Hebbian learning in linear-nonlinear networks with tuning curves leads to near-optimal, multi-alternative decision making

Decision making in numeracy tasks with spatially continuous scales

A diffusion model of decision making on continuous response scales is applied to three numeracy tasks. The goal is to explain the distributions of responses on the continuous response scale and the time taken to make decisions. In the model, information from a stimulus is spatially continuously distributed, the response is made by accumulating information to a criterion, which is a 1D line, and the noise in the accumulation process is continuous Gaussian process noise over spatial position. The model is fit to the data from three experiments. In one experiment, a one or two digit number is displayed and the task is to point to its location on a number line ranging from 1 to 100. This task is used extensively in research in education but there has been no model for it that accounts for both decision times and decision choices. In the second task, an array of dots is displayed and the task is to point to the position of the number of dots on an arc ranging from 11 to 90. In a third task, an array of dots is displayed and the task is to speak aloud the number of dots. The model we propose accounts for both accuracy and response time variables, including the full distributions of response times. It also provides estimates of the acuity of decisions (standard deviations in the evidence distributions) and it shows how representations of numeracy information are task-dependent. We discuss how our model relates to research on numeracy and the neuroscience of numeracy, and how it can produce more comprehensive measures of individual differences in numeracy skills in tasks with continuous response scales than have hitherto been available.

Evidence Accumulator or Decision Threshold - Which Cortical Mechanism are We Observing?

Most psychological models of perceptual decision making are of the accumulation-to-threshold variety. The neural basis of accumulation in parietal and prefrontal cortex is therefore a topic of great interest in neuroscience. In contrast, threshold mechanisms have received less attention, and their neural basis has usually been sought in subcortical structures. Here I analyze a model of a decision threshold that can be implemented in the same cortical areas as evidence accumulators, and whose behavior bears on two open questions in decision neuroscience: (1) When ramping activity is observed in a brain region during decision making, does it reflect evidence accumulation? (2) Are changes in speed-accuracy tradeoffs and response biases more likely to be achieved by changes in thresholds, or in accumulation rates and starting points? The analysis suggests that task-modulated ramping activity, by itself, is weak evidence that a brain area mediates evidence accumulation as opposed to threshold readout; and that signs of modulated accumulation are as likely to indicate threshold adaptation as adaptation of starting points and accumulation rates. These conclusions imply that how thresholds are modeled can dramatically impact accumulator-based interpretations of this data.

The reliability of artificial neural network in locating minor apical foramen: a cadaver study

INTRODUCTION

The purpose of this study was to evaluate the accuracy of the artificial neural network (ANN) in a human cadaver model in an attempt to simulate the clinical situation of working length determination.

METHODS

Fifty single-rooted teeth were selected from 19 male cadavers ranging in age from 49-73 years. Access cavities were prepared, a file was placed in the canals, and the working length was confirmed radiographically by endodontists. The location of the file in relation to the minor apical foramen was categorized as long, short, and exact by the ANN, by endodontists before extraction, and stereomicroscopically after extraction. The results were compared by using Friedman and Wilcoxon tests. The significance level was set at P <.05.

RESULTS

The Friedman test revealed a significant difference among groups (P < .001). There were significant differences between data obtained from endodontists and ANN (P = .001) and data obtained from endodontists and real measurements by stereomicroscope after extraction (P < .002). The correct assessment by the endodontists was accurate in 76% of the teeth. ANN determined the anatomic position correctly 96% of the time. The confidence interval for the correct result was 64.16-87.84 for endodontists and 90.57-101.43 for ANN.

CONCLUSIONS

ANN was more accurate than endodontists' determinations when compared with real working length measurements by using the stereomicroscope as a gold standard after tooth extraction. The artificial neural network is an accurate method for determining the working length.