Models, Inferences, and Decisions

What We Do

Recent research in cognitive and behavioral sciences is increasingly illuminating the basic mechanisms of human reasoning and cognition, as well as their limitations and systematic deviations from normative theories of rational inference and decision-making. It also raises interesting questions concerning the foundations and methods of different scientific disciplines, and the analysis of scientific reasoning in general.

This research line puts together theoretical and formal models of inference and decision-making with empirical approaches to the study of human reasoning and cognition. The aim is twofold: to better understand, and possibly improve, how people reason and make choices in different contexts, both in ordinary life and in science; and to clarify and strengthen the methodology and foundations of cognitive, behavioral, and social sciences.

Topics we work, or plan to work, on include:


Who We Are

Principal InvestigatorAssociate Professor, PhDScholar, ResearchGate, Personal page
Assistant Professor, PhDScholar
Research Collaborator, PhD
Research Collaborator, PhD
Research Collaborator
Research Collaborator, PhD
PhD StudentPersonal page
PhD StudentPersonal page
PhD Student
PhD Student
PhD Student
PhD Student
PhD Student

Guest members

CHIARA LUCIFORAPhDResearchGate, LinkedIn

Past members

LUCA POLONIOPhDScholar, ResearchGate

What We Publish

Abductive reasoning in cognitive neuroscience: weak and strong reverse inference

Calzavarini, F.; Cevolani, G. Synthese, 2022. DOI: 10.1007/s11229-022-03585-2
Reverse inference is a crucial inferential strategy used in cognitive neuroscience to derive conclusions about the engagement of cognitive processes from patterns of brain activation. While widely employed in experimental studies, it is now viewed with increasing scepticism within the neuroscience community. One problem with reverse inference is that it is logically invalid, being an instance of abduction in Peirce’s sense. In this paper, we offer the first systematic analysis of reverse inference as a form of abductive reasoning and highlight some relevant implications for the current debate. We start by formalising an important distinction that has been entirely neglected in the literature, namely the distinction between weak (strategic) and strong (justificatory) reverse inference. Then, we rely on case studies from recent neuroscientific research to systematically discuss the role and limits of both strong and weak reverse inference; in particular, we offer the first exploration of weak reverse inference as a discovery strategy within cognitive neuroscience.

Lateral reading and monetary incentives to spot disinformation about science

Folco Panizza, Piero Ronzani, Carlo Martini, Simone Mattavelli, Tiffany Morisseau & Matteo MotterliniScientific Reports, 2022. DOI: 10.1038/s41598-022-09168-y
Disinformation about science can impose enormous economic and public health burdens. A recentlyproposed strategy to help online users recognise false content is to follow the techniques ofprofessional fact checkers, such as looking for information on other websites (lateral reading) andlooking beyond the first results suggested by search engines (click restraint). In two preregisteredonline experiments (N = 5387), we simulated a social media environment and tested twointerventions, one in the form of a pop-up meant to advise participants to follow such techniques,the other based on monetary incentives. We measured participants’ ability to identify whetherinformation was scientifically valid or invalid. Analysis of participants’ search style reveals that bothmonetary incentives and pop-up increased the use of fact-checking strategies. Monetary incentiveswere overall effective in increasing accuracy, whereas the pop-up worked when the source ofinformation was unknown. Pop-up and incentives, when used together, produced a cumulative effecton accuracy. We suggest that monetary incentives enhance content relevance, and could be combinedwith fact-checking techniques to counteract disinformation.

A Unified Model of Ad Hoc Concepts in Conceptual Spaces

Coraci, D.Minds and Machines, 2022. DOI: 10.1007/s11023-021-09586-3
Ad hoc concepts (like “clothes to wear in the snow”, for instance) are highly-context dependent representations humans construct to deal with novel or uncommon situations and to interpret linguistic stimuli in communication. In the last decades, such concepts have been investigated both in experimental cognitive psychology and within pragmatics by proponents of so-called relevance theory. These two research lines have however proceeded in parallel, proposing two unconnected strategies to account for the construction and use of ad hoc concepts. The present work explores the relations between these two approaches and the possibility of merging them into a unique account of the internal structure of ad hoc representations and of the key processes involved in their constructions. To this purpose, we first present an integrated two-level account of the construction of ad hoc representations from lexical concepts; then, we show how our account can be embedded in a conceptual space framework that allows for a natural, geometrical interpretation of the main steps in such a construction process. After discussing in detail two main examples of the construction of ad hoc concepts within conceptual spaces, we conclude with some remarks on possible extensions of our approach.

Cerebral Organoids and Biological Hybrids as New Entities in the Moral Landscape

Alice Andrea Chinaia, Andrea LavazzaAJOB Neuroscience, 2022. DOI:10.1080/21507740.2022.2048732
Commentary without abstract.

Approaching deterministic and probabilistic truth: a unified account

Cevolani, G.; Festa, R.Synthese, 2021. DOI: 10.1007/s11229-021-03298-y 
The basic problem of a theory of truth approximation is defining when a theory is “close to the truth” about some relevant domain. Existing accounts of truthlikeness or verisimilitude address this problem, but are usually limited to the problem of approaching a “deterministic” truth by means of deterministic theories. A general theory of truth approximation, however, should arguably cover also cases where either the relevant theories, or “the truth”, or both, are “probabilistic” in nature. As a step forward in this direction, we first present a general characterization of both deterministic and probabilistic truth approximation; then, we introduce a new account of verisimilitude which provides a simple formal framework to deal with such issue in a unified way. The connections of our account with some other proposals in the literature are also briefly discussed. 

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Our Talks