Passive attention in artificial neural networks predicts human visual selectivity

1 Introduction . The last decade has witnessed the rise of artificial neural networks ( ANNs ) that can match and even exceed human performance on a variety of perceptual and cognitive tasks , ranging from image recognition [ 1 ] to natural language processing and reinforcement learning [ 2 ] . Alongside the rapid development of these technologies , a significant body of work aimed at improving the interpretability of these systems and comparing them to biological ones has also grown [ 3 , 4 , 5 , 6 , 7 ] . In computer vision , techniques for probing which visual regions ANNs “ attend to ” when classifying images have been developed to visualize the receptive fields of convolutional layers as well as regions of a visual input that most influence the class activations of the models [ 8 , 9 , 10 , 11 , 12 ] . In neuroscience , researchers began to quantify the functional fidelity of leading ANNs as models of the human visual system using both neural and behavioral benchmarks [ 13 , 14 , 15 ] . Finally , cognitive scientists have developed techniques to compare the structure of ANN learned representations to human 35th Conference on Neural Information Processing Systems ( NeurIPS 2021 ) psychological representations [ 16 , 17 , 18 ] . All these efforts have contributed to our understanding of the biological and psychological validity of leading ANNs as models of biological vision , beyond just assessing their performance on standard object categorization benchmarks . Many previous analyses of the correspondence between ANNs and human vision have focused on the representations used by the systems . However , a natural question is whether ANNs select information in the same way , and in particular whether they attend to the same visual regions as humans when extracting information for visual object recognition and localization . Prior work has developed ANNs trained explicitly to predict human visual gaze [ 19 ] , and even incorporated simulated foveated systems into the model design [ 20 ] . In addition , work comparing human attention to computational attention [ 21 , 22 , 23 , 6 , 7 ] revealed that computational attention tends not to resemble human attention . However , this work also provides evidence that using human attention to supervise the training of network architectures devised to emulate the parallel pathways in the human visual system can improve performance , and yields more human-like visual features [ 6 ] . Still , relatively little work has attempted a more comprehensive examination of how a wide range of ANNs compare to multiple human behavioral measures , using the large variety of interpretability techniques that are now available to probe what visual information ANNs use . Methods for gaining insight into what is learned by ANNs started with “ passive ” attention gradientbased approaches designed to reveal which visual inputs influence the class activation score the most [ 24 ] . More advanced techniques using deconvolution and guided backpropagation methods followed [ 9 , 25 ] as well as techniques that introduced novel design alterations , such as global average pooling layers and class activation mapping ( CAM ) to localize class-specific visual regions in the input images [ 10 ] . Finally , more general approaches that could be applied to architectures without global average pooling [ 26 ] appeared , with some of the most recent contributions proposing class activation mapping techniques that do not rely on gradients at all [ 27 ] . Aside from this range of “ passive ” techniques , computer scientists have also developed CNN models that incorporate end-to-end trainable attention modules [ 28 , 29 ] as a means for both improving interpretability and boosting performance . The full range of techniques now available for visualizing the information that is most relevant to ANNs offer an unprecedented opportunity to compare their results to biological analogues such as estimates of human attention , discrimination accuracy , and visual recognition over image regions . Since the early 19th century [ 30 ] vision scientists devoted to the study of biological vision have also developed a variety of experimental techniques for estimating the visual information used by the primate visual system when engaged in similar perceptual and cognitive tasks such as visual search , localization , and recognition . Among these are measures of visual change sensitivity ( discrimination accuracy ) , visuospatial memory ( spatial localization estimation ) , as well as explicit reports of visual recognizability . In this work , we obtained estimates from six different perceptual tasks , including explicit visual recognizability estimates using a recent behavioral task [ 31 , 32 ] ( Fig . 1A and Fig . 2A ) , a two-alternative forced choice ( 2AFC ) change sensitivity task ( Fig . 1B and Fig . 2A ) , eye-tracking fixations ( Fig . 1C and Fig . 2A ) , and a spatial localization task ( Fig . 1D and Fig . 2A ) . We then compared them to estimates of ANN visual selectivity using a variety of pretrained models and visualization techniques including guided backpropagation and techniques based on class activation mapping . We used a range of model types , including early convolutional networks like AlexNet [ 1 ] , as well as recent state-of the-art models , such as Visual Transformer ( ViT ) [ 33 ] and EfficientNet [ 34 ] models . We find that only a select class of ANN models and passive attention techniques capture the shared variance across all human visual selectivity measures . This work contributes to current efforts aimed at evaluating the biological and psychological validity of contemporary ANNs by investigating the similarity between artificial and biological vision systems at the level of the visual inputs rather than the learned representations or correspondence to patterns of neural activation in the visual cortex [ 13 ] . We see our primary contributions as two-fold : ( 1 ) In a departure from prior work , we use a larger range of human behavioral measures , ANN models , and attention techniques in our comparisons . These reveal a significant range in the degree to which different models combined with different attention techniques produce human-like results . In addition , they show that more human-like ANN attention captures a shared component of the joint variability between the different human measures , rather than any particular human measure . ( 2 ) We provide causal evidence that more human-like ANN attention directly influences both human classification performance as well as predictions made by ANNs . 2 Human visual selectivity measures . We computed 6 behavioral measures with 25 images , performing experiments in which a total of 4,050 participants took part ( see Table 1 in the Appendix ) . We ran experiments for 3 distinct human behavioral tasks for each image ( see Fig . 1 and Fig . 2 for representative examples ) . A total of 1,575 participants took part in the discrimination accuracy experiments ( an average of 63 participants for each of the 25 images ) , and a total of 225 participants took part in each of the image patch ratings tasks ( 9 participants per image ) . Finally , the spatial memory serial reproduction chain experiments were completed by a total of 2,250 participants ( an average of 90 participants for each of the images ) . Participants were recruited anonymously over Amazon Mechanical Turk ( AMT ) , and provided informed consent . Participants were paid approximately $ 7 per hour . Details of the experimental procedures , design , and map estimation for each task can be found in Appendix Fig . S1 . 1 . Visual recognizability . We adopted a recent behavioral task [ 31 , 32 ] designed to measure the informativeness and recognizability of local image regions by using explicit self-reports ( see Fig . 1A ) . In the task , participants view small circular image patches sampled from full images and rate how “ recognizable ” or “ informative ” the content of the patch is on a six-point Likert scale ranging from a rating of “ Very low recognizability ” to “ Very high recognizability ” . The patches were sampled from a regular 12 x 12 grid over the image . Fig . 2A shows representative examples of the results following averaging over all the ratings in different spatial areas of the images , smoothing , and interpolation to produce continuous maps for each image . We ran 25 experiments with an average of 9 participants per image ( see Appendix for details of the map generation procedure ) . A total of 225 participants took part in the patch ratings experiments . Participants were paid $ 2 to complete 144 experimental trials . 2 . Change sensitivity ( Discrimination ) . We measured change sensitivity using a two-alternative forced choice ( 2AFC ) discrimination task . In this task , participants viewed an image with a small red dot superimposed on it for 1000 milliseconds . Following a 1000 millisecond delay , the same image was presented again with the dot in either the same exact location or in a slightly displaced location ( see Fig 1B ) . Participants were then asked to indicate if the dot was shifted or unchanged in the second presentation . Crucially , the locations of the initial dot locations were sampled densely from all possible locations on a regular grid that spanned the dimensions of the image , in order to measure changes in visual change sensitivity over the entire image ( see Appendix for details ) . This task measures changes in visual acuity conditioned on different visual areas in an image , and has been used as a proxy for measuring variable encoding precision of different image regions [ 35 ] . We ran 25 experiments ( one for each image ) with an average of 63 participants per experiment . The overall number of participants for the discrimination tasks equalled 1,575 . Participants were paid $ 1.50 to complete 120 trials in the discrimination task . 3 . Visuospatial localization . We used a recent behavioral paradigm based on serial reproduction that can reveal intricate spatial memory priors that guide visual localization estimation in humans [ 35 ] . In this paradigm the first participant views a point superimposed on an image and then reproduces its location from memory . The next participant views the same image but with the point located in the position reconstructed by the previous participant . As in the “ telephone game , ” the process is repeated , forming a chain of participants . For each image , there were a total of 20 iterations in the chains , for 250 initial random seed dot positions . This experimental procedure is known to reveal the spatial landmarks in visual scenes that bias human allocentric visuospatial representations ( see [ 35 ] and Fig . 1D ) . We ran 25 experiments ( one for each image ) with an average of 90 participants each . The overall number of participants was 2,250 . Participants were paid a base rate of $ 1.00 for completing 105 trials in the spatial memory experiment but could earn up to $ 1.50 depending on accuracy in the task . Additional details are provided in the Appendix . 4 . Fixations . Finally , we used an existing dataset of human fixations obtained via eye-tracking when human participants were engaged in a free-viewing task , a cued object search task , and a saliency search task [ 36 ] ( Fig . 1C ) . We used the published data from the 75 experiments reported in [ 36 ] . 3 ANN models , passive and active attention . We evaluated three standard deep convolutional neural network architectures ( AlexNet [ 1 ] , VGGNet [ 24 ] , ResNet [ 37 ] ) , as well as two state-of-the-art architectures ( Vision Transformer ( ViT ) [ 33 ] and EfficientNet [ 34 ] ) . For each of these models , which do not have active attention modules , we obtained attention maps using a range of passive methods described below . We also evaluated two built-in end-to-end trainable attention modules , described below . All models were pretrained on ImageNet 2012 [ 38 ] , CIFAR-100 [ 39 ] , or Places365-Standard [ 40 ] . Unlike the ImageNet and CIFAR-100 datasets , which are comprised of images of objects , Places365-Standard is comprised of images of complex natural scenes . See Table 2 in the Appendix for a list of all models .

Summary: In this paper, the authors systematically compare visual saliency maps obtained from deep learning models against human visual selectivity maps obtained through a series of large-scale online experiments involving a variety of perceptual tasks. The correlation between maps greatly varies depending on neural network architecture, visualization technique and behavioral task. In some cases (e.g., SGBP technique applied to AlexNet architecture) the model’s map achieves a very good match with human’s maps, suggesting a remarkable similarity between humans and machines in terms of how they select information contained in visual stimuli. This finding is further corroborated by two recognition experiments, where performance of both humans and deep nets is shown to decrease when images are masked using “incorrect” maps.