Comparing probabilistic accounts of probability judgments

Bayesian theories of cognitive science hold that cognition is fundamentally probabilistic, but people’s explicit probability judgments often violate the laws of probability. Two recent proposals, the “Probability Theory plus Noise” (Costello & Watts, 2014) and “Bayesian Sampler” (Zhu et al., 2020) theories of probability judgments, both seek to account for these biases while maintaining that mental credences are fundamentally probabilistic. These theories fit quite differently into the larger project of Bayesian cognitive science, but their many similarities complicate comparisons of their predictive accuracy. In particular, comparing the models demands a careful accounting of model complexity. Here, I cast these theories into a Bayesian data analysis framework that supports principled model comparison using information criteria. Comparing the fits of both models on data collected by Zhu and colleagues (2020) I find the data are best explained by a modified version of the Bayesian Sampler model under which people may hold informative priors about probabilities.

Is a non-evolutionary psychology possible?

The last thirty years has seen the emergence of a self-styled ‘evolutionary’ paradigm within psychology (henceforth, EP). EP is often presented and critiqued as a distinctive, contentious paradigm, to be contrasted with other accounts of human psychology. However, little attention has been paid to the sense in which those other accounts are not evolutionary, or at least evolutionalizable. We distinguish between a commitment to evolution, and a more specific commitment to adaptationism. We argue that all formulable accounts of human psychology are evolutionary in a real sense: non-evolutionary psychology is impossible. Not all psychologies are explicitly adaptationist, but those that are not still draw on informal notions of organismal function, and thus implicitly require at least a weak version of adaptationism. We argue that the really distinctive and contentious feature of EP is not its commitment to evolution, or even adaptationism. It is the commitment to domain-specificity and the associated multiplicity of innately specialized psychological mechanisms. This commitment entails a narrow parsing of what an adaptive problem is, and has the consequence that the science of psychology ends up consisting of many narrow proximal explanations, rather than a few broad ones. We illustrate this thesis by examining a range of paradigms that can be seen as competitors to canonical EP: social role theory; cultural evolutionary psychology and dual inheritance theory; Bayesian cognitive science; and Giddens’ social theory. Narrow versus broad functional specialization emerges as the central locus of difference between the different psychologies we review.

Probabilistic Biases Meet the Bayesian Brain

In Bayesian cognitive science, the mind is seen as a spectacular probabilistic-inference machine. But judgment and decision-making (JDM) researchers have spent half a century uncovering how dramatically and systematically people depart from rational norms. In this article, we outline recent research that opens up the possibility of an unexpected reconciliation. The key hypothesis is that the brain neither represents nor calculates with probabilities but approximates probabilistic calculations by drawing samples from memory or mental simulation. Sampling models diverge from perfect probabilistic calculations in ways that capture many classic JDM findings, which offers the hope of an integrated explanation of classic heuristics and biases, including availability, representativeness, and anchoring and adjustment.

New Paradigms in the Psychology of Reasoning

The psychology of verbal reasoning initially compared performance with classical logic. In the last 25 years, a new paradigm has arisen, which focuses on knowledge-rich reasoning for communication and persuasion and is typically modeled using Bayesian probability theory rather than logic. This paradigm provides a new perspective on argumentation, explaining the rational persuasiveness of arguments that are logical fallacies. It also helps explain how and why people stray from logic when given deductive reasoning tasks. What appear to be erroneous responses, when compared against logic, often turn out to be rationally justified when seen in the richer rational framework of the new paradigm. Moreover, the same approach extends naturally to inductive reasoning tasks, in which people extrapolate beyond the data they are given and logic does not readily apply. We outline links between social and individual reasoning and set recent developments in the psychology of reasoning in the wider context of Bayesian cognitive science.

The imaginary fundamentalists: The unshocking truth about Bayesian cognitive science

If Bayesian Fundamentalism existed, Jones & Love's (J&L's) arguments would provide a necessary corrective. But it does not. Bayesian cognitive science is deeply concerned with characterizing algorithms and representations, and, ultimately, implementations in neural circuits; it pays close attention to environmental structure and the constraints of behavioral data, when available; and it rigorously compares multiple models, both within and across papers. J&L's recommendation of Bayesian Enlightenment corresponds to past, present, and, we hope, future practice in Bayesian cognitive science.