Summary
In this tutorial, we introduce a novel way to represent the structure of theories of consciousness and their predictions. We translate claims made by theories of consciousness into “prediction maps”. Our approach builds on i) the assumption that scientific theories can be represented as networks of beliefs; and ii) the Lakatosian standpoint that theories are structured around core claims and various belts of peripheral hypotheses. This helps visualize how impactful a prediction is for the theory: the closer the prediction is to the core, the more (dis)confirmatory power it will carry. Such a visualization can be used to assess the impact of theory testing, as well as to help define the proper target of investigation when designing experiments to test theories.
In our tutorial, we will first introduce the main philosophical assumptions driving the project, and then practically show how these can be applied to specific neuroscientific theories of consciousness. Furthermore, we will present how graph theory measures can be leveraged to individuate the most influential claims of the theories. Finally, we will examine some limitations of our current framework and avenues for future research.
This tutorial sits nicely at the intersection between philosophy of science and the empirical study of consciousness, reflecting the genuine interdisciplinary nature of ASSC. The tutorial will be of interest to both scholars interested in the theoretical aspects of consciousness science and those interested in its empirical study.
Rationale on speaker selection and proof of their expertise
For the past year, our team has been working on a project to individuate and structurally represent four neuroscientific theories of consciousness: Global Neuronal Workspace Theory (GNWT), Integrated Information Theory (IIT), Recurrent Processing Theory (RPT), and Higher-order Theories (HOTs). The project is led by Niccolò Negro, whose doctoral work focused on the philosophy of consciousness science, analysing in particular IIT from a philosophy of science perspective. This project builds on his paper “(Dis)confirming theories of consciousness and their predictions: towards a Lakatosian consciousness science”, published in Neuroscience of Consciousness.
This work is supervised by Prof. Liad Mudrik. Prof. Mudrik has been co-leading the first adversarial collaboration between IIT and GNWT. This puts her in a unique position to convey the importance of this work for empirical investigations into consciousness theories.
The other three members of the project, Eden Elbaz, Shai Fischer, and Maor Schreiber, are PhD students in Prof. Mudrik’s lab and have been playing a key role in shaping the course of the project and in designing the prediction maps for RPT, GNWT, and HOTs respectively. This required reading the relevant literature, cross-checking different works spanning decades of research, and extracting the structural claims of each theory while individuating their logical relationships with empirical predictions. Their expertise in practically building the predictions maps for the theories is an invaluable asset of this tutorial, since they can explain in detail how to translate a theory into a “prediction map”, as well as the specific challenges in doing so.
Desired educational expectations
In this tutorial, we plan to instruct the audience on how to approach theories of consciousness from a different angle. We expect participants to learn how to extract the crucial theoretical constructs of theories of consciousness, how they interact with various peripheral hypotheses, and how they are used to derive critical predictions. A further component of our framework is the distinction between different levels of periphery around the core. We expect attendees to learn how to assign meaning to each of these levels, and critically evaluate whether our proposed framework is the best way to represent theories of consciousness. Doing this helps determine the specific position each theoretical claim should take in the overall network. After this exercise, attendees will have all the key ingredients to build a prediction map for a given theory of consciousness. We believe this is a valuable result, as it pushes consciousness scholars to consider several questions that speak to both theoretical and empirical aspects of consciousness science, such as: what counts as a central claim of the theory, and which claims could instead be revised without major modifications to the overall theoretical structure? What is the logical relationship between theoretical claims? And what is the logical relationship between theoretical claims and empirical predictions?
This tutorial offers a novel tool to navigate these intricate issues.
Proposed audience engagement
In the tutorial, we will include an extensive interactive and dialogical component. We will reserve enough time for questions at the end of each presentation, and have two hands-on sessions in which participants will construct their own maps and analyze them. After presenting the philosophical foundations of our project, we will provide a step-by-step guide on how to build a prediction map for a theory of consciousness. After that, we will ask attendees to create a map for the theory of consciousness they are more familiar with. We will divide the room in groups, with each attendee joining the group working on their preferred theory (it is possible that there will be more groups working on the same theory). We will then ask each group to present their map. Participants will have the opportunity to share their thoughts on the map building process and on the specific challenges they faced.
The second hands-on section will involve participants computing various graph theory measures (e.g., centrality). Attendees will be instructed on how to compute centrality measures with Python, and will be able to do so in real time.
Planned structure
The tutorial will be divided into six sections, described in more details below:
Section 1
Introduction; 25 min (~20 + Q&A)
We will present the rationale of the project, why it is important for consciousness science, and its philosophical foundations.
Section 2
A recipe for prediction maps construction; 25 min (~20 + Q&A)
We will present how we practically built a prediction map, and we provide a step-by-step guide on how to do so.
Section 3
Participants practice and feedback; 1h
Attendees will be able to apply what they learnt in the previous section. They will work in groups to build a map of a theory of consciousness of their choice, and they will then share their results.
Section 4
Introduction to graph theory measures; 15 min (~10 min + Q&A)
We will give a brief overview of what graph theory measures are and why they are important in scoring the degree of influence of each claim for the overall theoretical network. Participants will be instructed on how to practically compute various measures.
Section 5
Participants practice on graph theory measures ;15 min
Attendees will be able to apply what they learnt about graph theory measures to a specific prediction map.
Section 6
Limitations, future directions, and general discussion; 30 min
We will examine some limitations of the current framework and we will offer some insights on how this research could develop and benefit consciousness science at large.
Rationale on panel inclusivity
Our research group is a diverse and inclusive team that brings together different expertise. Out of five members, three self-identify as males and two as females. The team includes Prof. Liad Mudrik, alongside one postdoctoral researcher and three PhD-level young researchers. We represent different cultural backgrounds, with one Italian member and four Israelis from various ethnicities. Academically, our group spans a broad range of disciplines, including philosophy, psychology, and computational neuroscience. Such diversity has only strengthened the cohesiveness and efficiency of our team; in fact, approaching research questions from multiple perspectives has greatly enriched our collaborative work.
