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The Cognitive Lunch talks will be on Wednesdays from 12:10 - 1:25 in the Psychology conference room (PY 128) located behind the main office.
Spring 2010 Schedule - 09/02 Organizational Meeting - Abstract
- 09/09 Nicole McNeil, University of Notre Dame - Abstract
- 09/16 Francisco Lara-Dammer, CRCC, Indiana University - Abstract
- 09/23 Jerome R. Busemeyer, Indiana University - Abstract
- 10/14 Larry Yaeger, School of Informatics, Indiana University - Abstract
- 10/21 Rich Shiffrin, PBS, Indiana University - Abstract
- 10/28 Melissa Gresalfi, Learning Sciences Program, Indiana University - Abstract
- 11/04 Rick Hullinger, Department of Psychological and Brain Sciences, Indiana University - Abstract
- 11/11 Ulrike Hahn, School of Psychology, Cardiff University, U.K. - Abstract
- 11/18 Kris Hauser, School of Informatics and Computing, Indiana University - Abstract
Abstract 9/2: Organizational Meeting
9/9: Nicole McNeil, University of Notre Dame
Limitations to teaching 2 + 2 = 4: Knowledge of traditional arithmetic
hinders understanding of mathematical equivalence
Why do children sometimes fail to learn new information, even after
substantial amounts of experience or instruction? Several prevailing
accounts suggest that learning difficulties are caused by something
that children lack (e.g., working-memory resources or proficiency with
prerequisite skills). In contrast, others argue that difficulties are
caused, at least in part, by something that children have--existing
knowledge. In this talk, I will focus on children's difficulties with
mathematical equivalence (i.e., the concept that the two sides of an
equation are equal and interchangeable), and I will present evidence
that children's existing knowledge of arithmetic contributes to these
difficulties. I will discuss how this evidence informs our
understanding of theoretical issues related to cognition and
development, as well as practical issues related to learning and
instruction in the domain of mathematics.9/16: Francisco Lara-Dammer, CRCC, Indiana University
Geometric Figures in the Human Imagination
This talk proposes a plausible mental representation of simple geometric shapes such as points, lines, and circles
when they are imagined by people (as opposed to being drawn on an external medium such as paper).
The representation is simulated with a computer program whose purpose is
to model geometric discovery. A consequence of the construction of mental figures is that it facilitates some tasks
(like analogy making and remembering) but can make other tasks difficult (it makes humans susceptible to illusions and errors).
The mental plane, as I call the place in the brain where this imagery occurs, has some similarities to other
systems of the brain such as the auditory system. I will give some examples illustrating their similarities.9/23: Jerome R. Busemeyer, Indiana University
Quantum Probability Explanations for Probability Judgment `Errors'
A quantum probability model is introduced and used to explain human probability judgment errors including the conjunction, disjunction, inverse, and conditional fallacies, as well as unpacking effects and partitioning effects. The quantum model provides a geometric view of probability theory, which is easy to understand, and it gives an interesting way to formalize the heuristic concepts of representativeness and availability. Quantum probability reduces to classic (Bayesian) probability under certain conditions, but it is argued that these conditions are overly restrictive for human judgments. The quantum model is compared to other models for these probability judgment errors including the averaging model and support theory.10/14: Larry Yaeger, School of Informatics, Indiana University
Evolutionary Trends in Neural Complexity and Network Structure
A model of an evolving ecosystem has been used to investigate evolutionary trends in an information-theoretic measure of neural complexity, and related those trends to behavioral adaptation to the environment. Multiple techniques for evolving non-adaptive complexity have been compared to the adaptive complexity arising due to natural selection in this environment, and differences in resulting agent behaviors identified. These methods have yielded insights into the effects of natural selection on evolutionary trends in complexity. Now graph theoretical analytical techniques are being brought to bear to understand the structural metrics and motifs that give rise to neural complexity.10/21: Rich Shiffrin, PBS, Indiana University
Rational Equilibria for Multiplayer Games
In the Prisoner's Dilemma each player gains more by defecting whatever the other player chooses: If both therefore defect (the Nash Equilibrium) they get a jointly bad result. I argue that rational players should cooperate and obtain a jointly good result (not because they have the goal of maximizing joint return, but because such a strategy gives a 'selfish' player a better return). Similarly consider a two trial 'centipede game': The first player can STOP and both get a return of zero, or can GO. Then the second player can STOP, getting 10 and giving the first player -1, or GO, giving both players 9. Usual game theory stipulates the following: Knowing the second player will STOP, the first player will STOP giving both zero. I argue that two rational players should both GO, giving both 9.
The present talk starts with such a premise and searches for algorithms that will find such 'rational' solutions for jointly rational players in multiplayer sequential decision games of arbitrary complexity and length. Such a solution plays a role similar to 'ideal observer theory', and provides a baseline to which human behavior can be compared. The basic assumptions: 1) All players are rational and know all players are rational. 2) All players are selfish and try to maximize personal return. 3) Players do not know other players utilities so only ordinal returns matter: More is better but how much more is irrelevant.
For two player games a unique rational solution is always available, and a relatively simple algorithm finds it. When there are more than two players, the complexities expand super-exponentially: Rational solutions do not always exist (as shown by examples). An extension of the two player algorithm will find an important class of solutions, but not all. I discuss attempts to find ways to determine all cases that do have rational solutions, and attempts to find algorithms that extend the class of cases for which solutions do exist.10/28: Melissa Gresalfi, Learning Sciences Program, Indiana University
Learning for a reason: Supporting students’ critical engagement with mathematics
The focus of this talk is on the ways students engage with mathematics—how they approach novel problems when given some level of freedom or agency in determining solutions. Ill-structured problems are more similar to the kinds of real-world problems that professionals encounter outside of the unique context of schooling, and thus are a crucial area of study as we prepare students for an increasingly unknown future. The talk will review a trajectory of designing for students’ engagement with content that involves making intentional choices about which procedures to leverage in order to support particular claims, which I call critical engagement.
In the talk, I will review three years of curriculum design to foster critical engagement with mathematics in the context of an online, multiplayer educational videogame called Quest Atlantis. In designing this unit, I sought to critical engagement in order to deepen and facilitate procedural and conceptual engagement. I argue that consequential engagement is a central aspect of deepening conceptual understanding, because when one uses disciplinary knowledge to examine the world, they gain richer insight into and from the world, while simultaneously pushing back on theories about the world. As such, these different ways of engaging are not separable, but interact and build upon each other. Conceptual engagement cannot occur without a robust understanding of procedures; likewise consequential engagement can create new opportunities to engage conceptually with content.
As I will show, iterative refinements of the unit supported increasing critical engagement with the content, and ultimately, in a comparison study, significantly higher learning gains. In this talk, I will highlight the particular relations between design decisions and student thinking. Want a teaser? Visit http://inkido.indiana.edu/barab_we/ and check out the “Ander City” worked example.11/4: Rick Hullinger, Department of Psychological and Brain Sciences, Indiana University
Evolution of Attention in Learning
A variety of phenomena in associative learning suggest that people and some animals are able to learn how to allocate attention across cues. Models of attentional learning are motivated by the need to account for these phenomena. We start with a different, more general motivation for learners, namely, the need to learn quickly. Using simulated evolution, with adaptive fitness measured as overall accuracy during a lifetime of learning, we show that evolution converges to architectures that incorporate attentional learning. We describe the specific environments where attentional mechanisms are shown to be adaptive, and we describe how we assess attentional learning in the evolved agents.
Anyone who is interested in this topic but is unable to attend the talk is encouraged to read our forthcoming chapter covering the same topic:
Kruschke, J. K., and Hullinger, R. A. (2010). Evolution of attention in learning. In: N. A. Schmajuk (Ed.), Computational models of conditioning. Cambridge University Press.
http://www.indiana.edu/~kruschke/articles/KruschkeH2009.pdf11/11: Ulrike Hahn, School of Psychology, Cardiff University, U.K.
What makes things similar?
Similarity plays an explanatory role in a wide range of cognitive contexts, from categorization,
memory, reasoning, problem-solving through to language development and language processing. Despite
the fact that it is intuitively obvious when two entities are similar, psychology has neither
settled the issue of how best to conceptualize similarity, nor how best to measure it. The talk
presents recent experimental evidence in favor of a transformational view of similarity, and
discusses the relationship between this view and other theories of similarity.11/18: Kris Hauser, School of Informatics and Computing, Indiana University
The motion space complexity hypothesis and its effects on planning, control, and learning
The motion of an embodied agent can be represented as a point moving through a high-dimensional motion space, whose shape is dictated by the agent's morphology and environment. From an information-theoretic perspective, we argue that the geometric complexity of a motion space strongly affects the difficulty of operating the agent, whether the agent is mechanical or biological. For robots, "hard" spaces will demand more sophisticated planning and control algorithms, which has important consequences for mechanism design. Our case studies suggest that NASA should have looked toward nature for inspiration when designing its multi-million dollar six-legged ATHLETE robot, and that surgeons may be worse than computers at operating steerable needles, a new medical device with a highly unintuitive motion space. We also raise the hypothesis that evolution in biological systems may select for "simple" motion spaces as well as low-level reflexes that exploit motion space structure, in order to reduce cognitive load and encourage fast learning.
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