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The Principle-at-Risk Analysis (PaRA): Operationalising Digital Ethics by Bridging Principles and Operations of a Digital Ethics Advisory Panel Minds Mach. (IF 7.4) Pub Date : 2023-12-18 André T. Nemat, Sarah J. Becker, Simon Lucas, Sean Thomas, Isabel Gadea, Jean Enno Charton
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The Role of Naturalness in Concept Learning: A Computational Study Minds Mach. (IF 7.4) Pub Date : 2023-11-29 Igor Douven
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Encoding Ethics to Compute Value-Aligned Norms Minds Mach. (IF 7.4) Pub Date : 2023-11-22 Marc Serramia, Manel Rodriguez-Soto, Maite Lopez-Sanchez, Juan A. Rodriguez-Aguilar, Filippo Bistaffa, Paula Boddington, Michael Wooldridge, Carlos Ansotegui
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A Pragmatic Theory of Computational Artefacts Minds Mach. (IF 7.4) Pub Date : 2023-11-22 Alessandro G. Buda, Giuseppe Primiero
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Democratizing AI from a Sociotechnical Perspective Minds Mach. (IF 7.4) Pub Date : 2023-11-18 Merel Noorman, Tsjalling Swierstra
Artificial Intelligence (AI) technologies offer new ways of conducting decision-making tasks that influence the daily lives of citizens, such as coordinating traffic, energy distributions, and crowd flows. They can sort, rank, and prioritize the distribution of fines or public funds and resources. Many of the changes that AI technologies promise to bring to such tasks pertain to decisions that are
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Online Altruism: What it is and how it Differs from Other Kinds of Altruism Minds Mach. (IF 7.4) Pub Date : 2023-10-28 Katherine Lou, Luciano Floridi
Altruism is a well-studied phenomenon in the social sciences, but online altruism has received relatively little attention. In this article, we examine several cases of online altruism, and analyse the key characteristics of the phenomenon, in particular comparing and contrasting it against models of traditional donor behaviour. We suggest a novel definition of online altruism, and provide an in-depth
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The Ethics of Online Controlled Experiments (A/B Testing) Minds Mach. (IF 7.4) Pub Date : 2023-09-22 Andrea Polonioli, Riccardo Ghioni, Ciro Greco, Prathm Juneja, Jacopo Tagliabue, David Watson, Luciano Floridi
Online controlled experiments, also known as A/B tests, have become ubiquitous. While many practical challenges in running experiments at scale have been thoroughly discussed, the ethical dimension of A/B testing has been neglected. This article fills this gap in the literature by introducing a new, soft ethics and governance framework that explicitly recognizes how the rise of an experimentation culture
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Yet Another Impossibility Theorem in Algorithmic Fairness Minds Mach. (IF 7.4) Pub Date : 2023-09-08 Fabian Beigang
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Natural and Artificial Intelligence: A Comparative Analysis of Cognitive Aspects Minds Mach. (IF 7.4) Pub Date : 2023-09-07 Francesco Abbate
Moving from a behavioral definition of intelligence, which describes it as the ability to adapt to the surrounding environment and deal effectively with new situations (Anastasi, 1986), this paper explains to what extent the performance obtained by ChatGPT in the linguistic domain can be considered as intelligent behavior and to what extent they cannot. It also explains in what sense the hypothesis
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Avatars as Proxies Minds Mach. (IF 7.4) Pub Date : 2023-07-26 Paula Sweeney
Avatars will represent us online, in virtual worlds, and in technologically supported hybrid environments. We and our avatars will stand not in an identity relation but in a proxy relation, an arrangement that is significant not least because our proxies’ actions can be counted as our own. However, this proxy relation between humans and avatars is not well understood and its consequences under-explored
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Can Deep CNNs Avoid Infinite Regress/Circularity in Content Constitution? Minds Mach. (IF 7.4) Pub Date : 2023-07-17 Jesse Lopes
The representations of deep convolutional neural networks (CNNs) are formed from generalizing similarities and abstracting from differences in the manner of the empiricist theory of abstraction (Buckner, Synthese 195:5339–5372, 2018). The empiricist theory of abstraction is well understood to entail infinite regress and circularity in content constitution (Husserl, Logical Investigations. Routledge
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The Non-theory-driven Character of Computer Simulations and Their Role as Exploratory Strategies Minds Mach. (IF 7.4) Pub Date : 2023-07-11 Juan M. Durán
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Reasoning with Concepts: A Unifying Framework Minds Mach. (IF 7.4) Pub Date : 2023-07-11 Peter Gärdenfors, Matías Osta-Vélez
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An Alternative to Cognitivism: Computational Phenomenology for Deep Learning Minds Mach. (IF 7.4) Pub Date : 2023-06-29 Pierre Beckmann, Guillaume Köstner, Inês Hipólito
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Machines Learn Better with Better Data Ontology: Lessons from Philosophy of Induction and Machine Learning Practice Minds Mach. (IF 7.4) Pub Date : 2023-06-20 Dan Li
As scientists start to adopt machine learning (ML) as one research tool, the security of ML and the knowledge generated become a concern. In this paper, I explain how supervised ML can be improved with better data ontology, or the way we make categories and turn information into data. More specifically, we should design data ontology in such a way that is consistent with the knowledge that we have
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True Turing: A Bird’s-Eye View Minds Mach. (IF 7.4) Pub Date : 2023-06-19 Edgar Daylight
Alan Turing is often portrayed as a materialist in secondary literature. In the present article, I suggest that Turing was instead an idealist, inspired by Cambridge scholars, Arthur Eddington, Ernest Hobson, James Jeans and John McTaggart. I outline Turing’s developing thoughts and his legacy in the USA to date. Specifically, I contrast Turing’s two notions of computability (both from 1936) and distinguish
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Explainable AI and Causal Understanding: Counterfactual Approaches Considered Minds Mach. (IF 7.4) Pub Date : 2023-06-09 Sam Baron
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The Puzzling Resilience of Multiple Realization Minds Mach. (IF 7.4) Pub Date : 2023-06-03 Thomas W. Polger, Lawrence A. Shapiro
According to the multiple realization argument, mental states or processes can be realized in diverse and heterogeneous physical systems; and that fact implies that mental state or process kinds cannot be identified with particular kinds of physical states or processes. More specifically, mental processes cannot be identified with brain processes. Moreover, the argument provides a general model for
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Developing Artificial Human-Like Arithmetical Intelligence (and Why) Minds Mach. (IF 7.4) Pub Date : 2023-05-22 Markus Pantsar
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The Role of A Priori Belief in the Design and Analysis of Fault-Tolerant Distributed Systems Minds Mach. (IF 7.4) Pub Date : 2023-04-17 Giorgio Cignarale, Ulrich Schmid, Tuomas Tahko, Roman Kuznets
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From Monitors to Monitors: A Primitive History Minds Mach. (IF 7.4) Pub Date : 2023-04-05 Troy K. Astarte
As computers became multi-component systems in the 1950s, handling the speed differentials efficiently was identified as a major challenge. The desire for better understanding and control of ‘concurrency’ spread into hardware, software, and formalism. This paper examines the way in which the problem emerged and was handled across various computing cultures from 1955 to 1985. In the machinic culture
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A Dilemma for Dispositional Answers to Kripkenstein’s Challenge Minds Mach. (IF 7.4) Pub Date : 2023-03-27 Andrea Guardo
Kripkenstein’s challenge is usually described as being essentially about the use of a word in new kinds of cases ‒ the old kinds of cases being commonly considered as non-problematic. I show that this way of conceiving the challenge is neither true to Kripke’s intentions nor philosophically defensible: the Kripkean skeptic can question my answering “125” to the question “What is 68 plus 57?” even if
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Informational Equivalence but Computational Differences? Herbert Simon on Representations in Scientific Practice Minds Mach. (IF 7.4) Pub Date : 2023-03-17 David Waszek
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Autonomous Force Beyond Armed Conflict Minds Mach. (IF 7.4) Pub Date : 2023-03-13 Alexander Blanchard
Proposals by the San Francisco Police Department (SFPD) to use bomb disposal robots for deadly force against humans have met with widespread condemnation. Media coverage of the furore has tended, incorrectly, to conflate these robots with autonomous weapon systems (AWS), the AI-based weapons used in armed conflict. These two types of systems should be treated as distinct since they have different sets
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Computers as Interactive Machines: Can We Build an Explanatory Abstraction? Minds Mach. (IF 7.4) Pub Date : 2023-03-11 Alice Martin, Mathieu Magnaudet, Stéphane Conversy
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Attitudinal Tensions in the Joint Pursuit of Explainable and Trusted AI Minds Mach. (IF 7.4) Pub Date : 2023-02-26 Devesh Narayanan, Zhi Ming Tan
It is frequently demanded that AI-based Decision Support Tools (AI-DSTs) ought to be both explainable to, and trusted by, those who use them. The joint pursuit of these two principles is ordinarily believed to be uncontroversial. In fact, a common view is that AI systems should be made explainable so that they can be trusted, and in turn, accepted by decision-makers. However, the moral scope of these
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Leibniz and the Stocking Frame: Computation, Weaving and Knitting in the 17th Century Minds Mach. (IF 7.4) Pub Date : 2023-02-17 Michael Friedman
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The Blueprint for an AI Bill of Rights: In Search of Enaction, at Risk of Inaction Minds Mach. (IF 7.4) Pub Date : 2023-01-29 Emmie Hine, Luciano Floridi
The US is promoting a new vision of a “Good AI Society” through its recent AI Bill of Rights. This offers a promising vision of community-oriented equity unique amongst peer countries. However, it leaves the door open for potential rights violations. Furthermore, it may have some federal impact, but it is non-binding, and without concrete legislation, the private sector is likely to ignore it.
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Three Early Formal Approaches to the Verification of Concurrent Programs Minds Mach. (IF 7.4) Pub Date : 2023-01-23 Cliff B. Jones
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Grounding the Vector Space of an Octopus: Word Meaning from Raw Text Minds Mach. (IF 7.4) Pub Date : 2023-01-23 Anders Søgaard
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Enactivism Meets Mechanism: Tensions & Congruities in Cognitive Science Minds Mach. (IF 7.4) Pub Date : 2023-01-16 Jonny Lee
Enactivism advances an understanding of cognition rooted in the dynamic interaction between an embodied agent and their environment, whilst new mechanism suggests that cognition is explained by uncovering the organised components underlying cognitive capacities. On the face of it, the mechanistic model’s emphasis on localisable and decomposable mechanisms, often neural in nature, runs contrary to the
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The Switch, the Ladder, and the Matrix: Models for Classifying AI Systems Minds Mach. (IF 7.4) Pub Date : 2023-01-04 Jakob Mökander, Margi Sheth, David S. Watson, Luciano Floridi
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How a Minimal Learning Agent can Infer the Existence of Unobserved Variables in a Complex Environment Minds Mach. (IF 7.4) Pub Date : 2022-12-29 Benjamin Eva, Katja Ried, Thomas Müller, Hans J. Briegel
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A Model Solution: On the Compatibility of Predictive Processing and Embodied Cognition Minds Mach. (IF 7.4) Pub Date : 2022-11-29 Luke Kersten
Predictive processing (PP) and embodied cognition (EC) have emerged as two influential approaches within cognitive science in recent years. Not only have PP and EC been heralded as “revolutions” and “paradigm shifts” but they have motivated a number of new and interesting areas of research. This has prompted some to wonder how compatible the two views might be. This paper looks to weigh in on the issue
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The Turing Test is a Thought Experiment Minds Mach. (IF 7.4) Pub Date : 2022-11-27 Bernardo Gonçalves
The Turing test has been studied and run as a controlled experiment and found to be underspecified and poorly designed. On the other hand, it has been defended and still attracts interest as a test for true artificial intelligence (AI). Scientists and philosophers regret the test’s current status, acknowledging that the situation is at odds with the intellectual standards of Turing’s works. This article
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On the Advantages of Distinguishing Between Predictive and Allocative Fairness in Algorithmic Decision-Making Minds Mach. (IF 7.4) Pub Date : 2022-11-23 Fabian Beigang
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From Pluralistic Normative Principles to Autonomous-Agent Rules Minds Mach. (IF 7.4) Pub Date : 2022-10-29 Beverley Townsend, Colin Paterson, T. T. Arvind, Gabriel Nemirovsky, Radu Calinescu, Ana Cavalcanti, Ibrahim Habli, Alan Thomas
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Meta’s Oversight Board: A Review and Critical Assessment Minds Mach. (IF 7.4) Pub Date : 2022-10-24 David Wong, Luciano Floridi
Since the announcement and establishment of the Oversight Board (OB) by the technology company Meta as an independent institution reviewing Facebook and Instagram’s content moderation decisions, the OB has been subjected to scholarly scrutiny ranging from praise to criticism. However, there is currently no overarching framework for understanding the OB’s various strengths and weaknesses. Consequently
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The End of Vagueness: Technological Epistemicism, Surveillance Capitalism, and Explainable Artificial Intelligence Minds Mach. (IF 7.4) Pub Date : 2022-09-11 Alison Duncan Kerr, Kevin Scharp
Artificial Intelligence (AI) pervades humanity in 2022, and it is notoriously difficult to understand how certain aspects of it work. There is a movement—Explainable Artificial Intelligence (XAI)—to develop new methods for explaining the behaviours of AI systems. We aim to highlight one important philosophical significance of XAI—it has a role to play in the elimination of vagueness. To show this,
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Ethical Considerations in the Application of Artificial Intelligence to Monitor Social Media for COVID-19 Data Minds Mach. (IF 7.4) Pub Date : 2022-08-25 Lidia Flores, Sean D. Young
The COVID-19 pandemic and its related policies (e.g., stay at home and social distancing orders) have increased people’s use of digital technology, such as social media. Researchers have, in turn, utilized artificial intelligence to analyze social media data for public health surveillance. For example, through machine learning and natural language processing, they have monitored social media data to
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The US Algorithmic Accountability Act of 2022 vs. The EU Artificial Intelligence Act: what can they learn from each other? Minds Mach. (IF 7.4) Pub Date : 2022-08-18 Jakob Mökander, Prathm Juneja, David S. Watson, Luciano Floridi
On the whole, the US Algorithmic Accountability Act of 2022 (US AAA) is a pragmatic approach to balancing the benefits and risks of automated decision systems. Yet there is still room for improvement. This commentary highlights how the US AAA can both inform and learn from the European Artificial Intelligence Act (EU AIA).
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Contestable AI by Design: Towards a Framework Minds Mach. (IF 7.4) Pub Date : 2022-08-13 Kars Alfrink, Ianus Keller, Gerd Kortuem, Neelke Doorn
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Realising Meaningful Human Control Over Automated Driving Systems: A Multidisciplinary Approach Minds Mach. (IF 7.4) Pub Date : 2022-07-28 Filippo Santoni de Sio, Giulio Mecacci, Simeon Calvert, Daniel Heikoop, Marjan Hagenzieker, Bart van Arem
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Defining Explanation and Explanatory Depth in XAI Minds Mach. (IF 7.4) Pub Date : 2022-06-29 Stefan Buijsman
Explainable artificial intelligence (XAI) aims to help people understand black box algorithms, particularly of their outputs. But what are these explanations and when is one explanation better than another? The manipulationist definition of explanation from the philosophy of science offers good answers to these questions, holding that an explanation consists of a generalization that shows what happens
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Assembled Bias: Beyond Transparent Algorithmic Bias Minds Mach. (IF 7.4) Pub Date : 2022-06-18 Robyn Repko Waller, Russell L. Waller
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Ethics of Self-driving Cars: A Naturalistic Approach Minds Mach. (IF 7.4) Pub Date : 2022-06-09 Selene Arfini, Davide Spinelli, Daniele Chiffi
The potential development of self-driving cars (also known as autonomous vehicles or AVs – particularly Level 5 AVs) has called the attention of different interested parties. Yet, there are still only a few relevant international regulations on them, no emergency patterns accepted by communities and Original Equipment Manufacturers (OEMs), and no publicly accepted solutions to some of their pending
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Explainable Artificial Intelligence in Data Science Minds Mach. (IF 7.4) Pub Date : 2022-05-12 Joaquín Borrego-Díaz, Juan Galán-Páez
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From representations in predictive processing to degrees of representational features Minds Mach. (IF 7.4) Pub Date : 2022-05-03 Danaja Rutar, Wanja Wiese, Johan Kwisthout
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Playing Games with Ais: The Limits of GPT-3 and Similar Large Language Models Minds Mach. (IF 7.4) Pub Date : 2022-05-03 Adam Sobieszek, Tadeusz Price
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Correction to: What Might Machines Mean? Minds Mach. (IF 7.4) Pub Date : 2022-04-16 Mitchell Green,Jan G. Michel
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Local Explanations via Necessity and Sufficiency: Unifying Theory and Practice Minds Mach. (IF 7.4) Pub Date : 2022-03-16 David S. Watson, Limor Gultchin, Ankur Taly, Luciano Floridi
Necessity and sufficiency are the building blocks of all successful explanations. Yet despite their importance, these notions have been conceptually underdeveloped and inconsistently applied in explainable artificial intelligence (XAI), a fast-growing research area that is so far lacking in firm theoretical foundations. In this article, an expanded version of a paper originally presented at the 37th
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Scientific Exploration and Explainable Artificial Intelligence Minds Mach. (IF 7.4) Pub Date : 2022-03-10 Carlos Zednik, Hannes Boelsen
Models developed using machine learning are increasingly prevalent in scientific research. At the same time, these models are notoriously opaque. Explainable AI aims to mitigate the impact of opacity by rendering opaque models transparent. More than being just the solution to a problem, however, Explainable AI can also play an invaluable role in scientific exploration. This paper describes how post-hoc
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Simple Models in Complex Worlds: Occam’s Razor and Statistical Learning Theory Minds Mach. (IF 7.4) Pub Date : 2022-03-08 Falco J. Bargagli Stoffi, Gustavo Cevolani, Giorgio Gnecco
The idea that “simplicity is a sign of truth”, and the related “Occam’s razor” principle, stating that, all other things being equal, simpler models should be preferred to more complex ones, have been long discussed in philosophy and science. We explore these ideas in the context of supervised machine learning, namely the branch of artificial intelligence that studies algorithms which balance simplicity
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Why Indirect Harms do not Support Social Robot Rights Minds Mach. (IF 7.4) Pub Date : 2022-03-07 Paula Sweeney
There is growing evidence to support the claim that we react differently to robots than we do to other objects. In particular, we react differently to robots with which we have some form of social interaction. In this paper I critically assess the claim that, due to our tendency to become emotionally attached to social robots, permitting their harm may be damaging for society and as such we should
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Analogue Models and Universal Machines. Paradigms of Epistemic Transparency in Artificial Intelligence Minds Mach. (IF 7.4) Pub Date : 2022-03-05 Hajo Greif
The problem of epistemic opacity in Artificial Intelligence (AI) is often characterised as a problem of intransparent algorithms that give rise to intransparent models. However, the degrees of transparency of an AI model should not be taken as an absolute measure of the properties of its algorithms but of the model’s degree of intelligibility to human users. Its epistemically relevant elements are
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Correction to: (What) Can Deep Learning Contribute to Theoretical Linguistics? Minds Mach. (IF 7.4) Pub Date : 2022-03-01 Gabe Dupre
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Minds and Machines Special Issue: Machine Learning: Prediction Without Explanation? Minds Mach. (IF 7.4) Pub Date : 2022-03-01 F. J. Boge,P. Grünke,R. Hillerbrand
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Schema-Centred Unity and Process-Centred Pluralism of the Predictive Mind Minds Mach. (IF 7.4) Pub Date : 2022-02-25 Nina Poth
Proponents of the predictive processing (PP) framework often claim that one of the framework’s significant virtues is its unificatory power. What is supposedly unified are predictive processes in the mind, and these are explained in virtue of a common prediction error-minimisation (PEM) schema. In this paper, I argue against the claim that PP currently converges towards a unified explanation of cognitive