An evaluation of preference stability within MSWO preference assessments for children with autism.

Previous research has commonly evaluated preference stability over time and across multiple preference assessment administrations. No studies have evaluated shifts in preference across consecutive rounds of a single preference assessment, where rounds refer to each time the experimenter resets the stimulus-presentation array. The purpose of the present study was to examine the stability of stimulus selections across successive rounds of a multiple-stimulus-without-replacement (MSWO) preference assessment with different classes of stimuli for children with autism. The study involved a secondary data analysis and calculation of preference stability across consecutive rounds using Spearman rank-order correlation coefficients (Spearman's ρ ) for 17 participants across 40 MSWO preference assessments. Patterns of preference stability were observed in 24 out of the 40 assessments (60%) indicating that children's preferences in this study were slightly more likely to be classified as stable than other observed patterns of responding.

Opportunity cost determines free-operant action initiation latency and predicts apathy.

BACKGROUND: Apathy, a disabling and poorly understood neuropsychiatric symptom, is characterised by impaired self-initiated behaviour. It has been hypothesised that the opportunity cost of time (OCT) may be a key computational variable linking self-initiated behaviour with motivational status. OCT represents the amount of reward which is foregone per second if no action is taken. Using a novel behavioural task and computational modelling, we investigated the relationship between OCT, self-initiation and apathy. We predicted that higher OCT would engender shorter action latencies, and that individuals with greater sensitivity to OCT would have higher behavioural apathy. METHODS: We modulated the OCT in a novel task called the 'Fisherman Game', Participants freely chose when to self-initiate actions to either collect rewards, or on occasion, to complete non-rewarding actions. We measured the relationship between action latencies, OCT and apathy for each participant across two independent non-clinical studies, one under laboratory conditions (n = 21) and one online (n = 90). 'Average-reward' reinforcement learning was used to model our data. We replicated our findings across both studies. RESULTS: We show that the latency of self-initiation is driven by changes in the OCT. Furthermore, we demonstrate, for the first time, that participants with higher apathy showed greater sensitivity to changes in OCT in younger adults. Our model shows that apathetic individuals experienced greatest change in subjective OCT during our task as a consequence of being more sensitive to rewards. CONCLUSIONS: Our results suggest that OCT is an important variable for determining free-operant action initiation and understanding apathy.

A deep reinforcement learning algorithm for the rectangular strip packing problem.

As a branch of the two-dimensional (2D) optimal blanking problem, rectangular strip packing is a typical non-deterministic polynomial (NP-hard) problem. The classical packing solution method relies on heuristic and metaheuristic algorithms. Usually, it needs to be designed with manual decisions to guide the solution, resulting in a small solution scale, weak generalization, and low solution efficiency. Inspired by deep learning and reinforcement learning, combined with the characteristics of rectangular piece packing, a novel algorithm based on deep reinforcement learning is proposed in this work to solve the rectangular strip packing problem. The pointer network with an encoder and decoder structure is taken as the basic network for the deep reinforcement learning algorithm. A model-free reinforcement learning algorithm is designed to train network parameters to optimize the packing sequence. This design can not only avoid designing heuristic rules separately for different problems but also use the deep networks with self-learning characteristics to solve different instances more widely. At the same time, a piece positioning algorithm based on the maximum rectangles bottom-left (Maxrects-BL) is designed to determine the placement position of pieces on the plate and calculate model rewards and packing parameters. Finally, instances are used to analyze the optimization effect of the algorithm. The experimental results show that the proposed algorithm can produce three better and five comparable results compared with some classical heuristic algorithms. In addition, the calculation time of the proposed algorithm is less than 1 second in all test instances, which shows a good generalization, solution efficiency, and practical application potential.

Problematic social media use is statistically predicted by using social media for coping motives and by positive reinforcement processes in individuals with high COVID-19-related stress levels.

It is important to understand the relationship between stress and problematic use of social media (PUSM). However, no study to our knowledge has yet investigated the longitudinal relationship between perceived stress and PUSM via positive and negative reinforcement processes. The present study investigated relationships between COVID-19-pandemic-related stress and PUSM and possible moderating effects of motives for using social media (positive and/or negative reinforcement) during and following a COVID-19-pandemic-related lockdown. Six-hundred-and-sixty participants initially completed a survey including self-report measures of PUSM, COVID-19-pandemic-related stress, and motives for using social media (i.e., for negative reinforcement involving coping and conformity or positive reinforcements involving enhancement and social motives). During the COVID-19 outbreak recovery period, 117 participants again completed the survey. Bayesian analyses revealed that PUSM was associated with higher COVID-19-pandemic-related stress levels and use of social media for coping, conformity, and enhancement purposes. Longitudinally, PUSM symptom worsening was associated with increased use of social media for coping motives regardless of levels of perceived stress. Use of social media for conformity and enhancement purposes moderated relationships between stress levels during lockdown and PUSM symptoms worsening after lockdown. Our findings corroborate the hypothesis that negative reinforcement processes may be key factors in PUSM symptom worsening regardless of perceived stress. Concurrently, high levels of stress may worsen PUSM through positive reinforcement processes.

Superhuman performance on sepsis MIMIC-III data by distributional reinforcement learning.

We present a novel setup for treating sepsis using distributional reinforcement learning (RL). Sepsis is a life-threatening medical emergency. Its treatment is considered to be a challenging high-stakes decision-making problem, which has to procedurally account for risk. Treating sepsis by machine learning algorithms is difficult due to a couple of reasons: There is limited and error-afflicted initial data in a highly complex biological system combined with the need to make robust, transparent and safe decisions. We demonstrate a suitable method that combines data imputation by a kNN model using a custom distance with state representation by discretization using clustering, and that enables superhuman decision-making using speedy Q-learning in the framework of distributional RL. Compared to clinicians, the recovery rate is increased by more than 3% on the test data set. Our results illustrate how risk-aware RL agents can play a decisive role in critical situations such as the treatment of sepsis patients, a situation acerbated due to the COVID-19 pandemic (Martineau 2020). In addition, we emphasize the tractability of the methodology and the learning behavior while addressing some criticisms of the previous work (Komorowski et al. 2018) on this topic.

Using synchronous reinforcement to increase mask wearing in adults with intellectual and developmental disabilities.

In 2020 the Centers for Disease Control provided the public with recommendations to slow the spread of COVID-19 by wearing a mask in the community. In the current study, experimenters coached group home staff via telehealth to implement synchronous schedules of reinforcement to increase mask wearing for 5 adults with intellectual and developmental disabilities. Results showed the intervention effectively increased mask wearing for all participants for up to 30 min. Additionally, some participants for whom we assessed generalization of mask wearing demonstrated generalization to various community environments. Furthermore, procedural integrity data suggested staff could be coached via telehealth to implement the intervention, and staff surveys suggested the procedures and coaching were socially valid.

Agency rescues competition for credit assignment among predictive cues from adverse learning conditions.

A fundamental assumption of learning theories is that the credit assigned to predictive cues is not simply determined by their probability of reinforcement, but by their ability to compete with other cues present during learning. This assumption has guided behavioral and neural science research for decades, and tremendous empirical and theoretical advances have been made identifying the mechanisms of cue competition. However, when learning conditions are not optimal (e.g., when training is massed), cue competition is attenuated. This failure of the learning system exposes the individual's vulnerability to form spurious associations in the real world. Here, we uncover that cue competition in rats can be rescued when conditions are suboptimal provided that the individual has agency over the learning experience. Our findings reveal a new effect of agency over learning on credit assignment among predictive cues, and open new avenues of investigation into the underlying mechanisms.