The uses and abuses of the coherence – correspondence distinction

Kenneth Hammond introduced a distinction between coherence and correspondence criteria of rationality as a tool in the study of judgment and decision-making. This distinction has been widely used in the field. Yet, as this paper seeks to show, the relevant notions of coherence and correspondence have been progressively considered to be too narrow and have undergone non-trivial conceptual changes since their original introduction. I try to show, first, that the proliferation of conceptualizations of coherence and correspondence has created confusion in the literature and that appealing to such notions has not helped to elucidate discussions over the nature of rational judgment and decision-making. Nevertheless, I also argue for a reframing of the debate. In fact, what seems to underlie several contemporary appeals to the notions of coherence and correspondence is best explained in terms of a contrast between what I call here rule-based and goal-based rationality. Whilst these categories do need further refinement, they do seem to be useful in organizing and understanding research on rational judgment and decision-making

Is the Brain an Organ for Prediction Error Minimization?

An influential body of research in neuroscience and the philosophy of mind asserts that the brain is an organ for prediction error minimization. I clarify how this hypothesis should be understood, and I consider a prominent attempt to justify it, according to which prediction error minimization in the brain is a manifestation of a more fundamental imperative in all self-organizing systems to minimize (variational) free energy. I argue that this justification fails. The sense in which all self-organizing systems can be said to minimize free energy according to the free energy principle is fundamentally different from the alleged sense in which brains minimize prediction error. Thus, even if the free energy principle is true, it provides no support for a theory of the brain as an organ for prediction error minimization – or any other substantive theory of brain function

First principles in the life sciences: The free-energy principle, organicism, and mechanism

The free-energy principle claims that biological systems behave adaptively maintaining their physical integrity only if they minimize the free energy of their sensory states. Originally proposed to account for perception, learning, and action, the free-energy principle has been applied to the evolution, development, morphology, and function of the brain, and has been called a “postulate,” a “mandatory principle,” and an “imperative.” While it might afford a theoretical foundation for understanding the complex relationship between physical environment, life, and mind, its epistemic status and scope are unclear. Also unclear is how the free-energy principle relates to prominent theoretical approaches to life science phenomena, such as organicism and mechanicism. This paper clarifies both issues, and identifies limits and prospects for the free-energy principle as a first principle in the life sciences

Reasoning with conditionals

This paper reviews the psychological investigation of reasoning with conditionals, putting an emphasis on recent work. In the first part, a few methodological remarks are presented. In the second part, the main theories of deductive reasoning (mental rules, mental models, and the probabilistic approach) are considered in turn; their content is summarised and the semantics they assume for if and the way they explain formal conditional reasoning are discussed, in particular in the light of experimental work on the probability of conditionals. The last part presents the recent shift of interest towards the study of conditional reasoning in context, that is, with large knowledge bases and uncertain premises

Deep Analogical Inference as the Origin of Hypotheses

The ability to generate novel hypotheses is an important problem-solving capacity of humans. This ability is vital for making sense of the complex and unfamiliar world we live in. Often, this capacity is characterized as an inference to the best explanation—selecting the “best” explanation from a given set of candidate hypotheses. However, it remains unclear where these candidate hypotheses originate from. In this paper we contribute to computationally explaining these origins by providing the contours of the computational problem solved when humans generate hypotheses. The origin of hypotheses, otherwise known as abduction proper, is hallmarked by seven properties: (1) isotropy, (2) open-endedness, (3) novelty, (4) groundedness, (5) sensibility, (6) psychological realism, and (7) computational tractability. In this paper we provide a computational-level theory of abduction proper that unifies the first six of these properties and lays the groundwork for the seventh property of computational tractability. We conjecture that abduction proper is best seen as a process of deep analogical inference

Individual Differences in Cognitive Science: Conceptual and Methodological Issues

A primary aim of cognitive science is the investigation of psychological and neuroscientific generalizations that hold across subjects. Individual differences between people’s minds and brains are pervasive, however, even among subjects considered neurotypical. In this dissertation, I argue that both scientific practice and our philosophical understanding of science must be updated to reflect the presence of such individual differences. The first half of the dissertation proposes and applies a philosophical account of what it takes to explain variation, while the second half identifies several methods in psychology and neuroscience that demand reform in light of existing individual differences

Rethinking Rationality

We seek to understand rational decision making and if it exists whether finite (bounded) agents may be able to achieve its principles. This aim has been a singular objective throughout much of human science and philosophy, with early discussions identified since antiquity. More recently, there has been a thriving debate based on differing perspectives on rationality, including adaptive heuristics, Bayesian theory, quantum theory, resource rationality, and probabilistic language of thought. Are these perspectives on rationality mutually exclusive? Are they all needed? Do they undermine an aim to have rational standards in decision situations like politics, medicine, legal proceedings, and others, where there is an expectation and need for decision making as close to “optimal” as possible? This special issue brings together representative contributions from the currently predominant views on rationality, with a view to evaluate progress on these and related questions