There are plenty of strategies to train your dog, but is there a particularly effective method to get your pet pal to pay attention to you? A team of scientists believes the most successful technique likely involves combining two tried-and-true signals--and they gathered data from canines strapped with eye-tracking headgear to back up their theory. Dog owners frequently try communicating with their pets by looking or pointing directly at an object, but a team at the University of Veterinary Medicine Vienna recently wondered if either method (or a combination of the two) worked best. Led by comparative cognition postdoctoral candidate Christoph Völter, researchers introduced various communication scenarios to dogs to learn the answer. To evaluate the best human-to-dog strategy, a researcher first sat on their knees with a bowl on either side of them, only one of which contained a concealed treat.

Pet dogs know when you intend to give them a treat, even if you drop it where they can't get to it Dogs can understand when humans mean well, even if they don't get what they want from us. Prior to this work, the ability to distinguish between a human being unwilling or unable to perform a task had only been found in non-human primates. The close social bond between humans and canines is well established, but researchers have a limited understanding of if and how dogs comprehend human intent. To see if pet dogs can distinguish between intentional and accidental actions by strangers, Christoph Völter at the University of Veterinary Medicine Vienna in Austria and his colleagues ran tests with humans offering dogs food while the animals' body movements were tracked using eight cameras. Each dog and human were separated by a transparent plastic panel with holes that a slice of sausage could be passed through.

When 3D animated balls on a computer screen defy certain laws of physics, dogs act in a way that suggests they feel like their eyes are deceiving them. Pet dogs stare for longer and their pupils widen if virtual balls start rolling on their own rather than being set in motion by a collision with another ball. This suggests that the animals are surprised that the balls didn't move the way they had expected them to, says Christoph Völter at the University of Veterinary Medicine, Vienna. "This is the starting point for learning," says Völter. "You have expectations about the environment – regularities in your environment that are connected to physics – and then something happens that doesn't fit. And now you pay attention. And now you try to see what's going on."

This paper presents a new approach to identifying and eliminating mislabeled training instances for supervised learning. The goal of this approach is to improve classification accuracies produced by learning algorithms by improving the quality of the training data. Our approach uses a set of learning algorithms to create classifiers that serve as noise filters for the training data. We evaluate single algorithm, majority vote and consensus filters on five datasets that are prone to labeling errors. Our experiments illustrate that filtering significantly improves classification accuracy for noise levels up to 30 percent. An analytical and empirical evaluation of the precision of our approach shows that consensus filters are conservative at throwing away good data at the expense of retaining bad data and that majority filters are better at detecting bad data at the expense of throwing away good data. This suggests that for situations in which there is a paucity of data, consensus filters are preferable, whereas majority vote filters are preferable for situations with an abundance of data.