Time pressure changes how people explore and respond to uncertainty

How does time pressure influence exploration and decision-making? We investigated this question with several four-armed bandit tasks manipulating (within subjects) expected reward, uncertainty, and time pressure (limited vs. unlimited). With limited time, people have less opportunity to perform costly computations, thus shifting the cost-benefit balance of different exploration strategies. Through behavioral, reinforcement learning (RL), reaction time (RT), and evidence accumulation analyses, we show that time pressure changes how people explore and respond to uncertainty. Specifically, participants reduced their uncertainty-directed exploration under time pressure, were less value-directed, and repeated choices more often. Since our analyses relate uncertainty to slower responses and dampened evidence accumulation (i.e., drift rates), this demonstrates a resource-rational shift towards simpler, lower-cost strategies under time pressure. These results shed light on how people adapt their exploration and decision-making strategies to externally imposed cognitive constraints

Under pressure: The influence of time limits on human exploration

How does time pressure influence attitudes towards uncertainty? When time is limited, do people engage in different exploration strategies? We study human exploration in a range of four-armed bandit tasks with different reward distributions and manipulate the available time for each decision (limited vs. unlimited). Through multiple behavioral and model-based analyses, we show that reactions towards uncertainty are influenced by time pressure. Specifically, participants seek out uncertain options when time is unlimited, but avoid uncertainty under time pressure. Moreover, larger relative differences in uncertainty between options slowed down reaction times and dampened the drift rate of a linear ballistic accumulator model. These results shed new light on the differential effect of uncertainty and time pressure on human exploration

Premise Selection for Mathematics by Corpus Analysis and Kernel Methods

Smart premise selection is essential when using automated reasoning as a tool for large-theory formal proof development. A good method for premise selection in complex mathematical libraries is the application of machine learning to large corpora of proofs. This work develops learning-based premise selection in two ways. First, a newly available minimal dependency analysis of existing high-level formal mathematical proofs is used to build a large knowledge base of proof dependencies, providing precise data for ATP-based re-verification and for training premise selection algorithms. Second, a new machine learning algorithm for premise selection based on kernel methods is proposed and implemented. To evaluate the impact of both techniques, a benchmark consisting of 2078 large-theory mathematical problems is constructed,extending the older MPTP Challenge benchmark. The combined effect of the techniques results in a 50% improvement on the benchmark over the Vampire/SInE state-of-the-art system for automated reasoning in large theories.Comment: 26 page

Dopaminergic Differentiation of Human Embryonic Stem Cells on PA6-Derived Adipocytes.

Human embryonic stem cells (hESCs) are a promising source for cell replacement therapies. Parkinson's disease is one of the candidate diseases for the cell replacement therapy since the motor manifestations of the disease are associated with the loss of dopaminergic neurons in the substantia nigra pars compacta. Stromal cell-derived inducing activity (SDIA) is the most commonly used method for the dopaminergic differentiation of hESCs. This chapter describes a simple, reliable, and scalable dopaminergic induction method of hESCs using PA6-derived adipocytes. Coculturing hESCs with PA6-derived adipocytes markedly reduces the variable outcomes among experiments. Moreover, the colony differentiation step of this method can also be used for the dopaminergic induction of mouse embryonic stem cells and NTERA2 cells as well

A comparison of random forests, boosting and support vector machines for genomic selection

Genomic selection (GS) involves estimating breeding values using molecular markers spanning the entire genome. Accurate prediction of genomic breeding values (GEBVs) presents a central challenge to contemporary plant and animal breeders. The existence of a wide array of marker-based approaches for predicting breeding values makes it essential to evaluate and compare their relative predictive performances to identify approaches able to accurately predict breeding values. We evaluated the predictive accuracy of random forests (RF), stochastic gradient boosting (boosting) and support vector machines (SVMs) for predicting genomic breeding values using dense SNP markers and explored the utility of RF for ranking the predictive importance of markers for pre-screening markers or discovering chromosomal locations of QTLs

Deconstructing Weight Management Interventions for Young Adults: Looking Inside the Black Box of the EARLY Consortium Trials.

ObjectiveThe goal of the present study was to deconstruct the 17 treatment arms used in the Early Adult Reduction of weight through LifestYle (EARLY) weight management trials.MethodsIntervention materials were coded to reflect behavioral domains and behavior change techniques (BCTs) within those domains planned for each treatment arm. The analytical hierarchy process was employed to determine an emphasis profile of domains in each intervention.ResultsThe intervention arms used BCTs from all of the 16 domains, with an average of 29.3 BCTs per intervention arm. All 12 of the interventions included BCTs from the six domains of Goals and Planning, Feedback and Monitoring, Social Support, Shaping Knowledge, Natural Consequences, and Comparison of Outcomes; 11 of the 12 interventions shared 15 BCTs in common across those six domains.ConclusionsWeight management interventions are complex. The shared set of BCTs used in the EARLY trials may represent a core intervention that could be studied to determine the required emphases of BCTs and whether additional BCTs add to or detract from efficacy. Deconstructing interventions will aid in reproducibility and understanding of active ingredients

Carbon ion therapy for ameloblastic carcinoma

Ameloblastic carcinomas are rare odontogenic tumors. Treatment usually consists of surgical resection and sometimes adjuvant radiation. We report the case of a 71 year-old male patient undergoing carbon ion therapy for extensive local relapse of ameloblastic carcinoma. Treatment outcome was favourable with a complete remission at 6 weeks post completion of radiotherapy while RT-treatment itself was tolerated well with only mild side effects. High dose radiation hence is a potential alternative for patients unfit or unwilling to undergo extensive surgery or in cases when only a subtotal resection is planned or the resection is mutilating

Investigating the effect of independent blinded digital image assessment on the STOP GAP trial

Background Blinding is the process of keeping treatment assignment hidden and is used to minimise the possibility of bias. Trials at high risk of bias have been shown to report larger treatment effects than low risk studies. In dermatology, one popular method of blinding is to have independent outcome assessors who are unaware of treatment allocation assessing the end point using digital photographs. However, this can be complex, expensive and time-consuming. The objective of this study was to compare the effect of blinded and unblinded outcome assessment on the results of the STOP GAP trial. Methods The STOP GAP trial compared prednisolone to ciclosporin in treating pyoderma gangrenosum. Participants’ lesions were measured at baseline and 6 weeks to calculate the primary outcome, speed of healing. Independent blinded assessors obtained measurements from digital photographs using specialist software. In addition, unblinded treating clinicians estimated lesion area by measuring length and width. The primary outcome was determined using blinded measurements where available, otherwise unblinded measurements were used (method referred to as trial measurements). In this study, agreement between the trial and unblinded measurements was determined using the intraclass correlation coefficient (ICC). The STOP GAP primary analysis was repeated using unblinded measurements only. We introduced differential and non-differential error in unblinded measurements and investigated the effect on the STOP GAP primary analysis. Results 86 (80%) of the 108 patients were assessed using digital images. Agreement between trial and unblinded measurements was excellent (ICC=0.92 at baseline; 0.83 at 6 weeks). There was no evidence that the results of the trial primary analysis differed according to how the primary outcome was assessed (p-value for homogeneity = 1.00). Conclusions Blinded digital image assessment in STOP GAP did not meaningfully alter trial conclusions compared with unblinded assessment. However, as the process brought added accuracy and credibility to the trial it was considered worthwhile. These findings question the usefulness of digital image assessment in a trial with an objective outcome and where bias is not expected to be excessive. Further research should investigate if there are alternative, less complex ways of incorporating blinding in clinical trials