Deep Convolutional Neural Networks (CNNs) are gaining traction as the benchmark model of visual object recognition, with performance now surpassing humans. While CNNs can accurately assign one image to potentially thousands of categories, network performance could be the result of layers that are tuned to represent the visual shape of objects, rather than object category, since both are often confounded in natural images. Using two stimulus sets that explicitly dissociate shape from category, we correlate these two types of information with each layer of multiple CNNs. We also compare CNN output with fMRI activation along the human visual ventral stream by correlating artificial with neural representations. We find that CNNs encode category information independently from shape, peaking at the final fully connected layer in all tested CNN architectures. Comparing CNNs with fMRI brain data, early visual cortex (V1) and early layers of CNNs encode shape information. Anterior ventral temporal cortex encodes category information, which correlates best with the final layer of CNNs. The interaction between shape and category that is found along the human visual ventral pathway is echoed in multiple deep networks. Our results suggest CNNs represent category information independently from shape, much like the human visual system.

1Deep Convolutional Neural Networks (CNNs) are gaining traction as the benchmark model of 2 visual object recognition, with performance now surpassing humans. While CNNs can accurately 3 assign one image to potentially thousands of categories, network performance could be the result 4 of layers that are tuned to represent the visual shape of objects, rather than object category, since 5 both are often confounded in natural images. Using two stimulus sets that explicitly dissociate 6 shape from category, we correlate these two types of information with each layer of multiple 7CNNs. We also compare CNN output with fMRI activation along the human visual ventral 8 stream by correlating artificial with biological representations. We find that CNNs encode 9 category information independently from shape, peaking at the final fully connected layer in all 10 tested CNN architectures. Comparing CNNs with fMRI brain data, early visual cortex (V1) and 11 early layers of CNNs encode shape information. Anterior ventral temporal cortex encodes 12 category information, which correlates best with the final layer of CNNs. The interaction 13 between shape and category that is found along the human visual ventral pathway is echoed in 14 multiple deep networks. Our results suggest CNNs represent category information independently 15 from shape, much like the human visual system. 16 17

Some of the most impressive functional specialization in the human brain is found in occipitotemporal cortex (OTC), where several areas exhibit selectivity for a small number of visual categories, such as faces and bodies, and spatially cluster based on stimulus animacy.Previous studies suggest this animacy organization reflects the representation of an intuitive taxonomic hierarchy, distinct from the presence of face-and body-selective areas in OTC.Using human fMRI, we investigated the independent contribution of these two factors -the face-body division and taxonomic hierarchy -in accounting for the animacy organization of OTC, and whether they might also be reflected in the architecture of several deep neural networks. We found that graded selectivity based on animal resemblance to human faces and bodies masquerades as an apparent animacy continuum, which suggests that taxonomy is not a separate factor underlying the organization of the ventral visual pathway.

The ontogenetic development of human vision and the real-time neural processing of visual input exhibit a striking similarity—a sensitivity toward spatial frequencies that progresses in a coarse-to-fine manner. During early human development, sensitivity for higher spatial frequencies increases with age. In adulthood, when humans receive new visual input, low spatial frequencies are typically processed first before subsequent processing of higher spatial frequencies. We investigated to what extent this coarse-to-fine progression might impact visual representations in artificial vision and compared this to adult human representations. We simulated the coarse-to-fine progression of image processing in deep convolutional neural networks (CNNs) by gradually increasing spatial frequency information during training. We compared CNN performance after standard and coarse-to-fine training with a wide range of datasets from behavioral and neuroimaging experiments. In contrast to humans, CNNs that are trained using the standard protocol are very insensitive to low spatial frequency information, showing very poor performance in being able to classify such object images. By training CNNs using our coarse-to-fine method, we improved the classification accuracy of CNNs from 0% to 32% on low-pass-filtered images taken from the ImageNet dataset. The coarse-to-fine training also made the CNNs more sensitive to low spatial frequencies in hybrid images with conflicting information in different frequency bands. When comparing differently trained networks on images containing full spatial frequency information, we saw no representational differences. Overall, this integration of computational, neural, and behavioral findings shows the relevance of the exposure to and processing of inputs with variation in spatial frequency content for some aspects of high-level object representations.

A Demand-Side Program Simulation Tool is designed to predict the response from different deployment strategies of distributed domestic energy management. To date, there are several case studies of demand management and control projects from around the world. To achieve results with sufficient generality, case studies need to be conducted over long periods, with a reasonable number of diverse households. Such case studies require large capital to set up hardware and software.To bypass these financial and temporal investments, we have designed a simulator for energy suppliers to use in order to learn the likely performance of large-scale deployments. Of main interest is the prediction of not only the level and firmness of demand response in critical peak pricing trials, but also the household's comfortable level and satisfaction level. As an example of the power of the simulator we have used it to develop and test a new selfadaptive methodology to intelligently control the energy demand. The methodology is adaptive to global factors, such as the market energy price, as well as local conditions, such as the satisfaction level of households. This paper outlines self-adaptive methodologies used within the simulator. Experimental results show energy consumption under different control strategies and the improvement of system behaviour through adaptive design. With the self-adaptive demand management strategy, the total energy consumed by one million households' controllable loads has reduced dramatically while the satisfaction level of households is well maintained. This is one of the very first simulators that take into account both technical and human behaviour aspects.

Purpose People enjoy supervision during visual field assessment, although resource demands often make this difficult. We evaluated outcomes and subjective experience of methods of receiving feedback during perimetry, with specific goals to compare a humanoid robot to a computerized voice in participants with minimal prior perimetric experience. Human feedback and no feedback also were compared. Methods Twenty-two younger (aged 21–31 years) and 18 older (aged 52–76 years) adults participated. Visual field tests were conducted using an Octopus 900, controlled with the Open Perimetry Interface. Participants underwent four tests with the following feedback conditions: (1) human, (2) humanoid robot, (3) computer speaker, and (4) no feedback, in random order. Feedback rules for the speaker and robot were identical, with the difference being a social interaction with the robot before the test. Quantitative perimetric performance compared mean sensitivity (dB), fixation losses, and false-positives. Subjective experience was collected via survey. Results There was no significant effect of feedback type on the quantitative measures. For younger adults, the human and robot were preferred to the computer speaker ( P < 0.01). For older adults, the experience rating was similar for the speaker and robot. No feedback was the least preferred option of 77% younger and 50% older adults. Conclusions During perimetry, a social robot was preferred to a computer speaker providing the same feedback, despite the robot not being visible during the test. Making visual field testing more enjoyable for patients and operators may improve compliance and attitude to perimetry, leading to improved clinical outcomes. Translational Relevance Our data suggest that humanoid robots can replace some aspects of human interaction during perimetry and are preferable to receiving no human feedback.

In order to cope with the unpredictability of the energy market and provide rapid response when supply is strained by demand, an emerging technology, called energy demand management, enables appliances to manage and defer their electricity consumption when price soars. Initial experiments with our multi-agent, power load management simulator, showed a marked reduction in energy consumption when price-based constraints were imposed on the system. However, these results also revealed an unforeseen, negative effect: that reducing consumption for a bounded time interval decreases system stability. The reason is that price-driven control synchronizes the energy consumption of individual agents. Hence price, alone, is an insufficient measure to define global goals in a power load management system. In this article we explore the effectiveness of a multi-objective, system-level goal which combines both price and system stability. We apply the commonly known reinforcement learning framework, enabling the energy distribution system to be both cost saving and stable.

Studying illusions provides insight into the way the brain processes information. The Müller-Lyer Illusion (MLI) is a classical geometrical illusion of size, in which perceived line length is decreased by arrowheads and increased by arrowtails. Many theories have been put forward to explain the MLI, such as misapplied size constancy scaling, the statistics of image-source relationships and the filtering properties of signal processing in primary visual areas. Artificial models of the ventral visual processing stream allow us to isolate factors hypothesised to cause the illusion and test how these affect classification performance. We trained a feed-forward feature hierarchical model, HMAX, to perform a dual category line length judgment task (short versus long) with over 90% accuracy. We then tested the system in its ability to judge relative line lengths for images in a control set versus images that induce the MLI in humans. Results from the computational model show an overall illusory effect similar to that experienced by human subjects. No natural images were used for training, implying that misapplied size constancy and image-source statistics are not necessary factors for generating the illusion. A post-hoc analysis of response weights within a representative trained network ruled out the possibility that the illusion is caused by a reliance on information at low spatial frequencies. Our results suggest that the MLI can be produced using only feed-forward, neurophysiological connections.