A Brief but Comprehensive Review of Research on the Alternative DSM-5 Model for Personality Disorders.
Summary (2 min read)
Introduction
- A key assumption in many models is that the correct choice is fixed in time, i.e. decisions are made in a static environment.
- This assumption may hold in the laboratory, but natural environments are seldom static [15, 16].
- This model also suggests a biophysical neural implementation 2 for evidence integrators consisting of neural populations whose activity represents the evidence in favor of a particular choice.
II. OPTIMAL DECISIONS IN A STATIC ENVIRONMENT
- The authors develop their model in a way that parallels the case of a static environment with two possible states.
- To make a decision, an optimal observer integrates a stream of measurements to infer the present environmental state.
- In the static case, this can be done using sequential analysis [1, 9]:.
III. TWO ALTERNATIVES IN A CHANGING ENVIRONMENT
- The authors use the same assumptions to derive a recursive equation for the log likelihood ratio between to alternatives in a changing environment.
- The authors assume that + and − are known to the observer.
- The increase in accuracy in time is exceedingly slow for low m.
A. Equal switching rates between two states
- = + = −, the frequencies of switches between states are equal, also known as When.
- This distribution is concentrated around ȳ± = ± sinh−1 m2 , the fixed points of the deterministic counterpart of Eq. (9) obtained by setting Wτ ≡ 0.
- Aggregating new evidence then always tends to increase an optimal observer’s belief in one of the choices.
B. Linear approximation of the SDE
- An advantage of Eq. (6) is that it is amenable to standard methods of stochastic analysis.
- The authors can find an accurate piecewise linear approximation to Eq. (6), although, for simplicity, they focus on Eq. (9).
- Linear drift time nonlin. linear drift FIG.
- Eq. (11) can be integrated explicitly using standard methods in stochastic calculus [30].
IV. MULTIPLE ALTERNATIVES IN A CHANGING ENVIRONMENT
- The authors next extend their analysis of evidence accumulation in changing environments to the case of multiple alternatives.
- The authors again use sequential analysis to obtain the probabilities Ln,i = Pr(H(t) = Hi|ξ1:n) that the environment is in state.
- The index that maximizes the posterior probability, ı̂ = argmaxi Ln,i, corresponds to the most probable state, given the observations ξ1:n.
V. A CONTINUUM OF STATES IN A CHANGING ENVIRONMENT
- Lastly, the authors consider the case of a continuum of possible environmental states.
- (B) In quickly changing environments, the distribution does not have time to equilibriate between switches.
- The drift gθ(t) is maximal when θ agrees with the present environmental state.
VI. A NEURAL IMPLEMENTATION OF AN OPTIMAL OBSERVER
- Previous neural models of decision making typically relied on mutually inhibitory neural networks [12, 20, 33], with each population representing one alternative.
- H± and vanishes otherwise, W± are Wiener processes representing the variability in the input signal with covariance defined as in Eq. (14) .
- In this case coupling between populations is again excitatory (Fig. 7C).
VII. DISCUSSION
- The authors have derived a nonlinear stochastic model of optimal evidence accumulation in changing environments.
- The authors have made several assumptions about the model to simplify these initial derivations.
- The authors also assumed that changes in the environment follow a memoryless process.
- This allows for a straightforward approximation of the nonlinear model by a linear SDE, which can be analyzed fully.
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Frequently Asked Questions (13)
Q2. What future works have the authors mentioned in the paper "A brief but comprehensive review of research on the alternative dsm-5 model for personality disorders" ?
However, several questions remain unanswered and should be addressed in future research. Finally, future research should continue pursuing a comprehensive conceptualization of mental disorders that integrates major dimensions of personality and psychopathology [ 313–318 ]. Such limitations may be overcome by multitrait–multimethod designs, as demonstrated by a recent study on the construct validity of trait facets related to antagonism [ 306 ]. Currently, there is only a single study showing that the LPFS-BF can be used as an outcome measure in a 3- month residential treatment program [ 31 ].
Q3. Why did factor analyses fail to recover the theoretical structure of the LPFS?
factor analyses of individual items failed to recover the theoretical structure [137], which may in part be due to method factors of items with positive and negative valence.
Q4. What is the importance of a procedure to safeguard the validity of the PID-5?
To ascertain the validity of individual PID-5 results in higher stakes clinical situations, it is important that procedures are in place to safeguard scale interpretation from negligent or malingered response patterns.
Q5. What are the main issues that have been addressed in the PID-5?
Further issues that have been addressed include measurement invariance or item bias due to age [287, 288], gender [217, 289], and clinical status [290]; response styles in PID-5 self-reports [154, 155, 291, 292]; heritability and familial aggregation of maladaptive traits [39, 289, 293–295]; and perceived likability, impairment, functionality, as well as desire and ability for change of maladaptive traits [68, 158, 296–298].
Q6. What was the association between negative affectivity and low emotional stability?
negative affectivity was consistently associated with low emotional stability, detachment with low extraversion, antagonismwith low agreeableness, and disinhibition with low conscientiousness.
Q7. What was originally conceived of as an assessment of PD severity?
In the AMPD, the assessment of PD severity was originally conceived of as applying the LPFS as an expert rating on a single five-point scale [40].
Q8. What is the evidence in favor of the hypothesis that the PID-5 trait domains can be?
considerable evidence has accumulated in favor of the hypothesis that the PID-5 trait domains can be conceived of as maladaptive variants of general personality traits [77, 84, 144, 149, 152, 165, 168, 173–182].
Q9. What is the recent meta-analysis of the trait facets?
Two recent meta-analyses [141, 142] covering a large body of research including clinical and nonclinical samples from different countries have confirmed that the latent structure of trait facets is mainly in line with the five-factor model featured in the AMPD [55, 56, 59, 62, 67, 74–79, 86, 140, 143–151].
Q10. What should be done to improve the interpretation of test scores in single-case scenarios?
In addition, representative samples from the general population should be collected to establish normative values, which will greatly enhance the interpretation of test scores in single-case scenarios.
Q11. What is the first step in the implementation of the PD chapter?
In the first step, the practitioner examines whether the patient’s pathology corresponds to the general definition of PD (code: 6D10), which emphasizes longstanding problems in self and interpersonal functioning.
Q12. What is the purpose of the scale development for assessing severity of personality disorder?
Research on scale development for assessing severity according to ICD-11 is still in its beginnings and includes pilot studies on expert ratings [52] and the development of a brief self-report measure, the Standardized Assessment of Severity of Personality Disorder (SASPD) [37].
Q13. What is the need for research into the validity of the specific impairment criteria?
More research is needed into the validity of the specific PDs listed in the AMPD, incorporating the specific impairment criteria [88••] and using mixture modeling to test whether they indeed represent latent categories [312].