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The most plausible of Yarkoni's paths to recovery for psychology is the least radical one: psychologists need truly quantitative methods that exploit the informational power of variance and heterogeneity in multiple variables. If they drop ambitions to explain entire behaviors, they could find a box full of design and econometric tools in the parts of experimental economics that don't ape psychology.
Ainslie insightfully refines the concept of willpower by emphasizing low-effort applications of resolve. However, he gives undue weight to intertemporal discounting as the problem that willpower is needed to overcome. Nonhumans typically don't encounter choices that differ only in the time of consumption. Humans learn to transform uncertainty into problems they can solve using culturally evolved mechanisms for quantifying risk.
Leider and Griffiths clarify the basis for unification between mechanism-driven and solution-driven disciplines and methodologies in cognitive science. But, two outstanding issues arise for their model of resource-rationality: human brains co-process information with their environments, rather than merely adapt to them; and this is expressed in methodological differences between disciplines that complicate Leider and Griffiths’ proposed structural unification.
Use of network models to identify causal structure typically blocks reduction across the sciences. Entanglement of mental processes with environmental and intentional relationships, as Borsboom et al. argue, makes reduction of psychology to neuroscience particularly implausible. However, in psychiatry, a mental disorder can involve no brain disorder at all, even when the former crucially depends on aspects of brain structure. Gambling addiction constitutes an example.
Evidence for an EEA-derived domain-specific inference system must point to an active, latent representational structure. Otherwise we need to hypothesize only passive, virtual belief not over-ridden on the basis of the individual's experience. The folk economic beliefs identified by Boyer & Petersen (B&P), being with one exception about macroeconomics, might be virtual beliefs that people extrapolate across the micro–macro scale shift based on their experiences with markets.
Stanford casts original light on the question of why humans moralize some preferences. However, his account leaves some ambiguity around the relationship between the evolutionary function of moralization and the dynamics of tribal formation. Does the model govern these dynamics, or only explain why there are moralizing dispositions that more conventional modeling of the dynamics can exploit?
Gowdy & Krall (G&K) essentially recapitulate Malthus's classic argument for ecological pessimism in modern biological dress. Their reasoning also reproduces Malthus's blindness to the implications of technological innovation. Agriculture might have suppressed human individualism as G&K insist, but technology has tended to foster it. This complicates human ecological prospects in a non-Malthusian way, and it might additionally provide the resources for deliverance from disaster.
Bentley et al. say that economics is the science of their map's northwest quadrant, where choice is individual and transparent. This accepts the picture of the discipline common among behavioral economists who aim to drag economics southward but not eastward. In fact, leading economics journals regularly publish models located in all four quadrants, and the prominence of work from the eastern zone is increasing.
Classical probability models of incentive response are inadequate in “large worlds,” where the dimensions of relative risk and the dimensions of similarity in outcome comparisons typically differ. Quantum probability models for choice in large worlds may be motivated pragmatically – there is no third theory – or metaphysically: statistical processing in the brain adapts to the true scale-relative structure of the universe.
Clark expresses reservations about Friston's reductive interpretation of action-oriented predictive processing (AOPP) models of cognition, but he doesn't link these reservations to specific alternatives. Neuroeconomic models of sub-cognitive reward valuation, which, like AOPP, integrate attention with action based on prediction error, are such an alternative. They interpret reward valuation as an input to neocortical processing instead of reducing it.
McCullough et al. recognize that revenge and forgiveness jointly constitute a functional strategic complex. However, they model the halves of the complex as outputs of modules selected for regulating dyadic relationships. This is backwards. Forgiveness is a culturally evolved institution that can be exapted for use in dyadic contexts; it would be cheap talk among dyads were it not for the shadow of society.
Guala notes that low-cost punishment is the main mechanism that deters free-riding in small human communities. This mechanism is complemented by unusual human vulnerability to gossip. Defenders of an evolutionary discontinuity supporting human sociality might seize on this as an alternative to enjoyment of moralistic aggression as a special adaptation. However, the more basic adaptation of language likely suffices.
Cramer et al. persuasively conceptualize major depressive disorder (MDD) and generalized anxiety disorder (GAD) as network disorders, rejecting latent variable accounts. But how does their radical picture generalize across the suite of mental and personality disorders? Addictions are Axis I disorders that may be better characterized by latent variables. Their comorbidity relationships could be captured by inserting them as nodes in a super-network of Axis I conditions.
Ontic structural realism (OSR) is crucially motivated by empirical discoveries of fundamental physics. To this extent its potential to furnish a general metaphysics for science may appear limited. However, OSR also provides a good account of the progress that has been achieved over the decades in a formalized special science, economics. Furthermore, this has a basis in the ontology presupposed by economic theory, and is not just an artifact of formalization.
I distinguish between two styles of research that are both called “neuroeconomics”. Neurocellular economics (NE) uses the modelling techniques and mathematics of economics – constrained maximization and equilibrium analysis – to model relatively encapsulated functional parts of brains. This approach rests upon the fact that brains are, like markets, massively distributed information-processing networks over which executive systems can exert only limited and imperfect governance. Harrison's (2008) deepest criticisms of neuroeconomics do not apply to NE. However, the more famous style of neuroeconomics is behavioural economics in the scanner. This is often motivated by complaints about conventional economics frequently heard from behavioural economists. It attempts to use neuroimaging data to justify arguments for replacing standard aspects of microeconomic theory by facts and conjectures about human psychology. Harrison's grounds for unease about neuroeconomics apply to most BES, or at least to its explicit methodology. This methodology is naively reductionist and illegitimately assumes that economics should not do what all successful science does, namely, model abstract aspects of its target phenomena instead of would-be complete and fully ecologically situated facsimiles of them.