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1. The Digital Marshmallow Test (DMT) Diagnostic and Monitoring Mobile Health App for Impulsive Behavior: Development and Validation Study

   https://doi.org/10.2196/25018  

   Sobolev, Michael; Vitale, Rachel; Wen, Hongyi; Kizer, James; Leeman, Robert; Pollak, J P; Baumel, Amit; Vadhan, Nehal P; Estrin, Deborah; Muench, Frederick (January 2021, JMIR mHealth and uHealth)

   null (Ed.)

   Background The classic Marshmallow Test, where children were offered a choice between one small but immediate reward (eg, one marshmallow) or a larger reward (eg, two marshmallows) if they waited for a period of time, instigated a wealth of research on the relationships among impulsive responding, self-regulation, and clinical and life outcomes. Impulsivity is a hallmark feature of self-regulation failures that lead to poor health decisions and outcomes, making understanding and treating impulsivity one of the most important constructs to tackle in building a culture of health. Despite a large literature base, impulsivity measurement remains difficult due to the multidimensional nature of the construct and limited methods of assessment in daily life. Mobile devices and the rise of mobile health (mHealth) have changed our ability to assess and intervene with individuals remotely, providing an avenue for ambulatory diagnostic testing and interventions. Longitudinal studies with mobile devices can further help to understand impulsive behaviors and variation in state impulsivity in daily life. Objective The aim of this study was to develop and validate an impulsivity mHealth diagnostics and monitoring app called Digital Marshmallow Test (DMT) using both the Apple and Android platforms for widespread dissemination to researchers, clinicians, and the general public. Methods The DMT app was developed using Apple’s ResearchKit (iOS) and Android’s ResearchStack open source frameworks for developing health research study apps. The DMT app consists of three main modules: self-report, ecological momentary assessment, and active behavioral and cognitive tasks. We conducted a study with a 21-day assessment period (N=116 participants) to validate the novel measures of the DMT app. Results We used a semantic differential scale to develop self-report trait and momentary state measures of impulsivity as part of the DMT app. We identified three state factors (inefficient, thrill seeking, and intentional) that correlated highly with established measures of impulsivity. We further leveraged momentary semantic differential questions to examine intraindividual variability, the effect of daily life, and the contextual effect of mood on state impulsivity and daily impulsive behaviors. Our results indicated validation of the self-report sematic differential and related results, and of the mobile behavioral tasks, including the Balloon Analogue Risk Task and Go-No-Go task, with relatively low validity of the mobile Delay Discounting task. We discuss the design implications of these results to mHealth research. Conclusions This study demonstrates the potential for assessing different facets of trait and state impulsivity during everyday life and in clinical settings using the DMT mobile app. The DMT app can be further used to enhance our understanding of the individual facets that underlie impulsive behaviors, as well as providing a promising avenue for digital interventions. Trial Registration ClinicalTrials.gov NCT03006653; https://www.clinicaltrials.gov/ct2/show/NCT03006653 

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2. Early life adversity promotes resilience to opioid addiction-related phenotypes in male rats and sex-specific transcriptional changes

   https://doi.org/10.1073/pnas.2020173118  

   Ordoñes Sanchez, Evelyn; Bavley, Charlotte C.; Deutschmann, Andre U.; Carpenter, Rachel; Peterson, Drew R.; Karbalaei, Reza; Flowers, II, James; Rogers, Charleanne M.; Langrehr, Miranda G.; Ardekani, Cory S.; et al (February 2021, Proceedings of the National Academy of Sciences)

   Experiencing some early life adversity can have an “inoculating” effect that promotes resilience in adulthood. However, the mechanisms underlying stress inoculation are unknown, and animal models are lacking. Here we used the limited bedding and nesting (LBN) model of adversity to evaluate stress inoculation of addiction-related phenotypes. In LBN, pups from postnatal days 2 to 9 and their dams were exposed to a low-resource environment. In adulthood, they were tested for addiction-like phenotypes and compared to rats raised in standard housing conditions. High levels of impulsivity are associated with substance abuse, but in males, LBN reduced impulsive choice compared to controls. LBN males also self-administered less morphine and had a lower breakpoint on a progressive ratio reinforcement schedule than controls. These effects of LBN on addiction-related behaviors were not found in females. Because the nucleus accumbens (NAc) mediates these behaviors, we tested whether LBN altered NAc physiology in drug-naïve and morphine-exposed rats. LBN reduced the frequency of spontaneous excitatory postsynaptic currents in males, but a similar effect was not observed in females. Only in males did LBN prevent a morphine-induced increase in the AMPA/NMDA ratio. RNA sequencing was performed to delineate the molecular signature in the NAc associated with LBN-derived phenotypes. LBN produced sex-specific changes in transcription, including in genes related to glutamate transmission. Collectively, these studies reveal that LBN causes a male-specific stress inoculation effect against addiction-related phenotypes. Identifying factors that promote resilience to addiction may reveal novel treatment options for patients. 

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3. mPulse Mobile Sensing Model for Passive Detection of Impulsive Behavior: Exploratory Prediction Study

   https://doi.org/10.2196/25019  

   Wen, Hongyi; Sobolev, Michael; Vitale, Rachel; Kizer, James; Pollak, J P; Muench, Frederick; Estrin, Deborah (January 2021, JMIR Mental Health)

   null (Ed.)

   Background Mobile health technology has demonstrated the ability of smartphone apps and sensors to collect data pertaining to patient activity, behavior, and cognition. It also offers the opportunity to understand how everyday passive mobile metrics such as battery life and screen time relate to mental health outcomes through continuous sensing. Impulsivity is an underlying factor in numerous physical and mental health problems. However, few studies have been designed to help us understand how mobile sensors and self-report data can improve our understanding of impulsive behavior. Objective The objective of this study was to explore the feasibility of using mobile sensor data to detect and monitor self-reported state impulsivity and impulsive behavior passively via a cross-platform mobile sensing application. Methods We enrolled 26 participants who were part of a larger study of impulsivity to take part in a real-world, continuous mobile sensing study over 21 days on both Apple operating system (iOS) and Android platforms. The mobile sensing system (mPulse) collected data from call logs, battery charging, and screen checking. To validate the model, we used mobile sensing features to predict common self-reported impulsivity traits, objective mobile behavioral and cognitive measures, and ecological momentary assessment (EMA) of state impulsivity and constructs related to impulsive behavior (ie, risk-taking, attention, and affect). Results Overall, the findings suggested that passive measures of mobile phone use such as call logs, battery charging, and screen checking can predict different facets of trait and state impulsivity and impulsive behavior. For impulsivity traits, the models significantly explained variance in sensation seeking, planning, and lack of perseverance traits but failed to explain motor, urgency, lack of premeditation, and attention traits. Passive sensing features from call logs, battery charging, and screen checking were particularly useful in explaining and predicting trait-based sensation seeking. On a daily level, the model successfully predicted objective behavioral measures such as present bias in delay discounting tasks, commission and omission errors in a cognitive attention task, and total gains in a risk-taking task. Our models also predicted daily EMA questions on positivity, stress, productivity, healthiness, and emotion and affect. Perhaps most intriguingly, the model failed to predict daily EMA designed to measure previous-day impulsivity using face-valid questions. Conclusions The study demonstrated the potential for developing trait and state impulsivity phenotypes and detecting impulsive behavior from everyday mobile phone sensors. Limitations of the current research and suggestions for building more precise passive sensing models are discussed. Trial Registration ClinicalTrials.gov NCT03006653; https://clinicaltrials.gov/ct2/show/NCT03006653 

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4. Unobserved Is Not Equal to Non-existent: Using Gaussian Processes to Infer Immediate Rewards Across Contexts.

   Hamoon Azizsoltani, Yeo Jin (January 2019, In Proceedings of the 28th International Joint Conference on Artificial Intelligence)

   Learning optimal policies in real-world domains with delayed rewards is a major challenge in Reinforcement Learning. We address the credit assignment problem by proposing a Gaussian Process (GP)-based immediate reward approximation algorithm and evaluate its effectiveness in 4 contexts where rewards can be delayed for long trajectories. In one GridWorld game and 8 Atari games, where immediate rewards are available, our results showed that on 7 out 9 games, the proposed GP inferred reward policy performed at least as well as the immediate reward policy and significantly outperformed the corresponding delayed reward policy. In e-learning and healthcare applications, we combined GP-inferred immediate rewards with offline Deep Q-Network (DQN) policy induction and showed that the GP-inferred reward policies outperformed the policies induced using delayed rewards in both real-world contexts. 

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5. The conjunction fallacy in rats

   https://doi.org/10.3758/s13423-023-02251-z  

   González, Valeria V.; Sadeghi, Sowgol; Tran, Linh; Blaisdell, Aaron P. (August 2023, Psychonomic Bulletin & Review)

   Abstract Humans and other animals are capable of reasoning. However, there are overwhelming examples of errors or anomalies in reasoning. In two experiments, we studied if rats, like humans, estimate the conjunction of two events as more likely than each event independently, a phenomenon that has been called conjunction fallacy. In both experiments, rats learned through food reinforcement to press a lever under some cue conditions but not others. Sound B was rewarded whereas Sound A was not. However, when B was presented with the visual cue Y was not rewarded, whereas AX was rewarded (i.e., A-, AX+, B+, BY-). Both visual cues were presented in the same bulb. After training, rats received test sessions in which A and B were presented with the bulb explicitly off or occluded by a metal piece. Thus, on the occluded condition, it was ambiguous whether the trials were of the elements alone (A or B) or of the compounds (AX or BY). Rats responded on the occluded condition as if the compound cues were most likely present. The second experiment investigated if this error in probability estimation in Experiment 1, could be due to a conjunction fallacy, and if this could be attenuated by increasing the ratio of element/compound trials from the original 50-50 to 70-30 and 90-10. Only the 90-10 condition (where 90% of the training trials were of just A or just B) did not show a conjunction fallacy, though it emerged in all groups with additional training. These findings open new avenues for exploring the mechanisms behind the conjunction fallacy effect. 

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