Richard Stöckle-Schobel

Conditionals are basic for human reasoning. In our paper, we present two experiments, which for the first time systematically compare how people reason about indicative conditionals (Experiment 1) and counterfactual conditionals (Experiment 2) in causal and non-causal task settings (N = 80). The main result of both experiments is that conditional probability is the dominant response pattern and thus a key ingredient for modeling causal, indicative, and counterfactual conditionals. In the paper, we will give an overview of the main experimental results and discuss their relevance for understanding how people reason about conditionals

In my thesis, I argue that the philosophical and psychological study of concept-learning mechanisms has failed to take the diversity of learning mechanisms into account, and that consequently researchers should embrace a new way of thinking about concept learning: `concept learning' as a class of psychological mechanisms is not a natural kind lending itself to unified study and should be eliminated. To arrive at this, I discuss several concept-learning models that attempt to overcome Jerry Fodor's challenge and base my judgement on the plurality of feasible concept-learning mechanisms and on criteria for theoretical notions from the philosophy of science. Chapter 1 serves as an introduction to the topic `concept learning' and highlights its importance as a research topic in the study of the mind. I argue that a mechanistic understanding of the shape of concept learning is best suited to explain the phenomena, in line with the recent resurgence of mechanism-based explanation in the philosophy of mind. As the main challenge to the idea that concepts can be learnt, I proceed to set up Fodor's challenge for concept learning in Chapter 2. This challenge is the idea that concepts cannot be learnt given the logically possible mechanisms of concept learning. I lay out the argumentative structure and background assumptions that support Fodor's argument, and propose to scrutinise his empirically based premise most closely in my thesis: this empirically based premise is that the only possible mechanism of concept learning is the process of forming and testing hypotheses. As replies to Fodor's challenge, I discuss Perceptual Learning, Perceptual Meaning Analysis, Quinean Bootstrapping, pattern-governed learning, joint-attentional learning, and the Syndrome-Based Sustaining Mechanism Model. I argue that almost every mechanism I discuss has some leverage against Fodors argument, suggesting that there may be a wide variety of non-hypothesis-based concept-learning mechanisms. The final chapter of my thesis, Chapter 7, takes a step back and reviews the fate of the notion of concept learning in light of the diverse set of learning mechanisms brought up in my thesis. My first and main worry is that it is questionable whether the previously discussed mechanisms of concept learning share many scientifically relevant properties that would justify seeing them as instances of the natural kind 'concept learning mechanism'. I argue that the substantiation of this worry would necessitate the elimination of 'concept learning' and 'concept-learning mechanism' as terms of the cognitive sciences. The chapter lays out the argumentative structure on which Concept Learning Eliminativism rests, along with a discussion of questions about natural kinds and pragmatics in theory construction. This is inspired by Edouard Machery's argument for the elimination of 'concept', but independent of Machery's own project. With this in place, I go on to give a conclusive argument that supports CLE, based on the claims that 'concept learning' is not a natural kind and that there are pragmatic advantages to eliminating 'concept learning'. In this final chapter, I also raise pragmatic considerations that support the argument for CLE, and propose new research directions that could pro t from the eliminativist position.