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Impulsive Behavior

Impulsive Behavior: A pattern of acting rashly or without consideration of consequences, often driven by immediate gratification or lack of self-control.
This can manifest in various contexts, such as addiction, risk-taking, or emotional outbursts.
Effective management of impulsive behavior can improve quality of life and research outcomes.
PubCompare.ai's AI-powered tools can help identify the optimal research protocols and products to address this challenge, supporting data-driven decision making and enhancing the accuracy of your studies.

Most cited protocols related to «Impulsive Behavior»

1
Multidimensional Assessment of Impulsivity and Risky Behaviors
Cited 135 times

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Publication 2014
Alcoholics Alcohol Use Disorder Diagnosis Eating Disorders Ethnicity Feeding Behaviors Pharmaceutical Preparations Quercus Substance Use
2
Hierarchical Drift Diffusion Modeling
Cited 117 times
SSMs generally fall into one of two classes: (1) diffusion models which assume that relative evidence is accumulated over time and (2) race models which assume independent evidence accumulation and response commitment once the first accumulator crossed a boundary (LaBerge, 1962 (link); Vickers, 1970 (link)). Currently, HDDM includes two of the most commonly used SSMs: the drift diffusion model (DDM) (Ratcliff and Rouder, 1998 (link); Ratcliff and McKoon, 2008 (link)) belonging to the class of diffusion models and the linear ballistic accumulator (LBA) (Brown and Heathcote, 2008 (link)) belonging to the class of race models. In the remainder of this paper we focus on the more commonly used DDM.
As input these methods require trial-by-trial RT and choice data (HDDM currently only supports binary decisions) as illustrated in the below example table:
The DDM models decision-making in two-choice tasks. Each choice is represented as an upper and lower boundary. A drift-process accumulates evidence over time until it crosses one of the two boundaries and initiates the corresponding response (Ratcliff and Rouder, 1998 (link); Smith and Ratcliff, 2004 (link)) (see Figure 1 for an illustration). The speed with which the accumulation process approaches one of the two boundaries is called drift-rate v. Because there is noise in the drift process, the time of the boundary crossing and the selected response will vary between trials. The distance between the two boundaries (i.e., threshold a) influences how much evidence must be accumulated until a response is executed. A lower threshold makes responding faster in general but increases the influence of noise on decision-making and can hence lead to errors or impulsive choice, whereas a higher threshold leads to more cautious responding (slower, more skewed RT distributions, but more accurate). Response time, however, is not solely comprised of the decision-making process—perception, movement initiation and execution all take time and are lumped in the DDM by a single non-decision time parameter t. The model also allows for a prepotent bias z affecting the starting point of the drift process relative to the two boundaries. The termination times of this generative process gives rise to the response time distributions of both choices.
An analytic solution to the resulting probability distribution of the termination times was provided by Wald (1947 ); Feller (1968 ):
f(x|v,a,z)=πa2exp​(−vaz−v2x2)                     ×∑k=1∞k exp​(−k2π2x2a2)sin(kπz)
Since the formula contains an infinite sum, HDDM uses an approximation provided by Navarro and Fuss (2009 (link)).
Subsequently, the DDM was extended to include additional noise parameters capturing inter-trial variability in the drift-rate, the non-decision time and the starting point in order to account for two phenomena observed in decision-making tasks, most notably cases where errors are faster or slower than correct responses. Models that take this into account are referred to as the full DDM (Ratcliff and Rouder, 1998 (link)). HDDM uses analytic integration of the likelihood function for variability in drift-rate and numerical integration for variability in non-decision time and bias (Ratcliff and Tuerlinckx, 2002 (link)).
Wiecki T.V., Sofer I, & Frank M.J. (2013). HDDM: Hierarchical Bayesian estimation of the Drift-Diffusion Model in Python. Frontiers in Neuroinformatics, 7, 14.
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Publication 2013
Diffusion Impulsive Behavior Movement specific substance maruyama
3
Characterizing ADHD Symptom Distributions
Cited 97 times
A factor analysis was performed with SPSS, using the principal component method and varimax rotation. As expected, a 2-factor solution was obtained, Inattention representing the ADHD-INN domain and Hyperactivity/ Impulsivity representing the ADHD-HY/IMP domain. These two factors explained 77.78% of the variance, with the rotated factors each accounting for about the same percentage of variance in this sample (see Table 2). The Inattention factor accounted for 41.52% of the variance and the Hyperactivity/Impulsivity factor accounted for 36.26% of the variance.
The mean rating across all of the 847 children based on the 18 ADHD items (i. e., the ADHD-Combined summary score) was .54, with sd= .67 and skewness = 1.474. The mean, sd and skewness for the ADHD-INN subscale were .73, .86, and 1.12, respectively; for ADHD-HY/IMP, they were .34, .61, and 2.36 respectively. The population distribution of ADHD-Combined (ADHD-C) summary scores is presented in Figure 1.
As expected from a general (non-clinical) population, most scores were equal to or below 1.0, which represents a normal level of behavior (i.e., “Just a Little” or less) that would not meet the DSM-IV criteria for symptom presence. This extreme rightward skewness also held at the item level: 75% to 96% of the sample had scores of 1 or less (“Just a Little” or “Not at All”) on the ADHD items of the SNAP-IV. In this sample 79.9% of all students had scores equal to or below 1.0. Different procedures can be used to develop statistical norms based on absolute or relative evaluation of ADHD symptoms with respect to age and sex. In the example above, the average rating was calculated across all students, but averages based on age and gender subgroups have also been used (see Swanson, 1992 ; DuPaul, 1998 ; Conners, 2008 ). By the former absolute method, a higher percentage of young and male cases are expected to be identified as extreme (as in clinical practice), while by the latter relative method, an equal percentage of cases in the age and gender subgroups are expected to be identified as extreme.
As an exercise to demonstrate some consequences of using statistical cutoffs from a non-normal (skewed) distribution, we applied theoretical cutoffs based on the assumption that the distribution was normal. Using a standard theoretical cutoff equal to the mean + 1.65 sd, about 5% of the sample (about 42 cases) would be expected to be identified as extreme. However, as expected for a skewed distribution, this theoretical cutoff identified a significantly higher number of cases than expected. Based on the ADHD-C score, 71 individuals had scores above the theoretical 5% cutoff (8.4%). A comparison of the expected and observed proportions was statistically significant (Z = −4.53, p< .01).
As an additional exercise, we also used the theoretical cutoffs to estimate prevalence of ADHD based on exceeding the cutoff on domains of the SNAP-IV as an operational definition of symptom presence. In this sample, of n = 847, the difference between the observed percentage (9.2%) identified by the theoretical cutoff on the ADHD-INN subscale (78 cases rather than 42) was also statistically significant compared to the expected value of 5% (Z = −5.86, p < .01), as was the percentage (7.6%, or 64 cases) identified by the theoretical cutoff on the ADHD-HY/IMP subscale (Z = −3.47, p< .01).
A Venn diagram (see Figure 2) shows the overlap of extreme cases identified by the multiple cutoff values for the three subtypes: ADHD-C, ADHD-I, and ADHD-HI. This diagram makes it clear that few cases met the criteria for ADHD-C, ADHD-INN, and ADHD-HY/IMP simultaneously (n = 31, or 3.7%), or the criteria for ADHD-I only (n=28, 3.3%) or ADHD-HI only (n=13, 2.4%). Some cases met the criteria for ADHD-C but not ADHD-I (n = 20, 2.4%) or not ADHD-HI (n = 19, 2.2%). One case (0.1%) met the cutoff criteria for ADHD-C but not for ADHD-I or ADHD-HI. A total of 112 cases (n=28+19+31+20+13+1=112) or 13.2% met the cutoff criteria for these subtypes of ADHD.
Swanson J.M., Schuck S., Porter M.M., Carlson C., Hartman C.A., Sergeant J.A., Clevenger W., Wasdell M., McCleary R., Lakes K, & Wigal T. (2012). Categorical and Dimensional Definitions and Evaluations of Symptoms of ADHD: History of the SNAP and the SWAN Rating Scales. The International journal of educational and psychological assessment, 10(1), 51-70.
Publication 2012
ARID1A protein, human Child Disorder, Attention Deficit-Hyperactivity Males Student Symptom Evaluation
4
Parent-Rated SDQ Protocol for ADHD Evaluation
Cited 33 times
In this study, we used the parent-rated, English (United States) and Spanish versions of the SDQ (Goodman & Scott, 1999 (link); www.sdqinfo.org). The SDQ has been widely used and has been normed for the United States (Bourden et al., 2005 (link)). The SDQ consists of five subscales: Emotional Symptoms, Conduct Problems, Hyperactivity/Inattention, Peer Problems, and Prosocial Behavior. Each subscale has five items and each item is stated as a strength or weakness but not both. The number stated as weaknesses varies across these subscales. For Emotional Symptoms, all five are stated as weaknesses; for Conduct Problems, four of the five; for Hyperactivity/Inattention, three of the five; for Peer Problems, two of the five; and for Prosocial, none of the five (i.e., all are stated as strengths). The score on each subscale of the SDQ is determined by the summation of items stated as strengths and weaknesses. Weaknesses are scored 0 for “not true,” 1 for “somewhat true,” and 2 for “certainly true.” Reverse scoring is used for strengths, which are scored 2 for “not true,” 1 for “somewhat true,” and 0 for “certainly true.”
The SDQ Hyperactivity-Inattention subscale provides a rating reflecting the symptom domains of ADHD (i.e., inattention, hyperactivity, and impulsivity). It consists of three items stated as difficulties (1 = restless, overactive, cannot stay still for long; 2 = constantly fidgeting or squirming; and 3 = easily distracted, concentration wanders) and two items as strengths (4 = thinks things out before acting and 5 = sees tasks through to the end, good attention span).
As directed by the SDQ manual, the items stated as difficulties were scored to reflect the degree of psychopathology (0 = not true, 1 = somewhat true, and 2 = certainly true). The items stated as strengths were reverse scored (2 = not true, 1 = somewhat true, and 0 = certainly true). Thus, the minimum on each subscale is 0 (representing lack of psychopathology) and the maximum score on each subscale is 10 (representing presence of psychopathology, except in the case of the Prosocial Behavior subscale).
Lakes K.D., Swanson J.M, & Riggs M. (2011). The Reliability and Validity of the English and Spanish Strengths and Weaknesses of ADHD and Normal Behavior Rating Scales in a Preschool Sample: Continuum Measures of Hyperactivity and Inattention. Journal of attention disorders, 16(6), 510-516.
Publication 2011
Asthenia Attention Debility Disorder, Attention Deficit-Hyperactivity Emotions Hispanic or Latino Parent Problem Behavior Thinking Vision
5
Assessing Positive Emotion Regulation Difficulties
Cited 30 times
The Difficulties in Emotion Regulation Scale-Positive (DERS-Positive; Gratz, 2002 ) is a 15-item self-report measure developed to assess clinically relevant difficulties in the regulation of positive emotions. This measure was modeled after the original DERS (Gratz & Roemer, 2004 ), with items modified to assess difficulties stemming from the experience of positive emotions (vs. negative emotions). Specifically, rather than beginning with the stem “When I’m upset” like many of the original DERS items, the DERS-Positive items begin with the stem “When I’m happy.” DERS-Positive items were chosen to reflect difficulties within the following dimensions of emotion regulation: (a) acceptance of positive emotions; (b) ability to engage in goal-directed behavior when experiencing positive emotions; and (c) ability to control impulsive behaviors when experiencing positive emotions. Participants are asked to indicate how often the items apply to themselves, with responses ranging from 1 to 5, where 1 is almost never (0–10%), 2 is sometimes (11–35%), 3 is about half the time (36–65%), 4 is most of the time (66–90%), and 5 is almost always (91– 100%). Higher scores indicate greater difficulties in the regulation of positive emotions.
Weiss N.H., Gratz K.L, & Lavender J.M. (2015). Factor Structure and Initial Validation of a Multidimensional Measure of Difficulties in the Regulation of Positive Emotions: The DERS-Positive. Behavior modification, 39(3), 431-453.
Publication 2015
Emotional Regulation Emotions Impulsive Behavior Stem, Plant

Most recents protocols related to «Impulsive Behavior»

1
Delay Discounting and Substance Use
Delay Discounting Task: A 5-trial adjusted delay discounting task (28 (link)) was used to measure delay discounting. A subject’s temporal discounting rate is calculated as a “k” value (29 (link)). A higher temporal discounting rate (i.e., a higher “k” value) is associated with greater impulsivity (29 (link)). The logarithm of the k value [log10(k)] is calculated to obtain a more normal distribution across subjects (29 (link)).
Cocaine and Cannabis Use: The number of subjects with UDS positive for cocaine and cannabis are reported for descriptive purposes.
Tobacco use was assessed by the Fagerström Test of Nicotine Dependence (38 (link)). Subjects were classified as current tobacco users if they responded that they had used tobacco products within the past year, or non-current tobacco users if they responded that they had not used tobacco products within the past year.
Behavioral data were analyzed using the JMP statistical software package (JMP, Version 14. SAS Institute Inc., Cary, NC, 1989-2019). A two-sample T-test was performed to test for statistical significance between groups with respect to age, education, tobacco use, head motion (mFD score), and delay discounting task scores.
Woisard K., Steinberg J.L., Ma L., Zuniga E., Lennon M, & Moeller F.G. (2023). Executive control network resting state fMRI functional and effective connectivity and delay discounting in cocaine dependent subjects compared to healthy controls. Frontiers in Psychiatry, 14, 1117817.
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Publication 2023
Age Groups Cannabis Cocaine Head Nicotine Dependence Tobacco Products
2
IDDEAS Prototype for ADHD Diagnosis
The IDDEAS prototype allows for exploration of the ability of IDDEAS guidelines to provide decision support for diagnosis and treatment of attention deficit and hyperactivity disorder (ADHD) (see Figure 2). ADHD is a neurodevelopmental disorder characterized by inattention, hyperactivity, and impulsivity, ultimately causing impaired functioning for the individual (9 (link)). The IDDEAS prototype at this stage uses ADHD as the first clinical model paradigm. Preparation of IDDEAS includes the validation of the clinical materials and the user-interface. The IDDEAS guidelines were previously validated by the IDDEAS clinical research team using the DSM-5 and ICD-10 criteria. Focus groups were used to pre-test content prior to the IDDEAS prototype evaluation.
Each IDDEAS prototype evaluation session included having a clinician participant complete a concurrent, cognitive walk through/think-aloud procedure, as they critically appraised hypothetical patient case scenarios developed from real cases within CAMHS. A total of 20 patient case scenarios were collaboratively designed and validated by the IDDEAS team (BL, NS, RK). Out of the 20 possible cases, each participant was randomly assigned four to assess, two of which were to be assessed while using the IDDEAS prototype (ADHD modeled guidelines) and two without. Use of IDDEAS was similarly randomly assigned. Throughout the assessment of the four cases, participants were asked to follow a think-aloud procedure and provide a concurrent walk through of the clinical procedure they would follow if the patients were real. They were also asked to provide additional patient information they perceived to be potentially necessary to complete their clinical assessment. Finally, participants were asked to provide their overall perceptions of the IDDEAS prototype and its usability, functionality, and potential utility.
Clausen C., Leventhal B., Nytrø Ø., Koposov R., Røst T.B., Westbye O.S., Koochakpour K., Frodl T., Stien L, & Skokauskas N. (2023). Usability of the IDDEAS prototype in child and adolescent mental health services: A qualitative study for clinical decision support system development. Frontiers in Psychiatry, 14, 1033724.
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Publication 2023
Attention Deficit Disorder Cognition Diagnosis Disorder, Attention Deficit-Hyperactivity Neurodevelopmental Disorders Patients
3
Assessing Impulsivity, Self-Esteem, and Deviant Behavior
Impulsivity was measured using 3 items from the Barratt Impulsivity Scale originally developed by Barratt [35 ] and vali dated by Lee et al. [36 ]. For the current sample, Cronbach’s alpha was 0.73.
Self-esteem was measured using five positively worded items from the Rosenberg Self-esteem Scale [37 ]. This scale has shown good reliability [38 ]. In the current sample, Cronbach’s alpha was 0.87.
Childhood maltreatment was measured using eight items originally developed by Pennebaker and Susman [39 (link)]. Items are rated on a 4-point Likert scale. Cronbach’s alpha in the current sample was 0.76.
Deliberate self-harm was measured using three items (cutting, burning, and head banging) included in the Inventory of Statements About Self-injury [40 (link)]. Participants were asked to indicate how many times they had intentionally engaged without lethal intent in each of the behaviours listed during the past 12 months. The scale has shown good internal consistency and construct validity [40 (link),41 (link)].
Deviant behaviour problems included leaving home without notice, truancy at school, violence, peer victimization, bullying (including cyberbullying), and alcohol and substance problems experienced during the past 12 months, and were measured using 15 items developed by the National Youth Policy Institute [42 ]. All items were rated on a 5-point scale (never, 1 time, 2–3 times, 1–2 per week, every day). Cronbach’s alpha for the current sample was 0.79.
Lee J.S., Kwon H., Park J., Hong H.J, & Kweon Y.S. (2023). A Latent Class Analysis of Suicidal Behaviors in Adolescents. Psychiatry Investigation, 20(2), 93-100.
Publication 2023
Ethanol Problem Behavior Self Esteem Self Mutilation Victimization Youth
4
Measuring Substance Dependence and Personality Traits
Alcohol dependence was measured via the Alcohol Use Disorders Identification Test (AUDIT) (22 ), a 10-item self-report measure that identifies frequency and quantity of alcohol use, dependence symptoms, and alcohol-related consequences. The AUDIT has a high test-retest reliability and satisfactory internal consistency (23 (link)). Consistent with the literature, scores of 15 and above were considered indicative of alcohol dependence (22 ), with 161 (31.2%) of participants meeting this criteria.
Cannabis dependence was measured via the Cannabis Use Disorder Identification Test- Revised (CUDIT-R) (24 (link)), an 8-item self-report measure that assesses problematic cannabis use within the past 6 months. The CUDIT-R has been used in a variety of cannabis subpopulations and has been found to be both valid and reliable (25 (link), 26 (link)). Consistent with the literature, scores of 13 and above were considered indicative of cannabis dependence (24 (link)), with 150 (29.1%) of participants meeting this criteria.
Nicotine dependence was assessed using both the Fagerström Test for Cigarette Dependence (FTCD) (27 (link)) and the e-cigarette Fagerström Test of Cigarette Dependence [e-FTCD; (28 (link))], depending on whether the participant use cigarettes, e-cigarettes, or both. Participants who only used e-cigarettes were directed to complete E-cigarette Fagerström Test of Cigarette Dependence (e-FTCD) (28 (link)). The FTCD is a 6-item self-report measure that assesses a person's level of cigarette dependence. The FTCD is commonly used in nicotine and tobacco research and has good reliability and validity (27 (link)). The e-FTCD is a 6-item adapted version of the FTCD, modified by changing all references of cigarettes to e-cigarettes and all references of smoking to vaping. The e-FTCD has been proven reliable and valid for use with e-cigarette users (28 (link)). Scores of 4 and above were considered indicative of moderate nicotine dependence (29 (link)), with 292 (56.6%) participants meeting this criteria.
Personality traits of the participants were measured via the Substance Use Risk Profile Scale (SURPS) (16 (link)), a 23-item questionnaire that measures 4 distinct personality risk factors based on reinforcement-sensitivity models of substance use: hopelessness/ introversion (HI); anxiety sensitivity (AS); impulsivity (IMP), and sensation seeking (SS). The SURPS has been shown to have adequate psychometric properties (16 (link)).
Shephard A., Dölek Ş, & Barrett S.P. (2023). Investigating predictors of problematic alcohol, cannabis, and nicotine use among legal users of all three substances. Frontiers in Psychiatry, 14, 1110415.
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Publication 2023
Alcoholic Intoxication, Chronic Alcohol Use Disorder Anxiety Cannabis Cannabis Dependence Ethanol Hypersensitivity Introversion, Psychological Nicotine Nicotine Dependence Population Group Psychometrics Reinforcement, Psychological Substance Use Tobacco Products
5
Multidimensional Predictors of Substance Dependence
All statistical analyses were conducted using R version 4.2.1; there were no missing data in the analyses. To compare participants regardless of their preferred nicotine consumption method (cigarettes or e-cigarettes), a composite nicotine dependence variable was created by taking the highest value of the FTCD and e-FTCD scores; this gave each participant a single nicotine dependence score. Age of onset of regular use for each of the substances was calculated by subtracting reported years of regular use from current age; a composite nicotine age of onset was created by taking the lowest age between cigarette and e-cigarette age of onset variables. Three different hierarchical multiple regressions were conducted, in which dependent variables were levels of dependence on alcohol, cannabis, and nicotine, respectively. In the regression analyses, predictor variables were added in four stages: the first models contained demographic variables (age, sex, and education), the second models contained personality variables (hopelessness, anxiety sensitivity, impulsivity, and sensation seeking), the third models contained ages of onset of regular use across substances, and the fourth models contained dependence on the other two substances. Regarding nicotine dependence, the fourth model included an additional variable: dual use of cigarettes and e-cigarettes. The variables were added in this order as it was the most chronologically plausible order, as demographic variables are from birth, personality is mostly stable from childhood, and age of first use comes before substance dependence. Finally, post-hoc analyses were conducted on significant variables in the final models for each of the regressions. Participants were categorized by dependence on each substance (dependent vs. non-dependent) using the cut-offs described in Section 2.2. For continuous variables, the means of each group were compared via Welch's t-tests; for categorical variables (e.g., sex and dual use of cigarettes/e-cigarettes), the ratios were compared via Pearson's χ2 tests.
Shephard A., Dölek Ş, & Barrett S.P. (2023). Investigating predictors of problematic alcohol, cannabis, and nicotine use among legal users of all three substances. Frontiers in Psychiatry, 14, 1110415.
Full text: Click here
Publication 2023
Anxiety Birth Cannabis Ethanol Hypersensitivity Nicotine Nicotine Dependence Substance Dependence

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More about "Impulsive Behavior"

Impulsive behavior, also known as impulsiveness or impulsivity, is a pattern of acting hastily without considering the consequences.
This type of behavior is often driven by a desire for immediate gratification or a lack of self-control.
Impulsive behavior can manifest in various contexts, such as addiction, risk-taking, or emotional outbursts.
Effective management of impulsive behavior is crucial as it can significantly impact an individual's quality of life and research outcomes.
PubCompare.ai, an AI-powered platform, can help researchers and clinicians identify the optimal research protocols and products to address impulsive behavior.
PubCompare.ai's advanced comparison tools can assist in locating and optimizing research protocols from the literature, pre-prints, and patents.
This data-driven approach can enhance the accuracy of your studies and support decision-making processes.
SPSS (Statistical Package for the Social Sciences) versions 21, 22, 23, 25, and SPSS Statistics, as well as SAS 9.4 and MATLAB, can be utilized to analyze data and gain insights into impulsive behavior.
By leveraging the power of PubCompare.ai, researchers and clinicians can discover the most effective interventions and strategies to manage impulsive behavior, ultimately improving the quality of life for those affected and enhancing the reliability of research findings.
Embrace the advantages of data-driven decision making and unlock the potential of your studies today.