Children's Categorical Representation of Oblique Orientation

1982 ◽  
Vol 54 (2) ◽  
pp. 543-547 ◽  
Author(s):  
Anat Scher ◽  
David R. Olson

20 7-yr.-old children were given a memory task in which they were asked to compare successively presented oblique lines. The lines varied in respect to (1) their position within a square display and (2) their relation to the diagonal axis of the display. Children's performance suggests a categorical spatial representation system in which stimuli are encoded in terms of position and axis features. In comparing the orientation of two oblique lines, children match these coded categorical features (e.g., on axis vs off axis) and respond by the simple response rule: if a match say “same;” if a mismatch say “different,” so, children's recognition of oblique lines is often in error.

1982 ◽  
Vol 55 (3_suppl) ◽  
pp. 1317-1318
Author(s):  
Anat Scher

26 6-yr.-old children were given a memory task in which they were asked to compare the orientation of oblique lines. The performance of the children suggests a spatial representation system similar to that of older children. The representation assigned to obliques within a square display is characterized by the coding of position and axis information. Orientation comparisons are based on matching the coded information. As mental operations are limited the young children often do not respond correctly.


1996 ◽  
Vol 110 (5) ◽  
pp. 1006-1016 ◽  
Author(s):  
Sheri J. Y. Mizumori ◽  
Annette M. Lavoie ◽  
Anjali Kalyani

2021 ◽  
Vol 15 ◽  
Author(s):  
Louis Kang ◽  
Boyan Xu ◽  
Dmitriy Morozov

Persistent cohomology is a powerful technique for discovering topological structure in data. Strategies for its use in neuroscience are still undergoing development. We comprehensively and rigorously assess its performance in simulated neural recordings of the brain's spatial representation system. Grid, head direction, and conjunctive cell populations each span low-dimensional topological structures embedded in high-dimensional neural activity space. We evaluate the ability for persistent cohomology to discover these structures for different dataset dimensions, variations in spatial tuning, and forms of noise. We quantify its ability to decode simulated animal trajectories contained within these topological structures. We also identify regimes under which mixtures of populations form product topologies that can be detected. Our results reveal how dataset parameters affect the success of topological discovery and suggest principles for applying persistent cohomology, as well as persistent homology, to experimental neural recordings.


2021 ◽  
Vol 45 (1) ◽  
Author(s):  
Wenbo Tang ◽  
Shantanu P. Jadhav

When navigating through space, we must maintain a representation of our position in real time; when recalling a past episode, a memory can come back in a flash. Interestingly, the brain's spatial representation system, including the hippocampus, supports these two distinct timescale functions. How are neural representations of space used in the service of both real-world navigation and internal mnemonic processes? Recent progress has identified sequences of hippocampal place cells, evolving at multiple timescales in accordance with either navigational behaviors or internal oscillations, that underlie these functions. We review experimental findings on experience-dependent modulation of these sequential representations and consider how they link real-world navigation to time-compressed memories. We further discuss recent work suggesting the prevalence of these sequences beyond hippocampus and propose that these multiple-timescale mechanisms may represent a general algorithm for organizing cell assemblies, potentially unifying the dual roles of the spatial representation system in memory and navigation. Expected final online publication date for the Annual Review of Neuroscience, Volume 45 is July 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.


1992 ◽  
Vol 19 (1) ◽  
pp. 43-44 ◽  
Author(s):  
Kenneth A. Kiewra ◽  
Nelson F. DuBois

This article demonstrates how to teach operant concepts by means of a spatial representation system. The system is useful for teaching any set of related concepts.


2020 ◽  
Vol 14 ◽  
pp. 100386
Author(s):  
Eline Revdal ◽  
Vibeke Arntsen ◽  
Thanh Pierre Doan ◽  
Marte Kvello-Alme ◽  
Kjell Arne Kvistad ◽  
...  

2020 ◽  
Author(s):  
Louis Kang ◽  
Boyan Xu ◽  
Dmitriy Morozov

AbstractPersistent cohomology is a powerful technique for discovering topological structure in data. Strategies for its use in neuroscience are still undergoing development. We explore the application of persistent cohomology to the brain’s spatial representation system. We simulate populations of grid cells, head direction cells, and conjunctive cells, each of which span low-dimensional topological structures embedded in high-dimensional neural activity space. We evaluate the ability for persistent cohomology to discover these structures and demonstrate its robustness to various forms of noise. We identify regimes under which mixtures of populations form product topologies can be detected. Our results suggest guidelines for applying persistent cohomology, as well as persistent homology, to experimental neural recordings.


Science ◽  
2010 ◽  
Vol 328 (5985) ◽  
pp. 1576-1580 ◽  
Author(s):  
R. F. Langston ◽  
J. A. Ainge ◽  
J. J. Couey ◽  
C. B. Canto ◽  
T. L. Bjerknes ◽  
...  

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