BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Talks.cam//talks.cam.ac.uk//
X-WR-CALNAME:Talks.cam
BEGIN:VEVENT
SUMMARY:An image-computable model of speeded decision-making - Ivan Tomic
DTSTART:20250328T140000Z
DTEND:20250328T150000Z
UID:TALK229729@talks.cam.ac.uk
CONTACT:Adam Triabhall
DESCRIPTION:This week we will discuss and debate a recent paper by Jaffe a
 nd colleagues (2025).\n\nAbstract: “Evidence accumulation models (EAMs) 
 are the dominant framework for modeling response time (RT) data from speed
 ed decision-making tasks. While providing a good quantitative description 
 of RT data in terms of abstract perceptual representations\, EAMs do not e
 xplain how the visual system extracts these representations in the first p
 lace. To address this limitation\, we introduce the visual accumulator mod
 el (VAM)\, in which convolutional neural network models of visual processi
 ng and traditional EAMs are jointly fitted to trial-level RTs and raw (pix
 el-space) visual stimuli from individual subjects in a unified Bayesian fr
 amework. Models fitted to large-scale cognitive training data from a styli
 zed flanker task captured individual differences in congruency effects\, R
 Ts\, and accuracy. We find evidence that the selection of task-relevant in
 formation occurs through the orthogonalization of relevant and irrelevant 
 representations\, demonstrating how our framework can be used to relate vi
 sual representations to behavioral outputs. Together\, our work provides a
  probabilistic framework for both constraining neural network models of vi
 sion with behavioral data and studying how the visual system extracts repr
 esentations that guide decisions” (Jaffe et al.\, 2025).\n\nReference: J
 affe\, P. I.\, Santiago-Reyes\, G. X.\, Schafer\, R. J.\, Bissett\, P. G.\
 , & Poldrack\, R. A. (2025). An image-computable model of speeded decision
 -making. eLife\, 13. https://doi.org/10.7554/eLife.98351.3
LOCATION:https://cam-ac-uk.zoom.us/j/92612577704?pwd=MUtqMjVQdXNmUTVIYjRkM
 G1NUW9GZz09
END:VEVENT
END:VCALENDAR