A comparison of open and closed loop applications of the minimum distance guidance technique

A comparison is made of open and closed loop applications of a second order guidance algorithm, using the minimum distance strategy. A nonlinear reoptimization procedure is used as the ideal guidance history. The system model used for the comparison is a low-thrust vehicle performing a minimum time, three-dimensional, heliocentric Earth-Mars transfer. For the example problem considered, closed loop guidance proves to be much more accurate on satisfaction of the final state than the open loop procedure. On the other hand, closed loop guidance proves to be much more vulnerable to perturbation by highly nonlinear regions in the trajectory. Finally, the results indicate that for this problem the best loop closure interval is at each integration step, about one day, or more often, if possible

On the Computability of Solomonoff Induction and Knowledge-Seeking

Solomonoff induction is held as a gold standard for learning, but it is known to be incomputable. We quantify its incomputability by placing various flavors of Solomonoff's prior M in the arithmetical hierarchy. We also derive computability bounds for knowledge-seeking agents, and give a limit-computable weakly asymptotically optimal reinforcement learning agent.Comment: ALT 201

Attention with a mindful attitude attenuates subjective appetitive reactions and food intake following food-cue exposure.

BACKGROUND: Excessive energy intake that contributes to overweight and obesity is arguably driven by pleasure associated with the rewarding properties of energy-dense palatable foods. It is important to address influences of external food cues in food-abundant societies where people make over 200 food related decisions each day. This study experimentally examines protective effects of a mindful attention induction on appetitive measures, state craving and food intake following exposure to energy-dense foods. METHOD: Forty females were randomly allocated to a standard food-cue exposure condition in which attention is brought to the hedonic properties of food or food-cue exposure following a mindful attention induction. Appetitive reactions were measured pre, post and ten minutes after post-cue exposure, after which a plate of cookies was used as a surreptitious means of measuring food intake. RESULTS: Self-reported hunger remained unchanged and fullness significantly increased for the mindful attention group post-cue exposure whereas hunger significantly increased for the standard attention group and fullness remained unchanged. There was no significant between-group difference in state craving post-cue exposure and ten minutes later. Significantly more cookies were eaten by the standard attention group ten minutes post-cue exposure although no significant between-group differences in appetitive and craving measures were reported at that time. CONCLUSION: Our results point to a promising brief intervention strategy and highlights the importance of distinguishing mindful attention from attention. Results also demonstrate that mindful attention can influence food intake even when craving and hunger are experienced

Extreme State Aggregation Beyond MDPs

We consider a Reinforcement Learning setup where an agent interacts with an environment in observation-reward-action cycles without any (esp.\ MDP) assumptions on the environment. State aggregation and more generally feature reinforcement learning is concerned with mapping histories/raw-states to reduced/aggregated states. The idea behind both is that the resulting reduced process (approximately) forms a small stationary finite-state MDP, which can then be efficiently solved or learnt. We considerably generalize existing aggregation results by showing that even if the reduced process is not an MDP, the (q-)value functions and (optimal) policies of an associated MDP with same state-space size solve the original problem, as long as the solution can approximately be represented as a function of the reduced states. This implies an upper bound on the required state space size that holds uniformly for all RL problems. It may also explain why RL algorithms designed for MDPs sometimes perform well beyond MDPs.Comment: 28 LaTeX pages. 8 Theorem

Bayesian reinforcement learning with exploration

We consider a general reinforcement learning problem and show that carefully combining the Bayesian optimal policy and an exploring policy leads to minimax sample-complexity bounds in a very general class of (history-based) environments. We also prove lower bounds and show that the new algorithm displays adaptive behaviour when the environment is easier than worst-case

‘I can’t accept that feeling’: Relationships between interoceptive awareness, mindfulness and eating disorder symptoms in females with, and at-risk of an eating disorder

Mindfulness based therapies (MBTs) for eating disorders show potential benefit for outcomes yet evidence is scarce regarding the mechanisms by which they influence remission from symptoms. One way that mindfulness approaches create positive outcomes is through enhancement of emotion regulation skills. Maladaptive emotion regulation is a key psychological feature of all eating disorders. The aim of the current study was to identify facets of emotion regulation involved in the relationship between mindfulness and maladaptive eating behaviours. In three cross-sectional studies, clinical (n=39) and non-clinical (n=137 and 119) female participants completed: 1) the Eating Disorder Inventory (EDI) eating specific scales (drive-for-thinness and bulimia) and the EDI psychological symptom scales (emotion dysregulation and interoceptive deficits); and 2) mindfulness, impulsivity, and emotion regulation questionnaires. In all samples mindfulness was significantly and inversely associated with EDI eating and psychological symptom scales, and impulsivity. In non-clinical samples interoceptive deficits mediated the relationship between mindfulness and EDI eating specific scales. Non-acceptance of emotional experience, a facet of interoceptive awareness, mediated the relationship between mindfulness and eating specific EDI scores. Further investigations could verify relationships identified so that mindfulness based approaches can be optimised to enhance emotion regulation skills in sufferers, and those at-risk, of eating disorders

‘I can’t accept that feeling’: Relationships between interoceptive awareness, mindfulness and eating disorder symptoms in females with, and at-risk of an eating disorder

Mindfulness based therapies (MBTs) for eating disorders show potential benefit for outcomes yet evidence is scarce regarding the mechanisms by which they influence remission from symptoms. One way that mindfulness approaches create positive outcomes is through enhancement of emotion regulation skills. Maladaptive emotion regulation is a key psychological feature of all eating disorders. The aim of the current study was to identify facets of emotion regulation involved in the relationship between mindfulness and maladaptive eating behaviours. In three cross-sectional studies, clinical (n=39) and non-clinical (n=137 and 119) female participants completed: 1) the Eating Disorder Inventory (EDI) eating specific scales (drive-for-thinness and bulimia) and the EDI psychological symptom scales (emotion dysregulation and interoceptive deficits); and 2) mindfulness, impulsivity, and emotion regulation questionnaires. In all samples mindfulness was significantly and inversely associated with EDI eating and psychological symptom scales, and impulsivity. In non-clinical samples interoceptive deficits mediated the relationship between mindfulness and EDI eating specific scales. Non-acceptance of emotional experience, a facet of interoceptive awareness, mediated the relationship between mindfulness and eating specific EDI scores. Further investigations could verify relationships identified so that mindfulness based approaches can be optimised to enhance emotion regulation skills in sufferers, and those at-risk, of eating disorders

Mindfulness based emotional eating awareness training: taking the emotional out of eating.

Purpose: Emotional eating is important to study and address because it predicts poor outcome in weight loss interventions. Interventions have only touched the surface in terms of addressing emotional eating. Mindfulness approaches can address emotional eating by modification of emotion regulation and appetitive traits. The current study involved development of an emotional eating specific mindfulness intervention and assessment of its effect on appetitive traits associated with emotional eating. Methods: Participants (n = 14; Age M = 29yr; 90% female) completed baseline and end-of intervention self-report measures of emotional eating, food-cue reactivity, mindfulness, intuitive eating, emotional impulse regulation, stress, and a behavioural measure of inhibitory control. During the 6- week intervention, mindfulness meditation skills were taught weekly embedded in a psychoeducational curriculum about emotional eating. Results: Paired t-tests, controlled for type 1 error, revealed significant improvements in food-cue reactivity, intuitive eating, emotional impulse regulation, inhibitory control and stress (ps < .05; d: .58 to 1.54). Changes in emotional eating approached significance (p = .075, d = .66). Conclusion: The intervention purposefully did not focus on weight loss and recruited participants who had self-declared difficulties with emotional eating. The positive outcomes suggest that intervening with mindfulness training before weight loss is attempted has the potential to change psychological factors that underpin overeating and undermine weight loss efforts. The study provides proof of principle as a basis to design a randomized control trial to assess rigorously the effectiveness the intervention as a precursor to a weight loss intervention. Level of Evidence: Level IV, uncontrolled trial

Neuroscience in gambling policy and treatment: an interdisciplinary perspective

Neuroscientific explanations of gambling disorder can help people make sense of their experiences and guide the development of psychosocial interventions. However, the societal perceptions and implications of these explanations are not always clear or helpful. Two workshops in 2013 and 2014 brought together multidisciplinary researchers aiming to improve the clinical and policy-related effects of neuroscience research on gambling. The workshops revealed that neuroscience can be used to improve identification of the dangers of products used in gambling. Additionally, there was optimism associated with the diagnostic and prognostic uses of neuroscience in problem gambling and the provision of novel tools (eg, virtual reality) to assess the effectiveness of new policy interventions before their implementation. Other messages from these workshops were that neuroscientific models of decision making could provide a strong rationale for precommitment strategies and that interdisciplinary collaborations are needed to reduce the harms of gambling

Bayes-LQAS: Classifying the Prevalence of Global Acute Malnutrition

Lot Quality Assurance Sampling (LQAS) applications in health have generally relied on frequentist interpretations for statistical validity. Yet health professionals often seek statements about the probability distribution of unknown parameters to answer questions of interest. The frequentist paradigm does not pretend to yield such information, although a Bayesian formulation might. This is the source of an error made in a recent paper published in this journal. Many applications lend themselves to a Bayesian treatment, and would benefit from such considerations in their design. We discuss Bayes-LQAS (B-LQAS), which allows for incorporation of prior information into the LQAS classification procedure, and thus shows how to correct the aforementioned error. Further, we pay special attention to the formulation of Bayes Operating Characteristic Curves and the use of prior information to improve survey designs. As a motivating example, we discuss the classification of Global Acute Malnutrition prevalence and draw parallels between the Bayes and classical classifications schemes. We also illustrate the impact of informative and non-informative priors on the survey design. Results indicate that using a Bayesian approach allows the incorporation of expert information and/or historical data and is thus potentially a valuable tool for making accurate and precise classifications