Exaggerated claims about inaccuracy and downplaying veracity can also be found in research on memory. This commentary on Jussim's 2012 book analyzes these developments in connection with schema and the misinformation effect's purported role in memory distortion. It concludes by looking back to the locus classicus of memory distortion (viz. Bartlett 1932), which in fact provides a more nuanced account of inaccuracy.

In our commentary we ask whether we should ultimately endeavour to find the deep causes of behaviours? Then we discuss two extensions of the proposed framework: (1) Mendelian randomisation and (2) hypothesis-free gene–environment interaction (leveraging heterogeneity in genetic associations). These complementary methods help move us towards second-generation causal knowledge, ultimately understanding mechanistic pathways and identifying more effective intervention targets.

We compare the predictions of two important proposals made by De Neys to findings in the anchoring effect literature. Evidence for an anchoring-and-adjustment heuristic supports his proposal that system 1 and system 2 are non-exclusive. The relationship between psychophysical noise and anchoring effects, however, challenges his proposal that epistemic uncertainty determines the involvement of system 2 corrective processes in judgment.

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.

Howe et al.'s rejection of traditional discussion of talent is clearly acceptable, but their alternative has a weakness. They stress practice and hard work while referring vaguely to some basic biological substrate. High scores on a valid test of general intelligence provide a cultural-genetic basis for talented performance in a wide variety of specialties, ranging from engineering to the humanities. These choices may be entirely environmentally determined, and the highest levels of achievement do require practice and hard work.

This account of puritanical morality is useful and innovative, but makes two errors. First, it mischaracterizes the purity foundation as being unrelated to cooperation. Second, it makes the leap from cooperation (broadly construed) to a monist account of moral cognition (as harm or fairness). We show how this leap is both conceptually incoherent and inconsistent with empirical evidence about self-control moralization.

The CLASH model is not convincing for two reasons. First, it ignores prior research proposing very similar ideas in a more compelling fashion. Second, it dismisses the role of genetic factors in shaping criminal propensities across population groups, opting for a facultative view of life history evolution that does not seem to square with current evidence.

We are encouraged by the many positive commentaries on our target article. In this response, we recapitulate some of the points raised and identify synergies between them. We have arranged our response based on the tension between data and architecture that arises in the meta-learning framework. We additionally provide a short discussion that touches upon connections to foundation models.

An ideal vision model accounts for behavior and neurophysiology in both naturalistic conditions and designed lab experiments. Unlike psychological theories, artificial neural networks (ANNs) actually perform visual tasks and generate testable predictions for arbitrary inputs. These advantages enable ANNs to engage the entire spectrum of the evidence. Failures of particular models drive progress in a vibrant ANN research program of human vision.

Routes to achieving any sort of meaningful success in the enterprise of behavioural change requires an understanding of the rate of failure, and why failures occur. This commentary shows that there is more to diagnosis of failures than fixating on micro- rather than the macro-level behaviours.

The problems with polygenic scores (PGSs) have been understated. The fact that they are ancestry-specific means that biases related to sociodemographic factors would be impossible to avoid. Additionally, the requirement to obtain DNA would have profound impacts on study design and required resources, as well as likely introducing recruitment bias. PGSs are unhelpful for social science research.

We recognize today's deep neural network (DNN) models of language behaviors as engineering achievements. However, what we know intuitively and scientifically about language shows that what DNNs are and how they are trained on bare texts, makes them poor models of mind and brain for language organization, as it interacts with infant biology, maturation, experience, unique principles, and natural law.

Recent work by emotion researchers indicates that emotions have a multilevel structure. Sophisticated sentimentalists should take note of this work – for it better enables them to defend a substantive role for emotion in moral cognition. Contra May's rationalist criticisms, emotions are not only able to carry morally relevant information, but can also substantially influence moral judgment and reasoning.

I ask four questions: (1) Why should we think that our hominid ancestor's predation is not just a causal influence but the main causal factor responsible for human cruelty? (2) Why not think of human cruelty as a necessary part of a syndrome in which other phenomena are necessarily involved? (3) What definitions of cruelty does Nell propose that we operate with? And (4) what about the meaning of cruelty for human beings?

Ballard et al. show how control structures using minimal state can be made flexible enough for complex cognitive tasks by using deictic pointers, but they do so within a specific computational framework. We discuss broader implications in cognition and memory and provide biological evidence for their theory. We also suggest an alternative account of pointer binding, which may better explain their limited number.

Sensorimotor contingencies are certainly of great importance for perception but they are no substitute for the internal representation of perceived information. I argue that internal, non-iconic representations of perceptions must, and do, exist and that sensorimotor contingencies are an integral part of them. Further, I argue that mastery of the sensory apparatus or environment is not a prerequisite for perception and that perception is possible in the absence of any control over the perceptual process.

Cramer et al. present a thoughtful application of network analysis to symptoms, but certain questions remain open. These questions involve the intended causal interpretation, the critique of latent variables, individual variation in causal networks, Borsboom's idea of networks as measurement models, and how well the data support the stability of the network results.