Postdoctoral Research:

Project: From Spins to Birds to Traders: Model Adaptation in the Study of Critical Behavior (MACBeh) - Funded by The European Research Council (ERC)

PI: Patricia Palacios

Project description: Is there a connection between flocking starlings and human opinion polarisation? Scientists propose that collective behaviours in nature follow principles of criticality. While applications in biology and economics have increased, the use of these principles in other fields remains unclear. The ERC-funded MACBeh project will explore what elements of physics are applied to other sciences, what contributes to the predictive power of specific non-physical models, and how to improve model-building practices in criticality research.

Project webpage: https://cordis.europa.eu/project/id/101220703

Peer-reviewed publications:

Lauman-Lairson, J. A middle-range account of analogical inference: Target Domain Salience. Synthese 206, 42 (2025). https://doi.org/10.1007/s11229-025-05136-x

Under review:

Reverse Analogical Inference: Gerrymandering the Source Domain [Under review at Synthese]

I identify a form of analogical inference, which I call reverse analogical inference, that is not recognized in the analogical inference literature. Under current influential accounts, an analogical inference’s plausibility is determined by it having (1) a meaningful representation in the source domain and (2) relevant similarities between the source and target domain [Hesse 1966, Bartha 2009, Gentner 1997].

However, this results in a puzzle. The degree of similarity between source and target domain is determined by how the source and target domain are represented, and in cases of underdetermination there are multiple possible ways for the source and target domain to be represented. Thus, there are two possible ways of interpreting dissimilarity when evaluating an analogical inference. First, dissimilarity between source and target domain could indicate that the analogical inference is implausible (i.e. that the further feature of the source domain cannot be generalized to the target domain). This first type of interpretation is well discussed in the analogical inference literature. Alternatively, dissimilarity could be taken as justification for altering the representation in the source or target domain in order to increase the degree of similarity between source and target domain. This latter type of interpretation has not been examined in the analogical inference literature.

Using examples from microbiology and chemistry, I demonstrate instances where scientists respond to a lack of similarity between source and target domain in this latter way—not by questioning the plausibility of the analogical inference, but by altering the representation in the source domain. This gerrymandering of the source domain, which I call reverse analogical inference, can entail reevaluating what level of description is meaningful, what causal relations are important, or what the boundaries of different entities are. I provide a positive account that bridges the gap between the two-dimensional and empirical accounts, and includes criteria for reverse analogical inferences.

Works in Progress:

The Role of Cognitive Heuristics in Scientific Similarity Judgments [In preparation]

Psychology research shows that human similarity judgments are constructive and often non-veridical; innate cognitive heuristics systematically distort our perception of similarity in ways that were adaptive in our evolutionary history but can be unreliable for scientific inquiry. For instance, when presented with two stimuli, A and B, agents demonstrate asymmetric similarity judgments where A is judged to be more similar to B than B is to A, or vice versa, depending on the order or frequency at which A and B are shown to agents [Polk et al. 2002]. Evolved heuristics are integral to human reasoning but can contribute to error in contexts that diverge from the evolutionary problems they evolved to solve, such as in scientific inquiry.

Scientists are not immune to these cognitive heuristics; for instance, scientists overweight surface-level similarities when causal features are less perceptually salient, and underweight similarities that contradict socially learned rules, treating those resemblances as noise rather than evidence. This generates a puzzle. Scientific reasoning depends on appropriate similarity judgments, yet psychology research suggests that these judgments are shaped by heuristics that are not reliably truth-tracking. Cognitive heuristics that evolved to support adaptive behavior under uncertainty may introduce epistemic risk in scientific contexts that diverge from the environments in which they evolved. Scientists develop local norms for relevant similarity within their domains, which can incidentally modulate the effects of evolved cognitive heuristics, but these measures are piecemeal and insufficiently informed by empirical research on evolved similarity heuristics. Thus, much of the potential for targeted intervention on this source of bias remains untapped.

This paper analyses two case studies in the life sciences and argues that these demonstrate instances in which cognitive heuristics shaped scientists' similarity judgments, contributing to failed analogies and entrenched research programs. The paper lays the groundwork for a pluralistic epistemic toolkit that would provide empirically informed guidelines for refining local norms for relevant similarity. This project departs from standard debiasing approaches, which aim to reduce or correct biases in reasoning. Instead, I develop recommendations for restructuring scientific inquiry so that the similarity heuristics scientists inevitably use are situated in epistemic environments where they tend to yield reliable inferences.