Dogs recognize familiar faces from images

So far the specialized skill for recognizing facial features holistically has been assumed to be a quality that only humans and possibly primates possess. Although it’s well known, that faces and eye contact play an important role in the communication between dogs and humans, this was the first study, where facial recognition of dogs was investigated with eye movement tracking.

Main focus on spontaneous behavior of dogs

Typically animals’ ability to discriminate different individuals has been studied by training the animals to discriminate photographs of familiar and strange individuals. The researchers, led by Professor Outi Vainio at the University of Helsinki, tested dogs’ spontaneous behavior towards images – if the dogs are not trained to recognize faces are they able to see faces in the images and do they naturally look at familiar and strange faces differently?

“Dogs were trained to lie still during the image presentation and to perform the task independently. Dogs seemed to experience the task rewarding, because they were very eager to participate” says professor Vainio. Dogs’ eye movements were measured while they watched facial images of familiar humans and dogs (e.g. dog’s owner and another dog from the same family) being displayed on the computer screen. As a comparison, the dogs were shown facial images from dogs and humans that the dogs had never met.

Dogs preferred faces of familiar conspecifics

The results indicate that dogs were able to perceive faces in the images. Dogs looked at images of dogs longer than images of humans, regardless of the familiarity of the faces presented in the images. This corresponds to a previous study by Professor Vainio’s research group, where it was found that dogs prefer viewing conspecific faces over human faces.

Dogs fixed their gaze more often on familiar faces and eyes rather than strange ones, i.e. dogs scanned familiar faces more thoroughly.

In addition, part of the images was presented in inverted forms i.e. upside-down. The inverted faces were presented because their physical properties correspond to normal upright facial images e.g. same colors, contrasts, shapes. It’s known that the human brain process upside-down images in a different way than normal facial images. Thus far, it had not been studied how dogs gaze at inverted or familiar faces. Dogs viewed upright faces as long as inverted faces, but they gazed more at the eye area of upright faces, just like humans.

This study shows that the gazing behavior of dogs is not only following the physical properties of images, but also the information presented in the image and its semantic meaning. Dogs are able to see faces in the images and they differentiate familiar and strange faces from each other. These results indicate that dogs might have facial recognition skills, similar to humans.

Visual representations improved by reducing noise

Neuroscientist Suresh Krishna from the German Primate Center (DPZ) in cooperation with Annegret Falkner and Michael Goldberg at Columbia University, New York has revealed how the activity of neurons in an important area of the rhesus macaque’s brain becomes less variable when they represent important visual information during an eye movement task. This reduction in variability can improve the perceptual strength of attended or relevant aspects in a visual scene, and is enhanced when the animals are more motivated to perform the task.

Humans may see the same object again and again, but their brain response will be different each time, a phenomenon called neuronal noise. The same is true for rhesus macaques, which have a visual system very similar to that of humans. This variability often limits our ability to see a dim object or hear a faint sound. On the other hand, we benefit from variable responses as they are considered an essential part of the exploration stage of learning and for generating unpredictability during competitive interactions.

Despite this importance, brain variability is poorly understood. Neuroscientists Suresh Krishna of the DPZ and his colleagues Annegret Falkner and Michael Goldberg at Columbia University in New York examined the responses of neurons in the monkey brain’s lateral intraparietal area (LIP) while the monkey planned eye movements to spots of light at different locations on a computer screen. LIP is an area in the brain that is crucial for visual attention and for actively exploring visual scenes. To measure the activity of single LIP neurons, the scientists inserted electrodes thinner than a human hair into the monkey’s brain and recorded the neurons’ electrical activity. Because the brain is not pain-sensitive, this insertion of electrodes is painless for the animal.

Suresh Krishna and his colleagues could show how the activity of LIP neurons becomes less variable when the macaque performs a task and plans an eye movement. The reduction in variability was particularly strong where the monkey was planning to look and when the monkey was highly motivated to perform the task. This creation of a valley of reduced variability centered on relevant and interesting aspects of a visual scene may help the brain to filter the most important aspects from the sensory information delivered by the eye. The scientists developed a simple mathematical model that captures the patterns in the data and may also be a useful framework for the analysis of other brain areas.

"Our study represents one of the most detailed descriptions of neuronal variability in the brain. It offers important insights into fascinating brain functions as diverse as the focusing of visual attention and the control of eye movements during active viewing of visual scenes. The brain’s valley of variability that we discovered may help humans and animals to interact with their complex environment.", Suresh Krishna comments on the findings.

To predict, perchance to update: Neural responses to the unexpected

Among the brain’s many functions is the use of predictive models to processing expected stimuli or actions. In such a model, we experience surprise when presented with an unexpected stimulus – that is, one which the model evaluates as having a low probability of occurrence. Interestingly, there can be two distinct – but often experimentally correlated – responses to a surprising event: reallocating additional neural resources to reprogram actions, and updating the predictive model to account for the new environmental stimulus. Recently, scientists at Oxford University used brain imaging to identify separate brain systems involved in reprogramming and updating, and created a mathematical and neuroanatomical model of how brains adjust to environmental change, Moreover, the researchers conclude that their model may also inform models of neurological disorders, such as extinction, Balint syndrome and neglect, in which this adaptive response to surprise fails.

Research Fellow Jill X. O’Reilly discussed the research she and her colleagues conducted with Medical Xpress. “Sometimes we think of the brain as an input-output device which takes sensory information, processes it, and produces actions appropriately – but in fact, brains don’t passively ‘sit around’ waiting for sensory input,” O’Reilly explains. “Rather, they actively predict what is going to happen next, because by being prepared, they can process stimuli more efficiently.”

O’Reilly cites an important example of predictive processing, which the researchers used in their study: the control of eye movements. “You can actually only process quite a small portion of visual space accurately at any one time, which is why people tend to actively look at interesting objects,” O’Reilly tells Medical Xpress. “Parts of the brain that control eye movements – for example, the parietal cortex – are actively involved in trying to predict where visual objects that are worth looking at will occur next, in order to respond to them quickly and effectively.” Since the scientists were interested in how the brain forms predictions – such as where eye movements should be directed – they designed an experiment in which people’s expectations about where they should make eye movements were built up over time and then suddenly changed. (They did this moving the stimuli participants’ were instructed to fixate on to a different part of the computer screen.)

"However," notes O’Reilly, "we know from previous work that activity in many brain areas is evoked when people are expecting to make an eye movement to one place, and actually they have to make an eye movement to another. A lot of this brain activity has to do with reprogramming the eye movement itself, rather than learning about the changed environment. That means we needed to design an experiment in which re-planning of eye movements was sometimes accompanied by learning, and sometimes not." The researchers accomplished this by color-coding stimuli: participants knew that colorful stimuli indicated a real change in the environment, while grey stimuli were to be ignored.

To quantify how much participants learned on each trial of the experiment, the team constructed a computer participant that learned about the environment in the same way the real, human participants did. Because they could determine exactly what the computer participant knew or believed about the environment – that is, where it would need to look – on each trial, we could get mathematical measures of how surprising it found each stimulus (defined as how far the stimulus location was from where the computer participant expected it to be) and how much it learned on each trial.

Therefore, the computer participant allowed the scientists to separately measure the degree to which human participants had to respond to surprise in terms of reprogramming eye movements, and how much they learned on each trial. “We then needed to work out whether some parts of the brain were specifically involved in each of these processes,” O’Reilly continues. “To do this we used fMRI and looked for areas that increased their activity in proportion to how much the computer participant, and thereby the real participants, would need to reprogram their eye movements for each surprising stimulus – as well as the extent to which they’d have to update their predictions about future stimulus locations – on each trial.”

O’Reilly stresses that the computer participant was critical to addressing the challenges they encountered. “We had access to a complete model of what participants could know or should believe about where stimuli were expected to appear on each trial. That meant we could make very specific predictions about how much they should be surprised by certain stimuli and how much they learned from each stimulus.” The team checked these predictions by looking at behavioral measures like reaction time (participants were slower to move their eyes to surprising stimuli) and gaze dwell time (participants looked at stimuli for longer when the stimuli carried information about the possible locations of future stimuli).

O’Reilly describes how their study may inform understanding of neurological disorders in which this adjustment process fails by observing that a second saccade-sensitive region in the inferior posterior parietal cortex was activated by surprise and modulated by updating. “Some stroke victims are unable to move their eyes in order to look at stimuli that show up in their visual periphery, which turns out to be similar to the process of reprogramming to surprising stimuli in our model. In contrast,” she continues, “people with brain lesions in a slightly different brain region are able to move their eyes to look at stimuli, but seem unable to learn that stimuli could occur in some parts of space – usually towards the left of the body – even if given lots of hints and training.” Because the brain regions damaged in these two patient groups map onto the regions of parietal cortex active in the experiment’s reprogramming and updating conditions, the researchers think these two processes could be differentially affected in the two patient groups.

Moving forward, the researchers would like to test their paradigm in patients who have had strokes that damaged the different brain regions activated in their study. “We’d expect to find a difference between patients with damage in different parts of parietal cortex, such that one group might be slower to reprogram eye movements to all surprising stimuli whether these stimuli are informative about future stimulus locations or not,” O’Reilly concludes, “whereas the other group might have trouble learning that the location where stimuli are going to appear has changed.”

Impaired visual signals might contribute to schizophrenia symptoms

By observing the eye movements of schizophrenia patients while playing a simple video game, a University of British Columbia researcher has discovered a potential explanation for some of their symptoms, including difficulty with everyday tasks.

The research, published in a recent issue of the Journal of Neuroscience, shows that, compared to healthy controls, schizophrenia patients had a harder time tracking a moving dot on the computer monitor with their eyes and predicting its trajectory. But the impairment of their eye movements was not severe enough to explain the difference in their predictive performance, suggesting a breakdown in their ability to interpret what they saw.

Lead author Miriam Spering, an assistant professor of ophthalmology and visual sciences, says the patients were having trouble generating or using an “efference copy” – a signal sent from the eye movement system in the brain indicating how much, and in what direction, their eyes have moved. The efference copy helps validate visual information from the eyes.

"An impaired ability to generate or interpret efference copies means the brain cannot correct an incomplete perception," says Spering, who conducted the dot-tracking experiments as a postdoctoral fellow at New York University, and is now conducting similar studies at UBC. The brain might fill in the blanks by extrapolating from prior experience, contributing to psychotic symptoms, such as hallucinations.

My vision would be a mobile device that patients could use to practice that skill, so they could more easily do common tasks that involve motion perception, such as walking along a crowded sidewalk.

"But just as a person might, through practice, improve their ability to predict the trajectory of a moving dot, a person might be able to improve their ability to generate or use that efference copy," Spering says. "My vision would be a mobile device that patients could use to practice that skill, so they could more easily do common tasks that involve motion perception, such as walking along a crowded sidewalk."

Face Identification Accuracy is in the Eye (and Brain) of the Beholder

Though humans generally have a tendency to look at a region just below the eyes and above the nose toward the midline when first identifying another person, a small subset of people tend to look further down –– at the tip of the nose, for instance, or at the mouth. However, as UC Santa Barbara researchers Miguel Eckstein and Matthew Peterson recently discovered, “nose lookers” and “mouth lookers” can do just as well as everyone else when it comes to the split-second decision-making that goes into identifying someone. Their findings are in a recent issue of the journal Psychological Science.

"It was a surprise to us," said Eckstein, professor in the Department of Psychological & Brain Sciences, of the ability of that subset of "nose lookers" and "mouth lookers" to identify faces. In a previous study, he and postdoctoral researcher Peterson established through tests involving a series of face images and eye-tracking software that most humans tend to look just below the eyes when identifying another human being and when forced to look somewhere else, like the mouth, their face identification accuracy suffers.

The reason we look where we look, said the researchers, is evolutionary. With survival at stake and only a limited amount of time to assess who an individual might be, humans have developed the ability to make snap judgments by glancing at a place on the face that allows the observer’s eye to gather a massive amount of information, from the finer features around the eyes to the larger features of the mouth. In 200 milliseconds, we can tell whether another human being is friend, foe, or potential mate. The process is deceptively easy and seemingly negligible in its quickness: Identifying another individual is an activity on which we embark virtually from birth, and is crucial to everything from day-to-day social interaction to life-or-death situations. Thus, our brain devotes specialized circuitry to face recognition.

"One of, if not the most, difficult task you can do with the human face is to actually identify it," said Peterson, explaining that each time we look at someone’s face, it’s a little different –– perhaps the angle, or the lighting, or the face itself has changed –– and our brains constantly work to associate the current image with previously remembered images of that face, or faces like it, in a continuous process of recognition. Computer vision has nowhere near that capacity in identifying faces, yet.

So it would seem to follow that those who look at other parts of a person’s face might perform less well, and might be slower to recognize potential threats, or opportunities.

Or so the researchers thought. In a series of tests involving face identification tasks, the researchers found a small group that departed from the typical just-below-the-eyes gaze. The observers were Caucasian, had normal or corrected to normal vision, and no history of neurological disorders –– all qualities which controlled for cultural, physical, or neurological elements that could influence a person’s gaze.

But instead of performing less well, as would have been predicted by the theoretical analysis of the investigators, the participants were still able to identify faces with the same degree of accuracy as just-below-the-eyes lookers. Furthermore, when these nose-looking participants were forced to look at the eyes to do the identification, their accuracy degraded.

The findings both fascinate and set up a chicken-and-egg scenario for the researchers. One possibility is that people tailor their eye movement to the properties of their visual system –– everything from their eye structures to the brain functions they are born with and develop. If, for example, one is able to see well in the upper visual field (the region above where they look), they can afford to look lower on the face without losing the detail around the eyes when identifying someone. According to Eckstein, it is known that most humans tend to see better in the lower visual field.

The other possibility is the reverse –– that our visual systems adapt to our looking behavior. If at an early age a person developed the habit of looking lower on the face to identify someone else, over time brain circuits specialized for face identification could develop and arrange itself around that tendency.

"The main finding is that people develop distinct optimal face-looking strategies that maximize face identification accuracy," said Peterson. "In our framework, an optimized strategy or behavior is one that results in maximized performance. Thus, when we say that the observer-looking behavior was self-optimal, it refers to each individual fixating on locations that maximize their identification accuracy."

Future research will delve deeper into the mechanisms involved in those who look lower on the face to determine what could drive that gaze pattern and what information is gathered.

Never forget a face? Researchers find women have better memory recall than men

New research from McMaster University suggests women can remember faces better than men, in part because they spend more time studying features without even knowing it, and a technique researchers say can help improve anyone’s memories.

The findings help to answer long-standing questions about why some people can remember faces easily while others quickly forget someone they’ve just met.

“The way we move our eyes across a new individual’s face affects our ability to recognize that individual later,” explains Jennifer Heisz, a research fellow at the Rotman Research Institute at Baycrest Health Sciences and newly appointed assistant professor in the Department of Kinesiology at McMaster University.

She co-authored the paper with David Shore, psychology professor at McMaster and psychology graduate student Molly Pottruff.

“Our findings provide new insights into the potential mechanisms of episodic memory and the differences between the sexes. We discovered that women look more at new faces than men do, which allows them to create a richer and more superior memory,” Heisz says.

Eye tracking technology was used to monitor where study participants looked—be it eyes, nose or mouth—while they were shown a series of randomly selected faces on a computer screen. Each face was assigned a name that participants were asked to remember.

One group was tested over the course of one day, another group tested over the course of four days.

“We found that women fixated on the features far more than men, but this strategy operates completely outside of our awareness. Individuals don’t usually notice where their eyes fixate, so it’s all subconscious.”

The implications are exciting, she says, because it means anyone can be taught to scan more and potentially have better memory.

“The results open the possibility that changing our eye movement pattern may lead to better memory,” says Shore. “Increased scanning may prove to be a simple strategy to improve face memory in the general population, especially for individuals with memory impairment like older adults.”