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Understanding the Role of Scientific Models
This chapter delves into the essential nature of scientific models as simplified abstractions of the systems they represent. It highlights the risks of confusing models with the actual systems, which can hinder effective scientific inquiry and theory development.
Episode 136
I spoke with Judy Fan about:
* Our use of physical artifacts for sensemaking
* Why cognitive tools can be a double-edged sword
* Her approach to scientific inquiry and how that approach has developed
Enjoy—and let me know what you think!
Judy is Assistant Professor of Psychology at Stanford and director of the Cognitive Tools Lab. Her lab employs converging approaches from cognitive science, computational neuroscience, and artificial intelligence to reverse engineer the human cognitive toolkit, especially how people use physical representations of thought — such as sketches and prototypes — to learn, communicate, and solve problems.
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Outline:
* (00:00) Intro
* (00:49) Throughlines and discontinuities in Judy’s research
* (06:26) “Meaning” in Judy’s research
* (08:05) Production and consumption of artifacts
* (13:03) Explanatory questions, why we develop visual artifacts, science as a social enterprise
* (15:46) Unifying principles
* (17:45) “Hard limits” to knowledge and optimism
* (21:47) Tensions in different fields’ forms of sensemaking and establishing truth claims
* (30:55) Dichotomies and carving up the space of possible hypotheses, conceptual tools
* (33:22) Cognitive tools and projectivism, simplified models vs. nature
* (40:28) Scientific training and science as process and habit
* (45:51) Developing mental clarity about hypotheses
* (51:45) Clarifying and expressing ideas
* (1:03:21) Cognitive tools as double-edged
* (1:14:21) Historical and social embeddedness of tools
* (1:18:34) How cognitive tools impact our imagination
* (1:23:30) Normative commitments and the role of cognitive science outside the academy
* (1:32:31) Outro
Links:
* Selected papers (there are lots!)
* Overviews
* Drawing as a versatile cognitive tool (2023)
* Using games to understand the mind (2024)
* Socially intelligent machines that learn from humans and help humans learn (2024)
* Research papers
* Communicating design intent using drawing and text (2024)
* Creating ad hoc graphical representations of number (2024)
* Visual resemblance and interaction history jointly constrain pictorial meaning (2023)
* Explanatory drawings prioritize functional properties at the expense of visual fidelity (2023)
* SEVA: Leveraging sketches to evaluate alignment between human and machine visual abstraction (2023)
* Learning to communicate about shared procedural abstractions (2021)
* Visual communication of object concepts at different levels of abstraction (2021)
* Relating visual production and recognition of objects in the human visual cortex (2020)
* Collabdraw: an environment for collaborative sketching with an artificial agent (2019)
* Pragmatic inference and visual abstraction enable contextual flexibility in visual communication (2019)
* Common object representations for visual production and recognition (2018)
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