New subject: [Koglist] [CEU Cogsci Talks] TODAY: CCC Colloquium - Mark Ho - Monday, June 10

7 Jun 2024

Dear All,
The CEU Department of Cognitive Science and the Center for Cognitive Computation (CCC)
invites you to the following online talk.
*Please note that this is an online event only, next Monday, starting from 3 PM.*
Speaker: Mark Ho (New York University)
Title: How do people see, simplify, and solve problems?
Abstract: Everyday life often gives us new problems to solve. For example, imagine flying
back from a relaxing vacation and your return trip includes a layover in a new city.
You’ve just landed at your connecting destination but learn that the next flight has been
canceled. Suddenly, you need to start considering a whole new set of possibilities,
constraints, and goals: When is the next flight? Would it be nice to stay a few days and
explore? When do I have to be back at work? Could I take a car or train home instead? How
long would that take? How much would it cost? Then, you would actually need to do
something based on these considerations. Put another way, you need to come up with and
then execute a novel plan.
Our capacity to flexibly plan and act in new situations (such as deciding what to do when
a connecting flight gets canceled) is both quotidian and remarkable. This is because
planning is an exceptionally cognitively demanding process, and yet we are able to plan
successfully despite having limited attention, memory, and time. No other species and no
existing artificial intelligence system can plan as flexibly, generally, and efficiently
as we do on a routine basis. What explains our ability to plan?
To understand processes like planning, cognitive scientists often develop formal,
mathematical models that capture general principles underlying how people think and act
(similar to how Newton’s laws capture general principles underlying how physical objects
interact). One of the earliest and most influential models of planning, proposed by Newell
and Simon (1972), is based on the idea that people have a representation of a task (e.g.,
for chess, an understanding of how the pieces move and how to win) and then search for
good actions to take using that representation (e.g., by simulating and evaluating
different sequences of chess moves). Recently, we built on this original distinction
between representing tasks and searching for actions by formulating a new theory of
planning. Specifically, we propose that people not only search for good actions, but also
search for good task representations via a process that we refer to as value-guided
construal.
Our account of value-guided construal allows us to pose two broad questions—one conceptual
and one empirical. First, the conceptual question: What makes a task representation good?
We propose that a task representation is good if it is simple and planning with it leads
to a successful solution. Put another way, good task representations balance utility and
complexity. Our computational model allows us to quantify this tradeoff for a given task.
The second question is empirical: Do people form task representations that balance utility
and complexity? Answering this question requires data, so we designed a task in which
participants needed to plan how to navigate around obstacles in 2D mazes to reach a goal.
We wanted to understand how they represented the mazes to plan, and so in a series of
experiments, we assessed their memory of task elements after planning using post-trial
questions as well as their thought process during planning using mouse tracking. Across
these experiments, we found robust support for the idea that people trade off the utility
and complexity of a task representation, as predicted by our account of value-guided
construal.
Our results tell us whether people flexibly form task representations that trade-off
utility and complexity, but they also raise intriguing questions about how people might do
so. The current work does not provide a definitive answer to such questions, but we
explore how it might be done in principle. For example, one could start by solving the
simplest construal and only add in new details (such as obstacles) as needed. Background
knowledge about a domain could also guide which details to include in a construal—for
instance, in chess, knowing that the queen is the strongest piece on the board might bias
one to consider construals that include it.
The human capacity to plan in novel situations is both familiar and puzzling, and
understanding what makes it possible remains an important scientific challenge. The
framework of value-guided construal provides a new perspective on this old question by
drawing attention to the role that actively simplifying tasks plays in allowing people to
more efficiently use limited cognitive resources. Our hope is that this work can serve as
a starting point for further examination of the construal process itself, as well as other
cognitive mechanisms that interface with planning.
Original Article:
Ho, M. K., Abel, D., Correa, C. G., Littman, M. L., Cohen, J. D., & Griffiths, T. L.
(2022). People construct simplified mental representations to plan. Nature, 606(7912),
129–136. https://doi.org/10.1038/s41586-022-04743-9
Time: 3 PM, Monday, June 10, 2024
Zoom: Meeting ID: 927 6894
9309<https://ceu-edu.zoom.us/j/92768949309?pwd=KbnyxOa7YXxwOpbmIqQkjyi8Sk11ka.1>
Passcode: 245150
Please, be informed that video/photo recording might take place at the event and the
edited version of the video material might be published to communicate or promote CEU
PU's activities. Please, find our Privacy Notice
here<https://www.ceu.edu/privacy>cy>.
Best regards,
Ildikó Varga
Department Coordinator (Budapest)
Department of Cognitive Science
[cid:0d0b70b8-4628-4073-b8f8-374a7ae8abc7]
H-1051 Budapest
Nádor u. 15. FT room 404.
tel: +36-1 327-3000 2941
http://www.ceu.edu<http://www.ceu.edu/>
http://cognitivescience.ceu.edu<http://cognitivescience.ceu.edu/>
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[Koglist] [CEU Cogsci Talks] CCC Colloquium - Mark Ho - Monday, June 10