A naturalistic environment to study visual cognition in unrestrained monkeys.

Macaque monkeys are widely used to study vision. In the traditional approach, monkeys are brought into a lab to perform visual tasks while they are restrained to obtain stable eye tracking and neural recordings. Here, we describe a novel environment to study visual cognition in a more natural setting as well as other natural and social behaviors. We designed a naturalistic environment with an integrated touchscreen workstation that enables high-quality eye tracking in unrestrained monkeys. We used this environment to train monkeys on a challenging same-different task. We also show that this environment can reveal interesting novel social behaviors. As proof of concept, we show that two naive monkeys were able to learn this complex task through a combination of socially observing trained monkeys and solo trial-and-error. We propose that such naturalistic environments can be used to rigorously study visual cognition as well as other natural and social behaviors in freely moving monkeys.

Qualitative similarities and differences in visual object representations between brains and deep networks.

Deep neural networks have revolutionized computer vision, and their object representations across layers match coarsely with visual cortical areas in the brain. However, whether these representations exhibit qualitative patterns seen in human perception or brain representations remains unresolved. Here, we recast well-known perceptual and neural phenomena in terms of distance comparisons, and ask whether they are present in feedforward deep neural networks trained for object recognition. Some phenomena were present in randomly initialized networks, such as the global advantage effect, sparseness, and relative size. Many others were present after object recognition training, such as the Thatcher effect, mirror confusion, Weber's law, relative size, multiple object normalization and correlated sparseness. Yet other phenomena were absent in trained networks, such as 3D shape processing, surface invariance, occlusion, natural parts and the global advantage. These findings indicate sufficient conditions for the emergence of these phenomena in brains and deep networks, and offer clues to the properties that could be incorporated to improve deep networks.

How the forest interacts with the trees: Multiscale shape integration explains global and local processing.

Hierarchical stimuli have been widely used to study global and local processing. Two classic phenomena have been observed using these stimuli: the global advantage effect (we identify the global shape faster) and an interference effect (we identify shape slower when the global and local shapes are different). Because these phenomena have been observed during shape categorization tasks, it is unclear whether they reflect the categorical judgment or the underlying shape representation. Understanding the underlying shape representation is also critical because both global and local processing are modulated by stimulus properties. We performed two experiments to investigate these issues. In Experiment 1, we show that these phenomena can be observed in a same-different task, and that participants show systematic variation in response times across image pairs. We show that the response times to any pair of images can be accurately predicted using two factors: their dissimilarity and their distinctiveness relative to other images. In Experiment 2, we show that these phenomena can also be observed in a visual search task where participant did not have to make any categorical shape judgments. Here too, participants showed highly systematic variations in response time that could be explained as a linear sum of shape comparisons across global and local scales. Finally, the dissimilarity and distinctiveness factors estimated from the same-different task were systematically related to the search dissimilarities observed during visual search. In sum, our results show that global and local processing phenomena are properties of a systematic shape representation governed by simple rules.