307 research outputs found
The influence of temporal context on vision over multiple time scales
No full textData and analysis scripts for the study entitled “The influence of temporal context on vision across multiple time scales”, by Kacie Lee and Reuben Rideaux.
The raw EEG data can be accessed at the following OSF database: https://osf.io /cusyn
Violated predictions enhance the representational fidelity of visual features in perception
No full textData and analysis code for the study titled “Violated predictions enhance the representational fidelity of visual features in perception”, by Reuben Rideaux, Phuong Dang, Luke Jackel-David, Zak Buhmann, Dragan Rangelov & Jason B Mattingley.
The MATLAB circstat toolboxm(https://au.mathworks.com/matlabcentral/fileexchange/10676-circular-statistics-toolbox-directional-statistics), the shadedErrorBar function (https://au.mathworks.com/matlabcentral/fileexchange/26311-raacampbell-shadederrorbar), and the Robust correlation toolbox (https://sourceforge.net/projects/robustcorrtool/) are required to run the code
How multisensory neurons solve causal inference
No full textNetwork training data and results presented in 2021 study entitled "How multisensory neurons solve causal inference", by Rideaux, Storrs, Maiello, and Welchma
Philip Strong letter to Reuben Wood, January 27, 1852
No full textLegal correspondence written by Philip Strong to Governor Reuben Wood regarding a warrant to arrest Peyton Polly, dated January 27, 1852.
Reuben Wood was governor of Ohio from 1850 through 1853, and was closely involved with the Peyton Polly case and attempts to secure the Polly family's release. Peyton Polly and his family were freedmen living in Lawrence County, Ohio, when they were kidnapped on June 6, 1850, and sold back into slavery in Kentucky and Virginia
How multisensory neurons solve causal inference
No full textNetwork training data and results presented in 2021 study entitled "How multisensory neurons solve causal inference", by Rideaux, Storrs, Maiello, and Welchma
How multisensory neurons solve causal inference.
No full textSitting in a static railway carriage can produce illusory self-motion if the train on an adjoining track moves off. While our visual system registers motion, vestibular signals indicate that we are stationary. The brain is faced with a difficult challenge: is there a single cause of sensations (I am moving) or two causes (I am static, another train is moving)? If a single cause, integrating signals produces a more precise estimate of self-motion, but if not, one cue should be ignored. In many cases, this process of causal inference works without error, but how does the brain achieve it? Electrophysiological recordings show that the macaque medial superior temporal area contains many neurons that encode combinations of vestibular and visual motion cues. Some respond best to vestibular and visual motion in the same direction ("congruent" neurons), while others prefer opposing directions ("opposite" neurons). Congruent neurons could underlie cue integration, but the function of opposite neurons remains a puzzle. Here, we seek to explain this computational arrangement by training a neural network model to solve causal inference for motion estimation. Like biological systems, the model develops congruent and opposite units and recapitulates known behavioral and neurophysiological observations. We show that all units (both congruent and opposite) contribute to motion estimation. Importantly, however, it is the balance between their activity that distinguishes whether visual and vestibular cues should be integrated or separated. This explains the computational purpose of puzzling neural representations and shows how a relatively simple feedforward network can solve causal inference
How multisensory neurons solve causal inference
No full textNetwork training data and results presented in 2021 study entitled "How multisensory neurons solve causal inference", by Rideaux, Storrs, Maiello, and Welchma
Stanley Matthews letter to Reuben Wood, March 23, 1852
No full textLetter written to Governor Reuben Wood by Stanley Matthews in support of the appointment of Donn Piatt to a position in the Hamilton County Court of Common Pleas, March 23, 1852. Stanley Matthews (1824-1889) was at the time a judge in the court. He secured a seat in the Ohio Senate in 1856 before being appointed U.S. District Attorney for Southern Ohio in 1858, and later served as a justice of the U.S. Supreme Court from 1881 to 1889.
Reuben Wood was governor of Ohio from 1850 through 1853, and was closely involved with the Peyton Polly case and attempts to secure the Polly family's release. Peyton Polly and his family were freedmen living in Lawrence County, Ohio, when they were kidnapped on June 6, 1850, and sold back into slavery in Kentucky and Virginia
The racial romance of Amy Levy's "Reuben Sachs"
No full textOn its publication in 1888, Reuben Sachs by Amy Levy (1861-1889) was initially received as being anti-Semitic in both the Jewish and the mainstream presses. Many reviews were scathingly critical, and some singled out the author for special abuse ...Peer reviewedFinal article published
Information extraction during simultaneous motion processing
No full textAbstractWhen confronted with multiple moving objects the visual system can process them in two stages: an initial stage in which a limited number of signals are processed in parallel (i.e. simultaneously) followed by a sequential stage. We previously demonstrated that during the simultaneous stage, observers could discriminate between presentations containing up to 5 vs. 6 spatially localized motion signals (Edwards & Rideaux, 2013). Here we investigate what information is actually extracted during the simultaneous stage and whether the simultaneous limit varies with the detail of information extracted. This was achieved by measuring the ability of observers to extract varied information from low detail, i.e. the number of signals presented, to high detail, i.e. the actual directions present and the direction of a specific element, during the simultaneous stage. The results indicate that the resolution of simultaneous processing varies as a function of the information which is extracted, i.e. as the information extraction becomes more detailed, from the number of moving elements to the direction of a specific element, the capacity to process multiple signals is reduced. Thus, when assigning a capacity to simultaneous motion processing, this must be qualified by designating the degree of information extraction
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