Computer aids and human second reading as interventions in screening mammography: two systematic reviews to compare effects on cancer detection and recall rate

Background: There are two competing methods for improving the accuracy of a radiologist interpreting screening mammograms: computer aids (CAD) or independent second reading. Methods: Bibliographic databases were searched for clinical trials. Meta-analyses estimated impacts of CAD and double reading on odds ratios for cancer detection and recall rates. Sub-group analyses considered double reading with arbitration. Results: Ten studies compared single reading with CAD to single reading. Seventeen compared double to single reading. Double reading increases cancer detection and recall rates. Double reading with arbitration increases detection rate (CI: 1.02-1.15) and decreases recall rate (CI: 0.92-0.96). CAD does not have a significant effect on cancer detection rate (CI: 0.96-1.13) and increases recall rate (95% CI: 1.09-1.12). However, there is considerable heterogeneity in the impact on recall rate in both sets of studies. Conclusion: The evidence that double reading with arbitration enhances screening is stronger than that for single reading with CAD

Word Free Recall Search Scales Linearly With Number of Items Recalled

I find that the total search time in word free recall, averaged over item position, increases linearly with the number of items recalled. Thus the word free recall search algorithm scales the same as the low-error recognition of integers (Sternberg, 1966). The result suggests that both simple integer recognition and the more complex word free recall use the same search algorithm. The proportionality constant of 2-4 seconds per item (a hundred times larger than for integer recognition) is a power function of the proportion not remembered and seems to be the same function for word free recall in young and old subjects, high and low presentation rates and delayed and immediate free recall. The linear scaling of the search algorithm is different from what is commonly assumed to be the word free recall search algorithm, search by random sampling. The linearity of the word free recall extends down to 3 items which presents a challenge to the prevalent working memory theory in which 3-5 items are proposed to be stored in a separate high-availability store

Rapid presentation rate negatively impacts the contiguity effect in free recall

It is well-known that in free recall participants tend to recall words presented close together in time in sequence, reflecting a form of temporal binding in memory. This contiguity effect is robust, having been observed across many different experimental manipulations. In order to explore a potential boundary on the contiguity effect, participants performed a free recall task in which items were presented at rates ranging from 2 Hz to 8 Hz. Participants were still able to recall items even at the fastest presentation rate, though accuracy decreased. Importantly, the contiguity effect flattened as presentation rates increased. These findings illuminate possible constraints on the temporal encoding of episodic memories.http://sites.bu.edu/tcn/files/2019/05/RSVP_FR.pdfAccepted manuscrip

Revisiting Precision and Recall Definition for Generative Model Evaluation

In this article we revisit the definition of Precision-Recall (PR) curves for generative models proposed by Sajjadi et al. (arXiv:1806.00035). Rather than providing a scalar for generative quality, PR curves distinguish mode-collapse (poor recall) and bad quality (poor precision). We first generalize their formulation to arbitrary measures, hence removing any restriction to finite support. We also expose a bridge between PR curves and type I and type II error rates of likelihood ratio classifiers on the task of discriminating between samples of the two distributions. Building upon this new perspective, we propose a novel algorithm to approximate precision-recall curves, that shares some interesting methodological properties with the hypothesis testing technique from Lopez-Paz et al (arXiv:1610.06545). We demonstrate the interest of the proposed formulation over the original approach on controlled multi-modal datasets.Comment: ICML 201

Hellinger Distance Trees for Imbalanced Streams

Classifiers trained on data sets possessing an imbalanced class distribution are known to exhibit poor generalisation performance. This is known as the imbalanced learning problem. The problem becomes particularly acute when we consider incremental classifiers operating on imbalanced data streams, especially when the learning objective is rare class identification. As accuracy may provide a misleading impression of performance on imbalanced data, existing stream classifiers based on accuracy can suffer poor minority class performance on imbalanced streams, with the result being low minority class recall rates. In this paper we address this deficiency by proposing the use of the Hellinger distance measure, as a very fast decision tree split criterion. We demonstrate that by using Hellinger a statistically significant improvement in recall rates on imbalanced data streams can be achieved, with an acceptable increase in the false positive rate.Comment: 6 Pages, 2 figures, to be published in Proceedings 22nd International Conference on Pattern Recognition (ICPR) 201

The Recall and New Job Search of Laid-off Workers: A Bivariate Proportional Hazard Model with Unobserved Heterogeneity

Workers who lose their jobs can become re-employed either by being recalled to their previous employers or by finding new jobs. Workers' chances for recall should influence their job search strategies, so the rates of exit from unemployment by these two routes should be directly related. We solve a job search model to establish, in theory, a negative relationship between the recall and new job hazard rates. We look for evidence in the PSID by estimating a semi-parametric competing risks model with explicitly related hazards. We find only a small negative behavioral relationship between recall and new job hazard rates.

Unfamiliar voice identification: effect of post-event information on accuracy and voice ratings

This study addressed the effect of misleading post-event information (PEI) on voice ratings, identification accuracy, and confidence, as well as the link between verbal recall and accuracy. Participants listened to a dialogue between male and female targets, then read misleading information about voice pitch. Participants engaged in verbal recall, rated voices on a feature checklist, and made a lineup decision. Accuracy rates were low, especially on target-absent lineups. Confidence and accuracy were unrelated, but the number of facts recalled about the voice predicted later lineup accuracy. There was a main effect of misinformation on ratings of target voice pitch, but there was no effect on identification accuracy or confidence ratings. As voice lineup evidence from earwitnesses is used in courts, the findings have potential applied relevance

The Atkinson-Shiffrin model is ill-defined and does not correctly describe the Murdock free recall data

The Atkinson-Shiffrin (1968) model, the de facto standard model of short term memory cited thousands of times, fits the characteristically bowed free recall curves from Murdock (1962) well. However, it is long overdue to note that it is not a theoretically convincing explanation and that it does not fit all of the experimental relationships in the Murdock data.\ud To obtain a qualitatively correct fit of the bowing I show that four model concepts have to work together. “Long term memory” is needed in the short term memory experiment, conscious or subconscious rehearsal of four items has to take place, this “rehearsal buffer” has to drop items randomly rather than according to a first-in firstout model, and the rehearsal buffer has to be empty before the experiment starts.\ud Beyond the qualitative fit to the bowed recall curves, other relationships in the data are not borne out by the model. First, the “primacy strength”, the ratio of the probability of recall of the first item to the smallest probability of recall of an intermediate item, shows a significant experimental variation with presentation rate but no such variation is predicted by theory. Second, randomly emptying the rehearsal buffer predicts incorrectly that the number of recalled items should be the highest when the first recalled item is the last list item. Third, a simplified Atkinson-Shiffrin model is found to predict exact relationships between the recall probabilities of the initial items which do not seem to be borne out by the Murdock data. Fourth, the theory predicts a discontinuity in the differences between free recall graphs with different presentation rates for early list items which is probably not found in the Murdock data