UQAM - Université du Québec à Montréal Faculté des sciences humaines
Cognitives Sciences Institute


The program is now available. Please click here to view the document.

Or access the program at a glance (modifications: Terrence Stewart's conference has been moved June 22nd, 4:15. Monday 27th: Noah Goodman at 11:30, Jennifer Trueblood at 14:00, discussion at 3:00.

Abstracts and suggested reading for each speakers:

Roger Azevedo

North Carolina State University
Interdisciplinary Fusion: Reasoning about Cognitive, Metacognitive, and Affective Processes used during Complex Learning with Advanced Learning Technologies (Video available)

Abstract: Learning involves the real-time deployment of cognitive, affective, metacognitive, and motivational (CAMM) processes. Traditional methods of measuring self-regulatory processes severely limit our understanding of the temporal nature and role of these processes during learning, problem solving, etc. Researchers from different disciplines have recently used advanced learning technologies (e.g., intelligent tutoring systems, multi-agent systems, serious games, augmented reality) to measure (detect, track, model) and foster self-regulatory processes during learning and problem solving. Despite the emergence of interdisciplinary research, much work is still needed given the various theoretical models and assumptions, methodological approaches (e.g., eye-tracking, log-files, concurrent think-alouds), data types (e.g., physiological data), analytical methods, etc. As such, my overall goal is to present an interdisciplinary data fusion approach to measuring and fostering metacognition with advanced learning technologies. More specifically, I will focus on: (1) presenting major conceptual, theoretical, and methodological issues for a data fusion approach that focuses on the real-time detection, tracking, and modeling of CAMM processes; (2) presenting recent data using interdisciplinary approaches that uses a multitude of techniques to detect, track, model CAMM processes while learning with advanced learning technologies; and, (3) outlining an interdisciplinary research agenda that has the potential to significantly enhance advanced learning technologies’ ability to provide real-time, individualized adaptive support of learners’ CAMM processes.

Suggested readings:
  • Azevedo, R., Taub, M., Mudrick, N.V., & Grafsgaard, J. (in press). Using multi-channel trace data to infer and foster self-regulated learning between humans and advanced learning technologies. In D. Schunk & Greene, J.A (Eds.), Handbook of self-regulation of learning and performance (2nd ed.). New York, NY: Routledge.
  • Azevedo, R., Taub, M., Mudrick, N., Farnsworth, J., & Martin, S. (in press). Using research methods to investigate emotions in computer-based learning environments. In P. Schutz & M. Zembylas (Eds.), Methodological advances in research on emotion and education. Amsterdam, The Netherlands: Springer.
  • Azevedo, R., Martin, S. A., Taub, M., Mudrick, N., Millar, G., & Grafsgaard, J. (2016). Are pedagogical agents’ external regulation effective in fostering learning with intelligent tutoring systems? Proceedings of the 13th International Conference on Intelligent Tutoring Systems (ITS 2016), Zagreb, Croatia.
  • Taub, M., Mudrick, N., Azevedo, R., Millar, G. Rowe, J., & Lester, J. (2016). Using multi-level modeling with eye-tracking data to predict metacognitive monitoring and self-regulated learning with Crystal Island. Proceedings of the 13th International Conference on Intelligent Tutoring Systems (ITS 2016), Zagreb, Croatia.
  • Winne, P.H., & Azevedo, R. (2014). Metacognition. In K. Sawyer (Ed.), Cambridge Handbook of the Learning Sciences (2nd ed.) (pp. 63-87). Cambridge, MA: Cambridge University Press.
  • Harley, J., Carter, C., Papaionnou, N., Bouchet, F., Landis, R. S., Azevedo, R., & Karabachian, L. (2016). Examining the predictive relationship between personality and emotion traits and students’ agent-directed emotions: Towards emotionally-adaptive agent-based learning environments. User Modeling and User-Adapted Interaction: The Journal of Personalization Research, 2/3, 177-219.
  • Trevors, G., Feyzi-Behnagh, R., Azevedo, R., & Bouchet, F. (2016). Self-regulated learning processes vary as a function of epistemic beliefs and contexts: Evidence from eye tracking and concurrent and retrospective reports. Learning and Instruction, 42, 31-46.
  • Azevedo, R. (2015). Defining and measuring engagement and learning in science: Conceptual, theoretical, methodological, and analytical issues. Educational Psychologist, 50, 84-94.
  • Duffy, M., & Azevedo, R. (2015). Motivation matters: Interactions between achievement goals and agent scaffolding for self-regulated learning within an intelligent tutoring system. Computers in Human Behavior, 52, 338-348.
  • Duffy, M., Azevedo, R., Sun, N.-Z., Griscom, S., Stead, V., Dhillon, I., Crelinsten, L., Wiseman, J., Maniatis, T., & Lachapelle, K. (2015). Team regulation in a simulated medical emergency: A in-depth analysis of the cognitive, metacognitive, and affective processes. Instructional Science, 43, 401-426.
  • Harley, J. M., Bouchet, F., Hussain, S., Azevedo, R., & Calvo, R. (2015). A multi-componential analysis of emotions during complex learning with an intelligent multi-agent system. Computers in Human Behavior, 48, 615-625.

Linden Ball

University of Central Lancaster, UK
Dual-Reasoning Processes and the Resolution of Uncertainty: the Case of Belief Bias (Video available)

Abstract: Many researchers studying human reasoning appeal to a distinction between two forms of processing, one fast and intuitive, the other slow and reflective. Critics have questioned the validity of this distinction in terms of its capacity to generate falsifiable predictions, although those who defend a dual-process conceptualization have pointed out that falsifiable predictions can readily be derived from task-specific models. In this presentation I assess the credibility of dual-process models of people’s reasoning with belief-laden logical arguments that give rise to belief bias – a tendency to respond in line with a conclusion’s believability rather than its logical validity. I propose that dual-process models emphasizing either the sequential progression from intuitive to reflective thinking (default-interventionist models) or the parallel operation of intuitive and reflective processes (parallel-process models) struggle to capture some aspects of belief-bias data and also run into conceptual difficulties. Such challenges have led to the recent development of hybrid sequential/parallel models, which involve an initial stage of reasoning in which intuitive heuristic and intuitive logical processes function in parallel to deliver outputs that may concur or conflict. A conflict situation engenders metacognitive uncertainty, which triggers a second stage of reflective reasoning aimed at conflict resolution and uncertainty reduction. I suggest that a key strength of such hybrid models is their ability to explain a wide variety of belief-bias data, including response rates, eye-tracking measures, decision latencies and neuroscientific findings. Current limitations, however, arise from the fact that these models embody some rather paradoxical assumptions that are still in need of rigorous testing and corroboration.

Suggested readings:
  • Ball, L. J. (2013). Eye-tracking and reasoning: What your eyes tell about your inferences. In W. De Neys & M. Osman (Eds.), New approaches in reasoning research (pp. 51-69). Hove, UK: Psychology Press.
  • Ball, L. J., & Stupple, E. J. N. (2016). Dual reasoning processes and the resolution of uncertainty: The case of belief bias. In L. Macchi, M. Bagassi, & R. Viale (Eds.), Cognitive unconscious and human rationality (pp. 143-165). Cambridge, MA: MIT Press.

Guillaume Beaulac

Concordia University
Thinking about Cognitive Environments (Video available)

Abstract: Critical thinking is often taught with some emphasis on categories and operations of cognitive biases taught to individual students who are expected to carry these operations individually. The underlying thought is that knowledge of biases equips students to reduce them-this should, in other words, help them to reason better. There are however empirical grounds to doubt the effectiveness of this common and intuitive approach.
In this lecture, I develop a broader view of critical thinking strategies that offers us in return a broader view on reasoning. Reasoning well also means to use and modify our environment in specific ways to facilitate what is sometimes seen as a mostly if not uniquely individualistic activity. Based on previous work, I present a four-level taxonomy that enables a useful diagnosis of biasing factors and situations, and illuminates more strategies for effective bias mitigation located in the shaping of situational factors and reasoning infrastructure. While I do not reject the usefulness of usual individualistic strategies taught in critical thinking courses, I also suggest that we should expand our toolbox when it comes to teaching reasoning strategies.

Suggested readings:
  • Beaulac, G., & Kenyon, T. (2016). The Scope of Debiasing in the Classroom. Topoi.
  • Beaulac, G., & Robert, S. (2011). Théories à processus duaux et théories de l'éducation : Le cas de l'enseignement de la pensée critique et de la logique. Les ateliers de l'éthique, 6(1): 63-77.
  • Kenyon, T., & Beaulac, G. (2014). Critical Thinking Education and Debiasing. Informal Logic, 34(4): 341-363.
  • Soll J. B., Milkman K. L., & Payne J. W. (2015). A user's guide to debiasing. In G. Wu & G. Keren (Eds.), Blackwell Handbook of Judgment and Decision Making (2nd edition). Wiley-Blackwell.
  • Stanovich, K. E., & West, R. F. (2008). On the relative independence of thinking biases and cognitive ability. Journal of Personality and Social Psychology, 94(4): 672-695.
  • Wilson, T. D., & Brekke, N. (1994). Mental contamination and mental correction: Unwanted influences on judgments and evaluations. Psychological Bulletin, 116(1): 117-142.
  • Wilson, T. D., Centerbar, D. B., & Brekke, N. (2002). Mental Contamination and the Debiasing Problem. In T. Gilovich, D. Griffin, & D. Kahneman (Eds.) Heuristic and Biases: The Psychology of Intuitive Judgment. Cambridge University Press. 185-200.

Jean-Yves Béziau

Universidade Federal Rio de Janeiro
Can logic make us more rational? (Audio desyncronization. Our service provider is working on it. Follow us on Twitter to know when the video will be available. In the meantime, student may ask for access to this video if they need it for their work.)

Abstract: People like Pascal and Descartes didn't think that syllogistic was helpful to make us more rational. Were they wrong? Can we say that modern logic work better? That truth-tables, first-order classical logic or a non-classical logic make us more rational? In this lecture I will try to answer these questions investigating in parallel the nature of rationality and logic, discussing in particular the nature of negation.

Suggested readings:
  • J.-Y.Béziau, “Logic is not logic”, Abstracta 6 (2010), pp.73-102.
  • J.-Y.Béziau, “Round squares are no contradictions”, in New Directions in Paraconsistent Logic, Springer, New Delhi, 2016, pp.39-55.
  • J.-Y.Béziau and D.Jacquette (eds), Around and beyond the square of opposition, Birkhäuser, Basel, 2012

Jean-François Bonnefon

Toulouse School of Economics
Reasoning about preferences (Video available)

Abstract: Mindreading consists of predicting or explaining the behavior of others based on their presumed beliefs and desires. Although most research on mindreading focused on human children, adult patients, or nonhuman beings, there is increasing interest in how it is performed by typical human adults. In this talk, I will review research on reasoning about preferences which can be reframed as research on mindreading. Part of this research revolves around inferring what an individual will do based on information about what this individual can do and what would be the consequences of these actions. These inferences tie in with some currently hot topics of research such as selfishness and altruism. Another part of the research reviewed in this chapter deals with the interpretation of connectives and quantifiers (the basic tool words of reasoning) as a function of the preferences of the speaker and that of the listener. These inferences relate to current debates on the role of politeness n linguistic pragmatics. Overall, the talk will show how refocusing reasoning as thinking of others and their goals can bridge together such different themes as mindreading, altruism, and politeness.

Suggested readings:
  • Bonnefon, J.F. (2009). A theory of utility conditionals: Paralogical reasoning from decision-theoretic leakage. Psychological Review, 116, 888-907.
  • Bonnefon, J. F., Feeney, A., & De Neys, W. (2011). The risk of polite misunderstandings. Current Directions in Psychological Science, 20, 321-324.
  • Bonnefon, J. F., Haigh, M., & Stewart, A.J. (2013). Utility templates for the interpretation of conditional statements. Journal of Memory and Language, 68, 350-361.

Yves Bouchard

Reasoning in Epistemic Contexts (Video and handouts available)

Abstract: In this talk, I develop a Fitch-style natural deduction system (NDS) capable of expressing the core thesis of epistemological contextualism. The proposed NDS satisfies two epistemological constraints: (1) it allows for the differentiated expression of any concept of knowledge, and (2) it is explicit about the conditions under which a particular knowledge type can be transposed into another type. The general idea of this NDS takes his inspiration from the contextual logic developed by McCarthy and Buv_c (1996, 1997) in artificial intelligence. In the first part, I present the rationale of my proposal and the general framework of McCarthy and Buv_c. In the second part, I define the rules for the introduction and the elimination of the knowledge operator, and I discuss some epistemological problems in relation with theorems of the proposed NDS.

Suggested readings:
  • Buva_, Sa_a. 1996. Resolving Lexical Ambiguity Using a Formal Theory of Context. In Semantic Ambiguity and Underspecification, edited by K. van Deemter, and S. Peters. Stanford: CSLI Publications.
  • Buva_, Sa_a, Vanja Buva_, and Ian A. Mason. 1995. Metamathematics of contexts. Fundamenta Informaticae 23: 263-301.
  • McCarthy, John. 1993. Notes on formalizing context. In Proceedings of the 13th International Joint Conference on Artificial Intelligence (IJCAI), edited by R. Bajcsy. Chambéry: Morgan Kaufmann.
  • McCarthy, John, and Sa_a Buva_. 1997. Formalizing context (expanded notes). In Computing Natural Language, edited by A. Aliseda, R. van Glabbeek, and D. Westerståhl. Stanford: CSLI Publications.

Manne Bylund

Stockholm University
Learning to Think in a Second Language (Slides are avaliable.)

Abstract: Do speakers of different languages think differently? If so, what happens when you learn a second language? During the last decades, research within the cognitive sciences has shown that linguistic categories may influence the way we think about the world. These findings have potentially far-reaching consequences for second language learning, as they suggest that acquiring a new language may also entail learning a new way to think. Empirical research on this question shows that second language learners and bilinguals may indeed experience cognitive restructuring, as evidenced in their judgements of perceptual phenomena such as time, space, and colour. The extent to which such restructuring takes place depends on the learner’s proficiency with and frequency of use of the language. Interestingly, second language speakers have also been shown to possess two distinct sets of language-related behaviours, allowing them to behave differently depending on which language context they are in.

Suggested readings:
  • TBA

Ruth M. J. Byrne

Trinity College Dublin
Counterfactual thought: from logic to moral judgment (Video available (Sound has been recovered))

Abstract: People often create counterfactual alternatives to reality and imagine how the past could have been different ‘if only…’. Counterfactual thoughts can help explain past events, for example, by identifying causal relations, and they may also help prepare for the future, for example, by formulating intentions. Counterfactuals amplify emotions such as regret or guilt, relief or satisfaction, and they also support moral judgments such as blame, fault, and responsibility. People create counterfactuals by changing aspects of their mental representations of reality, and they reason about counterfactual conditionals by comparing alternative possibilities. Knowledge affects the plausibility of a counterfactual through a process of semantic and pragmatic modulation.

Suggested readings:
  • Byrne, R. M. (2016). Counterfactual thought. Annual review of psychology, 67, 135-157.

Balakrishnan Chandrasekaran

Ohio State University
Cognitive Architectures and Diagrammatic Reasoning (Video available)

Abstract: Diagrams are a form of spatial representation that supports reasoning and problem solving. Even when diagrams are external, not to mention when there are no external representations, problem solving often calls for internal representations, that is, representations in cognition, of diagrammatic elements and internal perceptions on them. General cognitive architectures—Soar and ACT-R, to name the most prominent—do not have representations and operations to support diagrammatic reasoning. In this article, we examine some requirements for such internal representations and processes in cognitive architectures. We discuss the degree to which DRS, our earlier proposal for such an internal representation for diagrams, meets these requirements. In DRS, the diagrams are not raw images, but a composition of objects that can be individuated and thus symbolized, while, unlike traditional symbols, the referent of the symbol is an object that retains its perceptual essence, namely, its spatiality. This duality provides a way to resolve what anti-imagists thought was a contradiction in mental imagery: the compositionality of mental images that seemed to be unique to symbol systems, and their support of a perceptual experience of images and some types of perception on them. We briefly review the use of DRS to augment Soar and ACT-R with a diagrammatic representation component. We identify issues for further research.

Suggested readings:
  • TBA

Cristina Conati

University of British Columbia
Representation and Reasoning for Intelligent Tutoring beyond problem solving (student may ask for access to this video - contact us)

Abstract: Intelligent Tutoring Systems (ITS) is the interdisciplinary field that investigates how to devise educational systems that provide instruction tailored to the needs of individual learners, as good teachers do. Research in this field has successfully delivered techniques and systems that provide adaptive support for student problem solving in a variety of domains. There are, however, other educational activities that can benefit from individualized computer-based support, such as studying examples, exploring interactive simulations and playing educational games. Providing individualized support for these activities poses unique challenges, because it requires having an ITS that can model and adapt to student behaviors, skills and mental states often not as structured and well-defined as those involved in traditional problem solving tasks. This paper presents a variety of projects that illustrate some of these challenges, our proposed solutions, and future opportunities

Suggested readings:
  • Conati C. and Kardan S. (2013). Student modeling: supporting personalized instruction, from problem solving to exploratory, open-ended interactions. AI Magazine, Vol 34, N. 3
  • Kardan S., Conati C. (2015) Providing Adaptive Support in an Interactive Simulation for Learning: an Experimental Evaluation. . Proceedings of CHI 2015, ACM SIGCHI Conference on Human Factors in Computing Systems , ACM, p. 3671-3680.
  • Conati C., Fratamico L., Kardan S., Roll I. (2015) Comparing representations for learner models in interactive simulations. . Proceedings of AIED 2015, 17th International Conference on Artificial Intelligence in Education , Springer LNCS, p. 74-83.

Helen de Cruz

University of Amsterdam
Can Numerical Cognition Tell us Anything about the Metaphysics of Numbers? (Video available)

Abstract: Humans and other animals have an evolved ability to detect discrete magnitudes (numerosities) in their environment. Does this observation support evolutionary debunking arguments against mathematical realism, as has been recently argued by Clarke-Doane, or does it bolster mathematical realism, as authors such as Joyce and Sinnott-Armstrong have assumed? To find out, we need to pay closer attention to the features of evolved numerical cognition. I provide a detailed examination of the functional properties of evolved numerical cognition, and propose that they prima facie favor a realist account of numbers.

Suggested readings:
  • Baker, A. (2005). Are there genuine mathematical explanations of physical phenomena? Mind, 114, 223–238.
  • Clarke-Doane, J. (2012). Morality and mathematics. The evolutionary challenge. Ethics, 122, 313–340.
  • De Cruz, H. (2008). An extended mind perspective on natural number representation. Philosophical Psychology, 21, 475–490.
  • De Cruz, H., Neth, H., & Schlimm, D. (2010). The cognitive basis of arithmetic. In: B. Löwe & T. Müller (Eds.), PhiMSAMP. Philosophy of mathematics: Sociological aspects and mathematical practice (pp. 59–106). London: College Publications.
  • Leng, M. (2005). Mathematical explanation. In C. Cellucci & D. Gillies (Eds.), Mathematical reasoning, heuristics and the development of mathematics (pp. 167– 189). London: King’s College Publications.

J. M. Ton De Jong

University of Twente, Netherlands
Reasoning and learning; Technological solutions for potential pitfalls in inquiry learning .

Abstract: Modern, active, forms of instruction, such as inquiry learning require from students a complex and multifaceted reasoning process. In practice we, therefore, see that students make many reasoning mistakes while engaged in inquiry and that they have trouble completing an effective inquiry learning process. These failures of students can be attributed to a mix of lacking skills and developmental issues. To support them in their inquiry learning process we develop technological tools (scaffolds) that support students in their reasoning processes, e.g., when creating hypotheses or designing experiments. In this presentation I will highlight the impasses that students experience and the tools that can support them overcoming these impasses.

Suggested readings:
  • de Jong, T., Linn, M.C., & Zacharia, Z.C. (2013). Physical and virtual laboratories in science and engineering education. Science, 340, 305-308
  • Kollöffel, B., & de Jong, T. (2013). Conceptual understanding of electrical circuits in secondary vocational engineering education: Combining traditional instruction with inquiry learning in a virtual lab. Journal of Engineering Education, 102, 375-393.
  • Gijlers, H., & de Jong, T. (2013). Using concept maps to facilitate collaborative simulation-based inquiry learning. The Journal of the Learning Sciences, 22, 340-374.
  • de Jong, T., Sotiriou, S., & Gillet, D. (2014). Innovations in STEM education: The Go-Lab federation of online labs. Smart Learning Environments, 1, 3.
  • Zacharia, Z.C., & de Jong, T. (2014). The effects on students’ conceptual understanding of electric circuits of introducing virtual manipulatives within a physical manipulatives oriented curriculum. Cognition & Instruction, 32, 101-158.
  • de Jong, T., & Lazonder, A. W. (2014). The guided discovery principle in multimedia learning. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (Second ed.), pp. 371-390. Cambridge: Cambridge University Press.
  • Mulder, Y., Bollen, L., de Jong, T., Lazonder, A.W. (2016). Scaffolding learning by modeling: The effects of partially worked out models. Journal of Research in Science Teaching, 53, 502-523.

Wim de Neys

CNRS and Université de Paris-Descartes
Heuristic Bias and Conflict Detection: Quo Vadis Dual Process Theory? (Video available)

Abstract: Decades of reasoning and decision-making research have established that human judgment is often biased by intuitive heuristics. Although this heuristic bias is well documented and widely featured in psychology textbooks, its precise nature is less clear. A key question is whether reasoners detect the biased nature of their judgments. My research is focusing on this detection process. In a nutshell, results indicate that despite their illogical response, people demonstrate a remarkable sensitivity to possible conflict between their heuristic judgment and elementary logical or probabilistic principles. In my lecture I will present an overview of the empirical studies and implications. I will clarify why the empirical detection findings have led me to hypothesize that people not only have heuristic intuitions but also logical intuitions. I will specifically focus on the opportunities and challenges of this idea for the dual process view of human thinking.

Suggested readings:
  • De Neys, W. (2015). Heuristic bias and conflict detection during thinking. In B. Ross (Ed.), The Psychology of Learning and Motivation, (pp. 1-32). Burlington: Academic Press.
  • De Neys, W. (2012). Bias and conflict: A case for logical intuitions. Perspectives on Psychological Science, 7, 28-38.

Catarina Dutilh Novaes

University of Groningen
The Phylogeny and Ontogeny of Deductive Reasoning: A Cultural Story (It is advised to get the handouts to follow the video.)

Abstract: Does ontogeny recapitulate phylogeny when it comes to mathematical thinking? A number of authors (Poincaré, Polya, Lakatos) have suggested that it does, at least in some respects. Drawing on historical sources, on the literature on deductive reasoning from psychology, and on findings from mathematics education, in my talk I explore this idea with respect to deductive reasoning specifically -- which would translate, among others, into the ability of producing and understanding mathematical proof. The key idea will be that both for phylogeny and for ontogeny, proof is best understood as an inherently dialogical notion.

Suggested readings:
  • C. Dutilh Novaes (2015) “A dialogical, multi-agent account of the normativity of logic”. Dialectica 69, 587-609.
  • C. Dutilh Novaes (2013) “A dialogical account of deductive reasoning as a case study for how culture shapes cognition”. Journal of Cognition and Culture 13, 453-476.

Jonathan Evans

Plymouth University, UK
From Dual Processes to Two Minds Theory (Video available (text read by Serge Robert))

Abstract: The distinction between two kinds of thinking, one fast and intuitive and the other slow and reflective has a long history in philosophy and psychology. Over the past 30 years or so, many authors have put forward dual process accounts in both cognitive and social psychology, often in ignorance of other similar theories in different fields. Attempts to describe a generic theory with features typical of the various accounts has led to problems, however. In particular, a number of supporters and critics have tended to write as though there was only one theory with all typical features necessarily associated with each other. I will explain the problems this has created. Another difficulty discussed here is that dualities in modes or styles of thinking have been confused with cognitively distinct types. I also show how dual process theories led first to the proposal of dual systems and then to that of two or more minds, with distinct evolutionary origins. Two minds theories can explain why we are in some ways very similar to other higher animals and in other ways very different indeed.

Suggested readings:
  • Evans, J. S. B. T., & Frankish, K. (2009). (Eds) In two minds: Dual processes and beyond. Oxford: Oxford University Press.
  • Evans, J. S. B. T., & Stanovich, K. E. (2013). Dual process theories of higher cognition: Advancing the debate. Perspectives on Psychological Science, 8, 223-241.
  • Kahneman, D. (2011). Thinking, fast and slow. New York: Farrar, Straus and Giroux.
  • Stanovich, K. E. (2011). Rationality and the reflective mind. New York: Oxford University Press.

Ulrich Furbach

Koblenz Universität
Automated Reasoning for Cognitive Computing (Video available)

Abstract: In this lecture we discuss the use of first order automated reasoning in question answering and cognitive computing. We will depict the state of the art in automated reasoning and the special constraints for its use within cognitive computing systems.
We will demonstrate that various AI techniques have to be combined such that natural language question answering can be tackled. This includes a treatment of query relaxation, web-services, large knowledge bases and co-operative answering.
Furthermore some attempts to model commonsense and human reasoning are presented.

Suggested readings:
  • Furbach, Ulrich, Björn Pelzer, and Claudia Schon. Automated Reasoning in the Wild. Automated Deduction-CADE-25. Springer International Publishing, LNCS 9195 (2015): 55-72.
  • Furbach, Ulrich, and Claudia Schon. Deontic logic for human reasoning. Advances in Knowledge Representation, Logic Programming, and Abstract Argumentation. Springer International Publishing, LNCS 9060 (2015): 63-80.
  • Furbach, Ulrich, Andrew S. Gordon, and Claudia Schon. Tackling Benchmark Problems of Commonsense Reasoning. Bridging the Gap between Human and Automated Reasoning (2015): 47.
  • Furbach, Ulrich, Ingo Glöckner, and Björn Pelzer. An application of automated reasoning in natural language question answering. Ai Communications 23.2-3 (2010): 241-265.

Jonathan Ginzberg

Université de Paris-Diderot
The View from Dialogue: Reasoning in Spontaneous, Emotionally Charged Conversation (Audio desyncronization. Our service provider is working on it. Follow us on Twitter to know when the video will be available. In the meantime, student may ask for access to this video if they need it for their work.)

Abstract: Spoken language interaction, as opposed to text, is often perceived as messy—- there are many fragmentary utterances, disfluency is rife and to cap it all gestural affect is superimposed. In fact, conversational interaction is the primary medium of language use, phylogenetically and ontogenetically. In understanding our reasoning capabilities it seems quite crucial, therefore, to ground such an account in conversational interaction. In this talk I will present an account of reasoning processes that occur in real time conversational interaction based on a variety of corpus and experimental studies and formulated in the framework of KoS, a semantic framework I have developed in recent years together with various collaborators (see references below). I will address two main issues—-how can we explicate the relevance of dialogue contributions and how can we compute the content of utterances given their extreme dependence on context. Indeed, a significant point I will argue for is that the reasoning that conversationalists make does not involve a single shared context, but an entity that is intrinsically individualised. This is crucially important in offering accounts of one of the phenomena that distinguishes spontaneous conversational interaction from text—-the ubiquity of self-repair (e.g., corrections and hesitations) and other-repair (clarification requests). I will then proceed to consider how this seemingly purely information—oriented perspective can be modified to accommodate emotionally—charged aspects of interaction, most specifically how we understand the import of laughter.

Suggested readings:
  • Cooper, R. and Ginzburg, J. (2015). Type theory with records for natural language semantics. In C. Fox and S. Lappin, editors, Handbook of Con- temporary Semantic Theory, second edition, Oxford. Blackwell.
  • Fernandez, R. (2006). Non-Sentential Utterances in Dialogue: Classification, Resolution and Use. Ph.D. thesis, King’s College, London.
  • Ginzburg, J. (1994). An update semantics for dialogue. In H. Bunt, editor, Proceedings of the 1st International Workshop on Computational Semantics. ITK, Tilburg University, Tilburg.
  • Ginzburg, J. (2012). The Interactive Stance: Meaning for Conversation. Oxford University Press, Oxford.
  • Ginzburg, J. and Fernandez, R. (2010). Computational models of dialogue. In A. Clark, C. Fox, and S. Lappin, editors, Handbook of Computational Linguistics and Natural Language, Oxford. Blackwell.
  • Jonathan Ginzburg, Raquel Fernndez and David Schlangen 2014 ‘Disfluencies as intra-utterance moves’. Semantics and Pragmatics 7 (9):1–64
  • Jonathan Ginzburg ‘The Semantics of Dialogue’ In: Maria Aloni and Paul Dekker (eds.) The Cambridge Handbook of Semantics, Cambridge University Press, Cambridge.
  • Ginzburg, J. and Poesio, M. (2016). Grammar is a system that characterizes talk in interaction. (under review)
  • Larsson, S. (2002). Issue based Dialogue Management. Ph.D. thesis, Gothenburg University.

Vinod Goel

York University
(Keynote lecture) Neuroscience and Reasoning: Is there a Module in the Brain for Reasoning? (Video available)

Abstract: TBA

Suggested readings:
  • TBA

Noah D. Goodman

Stanford University
Bayesian Models of Reasoning and Language Understanding (Audio desyncronization. Our service provider is working on it. Follow us on Twitter to know when the video will be available. In the meantime, student may ask for access to this video if they need it for their work.)

Abstract: TBA

Suggested readings:
  • TBA

Geoffrey Goodwin

University of Pennsylvania
How people judge the truth and probability of conditionals (Video available (sorry, no sound for that video. Still useful for the slides))

Abstract: In this talk I discuss two issues bearing on judgments the truth of conditional assertions: whether people think that the meaning of basic conditionals is probabilistic or deterministic, and whether they think that a conditional’s truth requires that its antecedent clause be true. Recent accounts in the literature have explicitly advocated the view that the meaning of conditionals is taken to be probabilistic rather than deterministic. Such accounts have sometimes also advocated the view that a conditional can only be true when its antecedent clause is true – an assumption that underpins 'defective' truth table accounts of the meaning of conditionals. In contrast to the first of these claims, I will present evidence from a range of tasks indicating that the predominant interpretation of basic conditionals is deterministic rather than probabilistic. In contrast to the second claim, I will present evidence showing that ordinary individuals find the truth of a conditional compatible with the falsity of its antecedent clause. Both lines of evidence constrain the acceptable theories of the meaning of conditionals and direct attention to several open questions.

Suggested readings:
  • Goodwin, G. P. (2014). Is the basic conditional probabilistic? Journal of Experimental Psychology: General, 143, 1214-1241.
  • Goodwin, G. P., & Johnson-Laird, P. N. (2016). The truth of conditional assertions. Manuscript submitted for publication.

Monique Grandbastien

Université de Lorraine
Reasoning at the core of ITS: stabilities and evolutions during the last 25 years. (Video available)

Abstract: I would start from the ITS definition as an Artificatial Intelligence (AI) based system that can reason upon models of knowledge useful for fostering and evaluating learning. The main function of an ITS is to adapt to the learner through an understanding or an awareness of her cognitive, meta-cognitive or affective states. Then I would briefly categorize reasoning processes used in ITS and show what can be considered as stable and what has evolved, appeared or is still evolving, using examples from the recent special issues of the International Journal of Artificial Intelligence in Education (IJAIED). The goal would be to ground historical concepts for the attendees.

Suggested readings:
  • TBA

Joseph Halpern

Cornell University
Decision theory with resource-bounded agents (Video available)

Abstract: There have been two major lines of research aimed at capturing resource-bounded players in game theory. The first, initiated by Rubinstein, charges an agent for doing costly computation; the second, initiated by Neyman does not charge for computation, but limits the computation that agents can do, typically by modeling agents as finite automata. We review recent work on applying both approaches in the context of decision theory. For the first approach, we take the objects of choice in a decision problem to be Turing machines, and charge players for the ``complexity'' of the Turing machine chosen (e.g., its running time). This approach can be used to explain well-known phenomena like first-impression-matters biases (i.e., people tend to put more weight on evidence they hear early on) and belief polarization (two people with different prior beliefs, hearing the same evidence, can end up with diametrically opposed conclusions) as the outcomes of quite rational decisions. For the second approach, we model people as finite automata, and provide a simple algorithm that, on a problem that captures a number of settings of interest, provably performs optimally as the number of states in the automaton increases. Perhaps more importantly, it seems to capture a number of features of human behavior, as observed in experiments.
This is joint work with Rafael Pass and Lior Seeman.
No previous background is assumed.

Suggested readings:
  • The paper Decision theory with resource-bounded agents (in Topics in Cognitive Science 6:2, 2014, pp. 245-257, joint with Rafael Pass and Lior Seeman), basically covers the material in this talk, and has additional references.

Pascal Hitzler

Wright State University, Dayton, Ohio
Ontology Modeling (Video available)

Abstract: Ontologies have become rather pervasive for the representation of domain knowledge in applications, including life and geosciences, the humanities, cybersecurity, ambient intelligence, entertainment, manufactoring, etc. At the same time, there is a significant lack of established best practices for ontology modeling, which results in ontologies of very mixed quality. In this talk, we will present recent discussions regarding quality modeling, with particular emphasis on ontology reuse. Most of the presentation will consist of a worked example. We will also present implications of a quality modeling approach on the underlying logic-based ontology languages, and on reasoning over them. We will also discuss in detail the GeoLink Modular Ontology which is currently being deployed for the ocean sciences.

Suggested readings:
  • Eva Blomqvist, Pascal Hitzler, Krzysztof Janowicz, Adila Krisnadhi, Thomas Narock, Monika Solanki, Considerations regarding Ontology Design Patterns. Semantic Web 7 (1), 2016, 1-7.
  • Adila A. Krisnadhi, Yingjie Hu, Krzysztof Janowicz, Pascal Hitzler, Robert Arko, Suzanne Carbotte, Cynthia Chandler, Michelle Cheatham, Douglas Fils, Tim Finin, Peng Ji, Matthew Jones, Nazifa Karima, Audrey Mickle, Tom Narock, Margaret O'Brien, Lisa Raymond, Adam Shepherd, Mark Schildhauer, Peter Wiebe, The GeoLink Modular Oceanography Ontology. In: Marcelo Arenas, Óscar Corcho, Elena Simperl, Markus Strohmaier, Mathieu d'Aquin, Kavitha Srinivas, Paul T. Groth, Michel Dumontier, Jeff Heflin, Krishnaprasad Thirunarayan, Steffen Staab (eds.), The Semantic Web - ISWC 2015 - 14th International Semantic Web Conference, Bethlehem, PA, USA, October 11-15, 2015, Proceedings, Part II. Lecture Notes in Computer Science 9367, Springer, Heidelberg, 2015, 301-309.
  • Yingjie Hu, Krzysztof Janowicz, David Carral, Simon Scheider, Werner Kuhn, Gary Berg-Cross, Pascal Hitzler, Mike Dean, Dave Kolas, A Geo-Ontology Design Pattern for Semantic Trajectories. In: Thora Tenbrink, John G. Stell, Antony Galton, Zena Wood (Eds.): Spatial Information Theory - 11th International Conference, COSIT 2013, Scarborough, UK, September 2-6, 2013. Proceedings. Lecture Notes in Computer Science Vol. 8116, Springer, 2013, pp. 438-456.
  • Adila Krisnadhi, Pascal Hitzler, Modeling With Ontology Design Patterns: Chess Games As a Worked Example. In: Aldo Gangemi, Pascal Hitzler, Krzysztof Janowicz, Adila Krisnadhi, Valentina Presutti (eds.), Ontology Engineering with Ontology Design Patterns: Foundations and Applications. IOS Press/AKA Verlag. To appear.
  • Adila Krisnadhi, Nazifa Karima, Pascal Hitzler, Reihaneh Amini, Michelle Cheatham, Víctor Rodríguez-Doncel, Krzysztof Janowicz, Ontology Design Patterns for Linked Data Publishing. In: Aldo Gangemi, Pascal Hitzler, Krzysztof Janowicz, Adila Krisnadhi, Valentina Presutti (eds.), Ontology Engineering with Ontology Design Patterns: Foundations and Applications. IOS Press/AKA Verlag. To appear.

Ian Horrocks

Oxford University
Logic ∧ Reasoning ∧ Scalability ⊨ ⊥ ? (Video available)

Abstract: Logic based Semantic Technologies are maturing rapidly, with RDF and OWL now being deployed in diverse application domains, and with major technology vendors starting to augment their existing systems accordingly. For example, the Optique project has successfully piloted Ontology Based Data Access in the energy domain, and Oracle Inc. has enhanced its well-known database management system with modules that use RDF/OWL ontologies to support semantic data management. Such applications critically depend on efficient query answering services, and this in turn depends on the provision of robustly scalable reasoning systems. In this talk I will review the evolution of Semantic Technologies to date, and show how research ideas from logic based knowledge representation developed into a mainstream technology. I will then go on to examine the scalability challenges arising from deployment in large scale applications, and discuss ongoing research aimed at addressing them.

Suggested readings:
  • Y. Zhou, B. Cuenca Grau, Y. Nenov, M. Kaminski, and I. Horrocks. PAGOdA: Pay-as-you-go ontology query answering using a datalog reasoner. J. of Artificial Intelligence Research, 54:309–367, 2015.
  • B. Motik, Y. Nenov, R. Piro, I. Horrocks, and D. Olteanu. Parallel materialisation of Datalog programs in centralised, main-memory RDF systems. In Proc. of the 28th Nat. Conf. on Artificial Intelligence (AAAI 14), pages 129–137. AAAI Press, 2014. [3] Roman Kontchakov, Martin Rezk, Mariano Rodriguez-Muro, Guohui Xiao, and Michael Zakharyaschev. An- swering SPARQL queries over databases under OWL 2 QL entailment regime. In: Proc. of the Int. Semantic Web Conference (ISWC). LNCS. Springer, 2014.

Sangeet Khemlani

Naval Research Laboratory, Washington, D.C.
mReasoner: A Computational Model of the Processes of Inference (Audio desyncronization. Our service provider is working on it. Follow us on Twitter to know when the video will be available. In the meantime, student may ask for access to this video if they need it for their work.)

Abstract: I describe mReasoner, a novel computational model of inferential processes. It implements mental model theory, which posits that when people reason, they construct small-scale mental simulations of the world. Mental models are discrete representations of real, hypothetical, or imaginary possibilities. They are iconic in that they mirror the relationships they represent. Hence, when a mental model represents a set of objects, the model contains multiple tokens representing multiple objects. mReasoner makes inferences the way humans do: it heuristically draws initial conclusions by analyzing the structure of mental models. In doing so, it predicts reasoners’ systematic errors and explains how they overcome them. The system can carry out multiple inferential tasks, such as assessing whether a given conclusion is possible, necessary, or consistent with the premises. Its parameters affect the size and contents of the models that the system builds, and also the propensity for the system to engage in deliberation, i.e., to search for alternative models and counterexamples. Hence, it can explain individual differences in reasoning, too. The system serves as an analytical tool that mimics both the frailties of human reasoning, e.g., systematic errors, as well as strengths of human inference, e.g., the ability to spontaneously generate relevant conclusions.

Suggested readings:
  • Johnson-Laird, P. N., & Khemlani, S. (2014). Toward a unified theory of reasoning. In B. Ross (Ed.), The Psychology of Learning and Motivation (pp. 1-42). Academic Press.
  • Johnson-Laird, P. N., Khemlani, S., & Goodwin, G.P. (2015). Logic, probability, and human reasoning. Trends in Cognitive Sciences, 19, 201-214.
  • Khemlani, S., & Johnson-Laird, P. N. (2013). The processes of inference. Argument & Computation, 4, 1-20.
  • Khemlani, S., Lotstein, M., Trafton, J.G., & Johnson-Laird, P. N. (2015). Immediate inferences from quantified assertions. Quarterly Journal of Experimental Psychology, 68, 2073–2096.
  • Khemlani, S., & Johnson-Laird, P. N. (2016). How people differ in syllogistic reasoning. In A. Papafragou, D. Grodner, D. Mirman, and J. Trueswell (Eds.), Proceedings of the 38th Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society.

Janet L. Kolodner

Georgia Institute of Technology, Atlanta
Cognitive Prosthetics for Fostering Learning: A View from the Learning Sciences (Video available)

Abstract: New technologies of the past decade have the potential to transform the ways we can imagine fostering learning in both formal and informal learning environments. I find several possibilities quite promising: (1) allowing learners to encounter, immerse themselves in, and manipulate phenomena, processes, and scenarios that would otherwise be too big, small, fast, slow, far away, expensive, dangerous, or otherwise impractical for them encounter in real life, (2) making it possible (and manageable) for learners to engage in the kinds of project work that professionals engage in, and (3) affording learners opportunities for expression that go way beyond what is easy to express verbally and in static diagrams, charts, and illustrations. Most of the focus of cognitive scientists up to now when thinking about design of learning technologies has been on tutoring. But taking into account affordances of new technologies for helping learners have experiences they could not otherwise have and for allowing them to express themselves much more easily and in more dynamic modes allows both imagination of new designs for learning environments and learning ecologies and new roles intelligent technologies might play in fostering learning of important content, skills, practices, habits, and dispositions. The expertise of AI researchers and practitioners will be critical to designing the learning environments of the future and the functions of technology in those environments, and my goal, in this talk, is to help those in the audience feel comfortable imagining a future of much more playful, engaging, and rich learning environments and identify issues that they might want to address so that cognitive science and AI researchers can inform those designs along with technology imagineers, educators, and learning scientists. My observations are based on learning sciences research of the past several decades, the possibilities of new technologies of the past few years, and my experience as program officer for the U.S. National Science Foundation’s Cyberlearning and Future Learning Technologies program.

Suggested readings:

David A. Lagnado

University College London
Causal networks in evidential reasoning (Video available (sorry, no sound for that video. Still useful for the slides))

Abstract: How do people reason in the face of complex and contradictory evidence? Focusing on investigative and legal contexts, we present an idiom-based approach to evidential reasoning, in which people combine and reuse causal schemas to capture large bodies of interrelated evidence and hypotheses. We examine both the normative and descriptive status of this framework, illustrating with real legal cases and empirical studies. We also argue that it is qualitative casual reasoning, rather than fully Bayesian computation, that lies at the heart of human evidential reasoning.

Suggested readings:

Susanne Lajoie

McGill University
Interdisciplinary Approaches to the Study of Learning Using Advanced Technologies (Video available)

Abstract: The capacity of instructional technologies to personalize instruction has progressively improved over the last decade, in conjunction with changes in learning theories that dictate what, when, and how to support learners. Learning is at its best when it is active, goal-oriented, contextualized, and interesting. Simulations, when designed well can provide opportunities for learners to interact with instructional materials; receive feedback through the structure of the environment and/or by human or computer agents that scaffold the learner; and present adaptive challenges to sustain attention and keep learners engaged. This presentation will describe a few technology rich learning environments that are investigated by members of the Learning Environments Across Disciplines partnership. The capabilities of these systems are discussed in terms of how they support cognition, positive affect and self-regulated learning. Specific examples of advanced technologies can support medical students during critical thinking and problem solving, collaboration, and communication will be presented.

Suggested readings:
  • Lajoie, Susanne P.. Multimedia Learning of Cognitive Processes, The Cambridge Handbook of Multimedia Learning. Ed.Richard E. Mayer.. 2nd ed. Cambridge: Cambridge University Press, 2014. 623-646. Cambridge Books Online. http://dx.doi.org/10.1017/CBO9781139547369.031.

Henry Markovits

A Dual-strategy Model of Conditional Reasoning (Video available (sorry, no sound for that video. Still useful for the slides))

Abstract: There is currently a major debate about the underlying processes involved in reasoning. Mental model based theories propose that reasoning involves (1) an internal figural representation of premises based on the partial semantics of the logical connector and (2) a search for potential counterexamples to a putative conclusion. Depending on the results of the latter, conclusions are dichotomously judged to be valid or invalid. Probabilistic theories suggest that reasoning involves the generation of an estimate of the likelihood of a putative conclusion, based on information about the premises available in memory. Reasoners must then transform this likelihood estimate into a dichotomous judgment of validity. Both approaches claim to provide unitary explanations of reasoning, but empirical evidence is mixed. Vershueuren et al. (2005a,b) proposed that reasoners in fact can use two strategies, the choice of which is determined by situational variables, cognitive capacity, and metacognitive control. The statistical strategy evaluates inferences probabilistically, accepting those with high conditional probability. The counterexample strategy rejects inferences when a counter-example shows the inference to be invalid.
This presentation will describe the latest iteration of the dual strategy model, and recent research that has provided empirical evidence supporting this model and clarifying the basic properties of each strategy (refs). The relation between the two strategies and reasoning about validity and explicitly probabilistic reasoning and the effects of context will be examined.

Suggested readings:
  • Markovits, H., Brunet, M.-L., Thompson, V., & Brisson, J. (2013). Direct evidence for a dual process model of deductive inference. Journal of Experimental Psychology: Learning, Memory, and Cognition, 39(4), 1213-1222.
  • Markovits, H., Brisson, J. & de Chantal, P.-L. (2015). Deductive updating is not Bayesian. Journal of Experimental Psychology: Learning, Memory and Cognition, http://dx.doi.org/10.1037/xlm0000092.
  • Markovits, H., Brisson, J. & de Chantal, P.-L. (2015). Additional evidence for a dual-strategy model of reasoning: Probabilistic reasoning is more invariant than reasoning about logical validity. Memory and Cognition, 43 (8), 1208-1215. doi : 10.3758/s13421-015-0535-1.<\li>

Sheila McIlraith

University of Toronto
Reasoning to Act: From Logic to Automated Planning (Video available)

Abstract: Reasoning about action and change is a fundamental human activity. In this talk I will overview the use of logic, and in particular the situation calculus, in studying the formal foundations of reasoning about action and change. A compelling application of reasoning about action and change is automated planning, the generation of a plan -- a set of actions and action ordering -- which, when executed by an agent, results in the achievement of a specified goal. Automated planning is an active area of research that is central to the development of intelligent agents and autonomous robots. I will first provide a specification of automated planning in logic. This will be followed by an overview of state-of-the-art techniques for automated planning via satisfiability (SAT) and heuristic search.

Suggested readings:
  • Knowledge in Action: Logical Foundations for Specifying and Implementing Dynamical Systems, Raymond Reiter, MIT Press, 2001.
  • Automated Planning: Theory & Practice, Malik Ghallab, Dana Nau, and Paulo Traverso, Morgan Kaufmann, 2004.
  • A Concise Introduction to Models and Methods for Automated Planning: Synthesis Lectures on Artificial Intelligence and Machine Learning, Hector Geffner and Blai Bonet, Morgan & Claypool, 2013.

Tassos. A. Mikropoulos

University of Ioannina, Greece
Educational Virtual Environments: Turning Affordances into Pedagogical Features through Reasoning (Video available)

Abstract: Virtual Reality (VR) technologies are used pedagogically through their unique attributes. These are 3D spatial representations namely virtual environments and worlds, natural semantics for the representation of objects and facts inside the virtual environments and worlds, autonomy, users’ representation through avatars, multisensory intuitive and real time interaction, first-order experiences, size in space and time, immersion, transduction and reification, and presence (Mikropoulos & Natsis, 2011). These attributes afford actions that could possibly be used in teaching and learning and lead to learning benefits. The actions can be considered as learning affordances since they support specific learners’ behaviors (Norman, 1988). The learning affordances of VR can be classified in general categories which include free navigation, creation, modeling and simulation, multichannel communication, collaboration and cooperation, content presentation and delivery. Learning affordances lead to learning benefits when are supported by a solid theoretical framework. Technological Pedagogical Content Knowledge (TPCK or TPACK) offers such a framework (Koehler & Mishra, 2008). TPACK introduces technology knowledge into Shulman’s framework of Pedagogical Content Knowledge (PCK) (Shulman, 1987). PCK reflects teachers’ pedagogical reasoning, a process in which teachers and learners are involved (Webb, 2014). With TPACK, technological knowledge, technological content knowledge and technological pedagogical knowledge have to be introduced in their pedagogical reasoning.
Educational scenarios, lesson plans and learning activities utilizing the learning affordances of VR have shown learning benefits. A constructivist approach for the understanding of the quantum atom in an immersive virtual environment has indicated that the sense of presence enhanced students’ mental models (Kontogeorgiou, Bellou, & Mikropoulos, 2008). A collaborative problem-based learning activity in a Multi-User Virtual Environment (MUVE) about the reflection of light has shown positive learning outcome, students’ satisfaction, high levels of engagement and social presence (Vrellis, Mikropoulos, & Avouris, 2016).
It seems that the TPACK framework inspired by pedagogical reasoning has to characterize both educational research and practice.

Suggested readings:
  • Koehler, M. J. & Mishra, P. (2008). Introducing Technological Pedagogical Content Knowledge. In AACTE (ed.), The Handbook of Technological Pedagogical Content Knowledge for Teaching and Teacher Educators (pp 3-29). New York: Routledge.
  • Kontogeorgiou. _. _., Bellou, J. and Mikropoulos, T. A. (2008). Being inside the Quantum Atom. PsychNology Journal, 6(1), 83-98.
  • Mikropoulos, T. A. & Natsis, A. (2011). Educational Virtual Environments: A Ten Year Review of Empirical Research (1999 – 2009). Computers & Education, 56(3), 769-780.
  • Norman, D. A. (1988). The psychology of everyday things. New York: Basic Books.
  • Shulman, L. S. (1987). Knowledge and teaching: Foundations of the new reform. Harvard Educational Review, 57(1), 1-22.
  • Vrellis, I., Mikropoulos, T. A., & Avouris, N. (2016). Learning outcome, presence and satisfaction from a science activity in Second Life. Australasian Journal of Educational Technology, 32(1), 59-77.
  • Webb, M. (2014). Pedagogy with information and communications technologies in transition. Education and Information Technologies, 19(2), 275-294.

Roger Nkambou & Serge Robert

Muse Logic: An Intelligent Tutoring System for the Teaching of Logic (Video available)

Abstract: Logic is a discipline in which students encounter difficulties, but also a very hard domain to master. We will talk about our participatory approach to design Logic-Muse, an Intelligent Tutoring System that helps learners develop reasoning skills in multiple contexts (situations). In fact, the study was conducted jointly with the active participation of experts in the field of logic and the psychology of reasoning. An explicit catalogue of systematic errors (syntactic and semantic) in classical logic and in many non-classical logics was built, followed by an explicit representation of the semantic knowledge behind reasoning as well as reasoning procedural structures and meta-structures. All these basic components were intensively argued with and internally validated by experts. The first implementation of Logic-Muse focused on classical propositional logic and is used to support learners in their reasoning in a wide range of situations including causal concrete authentic situations as well as abstract formal ones. During the reasoning problem solving, the system provides effective feedback following a thorough cognitive diagnosis of the learner errors. It also provides metacognitive support by making it possible to trace the reasoning procedure using logic meta-structures such as the Boolean lattice.
A preliminary formative evaluation of the expert and the learner components of Logic-Muse shows interesting results. From a database of 72 exercises, the ‘virtual’ expert demonstrates a perfect capability on conditional reasoning (after we compare its answers to those provided by human experts). Also, results (obtained using data mining techniques on data from 71 students) show an excellent prediction accuracy of the learner model.
Few ITS in logic exist but Logic-Muse innovates through its design rationale which leads to strong structures on which learning is based. It also innovates with the projection of reasoning skills in a variety of well-defined classes of situations making it possible to ensure an absolute mastery of reasoning skills regardless of the content effect.

Suggested readings:
  • Nkambou, R., Brisson, J., Kenfack, C., Robert, S., Kissok, P., Tato, A. (2015). The Participatory Design of Logic-Muse, an Intelligent Tutoring System for Logical Reasoning in Multiple Contexts. In S. Carliner, C. Fulford & N. Ostashewski (Eds.), Proceedings of EdMedia: World Conference on Educational Media and Technology 2015 (pp. 1729-1738). Association for the Advancement of Computing in Education (AACE).
  • Nkambou, R., Kenfack, C., Robert, S. & Brisson, J. (2015) « The Design Rationale of Logic-Muse, an ITS for Logical Reasoning in Multiple Contexts ». In Conati , C., Heffernan, N., Mitrovic, A. & M. Felisa Verdejo. (Eds.), Proceedings of the 17th International Conference, AIED 2015, Madrid, Spain.

David Over

Durham University, UK
Probabilistic Psychology of Reasoning (Video available (Sound recovered!))

Abstract: In most traditional experiments on the psychology of reasoning, the participants were asked to assume, in static reasoning, that given premises were true and to make inferences only from those assumptions. However, people cannot usually acquire rational beliefs, and make rational decisions, on the basis of mere assumptions about what holds at some static point of time. Most everyday and scientific reasoning takes place in a context of uncertainty and requires inferences from more or less strongly held beliefs or hypotheses. This reasoning is also primarily dynamic and directed at belief revision or updating over time. The new Bayesian and probabilistic approaches to the psychology of reasoning provide a framework for reasoning from uncertain premises. These new approaches extend the psychology of reasoning to the study of dynamic inference and belief change, and aim to integrate reasoning and decision making. This talk will introduce these new approaches to the psychology of reasoning, covering the fundamental concepts of coherence and probabilistic validity, and using conditional inferences as the main example.

Suggested readings:
  • A wide range of articles on the new Bayesian and probabilistic approaches to the psychology of reasoning (with an introduction) can be found in: Elqayam, S., Bonnefon, J. F., & Over, D. E. (Eds.) (2013). Basic and applied perspectives for new paradigm psychology of reasoning [Special issue]. Thinking & Reasoning, 19(3).
  • More recent relevant findings for the talk are in: Cruz, N., Baratgin, J., Oaksford, M., & Over, D. E. (2015). Bayesian reasoning with ifs and ands and ors. Frontiers in Psychology, 6, 192. and Evans, J. St. B. T., Thompson, V., & Over, D. E. (2015). Uncertain deduction and conditional reasoning. Frontiers in Psychology, 6, 398.
  • More formal points are covered in: Gilio, A., & Over, D. E. (2012). The psychology of inferring conditionals from disjunctions: A probabilistic study. Journal of Mathematical Psychology, 56, 118-131.

Guy Politzer

Université de Paris 8
An analogical model for deduction under uncertainty (Handouts avaliable.)

Abstract: From its inception, the psychological study of deductive reasoning has used classical logic as a normative framework. But its inadequacy to deal with nonmonotonicity and uncertainty has been underscored. The new paradigm in psychology proposes Bayesianism as a theoretical framework to account for the daily life inferences that have uncertain premises. However, critics sometimes point out its complexity at both the computational and the algorithmic levels. In this talk, an analogical physical model that embodies de Finetti's subjective approach to probability theory will be described. It will be shown (i) how one can determine the probability of the conclusion of arguments by qualitative transformations that exploit intuitive laws of conservation, so refuting the objection; (ii) how the analogy provides a counterpart of the Dutch book criterion to assess individuals' rationality: in violating the probability calculus they would commit themselves to executing physically impossible transformations.

Suggested readings:
  • de Finetti, B. (1937). La prévision, ses lois logiques, ses sources subjectives. Annales de l'Institut Henri Poincaré, VII, 1-67. [English translation: Foresight: Its logical laws, its subjective sources. In H. E. Kyburg Jr. , & H. E. Smokler (Eds.) (1964). Studies in subjective pobability (pp. 55-118). (New York: John Wiley)]
  • Pfeifer, N. , & Kleiter, G. D. (2009). Framing human inference by coherence based probability logic. Journal of Applied Logic, 7, 206-217.
  • Politzer, G. (2016). Deductive reasoning under uncertainty: A water tank analogy. Erkenntnis, 81(3), 479-506.
  • Politzer, G. , & Baratgin, J. (2016). Deductive schemas with uncertain premises using qualitative probability expressions. Thinking and Reasoning, 22(1), 78-98.

Paula Quinon

Lund University
Philosophical Problems Related to the Approximate Number System (Video available)

Abstract: It is well established by empirical studies that human sensitivity to quantities develops spontaneously from an early age, long before the onset of verbal counting (Wynn 1992, Dehaene 1997/2011, Carey 2009). This innate and non-symbolic ability to process quantitative information is explained by postulating existence of core cognition (see Spelke 2000, 2003). My lecture will be devoted to one of the core cognitive systems involved in quantity processing: the Approximate Number System (ANS). After presenting what is known today about ANS in developmental psychology, cognitive sciences and mathematics, I will provide an analysis of the conceptual content of numerical expressions (numerals and various types of quantifiers) that is possibly inherited from ANS. On the basis of these investigations I will ask the question about the value of empirical research conducted in cognitive sciences for philosophy of mathematics.

Suggested readings:
  • Good preparation for the lecture would be reading of at least few fragments from Dehaene’s Number Sense” (Stanislas Dehaene, The Number Sense: How the Mind Creates Mathematics, Revised and Updated Edition, Oxford University Press, 2011). This is an easy, but very informative reading. Otherwise the lecture will be self-contained.

Marco Ragni

Freiburg Universität
A neuro-cognitive model of relational reasoning (Video available)

Abstract: Communication in everyday life about humans, objects, space or time often use relational descriptions. However, to make implicit information explicit in the communication process can require to draw conclusions about the given information. Recent psychological research demonstrates that humans do process relational information in a specific way, constructing preferred models and neglecting others. In this lecture, I will present research from two perspectives: Starting from psychological and neuroscientific findings I will develop and test a neuro-cognitive model to explain the empirical data. In a second step I will analyze the possibility for a cognitive complexity measure and ist connection to measures from artificial intelligence. An outlook to other domains will conclude this talk.

Suggested readings:

Valerie Reyna

Cornell University
A Fuzzy Trace Theory of Risky Decision Making: Why Reasoning is More like Poetry than Logic (Video available)

Abstract: Fuzzy-trace theory draws on evidence for independent gist and verbatim memory representations of information, but differs from other dual-process theories in emphasizing that gist-based intuition is often an advanced form of reasoning. Such claims are based on empirical evidence comparing reasoning by children and adolescents to that of adults and reasoning of adult novices to that of experts. The theory predicts parallel development of verbatim-based analysis and gist-based intuition, which produces predicted developmental reversals (e.g., children outperform adults) under specific circumstances. For example, despite increasing competence in reasoning, many biases in judgment and decision making grow with age, producing more ‘‘irrational’’ violations of coherence among adults than children. The latter phenomena are linked to developmental increases in gist processing with age. Contrary to stereotypes and standard dual-process theories, risk seeking for rewards decreases from childhood to adolescence, and risk preferences diverge for rewards (gains) and losses. Risky choices in the laboratory predict real-world risk taking, supporting the surprising prediction that adolescents who reason logically and literally are more likely to take unhealthy risks than those who reason intuitively. Moreover, inducing gist-based reasoning (as assessed in randomized controlled experiments) reduces adolescent risk taking and promotes healthy outcomes. Thus, healthy reasoning about risk is more likely to be fuzzy, parallel (rather than serial as in logic), and impressionistic—more like understanding poetry than doing logic. Implications for well-being, brain organization, and new conceptions of rationality are discussed.

Suggested readings:
  • Reyna, V. F., Estrada, S. M., DeMarinis, J. A., Myers, R. M., Stanisz, J. M., & Mills, B. A. (2011). Neurobiological and memory models of risky decision making in adolescents versus young adults. Journal of Experimental Psychology: Learning, Memory, and Cognition, 37(5), 1125-1142. doi:10.1037/a0023943
  • Reyna, V. F., Wilhelms, E. A., McCormick, M. J., & Weldon, R. B. (2015). Development of risky decision making: Fuzzy-trace theory and neurobiological perspectives. Child Development Perspectives, 9(2), 122-127. doi:10.1111/cdep.12117
  • For publications, go to http://www.human.cornell.edu/hd/reyna/publications.cfm

Serge Robert

Between Logic and Cognition: A Computational Model of Reasoning (Video available)

Abstract: Since a few decades, the psychology of reasoning has established that spontaneous reasoners have a tendency to make systematic fallacies in certain contexts, that is to draw conclusions that they consider logically valid while they are not, and to make suppressions of valid inferences in other contexts, that is to refuse to draw logically valid conclusions. These phenomena have been explained mainly via theories of the algorithmic procedures that human reasoners would follow. This talk presents instead a computational modeling of these logical errors in terms of variations on the logical structures at work in reasoning. For example, in classical propositional logic, the Boolean lattice structure and the Klein group structure are at work. Many fallacies and suppressions will be presented as alterations to these structures. This computational detour has many heuristic consequences: 1) it allows predictions about not yet studied logical errors, 2) it helps to understand the cognitive functioning of reasoning and the role of logic in cognition, 3) it suggests evolutionary hypotheses about the emergence of human reasoning and 4) it also puts forward pedagogical strategies for the teaching of logic and for the production of an intelligent tutoring system for such a teaching.

Suggested readings:
  • Robert, S. & Brisson, J. “The Klein, Group, Squares of Opposition and the Explanation of Fallacies in Reasoning” in Logica Universalis, Springer International Publishing, volume 10, June 2016.

Craige Roberts

Ohio State University
Constrained Practical Reasoning in Linguistic Interpretation (Video available)

Abstract: TBA

Suggested readings:
  • TBA

Olivier Roy

Universität Bayreuth
Dynamic Models of Interactive Rationality (Video available)

Abstract: This will be a talk about strategic reasoning. We will first look at the so-called epistemic foundation of game theory, which tries to understand the conditions under which rational (Bayesian) agents will play according to classical solution concepts. Our main focus there will be on the notion of common belief in rationality and its consequences in extensive games: sub-game perfect equilibrium play. We will then consider dynamic models of strategic reasoning, which tries to understand how players can arrive at conditions like common belief in rationality by iteratively updating their beliefs in the rationality of others. We will see that these models suggest a number of interesting generalisations and extensions, both from a logical point of view and for the foundations of strategic reasoning.

Suggested readings:

Keith Stenning

University of Edinburgh
Reasoning as a Discourse Process: Consequences for Cognition (Video available)

Abstract: TBA

Suggested readings:
  • TBA

Terrence C. Stewart

University of Waterloo, Canada
Reasoning with Biologically Realistic Neural Representations (Video available)

Abstract: TBA

Suggested readings:
  • TBA

Matthew Stone

Rutgers University
Learning Cognitive Models of Meaning (Audio desyncronization. Our service provider is working on it. Follow us on Twitter to know when the video will be available. In the meantime, student may ask for access to this video if they need it for their work.)

Abstract: This talk begins with a brief tour of reasoning in utterance interpretation. The most famous kind of interpretive reasoning is Grice's theory of the conversational implicatures that speakers signal via cooperative principles. However, we need to distinguish implicatures from the commitments that speakers choose to reveal in their choice of actions, the influences on speakers' decisions that speakers can't help but leak, the abstract conclusions the speaker intends to prompt from the hearer's private information, and the open-ended insights that come when speakers invite hearers to think things through for themselves. The rapport and problem solving required in these different cases will help interlocutors track and contribute to conversation, but, I argue, need not be thought of as leading to enriched or revised meanings for utterances.The suprising bottom line is that if speakers' choices are straightforward then recognizing and learning meaning is also straightforward. I illustrate this effect with an empirical model of lexical choice in corpora. The model assumes speakers make heuristic decisions to provide true information and otherwise match what others tend to say. The model can be fit to attested utterances to learn the meanings of words, yielding a close fit to corpus data. However, as in David Lewis's approach to communication as signaling, its meanings constitute equilibria: a hearer observing a message learns only that the message is true.The talk draws on the arguments in Lepore and Stone's book Imagination and Convention (Oxford, 2015) and on results from McMahan and Stone (TACL, 2015).

Suggested readings:

Ron Sun

Rensselaer Polytechnic Institute, Troy, New York
Reasoning with the CLARION Cognitive Architecture (Audio desyncronization. Our service provider is working on it. Follow us on Twitter to know when the video will be available. In the meantime, student may ask for access to this video if they need it for their work.)

Abstract: I will discuss reasoning from the perspective of a computational cognitive architecture. First, the scope of reasoning phenomena to be accounted for will be reviewed. Some details of the cognitive architecture will be sketched, which serves as the foundation for accounting for these phenomena. I will then show how the cognitive architecture is able to explain a wide range of reasoning phenomena in a unified and detailed (mechanistic and process-based) way.

Suggested readings:
  • S. Helie and R. Sun, Incubation, insight, and creative problem solving: A unified theory and a connectionist model. Psychological Review, Vol.117, No.3, pp.994-1024. 2010.
  • R. Sun and S. Helie, Psychologically realistic cognitive agents: Taking human cognition seriously. Journal of Experimental and Theoretical Artificial Intelligence, Vol.25, pp.65-92. 2013.
  • R. Sun and X. Zhang, Accounting for a variety of reasoning data within a cognitive architecture. Journal of Experimental and Theoretical Artificial Intelligence, Vol.18, No.2, pp.169-191. 2006
  • J. Licato, R. Sun, and S. Bringsjord, Using a hybrid cognitive architecture to model children’s errors in an analogy task. Proceedings of the Annual Conference of Cognitive Science Society, Quebec City, Quebec, Canada. pp.857-862. Published by Cognitive Science Society, Austin, Texas. July, 2014.

Kristen Syrett

Rutgers University
Reasoning in Context in Child Language Acquisition (Audio desyncronization. Our service provider is working on it. Follow us on Twitter to know when the video will be available. In the meantime, student may ask for access to this video if they need it for their work.)

Abstract: While children display adult-level (or near adult-level) knowledge of many syntactic and semantic phenomena by age four or five, their pragmatic reasoning in many instances has been observed to lag behind that of adults until 9-11 years of age. For years, these findings with a very narrow set of phenomena were taken as evidence that children’s pragmatic abilities in general were slow to develop. However, in recent years, more and more evidence has surfaced that demonstrates that young children – even those at the preschool age – display a certain degree of pragmatic savviness when they are presented with linguistic material in a context that highlights infelicity or the relevance of pragmatic inferences, or that do not require reliance upon specific lexical knowledge. In these instances, children can instead call upon general principles of pragmatic reasoning, and appear to do so rather adeptly. I will present evidence from a number of studies in recent years with children ranging from three to six years of age demonstrating that young children not only demonstrate a sensitivity to non-literal meaning and can calculate implicatures, but that their ability to do so can be modulated by the type of pragmatic inference they are called upon to make and the contextual support they receive.

Suggested readings:
  • Clark, Eve, & Patricia Amaral. (2010). Children build on pragmatic information in language acquisition. Language and Linguistics Compass, 4, 445-457.

Paul Thagard

University of Waterloo, Canada
Why Reason? Inference, Reasoning and Social Communication (Video available)

Abstract: It is often assumed that inference and reasoning are the same process, but they are actually very different. Inference is a neural process that is private, parallel, multimodal, emotional, unconscious, fast, and automatic. Reasoning, in contrast, is usually public, serial, verbal, dispassionate, conscious, slow, and deliberate. So the contributions of reasoning to inference are unclear, and it is legitimate to ask why people such as teachers should bother with reasoning at all.
This talk will discuss this puzzle from the perspective of Chris Eliasmith’s new theory of mind, the Semantic Pointer Architecture. Semantic pointers are patterns of firing in groups of neurons that function like symbols while incorporating sensory, motor, and emotional information that can be recovered. Communication between people is not just transfer of words, but rather multimodal transmission of semantic pointers. Reasoning can be a useful although limited part of such communication, in teaching and in other social situations.

Suggested readings:

Valerie Thompson

University of Saskatchewan
Causes and Consequences of Confidence in Reasoning (Video available)

Abstract: The processes that underlie reasoning and deciding occur on a continuum from fast, intuitive ones to more deliberate, analytic ones. In this talk, I will present a theory of metareasoning whose goal is to predict the circumstances under which people reason with their “gut” instinct verses engaging a more thoughtful approach. Metareasoning refers to the processes that monitor and control reasoning, problem solving and decision-making (Ackerman & Thompson, 2014). That is, metareasoning processes determine how satisfied one is with a conclusion or decision, as well as whether, and how long, one allocates resources to a problem. Although it is tempting to think about metacognition in terms of deliberate, self-reflective processes, I will present evidence to the contrary. Instead, I will argue that metareasoning processes, like those involved in other cognitive domains, such as memory, are low-level cue-based processes that are sensitive to the experiences associated with drawing conclusions, such as the fluency or ease with which an answer comes to mind.

Suggested readings:
  • Thompson, V.A., Prowse-Turner, J., & Pennycook, G. (2011). Intuition, Metacognition, and Reason. Cognitive Psychology, 63, 107-140.

Jennifer Trueblood

Vanderbilt University
A Quantum Probability Approach to Causal Reasoning (Audio desyncronization. Our service provider is working on it. Follow us on Twitter to know when the video will be available. In the meantime, student may ask for access to this video if they need it for their work.)

Abstract: Decades of research have shown that human decision-making often violates the rules of classical probability theory. Quantum probability (QP) theory provides an exciting new framework to model human behavior. In this talk, I will compare quantum and classical probability models of human causal reasoning. Arguably, the most successful models of causal reasoning, Causal Graphical Models (CGMs), perform well in some situations, but there is considerable variation in how well they are able to account for data, both across scenarios and between individuals. I will discus a new framework for causal reasoning based on QP theory that accounts for behavior in situations where CGMs fail. In our approach, we postulate a hierarchy of mental representations, from fully quantum to fully classical, that could be adopted for different situations. We illustrate our approach with new experiments and model comparisons.

Suggested readings:

Michiel van Lambalgen

University of Amsterdam
Reasoning as a Discourse Process: Consequences for Logic (Video available (talk given by K. Stenning))

Abstract: TBA

Suggested readings:
  • TBA

Jeroen J. G. van Merrienboer

Maastricht University
Cognitive Basis for the Design of Instruction (Video available)

Abstract: This presentation will introduce four-component instructional design (4C/ID; van Merrienboer & Kirschner, 2013), which is an approach to the design of educational programs that stress reasoning, problem solving and decision making. 4C/ID provides guidelines for the analysis of real-life tasks (van Merrienboer, 2013) and the transition into a blueprint for an educational program. Its basic assumption is that blueprints for complex learning can always be described by four basic components, namely (a) learning tasks, (b) supportive information, (c) procedural information, and (d) part-task practice. Learning tasks provide the backbone of the educational program; they provide learning from varied experiences and explicitly aim at transfer of learning. The three other components are connected to this backbone. Supportive information aims at the development of mental models and cognitive strategies that help learners to perform the non-routine aspects of tasks (i.e. reasoning, problem solving, decision making). Procedural information aims at the development of cognitive rules that enable learners to perform the routine aspects of tasks. And part-task practice provides additional practice for routines that need to become fully automated. Applications of the 4C/ID model will be illustrated with examples from the medical domain (e.g., Postma & White, 2015).

Suggested readings:
  • Postma, T. C., & White, J. G. (2015). Developing clinical reasoning in the classroom – analysis of the 4C/ID-model. European Journal of Dental Education, 19, 74-80.
  • Van Merrienboer, J. J. G. (2013). Perspectives on problem solving and instruction. Computers and Education, 64, 153-160.
  • Van Merrienboer, J. J. G., & Kirschner, P. A. (2013). Ten steps to complex learning (2nd Rev. Ed.). New York: Routledge.
  • See also www.tensteps.info.

Malte Willer

University of Chicago
Reasoning with Epistemic Modals (Video and handouts available)

Abstract: Epistemic modals—modals that articulate what, in light of some body of information, might or must be the case—pose a number of interesting challenges to classical models of modal reasoning. I will consider one particular puzzle about epistemic modals in detail, highlight its connection with some classical results from the belief revision literature, and then explore a solution to the puzzle in detail. The solution will be based on a dynamic semantic analysis of the language of modal propositional logic together with an update centric conception of logical consequence. Of particular importance will be question of how the resulting logic differs from classical modal logic, and whether the differences can be motivated on independent conceptual grounds. The topic under consideration here thus lies at the intersection of philosophy of language with logic and linguistics, and it highlights how considerations about everyday discourse and reasoning can motivate the adoption of a nonclassical perspective on logic and semantics.

Suggested readings:
  • A. Gillies. “What Might Be the Case after a Change in View.” Journal of Philosophical Logic 35(2): 117–145.
  • F. Veltman. 1996. “Defaults in Update Semantics.” Journal of Philosophical Logic 25(3): 221–261. [§§1–2]
  • M. Willer. 2015. An Update on Epistemic Modals. Journal of Philosophical Logic 44(6): 835–849.

Beverly Woolf

University of Massachusetts, Amherst
Detecting and Responding to Student Emotion within an Online Tutor (Video available)

Abstract: TBA

Suggested readings:
  • TBA

UQAM - Université du Québec à Montréal  ›  Updated : July 6th, 2016