Memory for Radio Advertisements: the Effect of Program and Typicality

2013 ◽  
Vol 16 ◽  
Author(s):  
Beatriz Martín-Luengo ◽  
Karlos Luna ◽  
Malen Migueles
Keyword(s):  
False Alarm ◽  
Recognition Test ◽  
False Recognition ◽  
Response Criterion ◽  
False Alarms ◽  
Filler Task ◽  
Radio Program ◽  
False Alarm Rates ◽  
True False

AbstractWe examined the influence of the type of radio program on the memory for radio advertisements. We also investigated the role in memory of the typicality (high or low) of the elements of the products advertised. Participants listened to three types of programs (interesting, boring, enjoyable) with two advertisements embedded in each. After completing a filler task, the participants performed a true/false recognition test. Hits and false alarm rates were higher for the interesting and enjoyable programs than for the boring one. There were also more hits and false alarms for the high-typicality elements. The response criterion for the advertisements embedded in the boring program was stricter than for the advertisements in other types of programs. We conclude that the type of program in which an advertisement is inserted and the nature of the elements of the advertisement affect both the number of hits and false alarms and the response criterion, but not the accuracy of the memory.

scholarly journals Effects of fixed- and varied-context repetition on associative recognition in amnesia

2010 ◽  
Vol 16 (4) ◽  
pp. 596-602 ◽  
Author(s):  
DANIEL L. GREENBERG ◽  
MIEKE VERFAELLIE
Keyword(s):  
False Alarm ◽  
Recognition Task ◽  
Associative Recognition ◽  
False Alarms ◽  
Study Phase ◽  
Repetition Condition ◽  
Context Condition ◽  
False Alarm Rates

AbstractThis study compared the effects of fixed- and varied-context repetition on associative recognition in amnesia. Controls and amnesic participants were presented with a set of three-word phrases. Each was presented three times. In the varied-context condition, the verb changed with each presentation; in the fixed-context condition, it remained constant. At test, participants performed an associative-recognition task in which they were shown pairs of words from the study phase and asked to distinguish between intact and recombined pairs. For corrected recognition (hits – false alarms), controls performed better in the varied-context than in the fixed-context repetition condition, whereas amnesic participants’ performance did not differ between conditions. Similarly, controls had lower false-alarm rates in the varied-context condition, but there was no significant effect of condition for the amnesic participants. Thus, varied-context repetition does not improve amnesic participants’ performance on a recollection-dependent associative-recognition task, possibly because the amnesic participants were unable to take advantage of the additional cues that the varied-context encoding condition provided. (JINS, 2010, 16, 596–602.)

True-False Recognition Test

2014 ◽  
Author(s):  
Aileen Oeberst ◽  
Julienne Seidemann
Keyword(s):  
Recognition Test ◽  
False Recognition ◽  
True False

scholarly journals Reducing false alarm rates during change detection by modeling relief, shade and shadow of multi-temporal imagery.

2014 ◽  
Vol XL-4 ◽  
pp. 65-70 ◽  
Author(s):  
B. Gorte ◽  
C. van der Sande
Keyword(s):  
Change Detection ◽  
False Alarm ◽  
3D Model ◽  
Integrated Approach ◽  
False Alarms ◽  
The Sun ◽  
Geometric Image ◽  
Multi Temporal ◽  
False Alarm Rates

Change detection on the basis of multi-temporal imagery may lead to false alarms when the image has changed, whereas the scene has not. Geometric image differerences in an unchanged scene may be due to relief displacement, caused by diferent camera positions. Radiometric differences may be caused by changes in illumimation and shadow between the images, caused by a different position of the sun. The effects may be predicted, and after that compensated, if a 3d model of the scene is available. The paper presents an integrated approach to prediction of and compensation for relief displacement, shading and shadow.

Confirming Testlet Effects

2012 ◽  
Vol 36 (2) ◽  
pp. 104-121 ◽  
Author(s):  
Christine E. DeMars
Keyword(s):  
False Alarm ◽  
Sample Size ◽  
Statistical Significance ◽  
Information Criteria ◽  
False Alarms ◽  
Parameter Estimates ◽  
Significance Tests ◽  
Item Responses ◽  
False Alarm Rates ◽  
Essential Unidimensionality

A testlet is a cluster of items that share a common passage, scenario, or other context. These items might measure something in common beyond the trait measured by the test as a whole; if so, the model for the item responses should allow for this testlet trait. But modeling testlet effects that are negligible makes the model unnecessarily complicated and risks capitalization on chance, increasing the error in parameter estimates. Checking each testlet to see if the items within the testlet share something beyond the primary trait could therefore be useful. This study included (a) comparison between a model with no testlets and a model with testlet g, (b) comparison between a model with all suspected testlets and a model with all suspected testlets except testlet g, and (c) a test of essential unidimensionality. Overall, Comparison b was most useful for detecting testlet effects. Model comparisons based on information criteria, specifically the sample-size adjusted Bayesian Information Criteria (SSA-BIC) and BIC, resulted in fewer false alarms than statistical significance tests. The test of essential unidimensionality had true hit rates and false alarm rates similar to the SSA-BIC when the testlet effect was zero for all testlets except the studied testlet. But the presence of additional testlet effects in the partitioning test led to higher false alarm rates for the test of essential unidimensionality.

scholarly journals Machine Learning Approach to Combat False Alarms in Wireless Intrusion Detection System

2018 ◽  
Vol 11 (3) ◽  
pp. 67 ◽  
Author(s):  
D. Sudaroli Vijayakumar ◽  
S. Ganapathy
Keyword(s):  
Machine Learning ◽  
Wireless Networks ◽  
Intrusion Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Intrusion Detection Systems ◽  
False Alarms ◽  
Detection Systems ◽  
High Detection ◽  
False Alarm Rates

Wireless Networks facilitate the ease of communication for sharing the crucial information. Recently, most of the small and large-scale companies, educational institutions, government organizations, medical sectors, military and banking sectors are using the wireless networks. Security threats, a common term found both in wired as well as in wireless networks. However, it holds lot of importance in wireless networks because of its susceptible nature to threats. Security concerns in WLAN are studied and many organizations concluded that Wireless Intrusion Detection Systems (WIDS) is an essential element in network security infrastructure to monitor wireless activity for signs of attacks. However, it is an indisputable fact that the art of detecting attacks remains in its infancy. WIDS generally collect the activities within the protected network and analyze them to detect intrusions and generates an intrusion alarm. Irrespective of the different types of Intrusion Detection Systems, the major problems arising with WIDS is its inability to handle large volumes of alarms and more prone to false alarm attacks. Reducing the false alarms can improve the overall efficiency of the WIDS. Many techniques have been proposed in the literature to reduce the false alarm rates. However, most of the existing techniques are failed to provide desirable result and the high complexity to achieve high detection rate with less false alarm rates. This is the right time to propose a new technique for providing high detection accuracy with less false alarm rate. This paper made an extensive survey about the role of machine learning techniques to reduce the false alarm rate in WLAN IEEE 802.11. This survey proved that the substantial improvement has been achieved by reducing false alarm rate through machine learning algorithms. In addition to that, advancements specific to machine learning approaches is studied meticulously and a filtration technique is proposed.

scholarly journals Realizing Target Detection in SAR Images Based on Multiscale Superpixel Fusion

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1643
Author(s):  
Ming Liu ◽  
Shichao Chen ◽  
Fugang Lu ◽  
Mengdao Xing ◽  
Jingbiao Wei
Keyword(s):  
Synthetic Aperture Radar ◽  
False Alarm ◽  
False Alarm Rate ◽  
Target Detection ◽  
Synthetic Aperture ◽  
False Alarms ◽  
Sar Image ◽  
Constant False Alarm Rate ◽  
Sar Images ◽  
Complex Scenes

For target detection in complex scenes of synthetic aperture radar (SAR) images, the false alarms in the land areas are hard to eliminate, especially for the ones near the coastline. Focusing on the problem, an algorithm based on the fusion of multiscale superpixel segmentations is proposed in this paper. Firstly, the SAR images are partitioned by using different scales of superpixel segmentation. For the superpixels in each scale, the land-sea segmentation is achieved by judging their statistical properties. Then, the land-sea segmentation results obtained in each scale are combined with the result of the constant false alarm rate (CFAR) detector to eliminate the false alarms located on the land areas of the SAR image. In the end, to enhance the robustness of the proposed algorithm, the detection results obtained in different scales are fused together to realize the final target detection. Experimental results on real SAR images have verified the effectiveness of the proposed algorithm.

scholarly journals Irrelevant background context decreases mnemonic discrimination and increases false memory

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mihály Racsmány ◽  
Dorottya Bencze ◽  
Péter Pajkossy ◽  
Ágnes Szőllősi ◽  
Miklós Marián
Keyword(s):  
Recognition Test ◽  
False Recognition ◽  
Pattern Separation ◽  
Hippocampal Function ◽  
Memory Research ◽  
Early Dementia ◽  
Similar Information ◽  
Behavioral Score ◽  
The Cost ◽  
Background Context

AbstractOne of the greatest commonplaces in memory research is that context improves recall and enhances or leaves recognition intact. Here we present results which draw attention to the fact that the reappearance of irrelevant and unattended background contexts of encoding significantly impairs memory discrimination functions. This manuscript presents the results of two experiments in which participants made indoor/outdoor judgements for a large number of object images presented together with individual, irrelevant and presumably unattended background scenes. On a subsequent unexpected recognition test participants saw the incidentally encoded target objects, visually similar lures or new foil objects on the same or new background scenes. Our results showed that although the reappearance of the background scene raised the hit rate for target objects, it decreased mnemonic discrimination, a behavioral score for pattern separation, a hippocampal function that is affected in early dementia. Furthermore, the presence of the encoded background scene at the recognition test increased the false recognition of lure objects, even when participants were explicitly instructed to neglect the context scene. Altogether these results gave evidence that if context increases recognition hits for target memories, it does so at the cost of increasing false recognition and diminished discriminability for similar information.

scholarly journals Doorways do not always cause forgetting: a multimodal investigation

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Jessica McFadyen ◽  
Christopher Nolan ◽  
Ellen Pinocy ◽  
David Buteri ◽  
Oliver Baumann
Keyword(s):  
Working Memory ◽  
False Alarm ◽  
Memory Load ◽  
Real Life ◽  
Working Memory Load ◽  
Movement Data ◽  
Physical Environments ◽  
Series Of Experiments ◽  
False Alarm Rates ◽  
Location Updating

Abstract Background The ‘doorway effect’, or ‘location updating effect’, claims that we tend to forget items of recent significance immediately after crossing a boundary. Previous research suggests that such a forgetting effect occurs both at physical boundaries (e.g., moving from one room to another via a door) and metaphysical boundaries (e.g., imagining traversing a doorway, or even when moving from one desktop window to another on a computer). Here, we aimed to conceptually replicate this effect using virtual and physical environments. Methods Across four experiments, we measured participants’ hit and false alarm rates to memory probes for items recently encountered either in the same or previous room. Experiments 1 and 2 used highly immersive virtual reality without and with working memory load (Experiments 1 and 2, respectively). Experiment 3 used passive video watching and Experiment 4 used active real-life movement. Data analysis was conducted using frequentist as well as Bayesian inference statistics. Results Across this series of experiments, we observed no significant effect of doorways on forgetting. In Experiment 2, however, signal detection was impaired when participants responded to probes after moving through doorways, such that false alarm rates were increased for mismatched recognition probes. Thus, under working memory load, memory was more susceptible to interference after moving through doorways. Conclusions This study presents evidence that is inconsistent with the location updating effect as it has previously been reported. Our findings call into question the generalisability and robustness of this effect to slight paradigm alterations and, indeed, what factors contributed to the effect observed in previous studies.

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