Searcharxiv⌕ Search

arXiv · 2610.28357

What I Disbelieve: Quantifying Belief With Negative Ranking Functions

Abstract

A challenge in exploring the psychology of human uncertainty is its measurement and quantification. Prior work has focused almost exclusively on probability, while the formal theories developed in philosophy are rarely tested. We tested Wolfgang Spohn's ranking theory, a normative account in which belief is graded by ordinal ranks of disbelief that combine by minimization rather than addition, as a descriptive model of human belief. Across six experiments, participants assigned ranks of disbelief to propositions involving personal experience, chance devices, sequential learning, and an Ellsberg urn. Ranks were reliable and, through the theory's two-sided rank function, distinguished belief, disbelief, and suspension of judgment. Participants respected the Law of Negation, never disbelieving both a proposition and its complement, in every task except a lottery. Disjunctions did not follow the theory's Law of Disjunction. Instead of taking the minimum rank of the disjuncts, participants averaged them. In a learning task, ranks aligned more closely than did subjective probabilities with objective probabilities. In an Ellsberg Urn scenario, the paradoxical behavior seen with betting was markedly reduced by ranks. We conclude that human belief has the bipolar, negation-respecting structure that ranking theory proposes, but combines disjuncts by averaging rather than by minimization. As a clear account of human belief dynamics with metrics that can be reliably measured, Ranking Theory provides a valuable framework for investigating and contrasting probabilistic and non-probabilistic accounts of human belief.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Hanbin Go, Britt Anderson. 2026-10-20. What I Disbelieve: Quantifying Belief With Negative Ranking Functions. https://arxiv.org/abs/2610.28357

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Population Neuroscience 3.0: Bridging large-N and small-N paradigms in brain-wide association studies

Brain-wide association studies (BWAS) leveraging large datasets, have led to divergent and sometimes opposing views on research practices and priorities. The debates span multiple dimensions, including large-N vs.\ small-N sampling, cross-sectional vs.\ longitudinal, observational vs.\ interventional/causal, population generalizability vs.\ individual specificity, data-driven vs.\ hypothesis-driven, prediction-oriented vs.\ explanation-oriented, and molecular/cellular vs.\ systems-level. Taken together, these tensions are not only about methodology, but also about how large-N population studies and small-N, mechanism-focused studies should relate to one another. Shifts along these axes have sharpened disagreements and added to fragmentation in an already heterogeneous field of cognitive neuroscience. Here, we sketch an integrative response grounded in population neuroscience, organized around a closed-loop ``design--analysis--interpretation'' research cycle that aims to build consensus while bridging these divides. Our central claim is that population neuroscience offers a distinctive population-level vantage point for identifying general patterns, characterizing inter-individual variabilities, and benchmarking intra-individual changes, thereby providing reference frameworks through which small-N, mechanism-focused studies can co-evolve with large-N population studies. Population neuroscience is not simply about providing larger N for BWAS; more fundamentally, it is about accumulating cross-scale priors and shared infrastructures for design, analysis, and interpretation across human neuroscience studies. In this sense, we outline a ``third-generation'' agenda for population neuroscience that shifts emphasis from amassing isolated associations to building reference frameworks for cumulative mechanistic and translational research.

q-bio.NC↗

Bootstrapping Life-Inspired Machine Intelligence: The Biological Route from Chemistry to Cognition and Creativity

Achieving advanced machine intelligence remains a central challenge in AI research, often approached through scaling neural architectures and generative models. However, biological systems offer a broader repertoire of strategies for adaptive, goal-directed behavior - strategies that emerged long before nervous systems evolved. This paper advocates a genuinely life-inspired approach to machine intelligence, drawing on principles from biology that enable robustness, autonomy, and open-ended problem-solving across scales. We frame intelligence as flexible problem-solving, following William James, and develop the concept of "cognitive light cones" to characterize the continuum of intelligence in living systems and machines. We argue that biological evolution has discovered a scalable recipe for intelligence - and the progressive expansion of organisms' "cognitive light cone", predictive and control capacities. To explain how this is possible, we distill five design principles - multiscale autonomy, growth through self-assemblage of active components, continuous reconstruction of capabilities, exploitation of physical and embodied constraints, and pervasive signaling enabling self-organization and top-down control from goals - that underpin life's ability to navigate creatively diverse problem spaces. We discuss how these principles contrast with current AI paradigms and outline pathways for integrating them into future autonomous, embodied, and resilient artificial systems.

q-bio.NC↗

Predictive and feedback signals differently shape the formation of language representations

Adults vary greatly in how effectively they learn a new language, but the signals driving the learning processes and individual differences remain unclear. Over seven days, we tracked behavioral learning and collected fMRI data on day 1 from 102 adults as they learned an artificial language with corrective feedback. We trained matched transformer models to predict upcoming linguistic elements, learn grammaticality from corrective supervision or reinforcement-like reward signals in the feedback, and compared their internal representations with human brain activity. Representations derived from the prediction-focused model accounted for the largest share of unique neural variance at the group level, despite the human task being feedback-based. Across model-training stages, alignment between the predictive model and neural representations shifted from sensory to higher-order language and associative networks, indicating a model abstraction process. Conversely, neural patterns related to the feedback model were most useful for predicting individuals' future generalization outcomes on Day 7. Reward signals did not significantly account for learning variance of either type. These findings support a multi-signal model of adult language learning, in which prediction shapes a common neural learning architecture across learners, whereas feedback-related mechanisms better explain individual differences over time.

q-bio.NC↗