences, yet nevertheless maintain an optimistic stance toward a more cautious use.

We contend that social science variables are the product of multiple partly heritable traits. Genetic associations with socioeconomic status (SES) may differ across populations, but this is a consequence of the intermediary traits associated with SES differences also varying. Furthermore, genetic data allow social scientists to make causal statements regarding the aetiology and consequences of SES.

Burt's argument relies on a motte-and-bailey fallacy. Burt aims to argue against the value of genetics for social science; instead she argues against certain interpretations of a specific kind of genetics tool, polygenic scores (PGSs). The limitations, previously identified by behavioural geneticists including ourselves, do not negate the value of PGSs, let alone genetics in general, for social science.

In this response, I focus on clarifying my arguments, highlighting consensus, and addressing competing views about the utility of polygenic scores (PGSs) for social science. I also discuss an assortment of expansions to my arguments and suggest alternative approaches. I conclude by reiterating the need for caution and appropriate scientific skepticism.

An ideography is a general-purpose code made of pictures that do not encode language, which can be used autonomously – not just as a mnemonic prop – to encode information on a broad range of topics. Why are viable ideographies so hard to find? I contend that self-sufficient graphic codes need to be narrowly specialized. Writing systems are only an apparent exception: At their core, they are notations of a spoken language. Even if they also encode nonlinguistic information, they are useless to someone who lacks linguistic competence in the encoded language or a related one. The versatility of writing is thus vicarious: Writing borrows it from spoken language. Why is it so difficult to build a fully generalist graphic code? The most widespread answer points to a learnability problem. We possess specialized cognitive resources for learning spoken language, but lack them for graphic codes. I argue in favor of a different account: What is difficult about graphic codes is not so much learning or teaching them as getting every user to learn and teach the same code. This standardization problem does not affect spoken or signed languages as much. Those are based on cheap and transient signals, allowing for easy online repairing of miscommunication, and require face-to-face interactions where the advantages of common ground are maximized. Graphic codes lack these advantages, which makes them smaller in size and more specialized.

Morin argues that fully fledged ideography is hindered by a problem of standardization. In general, the argument makes a great deal of sense, but we find that it is easier to grasp if, unlike the author, we consider ideography a language – one whose symbols (graphs) are not as effortless to produce as those of spoken and sign languages (phones, gestures).

We posit a cognitive account of the puzzle of ideography, which complements the standardization account of Morin. Efficient standardization of spoken language is phenomenologically attributed to a modality effect coupled with chunking of cognitive representations, further aided by multisensory integration and the serialized nature of attention. These mechanisms explain why languages dominate graphic codes for general-purpose communication.

We argue that the problem of ideographic codes stems from neither learnability nor standardization, but from a general issue of pragmatic interpretation. As ideographic codes increase in expressive power, in order to reduce ambiguity, they must become more detailed – such that production becomes more cumbersome, and requires greater artistry on the part of users, limiting their capacity for growth.

The possibility of ideography is an empirical question. Prior examples of graphic codes do not provide compelling evidence for the infeasibility of ideography, because they fail to satisfy essential cognitive requirements that have only recently been revealed by studies of representational systems in cognitive science. Design criteria derived from cognitive principles suggest how effective graphic codes may be engineered.

All sufficiently large functional notations (ideographic or otherwise) are composites of discrete, structured elements (e.g., phonemes, morphemes, numerals). We must consider not only the modality but also the structure of the existing, workable ideographic/semasiographic systems we know (e.g., musical and numerical notation) to establish the cognitive limitations militating against humans memorizing and standardizing domain-general ideographies that would parallel written language.

Communication barriers long-associated with ideographs, including combinatorial grapholinguistic complexity, computational encoding–decoding complexity, and technological rendering and deployment, become trivialized through advancements in interoperable smart mobile digital devices. Such technologies impart unprecedented extended-reality user hazards only mitigated by unprecedented colloquial and bureaucratic societal norms. Digital age norms thus influence natural ideographic language origins and evolution in ways novel to human history.

Morin argues that ideographies are limited because graphic codes lack a capacity for proliferating standardization. However, natural graphic systems display rich standardization and can be placed in sequences using complex combinatorial structures. In contrast, ideographies are not natural, and their limitations lie in their attempts to artificially force a graphic system to behave like a writing system.