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Could simplified stimuli change how the brain p...
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David Nicholson
November 23, 2021
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Could simplified stimuli change how the brain performs visual search tasks?
flash talk for Neuromatch 4.0
David Nicholson
November 23, 2021
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Transcript
Could simplified stimuli change how the brain performs visual search
tasks? David Nicholson NMC4 December 2021
Introduction Visual search: a real-world behavior we engage in constantly
Introduction In the laboratory, visual search tasks use simplified stimuli
Peelen and Kastner, 2014
Introduction Hallmark of behavior exhibited in laboratory visual search tasks:
set size effects
Introduction The visual system is optimized to search natural images
Peelen and Kastner, 2014
Introduction → simplified stimuli change visual search behavior How could
we test this? Peelen and Kastner, 2014
Methods deep neural networks for image classification AlexNet DNN architecture
"plane"
Methods ~ state-of-the-art models of object recognition in the visual
system adapted from DiCarlo and Cox 2007 AlexNet ANN architecture: ~ primate ventral visual stream "retina space" "inferior temporal cortex space" separating hyperplane
Methods deep neural networks optimized for image classification (Kell McDermott
2019) step
Methods Transfer learning to adapt pre-trained DNNs to visual search
tasks https://github.com/NickleDave/searchstims
Methods Transfer learning to adapt pre-trained DNNs to visual search
tasks the Visual Search Difficulty dataset "How Hard Can It Be? Estimating the Difficulty of Visual Search in an Image". Ionescu, et al. 2016
Results DNNs exhibit set size effects
Results Set size effects result from optimizing DNNs to classify
natural images
Results Optimizing DNNs with natural images --> improved, human-like behavior
on search tasks with natural images
Results Optimizing DNNs with natural images --> improved, human-like behavior
on search tasks with natural images Training method Source dataset DNN architecture Accuracy (largest object) (mean (S. D.)) transfer ImageNet VGG16 0.786 (0.007) transfer ImageNet AlexNet 0.652 (0.010) initialize Pascal VOC AlexNet 0.390 (0.010) initialize Pascal VOC VGG16 0.353 (0.060) transfer search stimuli VGG16 0.262 (0.004) transfer search stimuli AlexNet 0.208 (0.000)
Results Optimizing DNNs with natural images --> improved, human-like behavior
on search tasks with natural images Training method Source dataset DNN architecture Accuracy (largest object) (mean (S. D.)) transfer ImageNet VGG16 0.786 (0.007) transfer ImageNet AlexNet 0.652 (0.010) initialize Pascal VOC AlexNet 0.390 (0.010) initialize Pascal VOC VGG16 0.353 (0.060) transfer search stimuli VGG16 0.262 (0.004) transfer search stimuli AlexNet 0.208 (0.000)
Results Optimizing DNNs with natural images --> improved, human-like behavior
on search tasks with natural images Training method Source dataset DNN architecture Accuracy (largest object) (mean (S. D.)) transfer ImageNet VGG16 0.786 (0.007) transfer ImageNet AlexNet 0.652 (0.010) initialize Pascal VOC AlexNet 0.390 (0.010) initialize Pascal VOC VGG16 0.353 (0.060) transfer search stimuli VGG16 0.262 (0.004) transfer search stimuli AlexNet 0.208 (0.000)
Results Optimizing DNNs with natural images --> improved, human-like behavior
on search tasks with natural images
Discussion Mismatch may be impeding our ability to understand visual
search behavior
Discussion Future work could compare behavior of different models on
a benchmark set of stimuli and tasks Guided Search 6.0, Wolfe 2021
NickleDave Thank you! Lifelong Learning Machines program, DARPA HR0011-18-2-0019 2017
William K. and Katherine W. Estes Fund to F. Pestilli, R. Goldstone and L. Smith, Indiana University Bloomington. nicholdav