Mean Decision Time Research Articles

Development of big data assisted effective enterprise resource planning framework for smart human resource management.

The planning of human resources and the management of enterprises consider the organization's size, the amount of effort put into operations, and the level of productivity. Inefficient allocation of resources in organizations due to skill-task misalignment lowers production and operational efficiency. This study addresses organizations' poor resource allocation and use, which reduces productivity and the efficiency of operations, and inefficiency may adversely impact company production and finances. This research aims to develop and assess a Placement-Assisted Resource Management Scheme (PRMS) to improve resource allocation and usage and businesses' operational efficiency and productivity. PRMS uses expertise, business requirements, and processes that are driven by data to match resources with activities that align with their capabilities and require them to perform promptly. The proposed system PRMS outperforms existing approaches on various performance metrics at two distinct levels of operations and operating levels, with a success rate of 0.9328% and 0.9302%, minimal swapping ratios of 12.052% and 11.658%, smaller resource mitigation ratios of 4.098% and 4.815%, mean decision times of 5.414s and 4.976s, and data analysis counts of 6387 and 6335 Success and data analysis increase by 9.98% and 8.2%, respectively, with the proposed strategy. This technique cuts the switching ratio, resource mitigation, and decision time by 6.52%, 13.84%, and 8.49%. The study concluded that PRMS is a solid, productivity-focused corporate improvement method that optimizes the allocation of resources and meets business needs.

Cognitive Development and Decision Making in Basketball: A Comparison between Male Players with and without Intellectual Impairment and across Different Age-Groups.

The aim of this study was to investigate the role of age and intellectual impairment (II) in decision-making in basketball. The current study investigated differences in decision making between equally well-trained adult basketball male players with intellectual impairment (players with II) (n = 93), adults without II (senior) (n = 44) and youth basketball players (under-14, n = 31; under-16, n = 25; under-18, n = 30). A computer test was developed composed by 20 photographs displaying various basketball game-situations, and participants had to decide as fast as possible what the player in ball possession should do: dribble, pass or shoot. Decision time and accuracy were recorded for every situation. Players with II had slower decision time (3.8 ± 1.8 s vs. 1.5 ± 0.5 s, p < 0.001) and less decision-making accuracy (15.7 ± 2.8 correct decisions vs. 17.9 ± 1.2 correct decisions, p < 0.001) compared to senior players without II. Discriminant analysis with speed and accuracy as independent variables classified 91.2% (CCA = 0.769) of the players correctly into their group: players with II or players without II. A Spearman correlation revealed that age correlated significantly (p < 0.001) with the number of correct decisions (rs = 0.269) and mean decision time (rs = -0.331). Our findings support that decision making in basketball develops with age and experience, but is significantly deteriorated in experienced adult players who have II. Decision-making should be considered as an important eligibility criterion to participate in competitive basketball events for male players with II.

Real-Time Monitoring and Control of the Breakthrough Stage in Ultrafast Laser Drilling Based on Sequential Three-Way Decision

Real-time monitoring and control of breakthrough stage are essential issues in ultrafast laser drilling (ULD) process. This paper proposed a novel intelligent methodology to address the decision bias problem in monitoring and control of breakthrough stage. Eight time-domain statistical features were extracted after analyzing the correlation between the optical emission signal and the drilling process and data processing. To build the identification model of breakthrough stage, support vector machine (SVM) method was adopted and the identification accuracy can up to 98.42%. Even so, there is a large deviation between the prediction breakthrough time by the identification model and the actual breakthrough time, which can up to 3.81s. Considering the decision bias, the comprehensive Gaussian weight sequential three-way decision (CGW-S3WD) method was proposed for the first time. Compared to the actual breakthrough time, the mean decision time is only delayed by 0.375s based on the combination of the identification model and CGW-S3WD method under simulated conditions. Finally, the method was applied to monitoring and control of breakthrough stage in the actual ULD process. Experimental results demonstrate that the control delay time is only 0.354s and the high-quality holes and protection of the back wall can be achieved simultaneously. The research confirms that the method can reduce the decision bias and provides a new perspective for accurate monitoring of complex industrial scenarios.

Factors associated with decision time to seek care in the face of ischemic stroke.

To verify the association between sociodemographic, clinical, environmental, cognitive, and emotional factors and the decision time of people with ischemic stroke to seek a health service after the onset of symptoms or wake up stroke. Cross-sectional study carried out from March to October 2019, with 304 patients, in a public hospital, a reference in neurology. Data obtained through interview and from medical records. Decision time was analyzed as a geometric mean. In the bivariate and multivariate analyses, linear regression was used and the Akaike Information Criterion was used to select the best model. Statistical significance of 5% was adopted. The geometric mean of decision time was 0.30h (95% CI 0.23-0.39). The final model explained this time in 41%, showing an increase of 0.5 min for people with arterial hypertension; 10.8 min for those who waited for symptoms to improve; 1.4 min for those who were alone at the onset of symptoms; 3.9 min for those at home; 3.2 min for the ones at work; and 2.1 for those on the street/public space. The mean decision time for seeking a health service was high and influenced by clinical, environmental, cognitive, and emotional variables. The results guide nurses regarding health education.

Decision for tPA Administration, Not Response Time, Contributes Most to Variation in Thrombolysis Times Onboard Mobile Stroke Unit

Background Hospitals have improved stroke thrombolysis times through rigorous assessment of care delays. However, this same rigor has not yet been applied to the novel setting of mobile stroke units (MSUs). Methods We reviewed all cases of intravenous tPA (tissue‐type plasminogen activator) administration onboard our MSU in Rochester, NY, since its first complete year of operation in 2019. The dispatch timeline was divided into 6 intervals: (1) response time, (2) onboarding time, (3) head computed tomography collection, (4) head computed tomography reading, (5) decision time, and (6) intravenous tPA administration. Results The mean±SD total time between MSU dispatch and tPA was 42±8.4 minutes (range, 26–60 minutes; N=53). The largest mean duration was in (1), between dispatch and arrival (12.0±4.7 minutes). However, the intercase variation was greatest in (5), between head computed tomography reading and treatment decision (6.3±6.2 minutes). After ranking cases by total time, the mean decision times of first and fourth quartile cases varied the most, by a factor of 2.45, whereas the mean times for the other intervals varied by &lt;1.50. Decision time was the interval most closely correlated with the mean total time (Spearman ρ=0.46; P =0.00051), which significantly decreased from 2019 to 2020 (Mann‐Whitney, Z=2.38; P =0.02). Decision time was negatively correlated with time since inception of the MSU (Spearman ρ=−0.29; P =0.037; Figure 2B), suggesting that decision time drove both variations and improvements in overall thrombolysis time. Conclusions The primary driver of variation in total time from emergency alert to tPA administration is provider decision time in our MSU program in Rochester, NY. This observation should be investigated in other settings but may ultimately inform MSU training and staffing to optimize MSU treatment timelines.

Using transcription-based detectors to emulate the behavior of sequential probability ratio-based concentration detectors.

The sequential probability ratio test (SPRT) from statistics is known to have the least mean decision time compared to other sequential or fixed-time tests for given error rates. In some circumstances, cells need to make decisions accurately and quickly, therefore it has been suggested that the SPRT may be used to understand the speed-accuracy tradeoff in cellular decision-making. It is generally thought that in order for cells to make use of the SPRT, it is necessary to find biochemical circuits that can compute the log-likelihood ratio needed for the SPRT. However, this paper takes a different approach. We recognize that the high-level behavior of the SPRT is defined by its positive detection or hit rate, and the computation of the log-likelihood ratio is just one way to realize this behavior. In this paper, we will present a method in which a transcription-based detector is used to emulate the hit rate of the SPRT without computing the exact log-likelihood ratio. We consider the problem of using a promoter with multiple binding sites to accurately and quickly detect whether the concentration of a transcription factor is above a target level. We show that it is possible to find binding and unbinding rates of the transcription factor to the promoter's binding sites so that the probability that the amount of mRNA produced will be higher than a threshold is approximately equal to the hit rate of the SPRT detector. Moreover, we show that the average time that this transcription-based detector needs to make a positive detection is less than or equal to that of the SPRT for a wide range of concentrations. We remark that the last statement does not contradict Wald's optimality result because our transcription-based detector uses an open-ended test.

Poster 212: The Cartilage Early Return for Transplant (CERT) Score: Predicting Early Patient Election to Proceed with Cartilage Transplant Following Chondroplasty of the Knee

Objectives:Surgical management of articular cartilage lesions often includes a staging or index chondroplasty which allows for debridement of unstable chondral flaps, accurate measurement of the size and location of lesions, and evaluation of co-existing pathology that might require subsequent treatment. Some patients obtain meaningful symptomatic improvement from this staging procedure allowing a delay or avoidance of definitive treatment, while others have continued discomfort and elect to move forward with transplantation. The purpose of this study was to (1) identify risk factors associated with early election to proceed with subsequent cartilage transplantation after a staging chondroplasty within 6 months and (2) develop a risk calculator with pre-operative prognostic value for conversion.Methods:A retrospective review of patients prospectively enrolled at the time of staging chondroplasty was performed, with early election defined as patient decision to proceed to cartilage transplantation within six months of chondroplasty. Cox proportional hazards analysis was used to determine univariate predictors of conversion, and a predictive calculator was formulated using stepwise regression employing the Akaike Information Criterion (AIC). Postoperative patient-reported outcomes were compared between failure and non-failure cohorts in the timepoints leading up to the mean decision time to validate that failure correlated with worse PROMs.Results:Sixty-five patients (67 knees) were included for analysis, with an overall election rate of 29.9% within six months after chondroplasty. Condylar involvement, number of lesions, lesion topology, subchondral involvement, AMADEUS grade, history of prior surgery, indication for realignment osteotomy, and lower preoperative baseline functional assessment by IKDC, KOOS Pain, KOOS ADL, KOOS Jr, VR12 Mental, and VR12 Physical were significantly associated with greater univariate risk of early election to transplant (p≤0.04). Based on multivariate results, the final AIC-driven CERT score employed preoperative KOOS Pain Score, VR12 Physical Score, condylar involvement, and AMADEUS score to generate 0–7-point risk stratification system with a 3% early election to proceed to transplant risk in the 0–2-point score group, 33% risk in the 3–4-point group, and 79% risk in the 5+ point group (p < 0.01) and an overall AUC of 0.906 (p<0.01). By 6 weeks, early-electing patients demonstrated significantly worse pain (61.71±17.97 vs 77.03±14.79, p=.003), daily living (84.75±14.26 vs 67.55±20.69, p=.002), sports (57.36±22.94 vs 32.14±28.67, p=.002), and quality of life scores (49.31±21.27 vs 22.33±20.33, p=<.001).Conclusions:The risk of a given patient electing to proceed with a cartilage restoration procedure within 6 months after failing to derive therapeutic benefit from staging chondroplasty is closely and additively associated with pre-operative KOOS Pain Score, VR12 Physical Score, anatomic area involved, and lesion AMADEUS score. Clinical utilization of the CERT score can help guide patient conversations regarding expected benefit from chondroplasty and determination whether a single or two stage definitive procedure can be considered.Figure 1.Early Cartilage Transplant Score CalculationFigure 2.ROC Curve for the study data. AUC: Area under the Curve.

An EZ-Diffusion Model Analysis of Attentional Ability in Patients With Retinal Pigmentosa.

Retinitis pigmentosa (RP) is characterized by visual acuity decrease and visual field loss. However, the impact of visual field loss on the cognitive performance of RP patients remains unknown. In the present study, in order to understand whether and how RP affects spatial processing and attentional function, one spatial processing task and three attentional tasks were conducted on RP patients and healthy controls. In addition, an EZ-diffusion model was performed for further data analysis with four parameters, mean decision time, non-decision time, drift rate, and boundary separation. It was found that in the spatial processing task, compared with the control group, the RP group exhibited a slower response speed in large and medium visual eccentricities, and slower drift rate for the large stimulus, which is strongly verified by the significant linear correlation between the visual field eccentricity with both reaction time (p = 0.047) and non-decision time (p = 0.043) in RP patients. In the attentional orienting task and the attentional switching task, RP exerted a reduction of speed and an increase of non-decision time on every condition, with a decrease of drift rate in the orienting task and boundary separation in the switching task. In addition, the switching cost for large stimulus was observed in the control group but not in the RP group. The stop-signal task demonstrated similar inhibition function between the two groups. These findings implied that RP exerted the impairment of spatial cognition correlated with the visual field eccentricity, mainly in the peripheral visual field. Moreover, specific to the peripheral visual field, RP patients had deficits in the attentional orienting and flexibility but not in the attentional inhibition.

The effect of a digital training tool to aid chest image interpretation: Hybridising eye tracking technology and a decision support tool

IntroductionEye tracking technology, checklists and search strategies have been demonstrated as useful aids in image interpretation. A training tool was developed, by the research team, which included these features. This study aimed to evaluate the effect of the training tool on participant image interpretation performance. MethodsThe study was carried out with reporting radiographers who had either commenced training in chest image interpretation (n = 12) or were trained in musculoskeletal image interpretation (n = 23) (total n = 35). Participants were allocated to a control or intervention group. Participants completed an initial assessment at recruitment and re-attended nine months later for a follow-up assessment. The intervention group were given unlimited access to a digital training tool.During assessments participants interpreted 20 chest images whilst using eye tracking technology (total of 1400 images were interpreted). A confidence level was obtained from participants on their diagnosis and a questionnaire, to obtain demographic data, was completed following the assessment. ResultsImprovements were seen in the confidence of intervention group participants (p < 0.05). False Positive (FP) scores decreased for both the control and intervention group (p < 0.05), this decrease was from 4.20 to 3.20 for the control group and from 5.87 to 3.27 for the intervention group. True Negative (TN) scores increased, from 5.13 to 6.73 for the intervention group (p < 0.05). Mean decision time decreased for both the control and intervention group. ConclusionThe tool led to positive effects on participant performance and could be a useful aid in chest image interpretation learning. Implications for practiceImprovements in performance were observed with a digital tool. The tool could improve image interpretation methods and training.

Finite Class Bayesian Inference System for Circle and Linear Walking Gait Event Recognition Using Inertial Measurement Units.

Accurate and fast human motion pattern recognition is the key to ensuring lower limb assistive devices' appropriate assistance. The research on human motion pattern recognition of lower limb assistive devices mainly focuses on sagittal gait. The motion pattern such as circular walking (CW) is asymmetric about the sagittal plane of the body. CW is common in daily living. However, the recognition algorithm of CW is rarely reported. Since lower limb assistive devices interact with humans, lacking the capability of recognizing CW is dangerous. Thus, to realize the accurate and fast recognition of CW, this article proposed a finite class Bayesian interference system (FC-BesIS). FC-BesIS is designed to recognize walking activities (linear walking and CW) and gait events (heel contact, load response, mid stance, terminal stance, pre-swing, initial swing, mid swing, and terminal swing). A finite class method which reduces the number of potential classes according to elimination rules before decision-making is introduced. Elimination rules are designed based on likelihood estimation and sensor information. The experiments show that walking activities and gait events can be accurately and fastly recognized by FC-BesIS. The experiments also show that the performance of FC-BesIS in mean recognition accuracy (MRA) and mean decision time (MDT) is improved compared with BesIS. The MRA of walking activities and gait events are 100% and 97.38%, respectively. The MDT of walking activities and gait events are 28.19 ms and 33.94 ms, respectively. Overall, FC-BesIS has been proved to be an accurate and fast recognition algorithm for human motion patterns using wearable sensors.

Computational Mechanisms of Effort and Reward Decisions in Patients With Depression and Their Association With Relapse After Antidepressant Discontinuation

Nearly 1 in 3 patients with major depressive disorder who respond to antidepressants relapse within 6 months of treatment discontinuation. No predictors of relapse exist to guide clinical decision-making in this scenario. To establish whether the decision to invest effort for rewards represents a persistent depression process after remission, predicts relapse after remission, and is affected by antidepressant discontinuation. This longitudinal randomized observational prognostic study in a Swiss and German university setting collected data from July 1, 2015, to January 31, 2019, from 66 healthy controls and 123 patients in remission from major depressive disorder in response to antidepressants prior to and after discontinuation. Study recruitment took place until January 2018. Discontinuation of antidepressants. Relapse during the 6 months after discontinuation. Choice and decision times on a task requiring participants to choose how much effort to exert for various amounts of reward and the mechanisms identified through parameters of a computational model. A total of 123 patients (mean [SD] age, 34.5 [11.2] years; 94 women [76%]) and 66 healthy controls (mean [SD] age, 34.6 [11.0] years; 49 women [74%]) were recruited. In the main subsample, mean (SD) decision times were slower for patients (n = 74) compared with controls (n = 34) (1.77 [0.38] seconds vs 1.61 [0.37] seconds; Cohen d = 0.52; P = .02), particularly for those who later relapsed after discontinuation of antidepressants (n = 21) compared with those who did not relapse (n = 39) (1.95 [0.40] seconds vs 1.67 [0.34] seconds; Cohen d = 0.77; P < .001). This slower decision time predicted relapse (accuracy = 0.66; P = .007). Patients invested less effort than healthy controls for rewards (F1,98 = 33.970; P < .001). Computational modeling identified a mean (SD) deviation from standard drift-diffusion models that was more prominent for patients than controls (patients, 0.67 [1.56]; controls, -0.71 [1.93]; Cohen d = 0.82; P < .001). Patients also showed higher mean (SD) effort sensitivity than controls (patients, 0.31 [0.92]; controls, -0.08 [1.03]; Cohen d = 0.51; P = .05). Relapsers differed from nonrelapsers in terms of the evidence required to make a decision for the low-effort choice (mean [SD]: relapsers, 1.36 [0.35]; nonrelapsers, 1.17 [0.26]; Cohen d = 0.65; P = .02). Group differences generally did not reach significance in the smaller replication sample (27 patients and 21 controls), but decision time prediction models from the main sample generalized to the replication sample (validation accuracy = 0.71; P = .03). This study found that the decision to invest effort was associated with prospective relapse risk after antidepressant discontinuation and may represent a persistent disease process in asymptomatic remitted major depressive disorder. Markers based on effort-related decision-making could potentially inform clinical decisions associated with antidepressant discontinuation.

Frequency-Sensitivity and Magnitude-Sensitivity in Decision-Making: Predictions of a Theoretical Model-Based Study

We theoretically study decision-making behaviour in a model-based analysis related to binary choices with pulsed stimuli. Assuming a strong coupling between external stimulus and its internal representation, we argue that the frequency of external periodic stimuli represents an important degree of freedom in decision-making which may modulate behavioural responses. We consider various different stimulus conditions, including varying overall magnitudes and magnitude ratios as well as varying overall frequencies and frequency ratios, and different duty cycles of the pulsed stimuli. Decision time distributions, mean decision times and choice probabilities are simulated and compared for two different models—a leaky competing accumulator model and a diffusion-type model with multiplicative noise. Our results reveal an interplay between the sensitivity of the model systems to both frequency and magnitude of the stimuli. In particular, we find that periodic stimuli may shape the decision time distributions resulting from both models by resembling the frequencies of the pulsed stimuli. We obtain significant frequency-sensitive effects on mean decision time and choice probability for a range of overall frequencies and frequency ratios. Our simulation analysis makes testable predictions that frequencies comparable with typical sensory processing and decision-making timescales may influence choice and response times in perceptual decisions. A possible experimental implementation is proposed.

A two-level iteration approach for modeling and analysis of rapid response process with multiple deteriorating patients

In acute care, a patient’s clinical deterioration is often a precursor to serious and often fatal outcomes. To reduce the severity and frequency of negative outcomes, care providers need to response rapidly by providing quick evaluation, triage, and treatment to patients with declining conditions. However, a provider’s availability to respond can be constrained when multiple patients are deteriorating at the same time. To study the multiple patients rapid response process, we introduce a network model with complex structures, such as split, merge, and parallel. Iterative methods are presented to evaluate the mean decision time (i.e., the average time from the detection of a patient’s declining to a physician’s treatment decision being made). It is shown that such methods lead to convergent results and high accuracy in performance evaluation. Such a model provides a quantitative tool for healthcare professionals to design and improve rapid response systems.

Adaptive Bayesian inference system for recognition of walking activities and prediction of gait events using wearable sensors

In this paper, a novel approach for recognition of walking activities and gait events with wearable sensors is presented. This approach, called adaptive Bayesian inference system (BasIS), uses a probabilistic formulation with a sequential analysis method, for recognition of walking activities performed by participants. Recognition of gait events, needed to identify the state of the human body during the walking activity, is also provided by the proposed method. In addition, the BasIS system includes an adaptive action–perception method for the prediction of gait events. The adaptive approach uses the knowledge gained from decisions made over time by the inference system. The action–perception method allows the BasIS system to autonomously adapt its performance, based on the evaluation of its own predictions and decisions made over time. The proposed approach is implemented in a layered architecture and validated with the recognition of three walking activities:level-ground, ramp ascent and ramp descent. The validation process employs real data from three inertial measurements units attached to the thigh, shanks and foot of participants while performing walking activities. The experiments show that mean decision times of 240 ms and 40 ms are needed to achieve mean accuracies of 99.87% and 99.82% for recognition of walking activities and gait events, respectively. The validation experiments also show that the performance, in accuracy and speed, is not significantly affected when noise is added to sensor measurements. These results show that the proposed adaptive recognition system is accurate, fast and robust to sensor noise, but also capable to adapt its own performance over time. Overall, the adaptive BasIS system demonstrates to be a robust and suitable computational approach for the intelligent recognition of activities of daily living using wearable sensors.

Abstract WP20: Reasons Patients Don’t Receive Endovascular Therapy and Their Outcomes

Objective: Many examine patient outcomes following endovascular therapy for acute ischemic stroke, but we wanted to better understand why patients presenting within the window for this therapy did not receive it and their outcomes. Methods: We retrospectively examined a cohort of 275 patients with a discharge diagnosis of ischemic stroke, NIHSS≥6 and last known normal (LKN) time&lt;6 hours prior to arrival. We determined why patients did not receive endovascular therapy and collected demographics, admission NIHSS, pre-admission modified Rankin scale (mRS), LKN time, decision time for no endovascular therapy, discharge NIHSS, disposition and mRS at follow-up. We used STATA 12.0 for statistical analysis and student’s t-test to compare mean decision time for individual groups of patients to other patients in the cohort. Results: Reasons patients did not receive intervention were patent major intracranial vessels (32%), no penumbra on CT or MRI (11.7%), large core infarct (16%), low NIHSS or significant improvement (8%), cerebral ischemia not suspected at admission (6.6%), poor baseline (6.2%) and advanced age (4.4%). Mean decision time for patients without penumbra or large core infarcts was not different from other patients in the cohort. Inability to treat patients within 6 hours was associated with a longer decision time (119 minutes longer, CI 54-185, p value =0.0004), but the time increase was due to transfer, not decision time relative to arrival at the tertiary care center. Patients for whom ischemic stroke was not considered at admission were most likely to present with seizure (33%) or posterior circulation symptoms (22%). Patients not receiving endovascular therapy due to mild/improving symptoms had significant deficits at discharge (mean mRS of 3; 18% discharged to a skilled nursing facility; 41% to an acute rehabilitation facility). At 3 months the mean mRS was 2. Conclusion: The most common reasons patients did not receive endovascular therapy were patent major intracranial vessels, no penumbra and large core infarct. These patients would be unlikely to benefit and might be harmed by intervention. Conversely, patients not treated due to mild/improving symptoms had significant deficits at discharge and 3 months and may have benefited from intervention.

Pharmacoeconomic Results Of Highly Innovative Drugs Approved For Temporary Reimbursement In The Czech Republic

Highly innovative drugs can be granted two plus one year of temporary reimbursement (TR) to provide timely access and to collect additional real-world evidence through registry. TR applicant does not need to comply with strict cost-effectiveness (CE) requirements and willingness-to-pay threshold (WTP) when there is no sufficient data about use in clinical practice or about CE at a time. After two/three years when switching to permanent reimbursement (PR), a drug must comply with strict CE and WTP requirements. The main objective was to analyse pharmacoeconomic results at the entry of drug into TR and compare them with results presented in consecutive PR procedure. All drugs approved for TR until 5/2017 were identified. Pharmacoeconomic results (i.e. type of analysis, incremental cost-effectiveness ratios (ICERs), net budget impact (netBI)) at the time of TR and consecutive PR application were analysed. We also examined therapeutic class, and compared the length of approval of TR versus PR. 40 TR decisions were identified. Majority (70%) belonged to oncology drugs, 33% were orphans. After the expiration of TR, 92% had positive subsequent decision on PR. Price negotiation with payers was present in 50% of cases. 8% did not manage to receive PR. Cost-utility analysis was used to present results of cost-effectiveness in 44% of TR procedures. Mean ICER (cost/QALY) of TR products was €93,087 (SD €64,550). In the subsequent PR procedure, the mean ICER was €40,937 (lower by 56%). NetBI lowered by 32% and 50% in 1st and 5th year respectively when applying for PR. Mean decision time about TR was longer (404 days; SD 194) than consecutive decision about PR (259 days; SD 251). Costly innovative drugs are accepted for a TR with higher ICERs and netBI than usually accepted in PR procedure. 92% of these drugs succeed in consecutive PR procedure.

A martingale analysis of first passage times of time-dependent Wiener diffusion models

Research in psychology and neuroscience has successfully modeled decision making as a process of noisy evidence accumulation to a decision bound. While there are several variants and implementations of this idea, the majority of these models make use of a noisy accumulation between two absorbing boundaries. A common assumption of these models is that decision parameters, e.g., the rate of accumulation (drift rate), remain fixed over the course of a decision, allowing the derivation of analytic formulas for the probabilities of hitting the upper or lower decision threshold, and the mean decision time. There is reason to believe, however, that many types of behavior would be better described by a model in which the parameters were allowed to vary over the course of the decision process.In this paper, we use martingale theory to derive formulas for the mean decision time, hitting probabilities, and first passage time (FPT) densities of a Wiener process with time-varying drift between two time-varying absorbing boundaries. This model was first studied by Ratcliff (1980) in the two-stage form, and here we consider the same model for an arbitrary number of stages (i.e. intervals of time during which parameters are constant). Our calculations enable direct computation of mean decision times and hitting probabilities for the associated multistage process. We also provide a review of how martingale theory may be used to analyze similar models employing Wiener processes by re-deriving some classical results. In concert with a variety of numerical tools already available, the current derivations should encourage mathematical analysis of more complex models of decision making with time-varying evidence.

Explicit moments of decision times for single- and double-threshold drift-diffusion processes

We derive expressions for the first three moments of the decision time (DT) distribution produced via first threshold crossings by sample paths of a drift-diffusion equation. The “pure” and “extended” diffusion processes are widely used to model two-alternative forced choice decisions, and, while simple formulae for accuracy, mean DT and coefficient of variation are readily available, third and higher moments and conditioned moments are not generally available. We provide explicit formulae for these, describe their behaviors as drift rates and starting points approach interesting limits, and, with the support of numerical simulations, discuss how trial-to-trial variability of drift rates, starting points, and non-decision times affect these behaviors in the extended diffusion model. Both unconditioned moments and those conditioned on correct and erroneous responses are treated. We argue that the results will assist in exploring mechanisms of evidence accumulation and in fitting parameters to experimental data.

Statistical Properties of Gray Color Categorization: Asymptotics of Psychometric Function

The results of our experiments on categorical perception of different shades of gray are reported. A special color generator was created for conducting the experiments on categorizing a random sequence of colors into two classes, light-gray and dark-gray. The collected data are analyzed based on constructing (i) the asymptotics of the corresponding psychometric functions and (ii) the mean decision time in categorizing a given shade of gray depending on the shade brightness (shade number). Conclusions about plausible mechanisms governing categorical perception, at least for the analyzed system, are drawn.

Probabilistic Decision Making with Spikes: From ISI Distributions to Behaviour via Information Gain.

Computational theories of decision making in the brain usually assume that sensory 'evidence' is accumulated supporting a number of hypotheses, and that the first accumulator to reach threshold triggers a decision in favour of its associated hypothesis. However, the evidence is often assumed to occur as a continuous process whose origins are somewhat abstract, with no direct link to the neural signals - action potentials or 'spikes' - that must ultimately form the substrate for decision making in the brain. Here we introduce a new variant of the well-known multi-hypothesis sequential probability ratio test (MSPRT) for decision making whose evidence observations consist of the basic unit of neural signalling - the inter-spike interval (ISI) - and which is based on a new form of the likelihood function. We dub this mechanism s-MSPRT and show its precise form for a range of realistic ISI distributions with positive support. In this way we show that, at the level of spikes, the refractory period may actually facilitate shorter decision times, and that the mechanism is robust against poor choice of the hypothesized data distribution. We show that s-MSPRT performance is related to the Kullback-Leibler divergence (KLD) or information gain between ISI distributions, through which we are able to link neural signalling to psychophysical observation at the behavioural level. Thus, we find the mean information needed for a decision is constant, thereby offering an account of Hick's law (relating decision time to the number of choices). Further, the mean decision time of s-MSPRT shows a power law dependence on the KLD offering an account of Piéron's law (relating reaction time to stimulus intensity). These results show the foundations for a research programme in which spike train analysis can be made the basis for predictions about behavior in multi-alternative choice tasks.