Controversial stimuli: Pitting neural networks against each other as models of human cognition
- aZuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027;
- bDepartment of Computer Science, Columbia University, New York, NY 10027;
- cDepartment of Psychology, Columbia University, New York, NY 10027;
- dDepartment of Neuroscience, Columbia University, New York, NY 10027;
- eDepartment of Electrical Engineering, Columbia University, New York, NY 10027
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Edited by Joshua B. Tenenbaum, Massachusetts Institute of Technology, Cambridge, MA, and accepted by Editorial Board Member Dale Purves September 15, 2020 (received for review November 13, 2019)

Abstract
Distinct scientific theories can make similar predictions. To adjudicate between theories, we must design experiments for which the theories make distinct predictions. Here we consider the problem of comparing deep neural networks as models of human visual recognition. To efficiently compare models’ ability to predict human responses, we synthesize controversial stimuli: images for which different models produce distinct responses. We applied this approach to two visual recognition tasks, handwritten digits (MNIST) and objects in small natural images (CIFAR-10). For each task, we synthesized controversial stimuli to maximize the disagreement among models which employed different architectures and recognition algorithms. Human subjects viewed hundreds of these stimuli, as well as natural examples, and judged the probability of presence of each digit/object category in each image. We quantified how accurately each model predicted the human judgments. The best-performing models were a generative analysis-by-synthesis model (based on variational autoencoders) for MNIST and a hybrid discriminative–generative joint energy model for CIFAR-10. These deep neural networks (DNNs), which model the distribution of images, performed better than purely discriminative DNNs, which learn only to map images to labels. None of the candidate models fully explained the human responses. Controversial stimuli generalize the concept of adversarial examples, obviating the need to assume a ground-truth model. Unlike natural images, controversial stimuli are not constrained to the stimulus distribution models are trained on, thus providing severe out-of-distribution tests that reveal the models’ inductive biases. Controversial stimuli therefore provide powerful probes of discrepancies between models and human perception.
- visual object recognition
- deep neural networks
- optimal experimental design
- adversarial examples
- generative modeling
Footnotes
- ↵1To whom correspondence may be addressed. Email: n.kriegeskorte{at}columbia.edu or tal.golan{at}columbia.edu.
Author contributions: T.G., P.C.R., and N.K. designed research; T.G. and P.C.R. performed research; and T.G. and N.K. wrote the paper.
The authors declare no competing interest.
This paper results from the Arthur M. Sackler Colloquium of the National Academy of Sciences, “Brain Produces Mind by Modeling,” held May 1–3, 2019, at the Arnold and Mabel Beckman Center of the National Academies of Sciences and Engineering in Irvine, CA. NAS colloquia began in 1991 and have been published in PNAS since 1995. From February 2001 through May 2019, colloquia were supported by a generous gift from The Dame Jillian and Dr. Arthur M. Sackler Foundation for the Arts, Sciences, & Humanities, in memory of Dame Sackler’s husband, Arthur M. Sackler. The complete program and video recordings of most presentations are available on the NAS website at http://www.nasonline.org/brain-produces-mind-by.
This article is a PNAS Direct Submission. J.B.T. is a guest editor invited by the Editorial Board.
This article contains supporting information online at https://www.pnas.org/lookup/suppl/doi:10.1073/pnas.1912334117/-/DCSupplemental.
Data Availability.
Optimization source code, synthesized images, and anonymized, detailed behavioral testing results are available at GitHub, https://github.com/kriegeskorte-lab/PNAS_2020_Controversial_Stimuli.
Published under the PNAS license.
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