Continuous Evolution of Statistical Estimators for Optimal Decision-Making

In many everyday situations, humans must make precise decisions in the presence of uncertain sensory information. For example, when asked to combine information from multiple sources we often assign greater weight to the more reliable information. It has been proposed that statistical-optimality often observed in human perception and decision-making requires that humans have access to the uncertainty of both their senses and their decisions. However, the mechanisms underlying the processes of uncertainty estimation remain largely unexplored. In this paper we introduce a novel visual tracking experiment that requires subjects to continuously report their evolving perception of the mean and uncertainty of noisy visual cues over time. We show that subjects accumulate sensory information over the course of a trial to form a continuous estimate of the mean, hindered only by natural kinematic constraints (sensorimotor latency etc.). Furthermore, subjects have access to a measure of their continuous objective uncertainty, rapidly acquired from sensory information available within a trial, but limited by natural kinematic constraints and a conservative margin for error. Our results provide the first direct evidence of the continuous mean and uncertainty estimation mechanisms in humans that may underlie optimal decision making

A nonlinear updating algorithm captures suboptimal inference in the presence of signal-dependent noise

Bayesian models have advanced the idea that humans combine prior beliefs and sensory observations to optimize behavior. How the brain implements Bayes-optimal inference, however, remains poorly understood. Simple behavioral tasks suggest that the brain can flexibly represent probability distributions. An alternative view is that the brain relies on simple algorithms that can implement Bayes-optimal behavior only when the computational demands are low. To distinguish between these alternatives, we devised a task in which Bayes-optimal performance could not be matched by simple algorithms. We asked subjects to estimate and reproduce a time interval by combining prior information with one or two sequential measurements. In the domain of time, measurement noise increases with duration. This property takes the integration of multiple measurements beyond the reach of simple algorithms. We found that subjects were able to update their estimates using the second measurement but their performance was suboptimal, suggesting that they were unable to update full probability distributions. Instead, subjects’ behavior was consistent with an algorithm that predicts upcoming sensory signals, and applies a nonlinear function to errors in prediction to update estimates. These results indicate that the inference strategies employed by humans may deviate from Bayes-optimal integration when the computational demands are high

Imaging of subsurface lineaments in the southwestern part of the Thrace Basin from gravity data

Linear anomalies, as an indicator of the structural features of some geological bodies, are very important for the interpretation of gravity and magnetic data. In this study, an image processing technique known as the Hough transform (HT) algorithm is described for determining invisible boundaries and extensions in gravity anomaly maps. The Hough function implements the Hough transform used to extract straight lines or circles within two-dimensional potential field images. It is defined as image and Hough space. In the Hough domain, this function transforms each nonzero point in the parameter domain to a sinusoid. In the image space, each point in the Hough space is transformed to a straight line or circle. Lineaments are depicted from these straight lines which are transformed in the image domain. An application of the Hough transform to the Bouguer anomaly map of the southwestern part of the Thrace Basin, NW Turkey, shows the effectiveness of the proposed approach. Based on geological data and gravity data, the structural features in the southwestern part of the Thrace Basin are investigated by applying the proposed approach and the Blakely and Simpson method. Lineaments identified by these approaches are generally in good accordance with previously-mapped surface faults

An evaluation of neuropsychiatric symptoms in Parkinson's disease patients

Objective: We aimed to examine neuropsychiatric symptoms of patients with early and advanced stage Parkinson’s disease (PD).Materials and Methods: The study was performed at Kocatepe University Neurology Department in Turkey, comprised 46 PD patients and 46 controls. Hoehn-Yahr (HY) scale was used to evaluate the clinical stages of PD and Unified Parkinson’s Disease Rating Scale (UPDRS) was used to evaluate the severity of clinical signs. Cognitive functions were evaluated by Mini-Mental State Examination (MMSE) and neuropsychiatric findings were evaluated by Beck Depression Inventory (BDI), Scale for the Assessment of Positive Symptoms (SAPS), and Scale for the Assessment of Negative Symptoms (SANS).Results: Significant difference was determined between BDI values of patients (13.28 ± 9.04) and control group (9.71 ± 5.19) (P = 0.02). Significant difference was determined with SANS (23.84 ± 15.42, 2.58 ± 3.13, P < 0.001) but not with SAPS (1.36 ± 4.16, 0.15 ± 0.43, P = 0.07). The patients were evaluated according to the HY stages and there was no significant difference between mild and severe symptom groups in respect of BDI, SAPS, and SANS values (P = 0.91, P = 0.31, and P = 0.29). According to gender, no significant difference was found between groups in respect of BDI, SAPS, and SANS values (P = 0.60, P = 0.54, and P = 0.67). No correlation was found between BDI, SAPS, SANS values, and HY stages.Conclusion: Higher rates of depression and negative symptoms were observed in patients with PD compared with healthy individuals. Results did not differ with different stages of PD. Therefore, it should be kept in mind that neuropsychiatric symptoms can be seen from the early stages of the disease and should be treated earlier.Keywords: Depression, Parkinson’s disease, psychosi

Author Correction: Prognosis and risk factors of ERCP pancreatitis in elderly (Scientific Reports, (2021), 11, 1, (15930), 10.1038/s41598-021-95484-8)

The original version of this Article contained an error in Table 3, where the “Complications” for “Syrén et al. (2019)36” was given in Turkish. “Belirtilmemiş” now reads: “Unspecified” The original Article has been corrected. © 2021, The Author(s)

Effects of Mast-Cell Stabilization in Cerulein-Induced Acute Pancreatitis in Rats.

Acute pancreatitis is characterized by inflammationof the pancreas that ranges from mild inflammationto severe necrotizing pancreatitis (1-3).Several factors can initiate a cascade of pathologic events that results in AP (4). A key feature in thispathogenesis is inflammatory reaction. However,causes of exaggerated inflammatory reaction are notwell understood (4,5). Since the pathogenesis of pancreatitisis still unclear, most of the treatment modalitiesare largely supportive and independent of thepathogenesis (1,2)