Cookies
We use essential cookies to run the site. Analytics cookies are optional and help us improve World Wide. Learn more.
Dr
Northwestern University
Showing your local timezone
Schedule
Thursday, December 2, 2021
4:00 PM Europe/London
Recording provided by the organiser.
Domain
NeuroscienceHost
Analogical Minds
Duration
90 minutes
Visual comparisons are ubiquitous, and they can also be an important source for learning (e.g., Gentner et al., 2016; Kok et al., 2013). In science, technology, engineering, and math (STEM), key information is often conveyed through figures, graphs, and diagrams (Mayer, 1993). Comparing within and across visuals is critical for gleaning insight into the underlying concepts, structures, and processes that they represent. This talk addresses how people make visual comparisons and how visual comparisons can be best supported to improve learning. In particular, the talk will present a series of studies exploring the Spatial Alignment Principle (Matlen et al., 2020), derived from Structure-Mapping Theory (Gentner, 1983). Structure-mapping theory proposes that comparisons involve a process of finding correspondences between elements based on structured relationships. The Spatial Alignment Principle suggests that spatially arranging compared figures directly – to support correct correspondences and minimize interference from incorrect correspondences – will facilitate visual comparisons. We find that direct placement can facilitate visual comparison in educationally relevant stimuli, and that it may be especially important when figures are less familiar. We also present complementary evidence illustrating the preponderance of visual comparisons in 7th grade science textbooks.
Nina Simms
Dr
Northwestern University
neuro
Nervous system function relies on the polarized architecture of neurons, established by directional transport of pre- and postsynaptic cargoes. While delivery of postsynaptic components depends on the
neuro
A combinatorial neural code for long-term motor memory
neuro
The postdoctoral researcher will work on an interdisciplinary project at the intersection of deep learning and comparative politics. The candidate will work in the Human-Centered Machine Learning (HuM
We are looking for highly motivated researchers to join our group in interdisciplinary projects that focus on the development of computational models to understand how linguistic information is repres
The CSNG Lab at the Faculty of Mathematics and Physics at the Charles University is seeking a highly motivated Postdoctoral Researcher to join our team to work on a digital twin model of the visual sy
PROJECT SUMMARY / ABSTRACT Cluster headache (CH), which affects about 1 in 1,000 people, is a severe and debilitating primary headache disorder characterized by repeated attacks occurring in clusters