proach to research on avoidance and anxiety informs several modern day clinical treatments, such as exposure and response prevention, and is supported by a strong program of basic science (e.g., Bouton et al.; Mineka & Zinbarg). Despite the central status given to avoidance in explaining anxiety disorders, behav-ior analysts have contributed ...
Mar 10, 2014 · Anxiety disorders are characterized by a longer-lasting, pathologically intensified, and unfounded emotion of anxiety, which palsies physical and mental functions and leads to avoidance behavior. Anxiety disorders are often associated with an underlying psychobiological dysfunction 59, 60 and lead to clinically significant distress, becoming a ...
The multidimensional anxiety theory has been broadly used in the field of sports, specifically athlete’s performance. Studies have based their theory on the connection between performance and physiological arousal making Marten’s Multidimensional Theory of Anxiety and the Catastrophe Model proposed by Hardy and Fazey (1987) two of the foremost used theories the …
Research in psychology has shown that attachment styles are best conceptualized and measured along two continuous, quasi-orthogonal dimensions called “attachment anxiety” and “attachment avoidance” (Brennan et al., 1998). Attachment anxiety is the extent to which a person worries that relationship partners might not be available in times of need; has a need for approval; and …
The multidimensional anxiety theory holds the premise that state anxiety is multidimensional , and that the two components of performance anxiety, that is , cognitive anxiety and somatic anxiety, influence performance differently.
The catastrophe model of anxiety suggests 4 specific relationships between cognitive anxiety, physiological arousal, and performance. It has been suggested that when cognitive anxiety levels are high, there is an increased level of physiological arousal that leads to a catastrophic drop in athletic performance.
There are some key terms we need to take into consideration when talking about the multidimensional anxiety theory and they are: 1 State anxiety: it is described as the emotional state marked by apprehension and tension. 2 Cognitive state anxiety: they are related to the worrying and negative perception or thoughts about self-performance. 3 Somatic state anxiety: it can be described as the changes in perceived physiological arousal.
Somatic state anxiety: it can be described as the changes in perceived physiological arousal. Anxiety in sports settings has been a common occurrence in competitive situations which can eventually result in reduced athlete’s performance.
Previous theory on the anxiety-performance relationship. For almost a decade the most used theory was the inverted-U hypothesis used to explain the relationship between anxiety and performance where the performance will be poor if there are lower levels of anxiety, optimal performance when having intermediate levels and then if anxiety goes ...
The catastrophe model (Catastrophe theory) was first proposed by French Mathematician Rene Thom in 1975 and later on, was adopted by Hardy & Fazey’s (1987) and introduced to the Behavioral Sciences.
In contrast, the CSAI-2 was used to measure and keep track of state anxiety as an existing/current emotional state character ized by feelings of apprehension and tension related to physiological arousal.
The Metacognitive model (MCM) of GAD proposed byWells(1995, 1999, 2004, 2005) posits that individuals with GADexperience two types of worry. When individuals are initiallyfaced with an anxiety-provoking situation, positive beliefs aboutworry are engendered (e.g., the belief that worry will help themcope with the situation). This process is known asType 1 worry,which Wells defines as worry about non-cognitive events such asexternal situations or physical symptoms (Wells, 2005). Type 1worry initially stimulates an anxiety response but later mayincrease or decrease anxiety, depending on whether the problemthat has stimulated the worry has been resolved. During the courseof Type 1 worry, negative beliefs about worry are activated (forWells’ theories on how negative beliefs about worry initiallydevelop, seeWells, 1995). Individuals with GAD begin to worryabout their Type 1 worry; they fear that the worry is uncontrollableor may even be inherently dangerous. This ‘‘worry about worry’’(i.e., ‘‘meta-worry’’) is labeled by Wells asType 2 worry.
The IUM posits the importance of four factors in distinguishingindividuals with GAD from healthy controls and other clinicalsamples: IU, positive beliefs about worry, cognitive avoidance, andnegative problem orientation. Two studies (Dugas, Marchand, &
A persistent fear of one or more social or performance situations in which the person is exposed to unfamiliar people or to possible scrutiny by others.The individual fears that he or she will act in a way (or show anxiety symptoms) that will be embarrassing and humiliating.
Excessive anxiety and worry (apprehensive expectation), occurring more days than not for at least 6 months, about a number of events or activities (such as work or school performance.
The 11 anxiety disorders described in the DSM-5 are separation anxiety disorder; selective mutism; specific phobia; SAD; panic disorder; agoraphobia; GAD; substance/medication-induced anxiety disorder; anxiety disorder due to another medical condition; other specified anxiety disorder; and unspecified anxiety disorder. Diagnosis of an anxiety disorder typically follows persistence of symptoms for 6 months [ ( 29 ), p. 189] and elimination of alternative explanations, including other psychiatric conditions.
This review makes reference to four types of computational model: reinforcement learning models, models of decision processes, Bayesian models, and network models. Each model type has already been applied to the study of anxiety in one form or another—and detailed accounts of exemplar studies will be provided in the section on “Existing Computational Studies of Trait Anxiety.” The current section provides brief introductions along with references to relevant tutorial material and intuitive suggestions about how these modeling approaches could be used to capture individual differences associated with pathological anxiety. This is intended to assist the reader in understanding the computationally informed conceptualization of trait anxiety introduced in the sections on “State Anxiety, Trait Anxiety, and Anxiety Disorders” and “Trait Anxiety: Targets for Computational Studies” and the more in-depth discussion of computational studies and open questions in the sections on “Existing Computational Studies of Trait Anxiety” and “Outstanding Computational Questions.”
The defining characteristic of an aversive stimulus, or punishment, is that it “is something an animal will work to escape or avoid” ( 159 ). Unlike pure associative learning, which underpins classical conditioning, successful avoidance requires implementation of an action. Among animals of a particular species, certain avoidance behaviors are instinctive punishment responses and can be transferred very easily from US to CS; others have to be learned based on their potential to facilitate escape [for a review, see Ref. ( 160 )]. Though analogous to the learning of appetitive behaviors to obtain rewards, avoidance learning poses an additional theoretical challenge because reinforcement in this case depends on non-occurrence of an aversive event. This problem of so-called negative reinforcement can be solved by assuming that an aversive CS first acquires the power to generate fear by classical conditioning and that any action serving to remove the CS will be reinforced because it reduces fear ( 161, 162 ).
As a self-report measure of a long-term behavioral trend, trait anxiety is not the most obvious subject for computational study. It seems too intangible and loosely defined to be approachable with the precision tools of computational modeling. However, this review has sought to demonstrate that modeling can be a useful addition to the study of trait anxiety precisely because it forces scientists to be explicit about the details and relevance of their hypotheses. In particular, the computational techniques of reinforcement learning, decision modeling, Bayesian modeling, and network analysis can all be used to address pressing questions about the processes underlying trait vulnerability to the development of anxiety disorders—as well as the potential relationships between them.