Screams contain a 'calling card' for the vocalizer's identity

"Our findings add to our understanding of how screams are evolutionarily important," says Emory psychologist Harold Gouzoules, senior author of the paper.

By Carol Clark

Human screams convey a level of individual identity that may help explain their evolutionary origins, finds a study by scientists at Emory University.

PeerJ published the research, showing that listeners can correctly identify whether pairs of screams were produced by the same person or two different people — a critical prerequisite to individual recognition.

“Our findings add to our understanding of how screams are evolutionarily important,” says Harold Gouzoules, senior author of the paper and an Emory professor of psychology. “The ability to identify who is screaming is likely an adaptive mechanism. The idea is that you wouldn’t respond equally to just anyone’s scream. You would likely respond more urgently to a scream from your child, or from someone else important to you.”

Jonathan Engelberg is first author of the paper and Jay Schwartz is a co-author. They are both Emory PhD candidates in Gouzoules’ Bioacoustics Lab.

The ability to recognize individuals by distinctive cues or signals is essential to the organization of social behavior, the authors note, and humans are adept at making identity-related judgements based on speech — even when the speech is heavily altered. Less is known, however, about identity cues in nonlinguistic vocalizations, such as screams.

Gouzoules first began researching monkey screams in 1980, before becoming one of the few scientists studying human screams about 10 years ago.

“The origin of screams was likely to startle a predator and make it jump, perhaps allowing the prey a small chance to escape,” Gouzoules says. “That’s very different from calling out for help.”

He theorizes that as some species became more social, including monkeys and other primates, screams became a way to recruit help from relatives and friends when someone got into trouble.

Previous research by Gouzoules and others suggests that non-human primates are able to identify whether a scream is coming from an individual that is important to them. Some researchers, however, have disputed the evidence, arguing that the chaotic and inconsistent nature of screams does not make them likely conduits for individual recognition.

Gouzoules wanted to test whether humans could determine if two fairly similar screams were made by the same person or a different person. His Bioacoustics Lab has amassed an impressive library of high-intensity, visceral sounds — from TV and movie performances to the screams of non-actors reacting to actual events on YouTube videos.

For the PeerJ paper, the lab ran experiments that included 104 participants. The participants listened to audio files of pairs of screams on a computer, without any visual cues for context. Each pair was presented two seconds apart and participants were asked to determine if the screams came from the same person or a different person.

In some trials, the two screams came from two different callers, but were matched by age, gender and the context of the scream. In other trials, the screams came from the same caller but were two different screams matched for context. And in a third trial, the stimulus pairs consisted of a scream and a slightly modified version of itself, to make it longer or shorter than the original.

For all three of the experiments, most of the participants were able to correctly judge most of the time whether the screams were from the same person or not.

“Our results provide empirical evidence that screams carry enough information for listeners to discriminate between different callers,” Gouzoules says. “Although screams may not be acoustically ideal for signaling a caller’s identity, natural selection appears to have adequately shaped them so they are good enough to do the job.”

The PeerJ paper is part of an extensive program of research into screams by Gouzoules. In previous work, his lab has found that listeners cannot distinguish acted screams from naturally occurring screams.

In upcoming papers, he is zeroing in on how people determine whether they are hearing a scream or some other vocalization and how they perceive the emotional context of a scream — judging whether it’s due to happiness, anger, fear or pain.

Photo: Getty Images

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The whisper of schizophrenia: Machine learning finds 'sound' words predict psychosis

"Machine learning technology is advancing so rapidly that it's giving us tools to data mine the human mind," says Emory psychologist Phillip Wolff, senior author of the study.

By Carol Clark

A machine-learning method discovered a hidden clue in people’s language predictive of the later emergence of psychosis — the frequent use of words associated with sound. The journal npj Schizophrenia published the findings by scientists at Emory University and Harvard University.

The researchers also developed a new machine-learning method to more precisely quantify the semantic richness of people’s conversational language, a known indicator for psychosis.

Their results show that automated analysis of the two language variables — more frequent use of words associated with sound and speaking with low semantic density, or vagueness — can predict whether an at-risk person will later develop psychosis with 93 percent accuracy.

Even trained clinicians had not noticed how people at risk for psychosis use more words associated with sound than the average, although abnormal auditory perception is a pre-clinical symptom.

“Trying to hear these subtleties in conversations with people is like trying to see microscopic germs with your eyes,” says Neguine Rezaii, first author of the paper. “The automated technique we’ve developed is a really sensitive tool to detect these hidden patterns. It’s like a microscope for warning signs of psychosis.”

Rezaii began work on the paper while she was a resident at Emory School of Medicine’s Department of Psychiatry and Behavioral Sciences. She is now a fellow in Harvard Medical School’s Department of Neurology.

“It was previously known that subtle features of future psychosis are present in people’s language, but we’ve used machine learning to actually uncover hidden details about those features,” says senior author Phillip Wolff, a professor of psychology at Emory. Wolff’s lab focuses on language semantics and machine learning to predict decision-making and mental health.

“Our finding is novel and adds to the evidence showing the potential for using machine learning to identify linguistic abnormalities associated with mental illness,” says co-author Elaine Walker, an Emory professor of psychology and neuroscience who researches how schizophrenia and other psychotic disorders develop.

The onset of schizophrenia and other psychotic disorders typically occurs in the early 20s, with warning signs — known as prodromal syndrome — beginning around age 17. About 25 to 30 percent of youth who meet criteria for a prodromal syndrome will develop schizophrenia or another psychotic disorder.

Using structured interviews and cognitive tests, trained clinicians can predict psychosis with about 80 percent accuracy in those with a prodromal syndrome. Machine-learning research is among the many ongoing efforts to streamline diagnostic methods, identify new variables, and improve the accuracy of predictions.

Currently, there is no cure for psychosis.

“If we can identify individuals who are at risk earlier and use preventive interventions, we might be able to reverse the deficits,” Walker says. “There are good data showing that treatments like cognitive-behavioral therapy can delay onset, and perhaps even reduce the occurrence of psychosis.”

For the current paper, the researchers first used machine learning to establish “norms” for conversational language. They fed a computer software program the online conversations of 30,000 users of Reddit, a social media platform where people have informal discussions about a range of topics. The software program, known as Word2Vec, uses an algorithm to change individual words to vectors, assigning each one a location in a semantic space based on its meaning. Those with similar meanings are positioned closer together than those with far different meanings.

The Wolff lab also developed a computer program to perform what the researchers dubbed “vector unpacking,” or analysis of the semantic density of word usage. Previous work has measured semantic coherence between sentences. Vector unpacking allowed the researchers to quantify how much information was packed into each sentence.

After generating a baseline of “normal” data, the researchers applied the same techniques to diagnostic interviews of 40 participants that had been conducted by trained clinicians, as part of the multi-site North American Prodrome Longitudinal Study (NAPLS), funded by the National Institutes of Health. NAPLS is focused on young people at clinical high risk for psychosis. Walker is the principal investigator for NAPLS at Emory, one of nine universities involved in the 14-year project.

The automated analyses of the participant samples were then compared to the normal baseline sample and the longitudinal data on whether the participants converted to psychosis.

The results showed that higher than normal usage of words related to sound, combined with a higher rate of using words with similar meaning, meant that psychosis was likely on the horizon.

Strengths of the study include the simplicity of using just two variables — both of which have a strong theoretical foundation — the replication of the results in a holdout dataset, and the high accuracy of its predictions, at above 90 percent.

“In the clinical realm, we often lack precision,” Rezaii says. “We need more quantified, objective ways to measure subtle variables, such as those hidden within language usage.”

Rezaii and Wolff are now gathering larger data sets and testing the application of their methods on a variety of neuropsychiatric diseases, including dementia.

“This research is interesting not just for its potential to reveal more about mental illness, but for understanding how the mind works — how it puts ideas together,” Wolff says. “Machine learning technology is advancing so rapidly that it’s giving us tools to data mine the human mind.”

The work was supported by grants from the National Institutes of Health and a Google Research Award.

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A focus on fathers: The science of dads

Anthropologist James Rilling with his son Toby, 8, and daughter Mia, 2. (Photo by Becky Stein)

Want to do something special for a father on June 16? Try asking him what he finds most rewarding — and most challenging — about being a dad.

James Rilling, an anthropologist at Emory University, recently completed in-depth interviews on that topic with 120 new fathers. Rilling and his colleague Craig Hadley, also an anthropologist at Emory, are still analyzing data from the interviews for a comprehensive study.

One result, however, is already clear. A positive-and-negative-affect scale administered to the subjects before and after the interviews shows how talking about fatherhood influenced their moods. “Most of them experienced an increase in how enthusiastic, proud and inspired they felt after talking about their experience as a father,” Rilling says. “They seemed to find it therapeutic to talk about their feelings surrounding being a father, particularly if they were struggling with some things. The challenges of being a mother are often much greater. So fathers may think that nobody really wants to hear about the things they are dealing with as a new parent.”

Read more about Rilling's work here, and learn five surprising facts about fathers.

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How chronic inflammation may drive down dopamine and motivation

“If our theory is correct, then it could have a tremendous impact on treating cases of depression and other behavioral disorders that may be driven by inflammation,” says co-author Andrew Miller, an Emory professor of psychiatry. (Getty Images)

By Carol Clark

Growing evidence shows that the brain’s dopamine system, which drives motivation, is directly affected by chronic, low-grade inflammation. A new paper proposes that this connection between dopamine, effort and the inflammatory response is an adaptive mechanism to help the body conserve energy.

Trends in Cognitive Sciences published the theoretical framework developed by scientists at Emory University. The authors also provided a computational method to experimentally test their theory.

“When your body is fighting an infection or healing a wound, your brain needs a mechanism to recalibrate your motivation to do other things so you don’t use up too much of your energy,” says corresponding author Michael Treadway, an associate professor in Emory’s Department of Psychology, who studies the relationship between motivation and mental illness. “We now have strong evidence suggesting that the immune system disrupts the dopamine system to help the brain perform this recalibration.”

The computational method will allow scientists to measure the effects of chronic inflammation on energy availability and effort-based decision-making. The method may yield insights into how chronic, low-grade inflammation contributes to motivational impairments in some cases of depression, schizophrenia and other medical disorders.

Co-author Andrew Miller, William P. Timmie Professor of Psychiatry and Behavioral Sciences in Emory’s School of Medicine and the Winship Cancer Institute, is a leader in this field and is pioneering the development of immunotherapeutic strategies for the treatment of psychiatric disorders.

“If our theory is correct, then it could have a tremendous impact on treating cases of depression and other behavioral disorders that may be driven by inflammation,” Miller says. “It would open up opportunities for the development of therapies that target energy utilization by immune cells, which would be something completely new in our field.”

Co-author Jessica Cooper, a post-doctoral fellow in Treadway’s lab, led the development of the computational model.

It has previously been shown that inflammatory cytokines — signaling molecules used by the immune system — impact the mesolimbic dopamine system. And recent research has revealed more insights into how immune cells can shift their metabolic states differently from most other cells.

The researchers built on these findings to develop their theoretical framework.

An immune-system mechanism to help regulate the use of energy resources during times of acute stress was likely adaptive in our ancestral environments, rife with pathogens and predators. In modern environments, however, many people are less physically active and may have low-grade inflammation due to factors such as chronic stress, obesity, metabolic syndrome, aging and other factors. Under these conditions, the same mechanism to conserve energy for the immune system could become maladaptive, the authors theorize.

Studies by Miller and others have provided evidence of an association between an elevated immune system, reduced levels of dopamine and motivation, and some diagnoses of depression, schizophrenia and other mental disorders.

“We’re not proposing that inflammation causes these disorders,” Treadway says. “The idea is that a subset of people with these disorders may have a particular sensitivity to the effects of the immune system and this sensitivity could contribute to the motivational impairments they are experiencing.”

The researchers are now using their computational method to test their theory in a clinical trial on depression.

The work for the current paper was supported by the National Institute of Mental Health.

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