What was the National Survey of Mental Health and Wellbeing?


The 2007 National Survey of Mental Health and Wellbeing (NSMHWB) was designed to provide lifetime prevalence estimates for mental disorders.


To gain statistics on key mental health issues including the prevalence of mental disorders, the associated disability, and the use of services.

As such the NSMHWB was a national epidemiological survey of mental disorders that used similar methodology to the NCS. It aimed to answer three main questions:

  1. How many people meet DSM-IV and ICD-10 diagnostic criteria for the major mental disorders?
  2. How disabled are they by their mental disorders? and
  3. How many have seen a health professional for their mental disorder?


Respondents were asked about experiences throughout their lifetime. In this survey, 12-month diagnoses were derived based on lifetime diagnosis and the presence of symptoms of that disorder in the 12 months prior to the survey interview. Assessment of mental disorders presented in this publication are based on the definitions and criteria of the World Health Organisation’s (WHO) International Classification of Diseases, Tenth Revision (ICD-10). Prevalence rates are presented with hierarchy rules applied (i.e. a person will not meet the criteria for particular disorders because the symptoms are believed to be accounted for by the presence of another disorder).


  • Among the 16,015,300 people aged 16-85 years, 45% (or 7,286,600 people) had a lifetime mental disorder (i.e. a mental disorder at some point in their life).
  • More than half (55% or 8,728,700 people) of people had no lifetime mental disorders.
  • Of people who had a lifetime mental disorder:
    • 20% (or 3,197,800 people) had a 12-month mental disorder and had symptoms in the 12 months prior to the survey interview; and
    • 25% (or 4,088,800 people) had experienced a lifetime mental disorder but did not have symptoms in the 12 months prior to the survey interview.

Prevalence of 12-Month Mental Health Disorders

Prevalence of mental disorders is the proportion of people in a given population who met the criteria for diagnosis of a mental disorder at a point in time

  • Among the 3,197,800 people (or 20% of people) who had a 12-month mental disorder and had symptoms in the 12 months prior to interview:
    • 14.4% had a 12-month Anxiety disorder (includes Panic disorder (2.6%); Agoraphobia (2.8%); Social Phobia (4.7%); Generalised Anxiety Disorder (2.7%); Obsessive-Compulsive Disorder (1.9%); and Post-Traumatic Stress Disorder (6.4%))
    • 6.2% had a 12-month Affective disorder (includes Depressive Episode (4.1%) (includes severe, moderate and mild depressive episodes); Dysthymia (1.3%); and Bipolar Affective Disorder (1.8%)), and
    • 5.1% had a 12-month Substance Use Disorder (includes Alcohol Harmful Use (2.9%); Alcohol Dependence (1.4%); and Drug Use Disorders (includes harmful use and dependence) (1.4%)).
  • Note that a person may have had more than one mental disorder.
    • The components when added may therefore not add to the total shown.
    • Includes Severe Depressive Episode, Moderate Depressive Episode, and Mild Depressive Episode.
    • Includes Harmful Use and Dependence.

There were 3.2 million people who had a 12-month mental disorder. In total, 14.4% (2.3 million) of Australians aged 16-85 years had a 12-month Anxiety disorder, 6.2% (995,900) had a 12-month Affective disorder and 5.1% (819,800) had a 12-month Substance Use disorder.

Women experienced higher rates of 12-month mental disorders than men (22% compared with 18%). Women experienced higher rates than men of Anxiety (18% and 11% respectively) and Affective disorders (7.1% and 5.3% respectively). However, men had twice the rate of Substance Use disorders (7.0% compared with 3.3% for women).

The prevalence of 12-month mental disorders varies across age groups, with people in younger age groups experiencing higher rates of disorder. More than a quarter (26%) of people aged 16-24 years and a similar proportion (25%) of people aged 25-34 years had a 12-month mental disorder compared with 5.9% of those aged 75-85 years old.

You can read the full survey results here and a shorter analysis can be found here.

What is the WHO Assessment Instrument for Mental Health Systems?


The World Health Organisation Assessment Instrument for Mental Health Systems (WHO-AIMS) is a new WHO tool for collecting essential information on the mental health system of a country or region.


The goal of collecting this information is to improve mental health systems and to provide a baseline for monitoring the change.

What is WHO-AIMS?

WHO-AIMS is a WHO tool for collecting essential information on the mental health system of a country or region. The goal of collecting this information is to improve mental health systems and to provide a baseline for monitoring the change.

For the purpose of WHO-AIMS, a mental health system is defined as all the activities whose primary purpose is to promote, restore or maintain mental health. WHO-AIMS is primarily intended for assessing mental health systems in low and middle income countries, but is also a valuable assessment tool for high resource countries.

Note: Great care has been taken to ensure the reliability of the data presented in the WHO-AIMS country reports. Data for WHO-AIMS are collected by a team led by a focal point within the country and are, in most cases, approved by the Ministry of Health. However, since WHO is not directly responsible for the data collection, WHO cannot independently verify the accuracy of any of the data presented in these reports.

WHO-AIMS Instrument, Version 2.2

You can find country reports, sub-regional reports, and other reports here (Pan American Health Organisation (PAHO) site).

What is Observational Learning?


Observational learning is learning that occurs through observing the behaviour of others.

It is a form of social learning which takes various forms, based on various processes. In humans, this form of learning seems to not need reinforcement to occur, but instead, requires a social model such as a parent, sibling, friend, or teacher with surroundings. Particularly in childhood, a model is someone of authority or higher status in an environment. In animals, observational learning is often based on classical conditioning, in which an instinctive behaviour is elicited by observing the behaviour of another (e.g. mobbing in birds), but other processes may be involved as well.

Human Observational Learning

Many behaviours that a learner observes, remembers, and imitates are actions that models display and display modelling, even though the model may not intentionally try to instil a particular behaviour. A child may learn to swear, smack, smoke, and deem other inappropriate behaviour acceptable through poor modelling. Albert Bandura claims that children continually learn desirable and undesirable behaviour through observational learning. Observational learning suggests that an individual’s environment, cognition, and behaviour all incorporate and ultimately determine how the individual functions and models.

Through observational learning, individual behaviours can spread across a culture through a process called diffusion chain. This basically occurs when an individual first learns a behaviour by observing another individual and that individual serves as a model through whom other individuals learn the behaviour, and so on.

Culture plays a role in whether observational learning is the dominant learning style in a person or community. Some cultures expect children to actively participate in their communities and are therefore exposed to different trades and roles on a daily basis. This exposure allows children to observe and learn the different skills and practices that are valued in their communities.

Albert Bandura, who is known for the classic Bobo doll experiment, identified this basic form of learning in 1961. The importance of observational learning lies in helping individuals, especially children, acquire new responses by observing others’ behaviour.

Albert Bandura states that people’s behaviour could be determined by their environment. Observational learning occurs through observing negative and positive behaviours. Bandura believes in reciprocal determinism in which the environment can influence people’s behaviour and vice versa. For instance, the Bobo doll experiment shows that the model, in a determined environment, affects children’s behaviour. In this experiment Bandura demonstrates that one group of children placed in an aggressive environment would act the same way, while the control group and the other group of children placed in a passive role model environment hardly shows any type of aggression.

In communities where children’s primary mode of learning is through observation, the children are rarely separated from adult activities. This incorporation into the adult world at an early age allows children to use observational learning skills in multiple spheres of life. This learning through observation requires keen attentive abilities. Culturally, they learn that their participation and contributions are valued in their communities. This teaches children that it is their duty, as members of the community, to observe others’ contributions so they gradually become involved and participate further in the community.

Influential Stages and Factors

The stages of observational learning include exposure to the model, acquiring the model’s behaviour and accepting it as one’s own.

Bandura’s social cognitive learning theory states that there are four factors that influence observational learning:

AttentionObservers cannot learn unless they pay attention to what’s happening around them. This process is influenced by characteristics of the model, such as how much one likes or identifies with the model, and by characteristics of the observer, such as the observer’s expectations or level of emotional arousal.
Retention/MemoryObservers must not only recognize the observed behaviour but also remember it at some later time. This process depends on the observer’s ability to code or structure the information in an easily remembered form or to mentally or physically rehearse the model’s actions.
Initiation/MotorObservers must be physically and/intellectually capable of producing the act. In many cases, the observer possesses the necessary responses. But sometimes, reproducing the model’s actions may involve skills the observer has not yet acquired. It is one thing to carefully watch a circus juggler, but it is quite another to go home and repeat those acts.
MotivationThe observer must have motivation to recreate the observed behaviour.

Bandura clearly distinguishes between learning and performance. Unless motivated, a person does not produce learned behaviour. This motivation can come from external reinforcement, such as the experimenter’s promise of reward in some of Bandura’s studies, or the bribe of a parent. Or it can come from vicarious reinforcement, based on the observation that models are rewarded. High-status models can affect performance through motivation. For example, girls aged 11 to 14 performed better on a motor performance task when they thought it was demonstrated by a high-status cheerleader than by a low-status model.

Some have even added a step between attention and retention involving encoding a behaviour.

Observational learning leads to a change in an individual’s behaviour along three dimensions:

  • An individual thinks about a situation in a different way and may have incentive to react to it.
  • The change is a result of a person’s direct experiences as opposed to being in-born.
  • For the most part, the change an individual has made is permanent.

Effect on Behaviour

According to Bandura’s social cognitive learning theory, observational learning can affect behaviour in many ways, with both positive and negative consequences. It can teach completely new behaviours, for one. It can also increase or decrease the frequency of behaviours that have previously been learned. Observational learning can even encourage behaviours that were previously forbidden (for example, the violent behaviour towards the Bobo doll that children imitated in Albert Bandura’s study). Observational learning can also influence behaviours that are similar to, but not identical to, the ones being modelled. For example, seeing a model excel at playing the piano may motivate an observer to play the saxophone.

Age Difference

Albert Bandura stressed that developing children learn from different social models, meaning that no two children are exposed to exactly the same modelling influence. From infancy to adolescence, they are exposed to various social models. A 2013 study found that a toddlers’ previous social familiarity with a model was not always necessary for learning and that they were also able to learn from observing a stranger demonstrating or modelling a new action to another stranger.

It was once believed that babies could not imitate actions until the latter half of the first year. However, a number of studies now report that infants as young as seven days can imitate simple facial expressions. By the latter half of their first year, 9-month-old babies can imitate actions hours after they first see them. As they continue to develop, toddlers around age two can acquire important personal and social skills by imitating a social model.

Deferred imitation is an important developmental milestone in a two-year-old, in which children not only construct symbolic representations but can also remember information. Unlike toddlers, children of elementary school age are less likely to rely on imagination to represent an experience. Instead, they can verbally describe the model’s behaviour. Since this form of learning does not need reinforcement, it is more likely to occur regularly.

As age increases, age-related observational learning motor skills may decrease in athletes and golfers. Younger and skilled golfers have higher observational learning compared to older golfers and less skilled golfers.

Observational Causal Learning

Humans use observational Moleen causal learning to watch other people’s actions and use the information gained to find out how something works and how we can do it ourselves.

A study of 25-month-old infants found that they can learn causal relations from observing human interventions. They also learn by observing normal actions not created by intentional human action.

Comparisons with Imitation

Observational learning is presumed to have occurred when an organism copies an improbable action or action outcome that it has observed and the matching behaviour cannot be explained by an alternative mechanism. Psychologists have been particularly interested in the form of observational learning known as imitation and in how to distinguish imitation from other processes. To successfully make this distinction, one must separate the degree to which behavioural similarity results from:

  • Predisposed behaviour.
  • Increased motivation resulting from the presence of another animal.
  • Attention drawn to a place or object.
  • Learning about the way the environment works, as distinguished from what we think of as.
  • Imitation (the copying of the demonstrated behaviour).

Observational learning differs from imitative learning in that it does not require a duplication of the behaviour exhibited by the model. For example, the learner may observe an unwanted behaviour and the subsequent consequences, and thus learn to refrain from that behaviour. For example, Riopelle (1960) found that monkeys did better with observational learning if they saw the “tutor” monkey make a mistake before making the right choice. Heyes (1993) distinguished imitation and non-imitative social learning in the following way: imitation occurs when animals learn about behaviour from observing conspecifics, whereas non-imitative social learning occurs when animals learn about the environment from observing others.

Not all imitation and learning through observing is the same, and they often differ in the degree to which they take on an active or passive form. John Dewey describes an important distinction between two different forms of imitation: imitation as an end in itself and imitation with a purpose.[19] Imitation as an end is more akin to mimicry, in which a person copies another’s act to repeat that action again. This kind of imitation is often observed in animals. Imitation with a purpose utilizes the imitative act as a means to accomplish something more significant. Whereas the more passive form of imitation as an end has been documented in some European American communities, the other kind of more active, purposeful imitation has been documented in other communities around the world.

Observation may take on a more active form in children’s learning in multiple Indigenous American communities. Ethnographic anthropological studies in Yucatec Mayan and Quechua Peruvian communities provide evidence that the home or community-centred economic systems of these cultures allow children to witness first-hand, activities that are meaningful to their own livelihoods and the overall well-being of the community. These children have the opportunity to observe activities that are relevant within the context of that community, which gives them a reason to sharpen their attention to the practical knowledge they are exposed to. This does not mean that they have to observe the activities even though they are present. The children often make an active decision to stay in attendance while a community activity is taking place to observe and learn. This decision underscores the significance of this learning style in many indigenous American communities. It goes far beyond learning mundane tasks through rote imitation; it is central to children’s gradual transformation into informed members of their communities’ unique practices. There was also a study, done with children, that concluded that Imitated behaviour can be recalled and used in another situation or the same.


Apprenticeship can involve both observational learning and modelling. Apprentices gain their skills in part through working with masters in their profession and through observing and evaluating the work of their fellow apprentices. Examples include renaissance inventor/painter Leonardo da Vinci and Michelangelo, before succeeding in their profession they were apprentices.

Learning Without Imitation

Michael Tomasello described various ways of observational learning without the process of imitation in animals (ethology):

ExposureIndividuals learn about their environment through close proximity to other individuals that have more experience. For example, a young dolphin learning the location of a plethora of fish by staying near its mother.
Stimulus EnhancementIndividuals become interested in an object from watching others interact with it. Increased interest in an object may result in object manipulation, which facilitates new object-related behaviours by trial-and-error learning. For example, a young killer whale might become interested in playing with a sea lion pup after watching other whales toss the sea lion pup around. After playing with the pup, the killer whale may develop foraging behaviours appropriate to such prey. In this case, the killer whale did not learn to prey on sea lions by observing other whales do so, but rather the killer whale became intrigued after observing other whales play with the pup. After the killer whale became interested, then its interactions with the sea lion resulted in behaviours that provoked future foraging efforts.
Goal EmulationIndividuals are enticed by the end result of an observed behaviour and attempt the same outcome but with a different method. For example, Haggerty (1909) devised an experiment in which a monkey climbed up the side of a cage, stuck its arm into a wooden chute, and pulled a rope in the chute to release food. Another monkey was provided an opportunity to obtain the food after watching a monkey go through this process on four separate occasions. The monkey performed a different method and finally succeeded after trial and error.

Peer Model Influences

Observational learning is very beneficial when there are positive, reinforcing peer models involved. Although individuals go through four different stages for observational learning: attention; retention; production; and motivation, this does not simply mean that when an individual’s attention is captured that it automatically sets the process in that exact order. One of the most important ongoing stages for observational learning, especially among children, is motivation and positive reinforcement.

Performance is enhanced when children are positively instructed on how they can improve a situation and where children actively participate alongside a more skilled person. Examples of this are scaffolding and guided participation. Scaffolding refers to an expert responding contingently to a novice so the novice gradually increases their understanding of a problem. Guided participation refers to an expert actively engaging in a situation with a novice so the novice participates with or observes the adult to understand how to resolve a problem.

Cultural Variation

Cultural variation can be seen by the extent of information learned or absorbed by children in non-Western cultures through learning by observation. Cultural variation is not restricted only to ethnicity and nationality, but rather, extends to the specific practices within communities. In learning by observation, children use observation to learn without verbal requests for further information, or without direct instruction. For example, children from Mexican heritage families tend to learn and make better use of information observed during classroom demonstration than children of European heritage. Children of European heritage experience the type of learning that separates them from their family and community activities. They instead participate in lessons and other exercises in special settings such as school. Cultural backgrounds differ from each other in which children display certain characteristics in regards to learning an activity. Another example is seen in the immersion of children in some Indigenous communities of the Americas into the adult world and the effects it has on observational learning and the ability to complete multiple tasks simultaneously. This might be due to children in these communities having the opportunity to see a task being completed by their elders or peers and then trying to emulate the task. In doing so they learn to value observation and the skill-building it affords them because of the value it holds within their community. This type of observation is not passive, but reflects the child’s intent to participate or learn within a community.

Observational learning can be seen taking place in many domains of Indigenous communities. The classroom setting is one significant example, and it functions differently for Indigenous communities compared to what is commonly present in Western schooling. The emphasis of keen observation in favour of supporting participation in ongoing activities strives to aid children to learn the important tools and ways of their community. Engaging in shared endeavours – with both the experienced and inexperienced – allows for the experienced to understand what the inexperienced need in order to grow in regards to the assessment of observational learning. The involvement of the inexperienced, or the children in this matter, can either be furthered by the children’s learning or advancing into the activity performed by the assessment of observational learning. Indigenous communities rely on observational learning as a way for their children to be a part of ongoing activities in the community.

Although learning in the Indigenous American communities is not always the central focus when participating in an activity, studies have shown that attention in intentional observation differs from accidental observation. Intentional participation is “keen observation and listening in anticipation of, or in the process of engaging in endeavors”. This means that when they have the intention of participating in an event, their attention is more focused on the details, compared to when they are accidentally observing.

Observational learning can be an active process in many Indigenous American communities. The learner must take initiative to attend to activities going on around them. Children in these communities also take initiative to contribute their knowledge in ways that will benefit their community. For example, in many Indigenous American cultures, children perform household chores without being instructed to do so by adults. Instead, they observe a need for their contributions, understand their role in their community, and take initiative to accomplish the tasks they have observed others doing. The learner’s intrinsic motivations play an important role in the child’s understanding and construction of meaning in these educational experiences. The independence and responsibility associated with observational learning in many Indigenous American communities are significant reasons why this method of learning involves more than just watching and imitating. A learner must be actively engaged with their demonstrations and experiences in order to fully comprehend and apply the knowledge they obtain.

Indigenous Communities of the Americas

Children from indigenous heritage communities of the Americas often learn through observation, a strategy that can carry over into adulthood. The heightened value towards observation allows children to multi-task and actively engage in simultaneous activities. The exposure to an uncensored adult lifestyle allows children to observe and learn the skills and practices that are valued in their communities. Children observe elders, parents, and siblings complete tasks and learn to participate in them. They are seen as contributors and learn to observe multiple tasks being completed at once and can learn to complete a task while still engaging with other community members without being distracted.

Indigenous communities provide more opportunities to incorporate children in everyday life. This can be seen in some Mayan communities where children are given full access to community events, which allows observational learning to occur more often. Other children in Mazahua, Mexico are known to observe ongoing activities intensely. In native northern Canadian and indigenous Mayan communities, children often learn as third-party observers from stories and conversations by others. Most young Mayan children are carried on their mother’s back, allowing them to observe their mother’s work and see the world as their mother sees it. Often, children in Indigenous American communities assume the majority of the responsibility for their learning. Additionally, children find their own approaches to learning. Children are often allowed to learn without restrictions and with minimal guidance. They are encouraged to participate in the community even if they do not know how to do the work. They are self-motivated to learn and finish their chores. These children act as a second set of eyes and ears for their parents, updating them about the community.

Children aged 6 to 8 in an indigenous heritage community in Guadalajara, Mexico participated in hard work, such as cooking or running errands, thus benefiting the whole family, while those in the city of Guadalajara rarely did so. These children participated more in adult regulated activities and had little time to play, while those from the indigenous-heritage community had more time to play and initiate in their after-school activities and had a higher sense of belonging to their community. Children from formerly indigenous communities are more likely to show these aspects than children from cosmopolitan communities are, even after leaving their childhood community

Within certain indigenous communities, people do not typically seek out explanations beyond basic observation. This is because they are competent in learning through astute observation and often nonverbally encourage to do so. In a Guatemalan footloom factory, amateur adult weavers observed skilled weavers over the course of weeks without questioning or being given explanations; the amateur weaver moved at their own pace and began when they felt confident. The framework of learning how to weave through observation can serve as a model that groups within a society use as a reference to guide their actions in particular domains of life. Communities that participate in observational learning promote tolerance and mutual understand of those coming from different cultural backgrounds.

Other Human and Animal Behaviour Experiments

When an animal is given a task to complete, they are almost always more successful after observing another animal doing the same task before them. Experiments have been conducted on several different species with the same effect: animals can learn behaviours from peers. However, there is a need to distinguish the propagation of behaviour and the stability of behaviour. Research has shown that social learning can spread a behaviour, but there are more factors regarding how a behaviour carries across generations of an animal culture.

Learning in Fish

Experiments with ninespine sticklebacks showed that individuals will use social learning to locate food.

Social Learning in Pigeons

A study in 1996 at the University of Kentucky used a foraging device to test social learning in pigeons. A pigeon could access the food reward by either pecking at a treadle or stepping on it. Significant correspondence was found between the methods of how the observers accessed their food and the methods the initial model used in accessing the food.

Acquiring Foraging Niches

Studies have been conducted at the University of Oslo and University of Saskatchewan regarding the possibility of social learning in birds, delineating the difference between cultural and genetic acquisition. Strong evidence already exists for mate choice, bird song, predator recognition, and foraging.

Researchers cross-fostered eggs between nests of blue tits and great tits and observed the resulting behaviour through audio-visual recording. Tits raised in the foster family learned their foster family’s foraging sites early. This shift – from the sites the tits would among their own kind and the sites they learned from the foster parents – lasted for life. What young birds learn from foster parents, they eventually transmitted to their own offspring. This suggests cultural transmissions of foraging behaviour over generations in the wild.

Social Learning in Crows

The University of Washington studied this phenomenon with crows, acknowledging the evolutionary tradeoff between acquiring costly information firsthand and learning that information socially with less cost to the individual but at the risk of inaccuracy. The experimenters exposed wild crows to a unique “dangerous face” mask as they trapped, banded, and released 7-15 birds at five different study places around Seattle, Washington. An immediate scolding response to the mask after trapping by previously captured crows illustrates that the individual crow learned the danger of that mask. There was a scolding from crows that were captured that had not been captured initially. That response indicates conditioning from the mob of birds that assembled during the capture.

Horizontal social learning (learning from peers) is consistent with the lone crows that recognized the dangerous face without ever being captured. Children of captured crow parents were conditioned to scold the dangerous mask, which demonstrates vertical social learning (learning from parents). The crows that were captured directly had the most precise discrimination between dangerous and neutral masks than the crows that learned from the experience of their peers. The ability of crows to learn doubled the frequency of scolding, which spread at least 1.2 km from where the experiment started to over a 5-year period at one site.

Propagation of Animal Culture

Researchers at the Département d’Etudes Cognitives, Institut Jean Nicod, Ecole Normale Supérieure acknowledged a difficulty with research in social learning. To count acquired behaviour as cultural, two conditions need must be met: the behaviour must spread in a social group, and that behaviour must be stable across generations. Research has provided evidence that imitation may play a role in the propagation of a behaviour, but these researchers believe the fidelity of this evidence is not sufficient to prove the stability of animal culture.

Other factors like ecological availability, reward-based factors, content-based factors, and source-based factors might explain the stability of animal culture in a wild rather than just imitation. As an example of ecological availability, chimps may learn how to fish for ants with a stick from their peers, but that behaviour is also influenced by the particular type of ants as well as the condition. A behaviour may be learned socially, but the fact that it was learned socially does not necessarily mean it will last. The fact that the behaviour is rewarding has a role in cultural stability as well. The ability for socially-learned behaviours to stabilise across generations is also mitigated by the complexity of the behaviour. Different individuals of a species, like crows, vary in their ability to use a complex tool. Finally, a behaviour’s stability in animal culture depends on the context in which they learn a behaviour. If a behaviour has already been adopted by a majority, then the behaviour is more likely to carry across generations out of a need for conforming.

Animals are able to acquire behaviours from social learning, but whether or not that behaviour carries across generations requires more investigation.

Hummingbird Experiment

Experiments with hummingbirds provided one example of apparent observational learning in a non-human organism. Hummingbirds were divided into two groups. Birds in one group were exposed to the feeding of a knowledgeable “tutor” bird; hummingbirds in the other group did not have this exposure. In subsequent tests the birds that had seen a tutor were more efficient feeders than the others.

Bottlenose Dolphin

Herman (2002) suggested that bottlenose dolphins produce goal-emulated behaviours rather than imitative ones. A dolphin that watches a model place a ball in a basket might place the ball in the basket when asked to mimic the behaviour, but it may do so in a different manner seen.

Rhesus Monkey

Kinnaman (1902) reported that one rhesus monkey learned to pull a plug from a box with its teeth to obtain food after watching another monkey succeed at this task.

Fredman (2012) also performed an experiment on observational behavior. In experiment 1, human-raised monkeys observed a familiar human model open a foraging box using a tool in one of two alternate ways: levering or poking. In experiment 2, mother-raised monkeys viewed similar techniques demonstrated by monkey models. A control group in each population saw no model. In both experiments, independent coders detected which technique experimental subjects had seen, thus confirming social learning. Further analyses examined copying at three levels of resolution.

The human-raised monkeys exhibited the greatest learning with the specific tool use technique they saw. Only monkeys who saw the levering model used the lever technique, by contrast with controls and those who witnessed poking. Mother-reared monkeys instead typically ignored the tool and exhibited fidelity at a lower level, tending only to re-create whichever result the model had achieved by either levering or poking.

Nevertheless, this level of social learning was associated with significantly greater levels of success in monkeys witnessing a model than in controls, an effect absent in the human-reared population. Results in both populations are consistent with a process of canalisation of the repertoire in the direction of the approach witnessed, producing a narrower, socially shaped behavioural profile than among controls who saw no model.

Light Box Experiment

Pinkham and Jaswal (2011) did an experiment to see if a child would learn how to turn on a light box by watching a parent. They found that children who saw a parent use their head to turn on the light box tended to do the task in that manner, while children who had not seen the parent used their hands instead.

Swimming Skill Performance

When adequate practice and appropriate feedback follow demonstrations, increased skill performance and learning occurs. Lewis (1974) did a study of children who had a fear of swimming and observed how modelling and going over swimming practices affected their overall performance. The experiment spanned nine days, and included many steps. The children were first assessed on their anxiety and swimming skills. Then they were placed into one of three conditional groups and exposed to these conditions over a few days.

At the end of each day, all children participated in a group lesson. The first group was a control group where the children watched a short cartoon video unrelated to swimming. The second group was a peer mastery group, which watched a short video of similar-aged children who had very good task performances and high confidence. Lastly, the third group was a peer coping group, whose subjects watched a video of similar-aged children who progressed from low task performances and low confidence statements to high task performances and high confidence statements.

The day following the exposures to each condition, the children were reassessed. Finally, the children were also assessed a few days later for a follow up assessment. Upon reassessment, it was shown that the two model groups who watched videos of children similar in age had successful rates on the skills assessed because they perceived the models as informational and motivational.

Do-as-I-do Chimpanzee

Flexible methods must be used to assess whether an animal can imitate an action. This led to an approach that teaches animals to imitate by using a command such as “do-as-I-do” or “do this” followed by the action that they are supposed to imitate. Researchers trained chimpanzees to imitate an action that was paired with the command. For example, this might include a researcher saying “do this” paired with clapping hands. This type of instruction has been utilised in a variety of other animals in order to teach imitation actions by utilising a command or request.

Observational Learning in Everyday Life

Observational learning allows for new skills to be learned in a wide variety of areas. Demonstrations help the modification of skills and behaviours.

Learning Physical Activities

When learning skills for physical activities can be anything that is learned that requires physical movement, this can include learning a sport, learning to eat with a fork, or learning to walk. There are multiple important variables that aid in modifying physical skills and psychological responses from an observational learning standpoint. Modelling is a variable in observational learning where the skill level of the model is considered. When someone is supposed to demonstrate a physical skill such as throwing a baseball the model should be able to execute the behaviour of throwing the ball flawlessly if the model of learning is a mastery model. Another model to utilise in observational learning is a coping model, which would be a model demonstrating a physical skill that they have not yet mastered or achieved high performance in. Both models are found to be effective and can be utilised depending on the what skills is trying to be demonstrated. These models can be used as interventions to increase observational learning in practice, competition, and rehabilitation situations.


Recent research in neuroscience has implicated mirror neurons as a neurophysiological basis for observational learning. These specialised visuomotor neurons fire action potentials when an individual performs a motor task and also fire when an individual passively observes another individual performing the same motor task. In observational motor learning, the process begins with a visual presentation of another individual performing a motor task, this acts as a model. The learner then needs to transform the observed visual information into internal motor commands that will allow them to perform the motor task, this is known as visuomotor transformation. Mirror neuron networks provide a mechanism for visuo-motor and motor-visual transformation and interaction. Similar networks of mirror neurons have also been implicated in social learning, motor cognition and social cognition.

Clinical Perspective

Autism Spectrum Disorder

Discreet trial training (DTT) is a structured and systematic approach utilized in helping individuals with autism spectrum disorder learn. Individuals with autism tend to struggle with learning through observation, therefore something that is reinforcing is necessary in order to motivate them to imitate or follow through with the task. When utilising DTT to teach individuals with autism modelling is utilised to aid in their learning. Modelling would include showing how to reach the correct answer, this could mean showing the steps to a math equation. Utilising DTT in a group setting also promotes observational learning from peers as well.

Who was Heather Ashton?


Heather Ashton FRCP (11 July 1929 to 15 September 2019) was a British psychopharmacologist and physician. She is best known for her clinical and research work on benzodiazepene dependence.


Chrystal Heather Champion was born in Dehradun, northern India, to Harry Champion, a British silviculturalist, and Chrystal (Parsons) Champion, a secretary. From the age of six, she attended a boarding school in Swanage, Dorset, England. When WWII began, she was evacuated to West Chester, Pennsylvania; during the crossing, her ship was attacked by a U-boat.

Ashton went on to study Medicine at Somerville College, Oxford, graduating with a First Class Honours Degree (BA) in Physiology in 1951. She earned her medical degree (DM) in 1956. She completed professional training at Middlesex Hospital. She was elected as a Fellow of the Royal College of Physicians, London, in 1975.

In 1965, Ashton joined the faculty at Newcastle University, first in the Department of Pharmacology and later in the Department of Psychiatry. From 1982 to 1994, she ran a benzodiazepine withdrawal clinic at the Royal Victoria Infirmary in Newcastle. She was on the executive committee of the North East Council on Addictions. Ashton also helped set up the British organisation Victims of Tranquillisers (VOT). She also gave evidence to British government committees on tobacco smoking, cannabis and benzodiazepines.

Ashton died on 15 September 2019 at her home in Newcastle upon Tyne, at age 90.


Ashton’s developed her expertise in the effects of psychoactive drugs and the effects of substances such as nicotine and cannabis on the brain.

During the 1960s, benzodiazepines, like diazepam and temazepam, had become popular and were seen as safe and effective treatments for anxiety or insomnia. One study found that the overdose death rate among patients taking both benzodiazepines and opioids was 10 times higher than among those who only took opioids.

Ashton’s research on these drugs found that they could be used in the short term, but could lead to physical dependence over the long-term. She also recognised that this benzodiazepine withdrawal syndrome was very different from those addicted to illegal drugs. This led to her writing an important manual to help those who were trying to stop their prescribed benzodiazepine. This manual is now used all over the world. This book, Benzodiazepines: How They Work and How to Withdraw, was first published in 1999; it has become known as the Ashton Manual and has been translated into 11 languages. Ashton’s research was influential, leading to changes in prescribing practices and guidelines recommended for benzodiazepines in 2013. Her research on psychotropic drugs led to over 200 journal articles, chapters and books, including over 50 papers concerning benzodiazepines alone.

Is there an Association between Metabolic Disorder & Cognitive Impairment in Patients with Early-Stage Schizophrenia?

Research Paper Title

The Association Between Metabolic Disturbance and Cognitive Impairments in Early-Stage Schizophrenia.


Cognitive impairment is one of the core symptoms of schizophrenia, which is considered to be significantly correlated to prognosis. In recent years, many studies have suggested that metabolic disorders could be related to a higher risk of cognitive defects in a general setting. However, there has been limited evidence on the association between metabolism and cognitive function in patients with early-stage schizophrenia.


In this study, the researchers recruited 172 patients with early-stage schizophrenia. Relevant metabolic parameters were examined and cognitive function was evaluated by using the MATRICS Consensus Cognitive Battery (MCCB) to investigate the relationship between metabolic disorder and cognitive impairment.


Generally, the prevalence of cognitive impairment among patients in our study was 84.7% (144/170), which was much higher than that in the general population. Compared with the general Chinese setting, the study population presented a higher proportion of metabolic disturbance. Patients who had metabolic disturbance showed no significant differences on cognitive function compared with the other patients. Correlation analysis showed that metabolic status was significantly correlated with cognitive function as assessed by the cognitive domain scores (p < 0.05), while such association was not found in further multiple regression analysis.


Therefore, there may be no association between metabolic disorder and cognitive impairment in patients with early-stage schizophrenia.


Peng, X-J., Hei, G-R., Li, R-R., Yang, Y., Liu, C-C., Xiao, J-M., Long, Y-J., Shao, P., Huang, J., Zhao, J-P. & Wu, R-R. (2021) The Association Between Metabolic Disturbance and Cognitive Impairments in Early-Stage Schizophrenia. Frontiers in Human Neuroscience. doi: 10.3389/fnhum.2020.599720. eCollection 2020.

Developing a Longitudinal Trajectory-Based Approach to Investigating Relapse Trend Differences in Mental Health Patients

Research Paper Title

Differences in Temporal Relapse Characteristics Between Affective and Non-affective Psychotic Disorders: Longitudinal Analysis.


Multiple relapses over time are common in both affective and non-affective psychotic disorders. Characterizing the temporal nature of these relapses may be crucial to understanding the underlying neurobiology of relapse.


Anonymised records of patients with affective and non-affective psychotic disorders were collected from SA Mental Health Data Universe and retrospectively analysed. To characterise the temporal characteristic of their relapses, a relapse trend score was computed using a symbolic series-based approach. A higher score suggests that relapse follows a trend and a lower score suggests relapses are random. Regression models were built to investigate if this score was significantly different between affective and non-affective psychotic disorders.


Logistic regression models showed a significant group difference in relapse trend score between the patient groups. For example, in patients who were hospitalized six or more times, relapse score in affective disorders were 2.6 times higher than non-affective psychotic disorders [OR 2.6, 95% CI (1.8-3.7), p < 0.001].


The results imply that the odds of a patient with affective disorder exhibiting a predictable trend in time to relapse were much higher than a patient with recurrent non-affective psychotic disorder. In other words, within recurrent non-affective psychosis group, time to relapse is random.


This study is an initial attempt to develop a longitudinal trajectory-based approach to investigate relapse trend differences in mental health patients. Further investigations using this approach may reflect differences in underlying biological processes between illnesses.


Immanuel, S.A., Schrader, G. & Bidargaddi, N. (2021) Differences in Temporal Relapse Characteristics Between Affective and Non-affective Psychotic Disorders: Longitudinal Analysis. Frontiers in Psychiatry. doi: 10.3389/fpsyt.2021.558056. eCollection 2021.

Can Lorazepam be Considered to be a Clinically Effective Means of Treating the Acutely Agitated Patient?

Research Paper Title

Treatment of Agitation With Lorazepam in Clinical Practice: A Systematic Review.


Acute agitation is a frequent occurrence in both inpatient and outpatient psychiatric settings, and the use of medication to calm a patient may be warranted to mitigate the situation. Lorazepam is a benzodiazepine that is widely used for management of acute agitation. Despite its widespread use, there is remarkably little clinical evidence for the benefits of lorazepam in acute agitation.


The researchers performed a systematic review with focus on lorazepam, including all randomised clinical trials on lorazepam in mental and behavioural disorders, excluding studies on dementia and paediatric patients and in mixed conditions.


A total of 11 studies met inclusion criteria, and all were in patients with mental and behavioural disorders. Most trials generally found improvements across a variety of outcomes related to agitation, although there was some disparity if specific outcomes were considered.

In the five studies with haloperidol, the combination of lorazepam and haloperidol was superior to either agent alone, but with no differences between monotherapy with the individual agents.


In the study comparing lorazepam to olanzapine, olanzapine was superior to lorazepam, and both were superior to placebo.

As expected, the safety of lorazepam among the different studies was consistent with its well-characterised profile with dizziness, sedation, and somnolence being the most common adverse events.

Based on this structured review, lorazepam can be considered to be a clinically effective means of treating the acutely agitated patient.


Amore, M., D’Andrea, M. & Fagiolini, F. (2021) Treatment of Agitation With Lorazepam in Clinical Practice: A Systematic Review.

Can the MHS: A Serve as a Clinically Useful Screening Tool for GAD?

Research Paper Title

A Brief Online and Offline (Paper-and-Pencil) Screening Tool for Generalized Anxiety Disorder: The Final Phase in the Development and Validation of the Mental Health Screening Tool for Anxiety Disorders (MHS: A).


Generalised anxiety disorder (GAD) can cause significant socioeconomic burden and daily life dysfunction; hence, therapeutic intervention through early detection is important.


This study was the final stage of a 3-year anxiety screening tool development project that evaluated the psychometric properties and diagnostic screening utility of the Mental Health Screening Tool for Anxiety Disorders (MHS: A), which measures GAD.


A total of 527 Koreans completed online and offline (i.e., paper-and pencil) versions of the MHS: A, Beck Anxiety Inventory (BAI), Generalised Anxiety Disorder-7 (GAD-7), and Penn State Worry Questionnaire (PSWQ). The participants had an average age of 38.6 years and included 340 (64.5%) females. Participants were also administered the Mini-International Neuropsychiatric Interview (MINI).

Internal consistency, convergent/criterion validity, item characteristics, and test information were assessed based on the item response theory (IRT), and a factor analysis and cut-off score analyses were conducted. The MHS: A had good internal consistency and good convergent validity with other anxiety scales.

The two versions (online/offline) of the MHS: A were nearly identical (r = 0.908). It had a one-factor structure and showed better diagnostic accuracy (online/offline: sensitivity = 0.98/0.90, specificity = 0.80/0.83) for GAD detection than the GAD-7 and BAI. The IRT analysis indicated that the MHS: A was most informative as a screening tool for GAD.


The MHS: A can serve as a clinically useful screening tool for GAD in Korea. Furthermore, it can be administered both online and offline and can be flexibly used as a brief mental health screener, especially with the current rise in telehealth.


Kim, S-H., Park, K., Yoon, S., Choi, Y., Lee, S-H. & Choi, K-H. (2021) A Brief Online and Offline (Paper-and-Pencil) Screening Tool for Generalized Anxiety Disorder: The Final Phase in the Development and Validation of the Mental Health Screening Tool for Anxiety Disorders (MHS: A). Frontiers in Psychology. doi: 10.3389/fpsyg.2021.639366. eCollection 2021.

Can We Improve Diagnosis of Depression with XGBOOST Machine Learning Model & a Large Biomarkers Dutch Dataset?

Research Paper Title

Improving Diagnosis of Depression With XGBOOST Machine Learning Model and a Large Biomarkers Dutch Dataset ( n = 11,081).


Machine Learning has been on the rise and healthcare is no exception to that. In healthcare, mental health is gaining more and more space. The diagnosis of mental disorders is based upon standardised patient interviews with defined set of questions and scales which is a time consuming and costly process.

The objective of the researchers was to apply the machine learning model and to evaluate to see if there is predictive power of biomarkers data to enhance the diagnosis of depression cases.

In this research paper, they aimed to explore the detection of depression cases among the sample of 11,081 Dutch citizen dataset. Most of the earlier studies have balanced datasets wherein the proportion of healthy cases and unhealthy cases are equal but in their study, the dataset contains only 570 cases of self-reported depression out of 11,081 cases hence it is a class imbalance classification problem. The machine learning model built on imbalance dataset gives predictions biased toward majority class hence the model will always predict the case as no depression case even if it is a case of depression.

The researchers used different resampling strategies to address the class imbalance problem. They created multiple samples by under sampling, over sampling, over-under sampling and ROSE sampling techniques to balance the dataset and then, they applied machine learning algorithm “Extreme Gradient Boosting” (XGBoost) on each sample to classify the mental illness cases from healthy cases.

The balanced accuracy, precision, recall and F1 score obtained from over-sampling and over-under sampling were more than 0.90.


Sharma, A. & Verbeke, W.J.M.I. (2021) Improving Diagnosis of Depression With XGBOOST Machine Learning Model and a Large Biomarkers Dutch Dataset ( n = 11,081). Frontiers in Big Data. doi: 10.3389/fdata.2020.00015. eCollection 2020.

Linking Boundary Sharpness Coefficient & Cortical Development in Autism Spectrum Disorders

Research Paper Title

Examining the Boundary Sharpness Coefficient as an Index of Cortical Microstructure in Autism Spectrum Disorder.


Autism spectrum disorder (ASD) is associated with atypical brain development. However, the phenotype of regionally specific increased cortical thickness observed in ASD may be driven by several independent biological processes that influence the gray/white matter boundary, such as synaptic pruning, myelination, or atypical migration.

Here, the researchers propose to use the boundary sharpness coefficient (BSC), a proxy for alterations in microstructure at the cortical gray/white matter boundary, to investigate brain differences in individuals with ASD, including factors that may influence ASD-related heterogeneity (age, sex, and intelligence quotient).


This was a vertex-based meta-analysis and a large multicentre structural magnetic resonance imaging (MRI) dataset, with a total of 1136 individuals, 415 with ASD (112 female; 303 male), and 721 controls (283 female; 438 male).


The researchers observed that individuals with ASD had significantly greater BSC in the bilateral superior temporal gyrus and left inferior frontal gyrus indicating an abrupt transition (high contrast) between white matter and cortical intensities.

Individuals with ASD under 18 had significantly greater BSC in the bilateral superior temporal gyrus and right postcentral gyrus; individuals with ASD over 18 had significantly increased BSC in the bilateral precuneus and superior temporal gyrus.

Increases were observed in different brain regions in males and females, with larger effect sizes in females. BSC correlated with ADOS-2 Calibrated Severity Score in individuals with ASD in the right medial temporal pole. Importantly, there was a significant spatial overlap between maps of the effect of diagnosis on BSC when compared with cortical thickness.


These results invite studies to use BSC as a possible new measure of cortical development in ASD and to further examine the microstructural underpinnings of BSC-related differences and their impact on measures of cortical morphology.


Olafson, E., Bedford, S.A., Devenyi, G.A., Patel, R., Tullo, S., Park, M.T.M., Parent, O., Anagnostou, E., Baron-Cohen, S., Bullmore, E.T., Chura, L.R., Craig, M.C., Ecker, C., Floris, D.L., Holt, R.J., Lenroot, R., Lerch, J.P., Lombardo, M.V., Murphy, D.G.M., Raznahan, A., Ruigrok, A.N.V., Spncer, M.D., Suckling, Taylor, M.J., MRC AIMS Consortium, Lai, M-C. & Chakravarty, M.M. (2021) Examining the Boundary Sharpness Coefficient as an Index of Cortical Microstructure in Autism Spectrum Disorder. Cerebral Cortex (New York, N.Y.: 1991). doi: 10.1093/cercor/bhab015. Online ahead of print.