Machines Who Think

Recent commentary about the 25th anniversary edition of Machines Who Think.

"Over the course of the last half-century, a number of books have sought to explain AI to a larger audience and many more devoted to writing the formal history of AI. It is a tribute to her powers of observation and her conversational style that none has really proven more successful than Pamela McCorduck's Machines Who Think now approaching the quarter century mark. Currently, it is the first source cited on the AI Topics web site on the history of AI. Based on extensive interviews with many of the early key players, it managed to forge the template for most subsequent histories, in the sense of providing them both the time line and the larger frame tale."

AI Magazine, Winter 2004 "In summary, if you are interested in the story of how the pioneers of AI approached the problem of getting a machine to think like a human, a story told with verve, wit, intelligence and perception, there is no better place to go than this book."

Nature "The enormous, if stealthy, influence of AI bears out many of the wonders foretold 25 years ago in Machines Who Think, Pamela McCorduck's groundbreaking survey of the history and prospects of the field…. [T]aken together, the original and the afterword form a rich and fascinating history." –Scientific American, May 2004Machines Who Think was conceived as a history of artificial intelligence, beginning with the first dreams of the classical Greek poets (and the nightmares of the Hebrew prophets), up through its realization as twentieth-century science.

The interviews with AI's pioneer scientists took place when the field was young and generally unknown. They were nearly all in robust middle age, with a few decades of fertile research behind them, and luckily, more to come. Thus their explanations of what they thought they were doing were spontaneous, provisional, and often full of glorious fun. Tapes and transcriptions of these interviews, along with supporting material and working drafts of the manuscript, can be found in the Pamela McCorduck Collection at the Carnegie Mellon University Library Archives. If you believe (and I do) that artificial intelligence is not only one of the most audacious of scientific undertakings, but also one of the most important, these interviews are a significant record of the original visionaries, whose intellectual verve set the tone for the field for years to come. That verve–that arrogance, some people thought–also set teeth on edge, as I've pointed out.

Practicing scientists, more interested in what will happen than what once did, are apt to forget their field's history. The new is infinitely seductive. But an interesting characteristic of AI research is how often good ideas were proposed, tried, then dropped, as the technology of the moment failed to allow a good idea to flourish. Then technology would catch up, and a whole new set of possibilities would emerge; another generation would rediscover a good idea, and the dance would begin once more. Meanwhile, new ideas have come up alongside the old: far from "the failure" its critics love to claim, the field thrives and already permeates everyday life.

But above all, the history of AI is a splendid tale in its own right–the search for intelligence outside the human cranium. It entailed defining just what "intellience" might be (disputed territory even yet) and which Other, among several candidates, might exhibit it. The field called up serious ethical and moral questions, and still does. It all happens to be one of the best tales of our times.

From the new foreword:"Machines Who Think has its own modest history that may be worth telling. In the early summer of 1974, John McCarthy made an emergency landing in his small plane in Alaska, at a place called (roughly translated) the Pass of Much Caribou Dung, so remote a spot he could not radio for help."

From the 30,000-word afterword, that summarizes the field since the original was published:

"In the late 1970s and early 1980s, artificial intelligence moved from the fringes to become a celebrity science. Seen in the downtown clubs, boldface in the gossip columns, stalked by paparazzi, it was swept up in a notorious publicity and commercial frenzy."

The new edition also has two separate time-lines, one tracing the evolution of AI in its narrowest sense, and a second one taking a much broader view of intellectual history, and placing AI in the context of all human information gathering, organizing, propagation, and discovery, a central place for AI that has only become apparent with the development of the second generation World Wide Web, which will depend deeply on AI techniques for finding, shaping and inventing knowledge.

Herb Simon himself urged me to re-publish. "Pamela," he wrote in email a few months before he died. "Do consider what might be done about bringing Machines Who Think back into print. More machines are thinking every day, and I would expect that every one of them would want to buy a copy. Soccer robots alone should account for a first printing."

FAQ answered by Pamela McCorduck: Q: How long has the human race dreamed about thinking machines?A: Since at least the time of classical Greece, when Homer's Iliad tells us about robots that are made by the Greek god Hephaestos, also known, in Roman mythology, as Vulcan. Some of these robots are human-like, and some of them are just machines–for example, golden tripods that serve food and wine at banquets. At about the same time, the Chinese were also telling tales of human-like machines that could think. It's also important to remember that this is the time in human history when the Second Commandment was codified, prohibiting the making of graven images, which in reality forbids humans to take on the creative privileges of divinities. In my book, I describe each attitude: I call one the Hellenic point of view, meaning out of Greece, and generally welcoming the idea of thinking machines. The other I call the Hebraic, which finds the whole idea of thinking machines wicked, even blasphemous. These two attitudes are very much alive today. The history of thinking machines is extremely rich: every century has its version. The 19th century was particularly fertile: Frankenstein and the Tales of E. T. A. Hoffman were published, and the fake chess machine called "The Turk" was on exhibit. Q: What's the difference between all those tall tales and what you're writing about?A: They were exactly that–tall tales. However, by the middle of the 20th century, a small group of farsighted scientists understood that the computer would allow them to actually realize this longstanding dream of a thinking machine.  Q: What does it mean that a machine beat Garry Kasparov, the world's chess champion?A: It's a tremendous achievement for human scientists to design a machine smart enough to beat not only the reigning chess champion, but also a man said to be the best chess champion ever. Kasparov, for his part, claims that these programs are making him a better chess player. Q: And what about the recent wins by IBM's program Watson at the guessing game, Jeopardy?This was spectacular. Watson had to understand natural language—in this case, English—to the point where it (yes, everyone on the Jeopardy program was referring to it as "he," but I'll continue to say "it") could outguess two of the best human players ever. To play Jeopardy, you must be able to crack riddles, puns, puzzles, and interpret ambiguous statements. Watson is a tremendous achievement. Q: Does this mean that machines are smarter than we are?A: Machines have been "smarter" than us in many ways for a while. Chess and Jeopardy are the best-known achievements, but many artificially intelligent programs have been at work for more than two decades in finance, in many sciences, such as molecular biology and high-energy physics, and in manufacturing and business processes all over the world. We've lately seen a program that has mastered the discovery process in a large, complex legal case, using a small fraction of the time, an even smaller fraction of costs—and it's more accurate. So if you include arithmetic, machines have been "smarter" than us for more than a century. People no longer feel threatened by machines that can add, subtract, and remember faster and better than we can, but machines that can manipulate and even interpret symbols better than we can give us pause. Q: Those are very narrow domains. Do general-purpose intelligent machines as smart as humans exist?A: Not yet. But scientists are trying to figure out how to design a machine that exhibits general intelligence, even if that means sacrificing a bit of specialized intelligence. Q: If the human chess champion has finally been defeated, and the best human Jeopardy players went down, what's the next big goal? A: It took fifty years between the time scientists first proposed the goal of a machine that could be the world's chess champion, and when that goal was reached. It took another fourteen years for Watson to emerge as the Jeopardy champion. In the late 1990s, a major new goal was set. In fifty years, AI should field a robot team of soccer players to compete with and defeat the human team of champions at the World's Cup. In the interim, more modestly accomplished soccer robots are teaching scientists a great deal about physical coordination in the real world, pattern recognition, teamwork, and real-time tactics and strategy under stress. Scientists from all over the world are fielding teams right now–one of the most obvious signs of how international artificial intelligence research has become. Q: Artificial intelligence–is it real?A: It's real. For more than two decades, your credit card company has employed various kinds of artificial intelligence programs to tell whether or not the transaction coming in from your card is typical for you, or whether it's outside your usual pattern. Outside the pattern, a warning flag goes up. The transaction might even be rejected. This isn't usually an easy, automatic judgment–many factors are weighed as the program is deciding. In fact, finance might be one of the biggest present-day users of AI. Utility companies employ AI programs to figure out whether small problems have the potential to be big ones, and if so, how to fix the small problem. Many medical devices now employ AI to diagnose and manage the course of therapy. Construction companies use AI to figure out schedules and manage risks. The U.S. armed forces uses all sorts of AI programs–to manage battles, to detect real threats out of possible noise, and so on. Though these programs are usually smarter than humans could be, they aren't perfect. Sometimes, like humans, they fail. Q: What so-called smart computers do–is that really thinking?A: No, if you insist that thinking can only take place inside the human cranium. But yes, if you believe that making difficult judgments, the kind usually left to experts, choosing among plausible alternatives, and acting on those choices, is thinking. That's what artificial intelligences do right now. Along with most people in AI, I consider what artificial intelligences do as a form of thinking, though I agree that these programs don't think just like human beings do, for the most part. I'm not sure that's even desirable. Why would we want AIs if all we want is human-level intelligence? There are plenty of humans on the planet. The field's big project is to make intelligences that exceed our own. As these programs come into our lives in more ways, we'll need programs that can explain their reasoning to us before we accept their decisions. Q: But doesn't that mean our own machines will replace us?A: This continues to be debated both inside and outside the field. Some people fear this–that smart machines will eventually get smart enough to come in and occupy our ecological niche, and that will be that. So long, human race. Some people think that the likeliest scenario is that smart machines will help humans become smarter, the way Garry Kasparov feels that smart chess-playing machines have made him a better player. Some people think that smart machines won't have any desire to occupy our particular niche: instead, being smarter than we are, they'll lift the burden of managing the planet off our shoulders, and leave us to do the things we do best–a rather pleasant prospect. But a few years ago, Bill Joy, an eminent computer scientist who helped found Sun Microsystems, was worried enough to write an article that calls for a halt in AI and some other kinds of research. He's far from the first, by the way. Most of the arguments against halting suggest that the benefits will outweigh the dangers. But nobody believes that there's no chance of danger. I should add that forbidding AI research is pretty hopeless. Research isn't being done on some mountaintop in secret. It's being done all over the planet. Suppose a group of nations (or firms, or universities) decided to stop doing AI research. Would that stop other researchers elsewhere? No, the perceived advantage of continuing this research would make at least a small group continue. The abstainers would be forced to continue themselves for their own protection. Q: Aren't you yourself worried?A: I agree that the dangerous scenarios are entirely plausible. I explore that further in my book. But I also believe that the chance is worth taking. The benefits could be tremendous. Let's take some examples. Scientists are at work right now on robots that will help the elderly stay independently in their own homes longer than otherwise. I think that's terrific. At the 2003 Superbowl (and presumably at the 2004 Superbowl too) a kind of artificial intelligence called "smart dust"–smart sensors a millimeter by a millimeter–was deployed to sense and report on unusual activity, looking for terrorists. Scientists are also at work on a machine that can detect the difference between a natural disease outbreak and a bio-terror attack. Unfortunately, these are issues we must address for the foreseeable future. We've recently had a lot of bad news about cheating going on in the financial sector. At least one part of that sector, the National Association of Security Dealers, uses AI to monitor the activities of its traders, looking not only at the trading patterns of individual traders, but at articles in newspapers and other possible influences. Q: Whoa! Isn't that a big invasion of privacy? In fact, didn't we hear that AI was going to be used for the government's Total Information Awareness project? That makes me very uncomfortable. A: Americans cherish their privacy, and so they should. American ideas about privacy have evolved legally and socially over a long period. Moreover, Americans aren't the only ones with such concerns–the European Union is even stricter about the use of personal information than the U.S. But the European Union also understands that the best defense against terrorism is to be able to detect patterns of behavior that might alert law enforcement officers to potential terrorism before it happens. Like the privacy you give up for the convenience of using a credit card, it's a trade-off. I think that trade-off should be publicly debated, with all the gravity it deserves. Q: Shouldn't we just say no to intelligent machines? Aren't the risks too scary?A: The risks are scary; the risks are real; but I don't think we should say no. In my book, I go further. I don't think we can say no. Here's what I mean: one of the best things humans have ever done for themselves was to collect, organize, and distribute information in the form of libraries and encyclopedias. We have always honored that effort, because we understand that no human can carry everything worth knowing inside a single head. The World Wide Web is this generation's new giant encyclopedia, and the Semantic Web, which is the next generation Web, will have intelligence built in. It will be as if everybody with access to a computer can have the world's smartest reference librarian at their fingertips, ready to help find exactly what you need, no matter how ill-formed your question is. And it will be able to offer some assurance that the information you are getting is reliable–the present World Wide Web cannot do that. In other words, intelligent machines seem to be part of a long human impulse to educate ourselves better and better, to make life better for each of us. Q: What's ahead as AI succeeds even more?A: Many of us already deal with limited AI in our daily lives–credit cards, search engines like Google, automated voice instructions from our GPS devices to help us drive to our destinations; we order prescriptions over the phone from semi-intelligent voice machines. But visionary projects are underway to make–hey, read my book! Q: Would you consider yourself an AI optimist?On the whole, yes, though I'm not nearly as certain that AI will succeed on the time scale that some observers, such as Ray Kurzweil, believe. However, I'm re-thinking my skepticism as programs like Watson exceed my expectations. I've always thought that significant AI would come to us, but not in a rush. Now I'm not so sure—it might be sooner than I expected. Maybe much sooner. My book talks about my experience with intelligent robots at a meeting in the summer of 2003. Some people have said I was too critical, too negative about that. But in March 2004, DARPA staged a race for autonomous vehicles over a 30-mile desert course. The best vehicle (Carnegie-Mellon's entry) did just over 7 miles before it quit. Some vehicles didn't even get started. The following year, however, the winning intelligent, autonomous car did its entire course without mishap, and it had a few others in the rearguard doing just fine too. These DARPA competitions have continued with ever more difficult problems posed and solved. But we still have a way to go, and no wonder. In just a few decades, we're trying to mimic and even surpass millions of years of natural evolution. Q: How do you feel about what's called "the singularity"? A: Oh, boy. I've long felt that "the singularity"—the moment when machine intelligence exceeds human intelligence—was so far off (if it happened at all) that anything I could say about it could only be hot air. With AI climbing the learning curve as fast as it has been lately, I've needed to revisit that stance. For now, I still maintain that if and when it happens, humans then and there will have the best opinions on how to confront this unprecedented event. We also need to question whether this singularity will arrive, as most proponents seem to think, in the form of a homogenous membrane, spreading all over the planet all at once. It makes more sense to me that it will arrive in fits and starts, and will sometimes be self-contradictory—which would raise other kinds of problems for us, and for it. I wouldn't be astonished if, at that point, we turn to our own smart machines for advice on what the best next move is for the human race.

Computational cognition

From Wikipedia, the free encyclopedia

Computational cognition (sometimes referred to as computational cognitive science or computational psychology) is the study of the computational basis of learning and inference by mathematical modeling, computer simulation, and behavioral experiments. In psychology, it is an approach which develops computational models based on experimental results. It seeks to understand the basis behind the human method of processing of information. Early on computational cognitive scientists sought to bring back and create a scientific form of Brentano’s psychology.^[1]

Artificial intelligence

There are two main purposes for the productions of artificial intelligence: to produce intelligent behaviors regardless of the quality of the results, and to model after intelligent behaviors found in nature.^[2] In the beginning of its existence, there was no need for artificial intelligence to emulate the same behavior as human cognition. Until 1960s, economist Herbert Simon and Allen Newell attempted to formalize human problem-solving skills by using the results of psychological studies to develop programs that implement the same problem-solving techniques as people would. Their works laid the foundation for symbolic AI and computational cognition, and even some advancements for cognitive science and cognitive psychology.^[3]
The field of symbolic AI is based on the physical symbol systems hypothesis by Simon and Newell, which states that expressing aspects of cognitive intelligence can be achieved through the manipulation of symbols.^[4] However, John McCarthy focused more on the initial purpose of artificial intelligence, which is to breakdown the essence of logical and abstract reasoning regardless of whether or not human employs the same mechanism.^[2]

Over the next decades, the progress made in artificial intelligence started to be focused more on developing logic-based and knowledge-based programs, veering away from the original purpose of symbolic AI. Researchers started to believe that artificial intelligence may never be able to imitate some intricate processes of human cognition like perception or learning. A chief failing of AI is not being able to achieve a complete likeness to human cognition due to the lack of emotion and the impossibility of implementing it into an AI. ^[5]They began to take a “sub-symbolic” approach to create intelligence without specifically representing that knowledge. This movement led to the emerging discipline of computational modeling, connectionism, and computational intelligence.^[4]

Computational modeling

As it contributes more to the understanding of human cognition than artificial intelligence, computational cognitive modeling emerged from the need to define various cognition functionalities (like motivation, emotion, or perception) by representing them in computational models of mechanisms and processes.^[6] Computational models study complex systems through the use of specific algorithms and extensive computational resources, or variables, to produce computer simulation.^[7] Simulation is achieved by adjusting the variables, changing one alone or even combining them together, to observe the effect on the outcomes. The results help experimenters make predictions about what would happen in the real system if those similar changes were to occur. ^[8]

When computational models attempt to mimic human cognitive functioning, all the details of the function must be known for them to transfer and display properly through the models, allowing researchers to thoroughly understand and test an existing theory because no variables are vague and all variables are modifiable. Consider a model of memory built by Atkinson and Shiffrin in 1968, it showed how rehearsal leads to long-term memory, where the information being rehearsed would be stored. Despite the advancement it made in revealing the function of memory, this model fails to provide answers to crucial questions like: how much information can be rehearsed at a time? How long does it take for information to transfer from rehearsal to long-term memory? Similarly, other computational models raise more questions about cognition than they answer, making their contributions much less significant for the understanding of human cognition than other cognitive approaches.^[9] An additional shortcoming of computational modeling is its reported lack of objectivity.^[10]

John Anderson in his ACT-R uses the functions of computational models and the findings of cognitive science. Adaptive Control of Thought-Rational is based on the theory that the brain consists of several modules which perform specialized functions separate of each other.^[9] The ACT-R model is classified as a symbolic approach to cognitive science.^[11]

Connectionist network

Another approach which deals more with the semantic content of cognitive science is connectionism or neural network modeling. Connectionism relies on the idea that the brain consists of simple units or nodes and the behavioral response comes primarily from the layers of connections between the nodes and not from the environmental stimulus itself.^[9]

Connectionist network differs from computational modeling specifically because of two functions: neural back-propagation and parallel-processing. Neural back-propagation is a method utilized by connectionist network to show evidence of learning. After a connectionist network produce a response, the stimulated results are compared to real-life situational results. The feedback provided by the backward propagation of errors would be used to improve accuracy for the network’s subsequent responses.^[12] The second function, parallel-processing, stemmed from the belief that knowledge and perception are not limited to specific modules but rather are distributed throughout the cognitive networks. The present of parallel distributed processing has been shown in psychological demonstrations like the Stroop effect, where the brain seems to be analyzing the perception of color and meaning of language at the same time.^[13] However, this theoretical approach has been continually disproved because the two cognitive functions for color-perception and word-forming are operating separately and simultaneously, not parallel of each other.^[14]

The field of cognition may have benefitted from the use of connectionist network but because of the completed system, setting up the neural network models can be quite a tedious task and the results may be less interpretable than the system they are trying to model. Therefore, the results can be used as evidence for broad theory of cognition without explaining the particular process happening within the cognitive function. Other disadvantages of connectionism lie in the research methods it employs or hypothesis it tests, which has been proven inaccurate or ineffective often, taking connectionist models further from an accurate representation of how the brain functions. These issues cause neural network models to be ineffective on studying higher forms of information-processing, and hinder connectionism from advancing the general understanding of human cognition.^[15]

Cognitive model

From Wikipedia, the free encyclopedia

A cognitive model is an approximation to animal cognitive processes (predominantly human) for the purposes of comprehension and prediction. Cognitive models can be developed within or without a cognitive architecture, though the two are not always easily distinguishable.

In contrast to cognitive architectures, cognitive models tend to be focused on a single cognitive phenomenon or process (e.g., list learning), how two or more processes interact (e.g., visual search and decision making), or to make behavioral predictions for a specific task or tool (e.g., how instituting a new software package will affect productivity). Cognitive architectures tend to be focused on the structural properties of the modeled system, and help constrain the development of cognitive models within the architecture. Likewise, model development helps to inform limitations and shortcomings of the architecture. Some of the most popular architectures for cognitive modeling include ACT-R, Clarion, and Soar.

History

Cognitive modeling historically developed within cognitive psychology/cognitive science (including human factors), and has received contributions from the fields of machine learning and artificial intelligence to name a few. There are many types of cognitive models, and they can range from box-and-arrow diagrams to a set of equations to software programs that interact with the same tools that humans use to complete tasks (e.g., computer mouse and keyboard).

Box-and-arrow models

A number of key terms are used to describe the processes involved in the perception, storage, and production of speech. Typically, they are used by speech pathologists while treating a child patient. The input signal is the speech signal heard by the child, usually assumed to come from an adult speaker. The output signal is the utterance produced by the child. The unseen psychological events that occur between the arrival of an input signal and the production of speech are the focus of psycholinguistic models. Events that process the input signal are referred to as input processes, whereas events that process the production of speech are referred to as output processes. Some aspects of speech processing are thought to happen online—that is, they occur during the actual perception or production of speech and thus require a share of the attentional resources dedicated to the speech task. Other processes, thought to happen offline, take place as part of the child’s background mental processing rather than during the time dedicated to the speech task. In this sense, online processing is sometimes defined as occurring in real-time, whereas offline processing is said to be time-free (Hewlett, 1990). In box-and-arrow psycholinguistic models, each hypothesized level of representation or processing can be represented in a diagram by a “box,” and the relationships between them by “arrows,” hence the name. Sometimes (as in the models of Smith, 1973, and Menn, 1978, described later in this paper) the arrows represent processes additional to those shown in boxes. Such models make explicit the hypothesized information- processing activities carried out in a particular cognitive function (such as language), in a manner analogous to computer flowcharts that depict the processes and decisions carried out by a computer program. Box-and-arrow models differ widely in the number of unseen psychological processes they describe and thus in the number of boxes they contain. Some have only one or two boxes between the input and output signals (e.g., Menn, 1978; Smith, 1973), whereas others have multiple boxes representing complex relationships between a number of different information-processing events (e.g., Hewlett, 1990; Hewlett, Gibbon, & Cohen- McKenzie,1998; Stackhouse & Wells, 1997). The most important box, however, and the source of much ongoing debate, is that representing the underlying representation (or UR). In essence, an underlying representation captures information stored in a child’s mind about a word he or she knows and uses. As the following description of several models will illustrate, the nature of this information and thus the type(s) of representation present in the child’s knowledge base have captured the attention of researchers for some time. (Elise Baker et al. Psycholinguistic Models of Speech Development and Their Application to Clinical Practice. Journal of Speech, Language, and Hearing Research. June 2001. 44. p 685–702.)

Computational models

A computational model is a mathematical model in computational science that requires extensive computational resources to study the behavior of a complex system by computer simulation. The system under study is often a complex nonlinear system for which simple, intuitive analytical solutions are not readily available. Rather than deriving a mathematical analytical solution to the problem, experimentation with the model is done by changing the parameters of the system in the computer, and studying the differences in the outcome of the experiments. Theories of operation of the model can be derived/deduced from these computational experiments. Examples of common computational models are weather forecasting models, earth simulator models, flight simulator models, molecular protein folding models, and neural network models.

Subsymbolic

subsymbolic if it is made by constituent entities that are not representations in their turn, e.g., pixels, sound images as perceived by the ear, signal samples; subsymbolic units in neural networks can be considered particular cases of this category.

Hybrid

Hybrid computers are computers that exhibit features of analog computers and digital computers. The digital component normally serves as the controller and provides logical operations, while the analog component normally serves as a solver of differential equations. See more details at hybrid intelligent system.

Dynamical systems

In the traditional computational approach, representations are viewed as static structures of discrete symbols. Cognition takes place by transforming static symbol structures in discrete, sequential steps. Sensory information is transformed into symbolic inputs, which produce symbolic outputs that get transformed into motor outputs. The entire system operates in an ongoing cycle.

What is missing from this traditional view is that human cognition happens continuously and in real time. Breaking down the processes into discrete time steps may not fully capture this behavior. An alternative approach is to define a system with (1) a state of the system at any given time, (2) a behavior, defined as the change over time in overall state, and (3) a state set or state space, representing the totality of overall states the system could be in.^[1] The system is distinguished by the fact that all of these states belong together; that is, a change in any aspect of the system depends on other aspects of the system.^[2]

A typical dynamical model is formalized by several differential equations that describe how the system’s state changes over time. By doing so, the form of the space of possible trajectories and the internal and external forces that shape a specific trajectory that unfold over time, instead of the physical nature of the underlying mechanisms that manifest this dynamics, carry explanatory force. On this dynamical view, parametric inputs alter the system’s intrinsic dynamics, rather than specifying an internal state that describes some external state of affairs.

Early dynamical systems

Associative memory

Early work in the application of dynamical systems to cognition can be found in the model of Hopfield networks.^[3][4] These networks were proposed as a model for associative memory. They represent the neural level of memory, modeling systems of around 30 neurons which can be in either an on or off state. By letting the network learn on its own, structure and computational properties naturally arise. Unlike previous models, “memories” can be formed and recalled by inputting a small portion of the entire memory. Time ordering of memories can also be encoded. The behavior of the system is modeled with vectors which can change values, representing different states of the system. This early model was a major step toward a dynamical systems view of human cognition, though many details had yet to be added and more phenomena accounted for.

Language acquisition

By taking into account the evolutionary development of the human nervous system and the similarity of the brain to other organs, Elman proposed that language and cognition should be treated as a dynamical system rather than a digital symbol processor.^[5] Neural networks of the type Elman implemented have come to be known as Elman networks. Instead of treating language as a collection of static lexical items and grammar rules that are learned and then used according to fixed rules, the dynamical systems view defines the lexicon as regions of state space within a dynamical system. Grammar is made up of attractors and repellers that constrain movement in the state space. This means that representations are sensitive to context, with mental representations viewed as trajectories through mental space instead of objects that are constructed and remain static. Elman networks were trained with simple sentences to represent grammar as a dynamical system. Once a basic grammar had been learned, the networks could then parse complex sentences by predicting which words would appear next according to the dynamical model.^[6]

Cognitive development

A classic developmental error, the A-not-B error, has been investigated in the context of dynamical systems.^[7][8] This error is proposed to be not a distinct error occurring at a specific age (8 to 10 months), but a feature of a dynamic learning process that is also present in older children. Children 2 years old were found to make an error similar to the A-not-B error when searching for toys hidden in a sandbox. After observing the toy being hidden in location A and repeatedly searching for it there, the 2-year-olds were shown a toy hidden in a new location B. When they looked for the toy, they searched in locations that were biased toward location A. This suggests that there is an ongoing representation of the toy’s location that changes over time. The child’s past behavior influences its model of locations of the sandbox, and so an account of behavior and learning must take into account how the system of the sandbox and the child’s past actions is changing over time.^[8]

Locomotion

One proposed mechanism of a dynamical system comes from analysis of continuous-time recurrent neural networks (CTRNNs). By focusing on the output of the neural networks rather than their states and examining fully interconnected networks, three-neuron Central pattern generator (CPG) can be used to represent systems such as leg movements during walking.^[9] This CPG contains three motor neurons to control the foot, backward swing, and forward swing effectors of the leg. Outputs of the network represent whether the foot is up or down and how much force is being applied to generate torque in the leg joint. One feature of this pattern is that neuron outputs are either off or on most of the time. Another feature is that the states are quasi-stable, meaning that they will eventually transition to other states. A simple pattern generator circuit like this is proposed to be a building block for a dynamical system. Sets of neurons that simultaneously transition from one quasi-stable state to another are defined as a dynamic module. These modules can in theory be combined to create larger circuits that comprise a complete dynamical system. However, the details of how this combination could occur are not fully worked out.

Modern dynamical systems

Behavioral dynamics

Modern formalizations of dynamical systems applied to the study of cognition vary. One such formalization, referred to as “behavioral dynamics”,^[10] treats the agent and the environment as a pair of coupled dynamical systems based on classical dynamical systems theory. In this formalization, the information from the environment informs the agent's behavior and the agent's actions modify the environment. In the specific case of perception-action cycles, the coupling of the environment and the agent is formalized by two functions. The first function transforms the representation of the agents action into specific patterns of muscle activation that in turn produce forces in the environment. The second function transforms the information from the environment (i.e., patterns of stimulation at the agent's receptors that reflect the environment's current state) into a representation that is useful for controlling the agents actions. Other similar dynamical systems have been proposed (although not developed into a formal framework) in which the agent's nervous systems, the agent's body, and the environment are coupled together^[11][12]

Adaptive behaviors

Behavioral dynamics have been applied to locomotive behavior. Modeling locomotion with behavioral dynamics demonstrates that adaptive behaviors could arise from the interactions of an agent and the environment. According to this framework, adaptive behaviors can be captured by two levels of analysis. At the first level of perception and action, an agent and an environment can be conceptualized as a pair of dynamical systems coupled together by the forces the agent applies to the environment and by the structured information provided by the environment. Thus, behavioral dynamics emerge from the agent-environment interaction. At the second level of time evolution, behavior can be expressed as a dynamical system represented as a vector field. In this vector field, attractors reflect stable behavioral solutions, where as bifurcations reflect changes in behavior. In contrast to previous work on central pattern generators, this framework suggests that stable behavioral patterns are an emergent, self-organizing property of the agent-environment system rather than determined by the structure of either the agent or the environment.

Open dynamical systems

An “open dynamical system” is an extension of classical dynamical systems theory.^[15] Rather than coupling the environment's and the agent's dynamical systems to each other, an “open dynamical system” defines a “total system”, an “agent system”, and a mechanism to relate these two systems. The total system is a dynamical system that models an agent in an environment, whereas the agent system is a dynamical system that models an agent's intrinsic dynamics (i.e., the agent's dynamics in the absence of an environment). Importantly, the relation mechanism does not couple the two systems together, but rather continuously modifies the total system into the decoupled agent's total system. By distinguishing between total and agent systems, it is possible to investigate an agent's behavior when it is isolated from the environment and when it is embedded within an environment. This formalization can be seen as a generalization from the classical formalization, whereby the agent system in the classical formalization can be viewed as the agent system in an open dynamical system, and the agent coupled to the environment and the environment can be viewed as the total system in an open dynamical system.

Embodied cognition

In the context of dynamical systems and embodied cognition, representations can be conceptualized as indicators or mediators. In the indicator view, internal states carry information about the existence of an object in the environment, where the state of a system during exposure to an object is the representation of that object. On the mediator view, internal states carry information about the environment which is used by the system in obtaining its goals. In this more complex account, the states of the system carries information that mediates between the information the agent takes in from the environment, and the force exerted on the environment by the agents behavior. The application of open dynamical systems have been discussed for four types of classical embodied cognition examples:^[16]

1. Instances where the environment and agent must work together to achieve a goal, referred to as "intimacy". A classic example of intimacy is the behavior of simple agents working to achieve a goal (e.g., insects traversing the environment). The successful completion of the goal relies fully on the coupling of the agent to the environment.^[17]
2. Instances where the use of external artifacts improves the performance of tasks than performance without these artifacts, referred to as "offloading". A classic example of offloading is the behavior of Scrabble players; people are able to create more words when playing Scrabble if they have the tiles in front of them and are allowed to physically manipulate their arrangement. In this example, the Scrabble tiles allow the agent to offload working memory demands on to the tiles themselves.^[18]
3. Instances where a functionally equivalent external artifact replaces functions that are normally performed internally by the agent, which is a special case of offloading. One famous example is that of human (specifically the agents Otto and Inga) navigation in a complex environment with or without assistance of an artifact.^[19]
4. Instances where there is not a single agent. The individual agent is part of larger system that contains multiple agents and multiple artifacts. One famous example, formulated by Ed Hutchins in his book Cognition in the Wild, is that of navigating a naval ship.^[20]

The interpretations of these examples rely on the following logic: (1) the total system captures embodiment; (2) one or more agent systems capture the intrinsic dynamics of individual agents; (3) the complete behavior of an agent can be understood as a change to the agent's intrinsic dynamics in relation to its situation in the environment; and (4) the paths of an open dynamical system can be interpreted as representational processes. These embodied cognition examples show the importance of studying the emergent dynamics of an agent-environment systems, as well as the intrinsic dynamics of agent systems. Rather than being at odds with traditional cognitive science approaches, dynamical systems are a natural extension of these methods and should be studied in parallel rather than in competition.

Social anxiety disorder

From Wikipedia, the free encyclopedia

 

Social anxiety disorder

Synonyms	Social phobia

Social anxiety disorder (SAD), also known as social phobia, is an anxiety disorder characterized by a significant amount of fear in one or more social situations, causing considerable distress and impaired ability to function in at least some parts of daily life.^[1]:15 These fears can be triggered by perceived or actual scrutiny from others.

Physical symptoms often include excessive blushing, excess sweating, trembling, palpitations, and nausea. Stammering may be present, along with rapid speech. Panic attacks can also occur under intense fear and discomfort. Some sufferers may use alcohol or other drugs to reduce fears and inhibitions at social events. It is common for sufferers of social phobia to self-medicate in this fashion, especially if they are undiagnosed, untreated, or both; this can lead to alcoholism, eating disorders or other kinds of substance abuse. SAD is sometimes referred to as an illness of lost opportunities where "individuals make major life choices to accommodate their illness". According to ICD-10 guidelines, the main diagnostic criteria of social phobia are fear of being the focus of attention, or fear of behaving in a way that will be embarrassing or humiliating, avoidance and anxiety symptoms.^[4] Standardized rating scales can be used to screen for social anxiety disorder and measure the severity of anxiety.

The first line treatment for social anxiety disorder is cognitive behavioral therapy (CBT) with medications recommended only in those who are not interested in therapy.^[5] CBT is effective in treating social phobia, whether delivered individually or in a group setting.^[6] The cognitive and behavioral components seek to change thought patterns and physical reactions to anxiety-inducing situations. The attention given to social anxiety disorder has significantly increased since 1999 with the approval and marketing of drugs for its treatment. Prescribed medications include several classes of antidepressants: selective serotonin reuptake inhibitors (SSRIs), serotonin-norepinephrine reuptake inhibitors (SNRIs), and monoamine oxidase inhibitors (MAOIs).^[7] Other commonly used medications include beta blockers and benzodiazepines. It is the most common anxiety disorder with up to 10% of people being affected at some point in their life.^[8]

Signs and symptoms

Cognitive aspects

In cognitive models of social anxiety disorder, those with social phobias experience dread over how they will be presented to others. They may feel overly self-conscious, pay high self-attention after the activity, or have high performance standards for themselves. According to the social psychology theory of self-presentation, a sufferer attempts to create a well-mannered impression towards others but believes he or she is unable to do so. Many times, prior to the potentially anxiety-provoking social situation, sufferers may deliberately review what could go wrong and how to deal with each unexpected case. After the event, they may have the perception that they performed unsatisfactorily. Consequently, they will perceive anything that may have possibly been abnormal as embarrassing. These thoughts may extend for weeks or longer. Cognitive distortions are a hallmark, and are learned about in CBT (cognitive-behavioral therapy). Thoughts are often self-defeating and inaccurate. Those with social phobia tend to interpret neutral or ambiguous conversations with a negative outlook, and many studies suggest that socially anxious individuals remember more negative memories than those less distressed.^[9]

An example of an instance may be that of an employee presenting to their co-workers. During the presentation, the person may stutter a word, upon which he or she may worry that other people significantly noticed and think that their perceptions of him or her as a presenter have been tarnished. This cognitive thought propels further anxiety which compounds with further stuttering, sweating, and, potentially, a panic attack.

Behavioural aspects

Social anxiety disorder is a persistent fear of one or more situations in which the person is exposed to possible scrutiny by others and fears that he or she may do something or act in a way that will be humiliating or embarrassing. It exceeds normal "shyness" as it leads to excessive social avoidance and substantial social or occupational impairment. Feared activities may include almost any type of social interaction, especially small groups, dating, parties, talking to strangers, restaurants, interviews, etc.

Those who suffer from social anxiety disorder fear being judged by others in society. In particular, individuals with social anxiety are nervous in the presence of people with authority and feel uncomfortable during physical examinations.^[10] People who suffer from this disorder may behave a certain way or say something and then feel embarrassed or humiliated after. As a result, they often choose to isolate themselves from society to avoid such situations. They may also feel uncomfortable meeting people they do not know, and act distant when they are with large groups of people. In some cases, they may show evidence of this disorder by avoiding eye contact, or blushing when someone is talking to them.^[10][11]

According to psychologist B. F. Skinner, phobias are controlled by escape and avoidance behaviors. For instance, a student may leave the room when talking in front of the class (escape) and refrain from doing verbal presentations because of the previously encountered anxiety attack (avoid). Major avoidance behaviors could include an almost pathological/compulsive lying behavior in order to preserve self-image and avoid judgement in front of others. Minor avoidance behaviors are exposed when a person avoids eye contact and crosses his/her arms to avoid recognizable shaking.^[9] A fight-or-flight response is then triggered in such events.

Physiological aspects

Physiological effects, similar to those in other anxiety disorders, are present in social phobics.^[12] In adults, it may be tears as well as excessive sweating, nausea, difficulty breathing, shaking, and palpitations as a result of the fight-or-flight response. The walk disturbance (where a person is so worried about how they walk that they may lose balance) may appear, especially when passing a group of people. Blushing is commonly exhibited by individuals suffering from social phobia.^[9] These visible symptoms further reinforce the anxiety in the presence of others. A 2006 study found that the area of the brain called the amygdala, part of the limbic system, is hyperactive when patients are shown threatening faces or confronted with frightening situations. They found that patients with more severe social phobia showed a correlation with the increased response in the amygdala.^[13]

Comorbidity

SAD shows a high degree of co-occurrence with other psychiatric disorders. In fact, a population-based study found that 66% of those with SAD had one or more additional mental health disorders.^[14] SAD often occurs alongside low self-esteem and most commonly clinical depression, perhaps due to a lack of personal relationships and long periods of isolation related to social avoidance.^[15] Clinical depression is 1.49 to 3.5 times more likely to occur in those with SAD.  Anxiety disorders are also very common in patients with SAD, in particular generalized anxiety disorder.^[18][19] Avoidant personality disorder is also highly correlated with SAD, with comorbidity rates ranging from 25% to 89%.^[15][20][21]

To try to reduce their anxiety and alleviate depression, people with social phobia may use alcohol or other drugs, which can lead to substance abuse. It is estimated that one-fifth of patients with social anxiety disorder also suffer from alcohol dependence.^[22] However, some research suggests SAD is unrelated to, or even protective against alcohol-related problems.^[23][24] Those who suffer from both alcoholism and social anxiety disorder are more likely to avoid group-based treatments and to relapse compared to people who do not have this combination.^[25]

Differential diagnosis

The DSM-IV criteria stated that an individual cannot receive a diagnosis of social anxiety disorder if their symptoms are better accounted for by one of the autism spectrum disorders such as autism and Asperger syndrome.^[26]

Because of its close relationship and overlapping symptoms, treating people with social phobia may help to understand the underlying connections to other mental disorders. Social anxiety disorder is often linked to bipolar disorder and attention deficit hyperactivity disorder (ADHD) and some believe that they share an underlying cyclothymic-anxious-sensitive disposition.^[27][28] The co-occurrence of ADHD and social phobia is very high; especially when SCT symptoms are present.^[29]

Causes

Research into the causes of social anxiety and social phobia is wide-ranging, encompassing multiple perspectives from neuroscience to sociology. Scientists have yet to pinpoint the exact causes. Studies suggest that genetics can play a part in combination with environmental factors. Social phobia is not caused by other mental disorders or by substance abuse.^[30] Generally, social anxiety begins at a specific point in an individual's life. This will develop over time as the person struggles to recover. Eventually, mild social awkwardness can develop into symptoms of social anxiety or phobia.

Genetics

It has been shown that there is a two to threefold greater risk of having social phobia if a first-degree relative also has the disorder. This could be due to genetics and/or due to children acquiring social fears and avoidance through processes of observational learning or parental psychosocial education. Studies of identical twins brought up (via adoption) in different families have indicated that, if one twin developed social anxiety disorder, then the other was between 30 percent and 50 percent more likely than average to also develop the disorder.^[31] To some extent this 'heritability' may not be specific – for example, studies have found that if a parent has any kind of anxiety disorder or clinical depression, then a child is somewhat more likely to develop an anxiety disorder or social phobia.^[32] Studies suggest that parents of those with social anxiety disorder tend to be more socially isolated themselves (Bruch and Heimberg, 1994; Caster et al., 1999), and shyness in adoptive parents is significantly correlated with shyness in adopted children (Daniels and Plomin, 1985).

Growing up with overprotective and hypercritical parents has also been associated with social anxiety disorder.^[10][33] Adolescents who were rated as having an insecure (anxious-ambivalent) attachment with their mother as infants were twice as likely to develop anxiety disorders by late adolescence, including social phobia.^[34]

A related line of research has investigated 'behavioural inhibition' in infants – early signs of an inhibited and introspective or fearful nature. Studies have shown that around 10–15 percent of individuals show this early temperament, which appears to be partly due to genetics. Some continue to show this trait into adolescence and adulthood, and appear to be more likely to develop social anxiety disorder.^[35]

Social experiences

A previous negative social experience can be a trigger to social phobia,^[36][37] perhaps particularly for individuals high in 'interpersonal sensitivity'. For around half of those diagnosed with social anxiety disorder, a specific traumatic or humiliating social event appears to be associated with the onset or worsening of the disorder;^[38] this kind of event appears to be particularly related to specific (performance) social phobia, for example regarding public speaking (Stemberg et al., 1995). As well as direct experiences, observing or hearing about the socially negative experiences of others (e.g. a faux pas committed by someone), or verbal warnings of social problems and dangers, may also make the development of a social anxiety disorder more likely.^[39] Social anxiety disorder may be caused by the longer-term effects of not fitting in, or being bullied, rejected or ignored (Beidel and Turner, 1998). Shy adolescents or avoidant adults have emphasised unpleasant experiences with peers^[40] or childhood bullying or harassment (Gilmartin, 1987). In one study, popularity was found to be negatively correlated with social anxiety, and children who were neglected by their peers reported higher social anxiety and fear of negative evaluation than other categories of children.^[41] Socially phobic children appear less likely to receive positive reactions from peers^[42] and anxious or inhibited children may isolate themselves.^[43]

Cultural influences

Cultural factors that have been related to social anxiety disorder include a society's attitude towards shyness and avoidance, affecting the ability to form relationships or access employment or education, and shame.^[44] One study found that the effects of parenting are different depending on the culture: American children appear more likely to develop social anxiety disorder if their parents emphasize the importance of others' opinions and use shame as a disciplinary strategy (Leung et al., 1994), but this association was not found for Chinese/Chinese-American children. In China, research has indicated that shy-inhibited children are more accepted than their peers and more likely to be considered for leadership and considered competent, in contrast to the findings in Western countries.^[45] Purely demographic variables may also play a role.

Problems in developing social skills, or 'social fluency', may be a cause of some social anxiety disorder, through either inability or lack of confidence to interact socially and gain positive reactions and acceptance from others. The studies have been mixed, however, with some studies not finding significant problems in social skills^[46] while others have.^[47] What does seem clear is that the socially anxious perceive their own social skills to be low.^[48] It may be that the increasing need for sophisticated social skills in forming relationships or careers, and an emphasis on assertiveness and competitiveness, is making social anxiety problems more common, at least among the 'middle classes'.^[49] An interpersonal or media emphasis on 'normal' or 'attractive' personal characteristics has also been argued to fuel perfectionism and feelings of inferiority or insecurity regarding negative evaluation from others. The need for social acceptance or social standing has been elaborated in other lines of research relating to social anxiety.^[50]

Substance-induced

While alcohol initially relieves social phobia, excessive alcohol misuse can worsen social phobia symptoms and can cause panic disorder to develop or worsen during alcohol intoxication and especially during alcohol withdrawal syndrome. This effect is not unique to alcohol but can also occur with long-term use of drugs which have a similar mechanism of action to alcohol such as the benzodiazepines which are sometimes prescribed as tranquillisers.^[51] Benzodiazepines possess anti-anxiety properties and can be useful for the short-term treatment of severe anxiety. Like the anticonvulsants, they tend to be mild and well tolerated, although there is a risk of habit-forming. Benzodiazepines are usually administered orally for the treatment of anxiety; however, occasionally lorazepam or diazepam may be given intravenously for the treatment of panic attacks.^[52]

The World Council of Anxiety does not recommend benzodiazepines for the long-term treatment of anxiety due to a range of problems associated with long-term use including tolerance, psychomotor impairment, cognitive and memory impairments, physical dependence and a benzodiazepine withdrawal syndrome upon discontinuation of benzodiazepines.^[53] Despite increasing focus on the use of antidepressants and other agents for the treatment of anxiety, benzodiazepines have remained a mainstay of anxiolytic pharmacotherapy due to their robust efficacy, rapid onset of therapeutic effect, and generally favorable side effect profile.^[54] Treatment patterns for psychotropic drugs appear to have remained stable over the past decade, with benzodiazepines being the most commonly used medication for panic disorder.^[55]

Approximately half of patients attending mental health services for conditions including anxiety disorders such as panic disorder or social phobia are the result of alcohol or benzodiazepine dependence.^{[citation needed]} Sometimes anxiety pre-existed alcohol or benzodiazepine dependence but the alcohol or benzodiazepine dependence act to keep the anxiety disorders going and often progressively making them worse.^{[citation needed]} Many people who are addicted to alcohol or prescribed benzodiazepines when it is explained to them they have a choice between ongoing ill mental health or quitting and recovering from their symptoms decide on quitting alcohol or their benzodiazepines.^[56] It was noted that every individual has an individual sensitivity level to alcohol or sedative hypnotic drugs and what one person can tolerate without ill health another will suffer very ill health and that even moderate drinking can cause rebound anxiety syndromes and sleep disorders.^{[citation needed]} A person who is suffering the toxic effects of alcohol or benzodiazepines will not benefit from other therapies or medications as they do not address the root cause of the symptoms.^{[citation needed]} Symptoms may temporarily worsen however, during alcohol withdrawal or benzodiazepine withdrawal.^[56]

Psychological factors

Research has indicated the role of 'core' or 'unconditional' negative beliefs (e.g. "I am inept") and 'conditional' beliefs nearer to the surface (e.g. "If I show myself, I will be rejected"). They are thought to develop based on personality and adverse experiences and to be activated when the person feels under threat.^[57] One line of work has focused more specifically on the key role of self-presentational concerns.^[58][59] The resulting anxiety states are seen as interfering with social performance and the ability to concentrate on interaction, which in turn creates more social problems, which strengthens the negative schema. Also highlighted has been a high focus on and worry about anxiety symptoms themselves and how they might appear to others.^[60] A similar model^[61] emphasizes the development of a distorted mental representation of the self and overestimates of the likelihood and consequences of negative evaluation, and of the performance standards that others have. Such cognitive-behavioral models consider the role of negatively biased memories of the past and the processes of rumination after an event, and fearful anticipation before it. Studies have also highlighted the role of subtle avoidance and defensive factors, and shown how attempts to avoid feared negative evaluations or use of 'safety behaviors' (Clark & Wells, 1995) can make social interaction more difficult and the anxiety worse in the long run. This work has been influential in the development of Cognitive Behavioral Therapy for social anxiety disorder, which has been shown to have efficacy.

Mechanisms

There are many studies investigating neural bases of social anxiety disorder.^[62][63] Although the exact neural mechanisms have not been found yet, there is evidence relating social anxiety disorder to imbalance in some neurochemicals and hyperactivity in some brain areas.

Neurotransmitters

Sociability is closely tied to dopamine neurotransmission.^[64] Misuse of stimulants like amphetamines to increase self-confidence and improve social performance is common.^{[citation needed]} In a recent study a direct relation between social status^[clarify]of volunteers and binding affinity of dopamine D2/3 receptors in the striatum was found.^[65] Other research shows that the binding affinity of dopamine D2 receptors in the striatum of social anxiety sufferers is lower than in controls.^[66] Some other research shows an abnormality in dopamine transporter density in the striatum of social anxiety sufferers.^[67][68] However, some researchers have been unable to replicate previous findings of evidence of dopamine abnormality in social anxiety disorder.^[69] Studies have shown high prevalence of social anxiety in Parkinson's disease and schizophrenia. In a recent study, social phobia was diagnosed in 50% of Parkinson's disease patients.^[70] Other researchers have found social phobia symptoms in patients treated with dopamine antagonists like haloperidol, emphasizing the role of dopamine neurotransmission in social anxiety disorder.^[71] Also, concentration problems, mental and physical fatigue, anhedonia and decreased self-confidence can be seen in those with social anxiety disorder.^{[citation needed]}

Some evidence points to the possibility that social anxiety disorder involves reduced serotonin receptor binding.^[72] A recent study reports increased serotonin transporter binding in psychotropic medication-naive patients with generalized social anxiety disorder.^[67] Although there is little evidence of abnormality in serotonin neurotransmission, the limited efficacy of medications which affect serotonin levels may indicate the role of this pathway. Paroxetine and sertraline are two SSRIs that have been approved by the FDA to treat social anxiety disorder. Some researchers believe that SSRIs decrease the activity of the amygdala.^[62] There is also increasing focus on other candidate transmitters, e.g. norepinephrine and glutamate, which may be over-active in social anxiety disorder, and the inhibitory transmitter GABA, which may be under-active in the thalamus.^[62][73]

Brain areas

The amygdala is part of the limbic system which is related to fear cognition and emotional learning. Individuals with social anxiety disorder have been found to have a hypersensitive amygdala; for example in relation to social threat cues (e.g. perceived negative evaluation by another person), angry or hostile faces, and while waiting to give a speech.^[74] Recent research has also indicated that another area of the brain, the anterior cingulate cortex, which was already known to be involved in the experience of physical pain, also appears to be involved in the experience of 'social pain', for example perceiving group exclusion.^[75] A 2007 meta-analysis also found that individuals with social anxiety had hyperactiviation in the amygdala and insula areas which are frequently associated with fear and negative emotional processing.^[76]

Diagnosis

ICD-10 defines social phobia as a fear of scrutiny by other people leading to avoidance of social situations. The anxiety symptoms may present as a complaint of blushing, hand tremor, nausea or urgency of micturition. Symptoms may progress to panic attacks.^[4]

Standardized rating scales such as the Social Phobia Inventory, the SPAI-B, Liebowitz Social Anxiety Scale, and the Social Interaction Anxiety Scale can be used to screen for social anxiety disorder and measure the severity of anxiety.^{[77][78][79][80][81]}

Prevention

Prevention of anxiety disorders is one focus of research.^[82][83] Use of CBT and related techniques may decrease the number of children with social anxiety disorder following completion of prevention programs.^[84]

Treatment

Psychotherapies

The first line treatment for social anxiety disorder is cognitive behavioral therapy (CBT) with medications such as selective serotonin reuptake inhibitors (SSRIs) used only in those who are not interested in therapy.^[1]:191[5] Self-help based on principles of CBT is a second-line treatment.^{[1]:191[85][86]}

There is some emerging evidence for the use of acceptance and commitment therapy (ACT) in the treatment of social anxiety disorder. ACT is considered an offshoot of traditional CBT and emphasizes accepting unpleasant symptoms rather than fighting against them, as well as psychological flexibility – the ability to adapt to changing situational demands, to shift one's perspective, and to balance competing desires.^[87] ACT may be useful as a second line treatment for this disorder in situations where CBT is ineffective or refused.^[88]

Some studies have suggested social skills training (SST) can help with social anxiety.^[89][90] Examples of social skills focused on during SST for social anxiety disorder include: initiating conversations, establishing friendships, interacting with members of the preferred sex, constructing a speech and assertiveness skills.^[91] However, it is not clear whether specific social skills techniques and training are required, rather than just support with general social functioning and exposure to social situations.^[92]

Given the evidence that social anxiety disorder may predict subsequent development of other psychiatric disorders such as depression, early diagnosis and treatment is important.^[16][17] Social anxiety disorder remains under-recognized in primary care practice, with patients often presenting for treatment only after the onset of complications such as clinical depression or substance abuse disorders.^[93][94][95]

A 2014 systematic review of interventions for adults with social anxiety disorder identified the following psychotherapeutic interventions from published and unpublished sources:^[96]

Intervention	Trials	Participants	Effect
Exercise promotion	1	18	-0.36
Exposure and social skills	10	227	-0.86
Exposure in vivo	9	199	-0.83
Social skills training	1	28	-0.88
Group cognitive behavioural therapy	28	984	-0.92
Heimberg model	11	338	-0.80
Other (no model specified)	16	583	-0.85
Enhanced cognitive behavioral therapy	1	63	-1.10
Individual cognitive behavioral therapy	15	562	-1.19
Hope, Heimberg and Turk Model	2	53	-1.02
Other (no model specified)	6	163	-1.19
Clark and Wells cognitive therapy model	3	97	-1.56
Clark and Wells cognitive therapy shortened sessions	4	249	-0.97
Other psychological therapy	7	182	-0.36
Interpersonal psychotherapy	2	64	-0.43
Mindfulness training	3	64	-0.39
Supportive therapy	2	54	-0.26
Psychodynamic psychotherapy	3	185	-0.62
Self-help with support	16	748	-0.86
Book with support	3	52	-0.85
Internet with support	13	696	-0.88
Self-help without support	9	406	-0.75
Book without support	4	136	-0.84
Internet without support	5	270	-0.66

Medications

SSRIs

Selective serotonin reuptake inhibitors (SSRIs), a class of antidepressants, are first choice medication for generalized social phobia but a second line treatment.^[1]:191 Compared to older forms of medication, there is less risk of tolerability and drug dependency associated with SSRIs.^[97]

In a 1995 double-blind, placebo-controlled trial, the SSRI paroxetine was shown to result in clinically meaningful improvement in 55% of patients with generalized social anxiety disorder, compared with 23.9% of those taking placebo.^[98] An October 2004 study yielded similar results. Patients were treated with either fluoxetine, psychotherapy, or a placebo. The first four sets saw improvement in 50.8 to 54.2 percent of the patients. Of those assigned to receive only a placebo, 31.7% achieved a rating of 1 or 2 on the Clinical Global Impression-Improvement scale. Those who sought both therapy and medication did not see a boost in improvement.^[99]

General side-effects are common during the first weeks while the body adjusts to the drug. Symptoms may include headaches, nausea, insomnia and changes in sexual behavior. Treatment safety during pregnancy has not been established.^[100] In late 2004 much media attention was given to a proposed link between SSRI use and suicidality [a term that encompasses suicidal ideation and attempts at suicide as well as suicide]. For this reason, [although evidential causality between SSRI use and actual suicide has not been demonstrated] the use of SSRIs in pediatric cases of depression is now recognized by the Food and Drug Administration as warranting a cautionary statement to the parents of children who may be prescribed SSRIs by a family doctor.^[101] Recent studies have shown no increase in rates of suicide.^[102] These tests, however, represent those diagnosed with depression, not necessarily with social anxiety disorder.

In addition, studies show that more socially phobic patients treated with anti-depressant medication develop hypomania than non-phobic controls. The hypomania can be seen as the medication creating a new problem.^[103][104]

Other drugs

Other prescription drugs are also used, if other methods are not effective. Before the introduction of SSRIs, monoamine oxidase inhibitors (MAOIs) such as phenelzine were frequently used in the treatment of social anxiety.^[7] Evidence continues to indicate that MAOIs are effective in the treatment and management of social anxiety disorder and they are still used, but generally only as a last resort medication, owing to concerns about dietary restrictions, possible adverse drug interactions and a recommendation of multiple doses per day.^[105] A newer type of this medication, Reversible inhibitors of monoamine oxidase subtype A (RIMAs) such as the drug moclobemide, bind reversibly to the MAO-A enzyme, greatly reducing the risk of hypertensive crisis with dietary tyramine intake.^[106]

Benzodiazepines such as clonazepam are an alternative to SSRIs. These drugs are often used for short-term relief of severe, disabling anxiety.^[107] Although benzodiazepines are still sometimes prescribed for long-term everyday use in some countries, there is concern over the development of drug tolerance, dependency and misuse. It has been recommended that benzodiazepines be considered only for individuals who fail to respond to other medications.^[108] Benzodiazepines augment the action of GABA, the major inhibitory neurotransmitter in the brain; effects usually begin to appear within minutes or hours. In most patients, tolerance rapidly develops to the sedative effects of benzodiazepines, but not to the anxiolytic effects.^{[citation needed]} Long-term use of benzodiazepine may result in physical dependence, and abrupt discontinuation of the drug should be avoided due to high potential for withdrawal symptoms (including tremor, insomnia, and in rare cases, seizures). A gradual tapering of the dose of clonazepam (a decrease of 0.25 mg every 2 weeks), however, has been shown to be well tolerated by patients with social anxiety disorder. Benzodiazepines are not recommended as monotherapy for patients who have major depression in addition to social anxiety disorder and should be avoided in patients with a history of substance abuse.^[10]

Certain anticonvulsant drugs such as gabapentin are effective in social anxiety disorder and may be a possible treatment alternative to benzodiazepines.^[109][110]

Serotonin-norepinephrine reuptake inhibitors (SNRIs) such as venlafaxine^{[111][112][113]} have shown similar effectiveness to the SSRIs. In Japan, Milnacipran is used in the treatment of Taijin kyofusho, a Japanese variant of social anxiety disorder. The atypical antidepressants mirtazapine and bupropion have been studied for the treatment of social anxiety disorder, and rendered mixed results.

Some people with a form of social phobia called performance phobia have been helped by beta-blockers, which are more commonly used to control high blood pressure. Taken in low doses, they control the physical manifestation of anxiety and can be taken before a public performance.

A novel treatment approach has recently been developed as a result of translational research. It has been shown that a combination of acute dosing of d-cycloserine (DCS) with exposure therapy facilitates the effects of exposure therapy of social phobia.^[117] DCS is an old antibiotic medication used for treating tuberculosis and does not have any anxiolytic properties per se. However, it acts as an agonist at the glutamatergic N-methyl-D-aspartate (NMDA) receptor site, which is important for learning and memory.^[118]

Kava-kava has also attracted attention as a possible treatment,^[119] although safety concerns exist.

Epidemiology

Social anxiety disorder is known to appear at an early age in most cases. Fifty percent of those who develop this disorder have developed it by the age of 11, and 80% have developed it by age 20.^{[citation needed]} This early age of onset may lead to people with social anxiety disorder being particularly vulnerable to depressive illnesses, drug abuse and other psychological conflicts.^[134]

When prevalence estimates were based on the examination of psychiatric clinic samples, social anxiety disorder was thought to be a relatively rare disorder. The opposite was found to be true; social anxiety was common, but many were afraid to seek psychiatric help, leading to an underrecognition of the problem.^[9]

The National Comorbidity Survey of over 8,000 American correspondents in 1994 revealed 12-month and lifetime prevalence rates of 7.9 percent and 13.3 percent, respectively; this makes it the third most prevalent psychiatric disorder after depression and alcohol dependence, and the most common of the anxiety disorders.^[135] According to U.S. epidemiological data from the National Institute of Mental Health, social phobia affects 15 million adult Americans in any given year.^[136] Estimates vary within 2 percent and 7 percent of the U.S. adult population.^[137]

The mean onset of social phobia is 10 to 13 years.^[138] Onset after age 25 is rare and is typically preceded by panic disorder or major depression.^[139] Social anxiety disorder occurs more often in females than males.^[140] The prevalence of social phobia appears to be increasing among white, married, and well-educated individuals. As a group, those with generalized social phobia are less likely to graduate from high school and are more likely to rely on government financial assistance or have poverty-level salaries.^[141] Surveys carried out in 2002 show the youth of England, Scotland, and Wales have a prevalence rate of 0.4 percent, 1.8 percent, and 0.6 percent, respectively.^[142] In Canada, the prevalence of self-reported social anxiety for Nova Scotians older than 14 years was 4.2 percent in June 2004 with women (4.6 percent) reporting more than men (3.8 percent).^[143] In Australia, social phobia is the 8th and 5th leading disease or illness for males and females between 15–24 years of age as of 2003.^[144] Because of the difficulty in separating social phobia from poor social skills or shyness, some studies have a large range of prevalence.^[145] The table also shows higher prevalence in Sweden.

History

Literary descriptions of shyness can be traced back to the days of Hippocrates around 400 B.C. Hippocrates described someone who "through bashfulness, suspicion, and timorousness, will not be seen abroad; loves darkness as life and cannot endure the light or to sit in lightsome places; his hat still in his eyes, he will neither see, nor be seen by his good will. He dare not come in company for fear he should be misused, disgraced, overshoot himself in gesture or speeches, or be sick; he thinks every man observes him."^[146]

The first mention of the psychiatric term social phobia (phobie des situations sociales), was made in the early 1900s.^[147] Psychologists used the term "social neurosis" to describe extremely shy patients in the 1930s. After extensive work by Joseph Wolpe on systematic desensitization, research on phobias and their treatment grew. The idea that social phobia was a separate entity from other phobias came from the British psychiatrist Isaac Marks, in the 1960s. This was accepted by the American Psychiatric Association and was first officially included in the third edition of the Diagnostic and Statistical Manual of Mental Disorders. The definition of the phobia was revised in 1989 to allow comorbidity with avoidant personality disorder, and introduced generalized social phobia.^[9] Social phobia had been largely ignored prior to 1985.^[148]

After a call to action by psychiatrist Michael Liebowitz and clinical psychologist Richard Heimberg, there was an increase in attention to and research on the disorder. The DSM-IV gave social phobia the alternative name social anxiety disorder. Research on the psychology and sociology of everyday social anxiety continued. Cognitive Behavioural models and therapies were developed for social anxiety disorder. In the 1990s, paroxetine became the first prescription drug in the U.S. approved to treat social anxiety disorder, with others following.