A signal-detection-based diagnostic-feature-detection model of eyewitness identification.

2014 ◽  
Vol 121 (2) ◽  
pp. 262-276 ◽  
Author(s):  
John T. Wixted ◽  
Laura Mickes
Keyword(s):  
Signal Detection ◽  
Feature Detection ◽  
Eyewitness Identification ◽  
Diagnostic Feature ◽  
Detection Model

scholarly journals Testing encoding specificity and the diagnostic feature-detection theory of eyewitness identification, with implications for showups, lineups, and partially disguised perpetrators

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Curt A. Carlson ◽  
Jacob A. Hemby ◽  
Alex R. Wooten ◽  
Alyssa R. Jones ◽  
Robert F. Lockamyeir ◽  
...  
Keyword(s):  
Feature Detection ◽  
Eyewitness Identification ◽  
Detection Theory ◽  
Diagnostic Information ◽  
Diagnostic Feature ◽  
Exterior Region ◽  
Facial Information ◽  
The Face ◽  
Encoding Specificity ◽  
Encoding And Retrieval

AbstractThe diagnostic feature-detection theory (DFT) of eyewitness identification is based on facial information that is diagnostic versus non-diagnostic of suspect guilt. It primarily has been tested by discounting non-diagnostic information at retrieval, typically by surrounding a single suspect showup with good fillers to create a lineup. We tested additional DFT predictions by manipulating the presence of facial information (i.e., the exterior region of the face) at both encoding and retrieval with a large between-subjects factorial design (N = 19,414). In support of DFT and in replication of the literature, lineups yielded higher discriminability than showups. In support of encoding specificity, conditions that matched information between encoding and retrieval were generally superior to mismatch conditions. More importantly, we supported several DFT and encoding specificity predictions not previously tested, including that (a) adding non-diagnostic information will reduce discriminability for showups more so than lineups, and (b) removing diagnostic information will lower discriminability for both showups and lineups. These results have implications for police deciding whether to conduct a showup or a lineup, and when dealing with partially disguised perpetrators (e.g., wearing a hoodie).

Do Sequential Lineups Impair Discriminability?

2020 ◽  
Author(s):  
Matthew Philip Kaesler ◽  
John C Dunn ◽  
Keith Ransom ◽  
Carolyn Semmler
Keyword(s):  
Human Behavior ◽  
Feature Detection ◽  
Eyewitness Memory ◽  
Simultaneous Presentation ◽  
Diagnostic Feature ◽  
Sequential Presentation ◽  
Sequential Lineups ◽  
Measurement Models ◽  
Sequential Lineup ◽  
Lineup Procedures

The debate regarding the best way to test and measure eyewitness memory has dominated the eyewitness literature for more than thirty years. We argue that to resolve this debate requires the development and application of appropriate measurement models. In this study we develop models of simultaneous and sequential lineup presentations and use these to compare the procedures in terms of discriminability and response bias. We tested a key prediction of the diagnostic feature detection hypothesis that discriminability should be greater for simultaneous than sequential lineups. We fit the models to the corpus of studies originally described by Palmer and Brewer (2012, Law and Human Behavior, 36(3), 247-255) and to data from a new experiment. The results of both investigations showed that discriminability did not differ between the two procedures, while responses were more conservative for sequential presentation compared to simultaneous presentation. We conclude that the two procedures do not differ in the efficiency with which they allow eyewitness memory to be expressed. We discuss the implications of this for the diagnostic feature detection hypothesis and other sequential lineup procedures used in current jurisdictions.

scholarly journals Influence of Features on Accuracy of Anomaly Detection for an Energy Trading System

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4237
Author(s):  
Hoon Ko ◽  
Kwangcheol Rim ◽  
Isabel Praça
Keyword(s):  
Anomaly Detection ◽  
Real Time ◽  
Feature Detection ◽  
Trading System ◽  
Judgment Accuracy ◽  
Energy Trading ◽  
Real Time Processing ◽  
Detection Model ◽  
Time Required ◽  
The Relationship

The biggest problem with conventional anomaly signal detection using features was that it was difficult to use it in real time and it requires processing of network signals. Furthermore, analyzing network signals in real-time required vast amounts of processing for each signal, as each protocol contained various pieces of information. This paper suggests anomaly detection by analyzing the relationship among each feature to the anomaly detection model. The model analyzes the anomaly of network signals based on anomaly feature detection. The selected feature for anomaly detection does not require constant network signal updates and real-time processing of these signals. When the selected features are found in the received signal, the signal is registered as a potential anomaly signal and is then steadily monitored until it is determined as either an anomaly or normal signal. In terms of the results, it determined the anomaly with 99.7% (0.997) accuracy in f(4)(S0) and in case f(4)(REJ) received 11,233 signals with a normal or 171anomaly judgment accuracy of 98.7% (0.987).

Phantom ROC: Recollection rejection in a hybrid conjoint recognition signal detection model

Memory ◽  
2006 ◽  
Vol 14 (6) ◽  
pp. 655-671 ◽  
Author(s):  
James Michael Lampinen ◽  
Kristina N. Watkins ◽  
Timothy N. Odegard
Keyword(s):  
Signal Detection ◽  
Detection Model ◽  
Recollection Rejection

The Four-States Model of Memory Retrieval Experiences

2007 ◽  
Vol 215 (1) ◽  
pp. 61-71 ◽  
Author(s):  
Edgar Erdfelder ◽  
Lutz Cüpper ◽  
Tina-Sarah Auer ◽  
Monika Undorf
Keyword(s):  
Signal Detection ◽  
Memory Retrieval ◽  
Measurement Model ◽  
Standard Errors ◽  
Parameter Estimates ◽  
Unequal Variances ◽  
One Dimensional ◽  
Detection Model ◽  
Memory States ◽  
The One

Abstract. A memory measurement model is presented that accounts for judgments of remembering, knowing, and guessing in old-new recognition tasks by assuming four disjoint latent memory states: recollection, familiarity, uncertainty, and rejection. This four-states model can be applied to both Tulving's (1985) remember-know procedure (RK version) and Gardiner and coworker's ( Gardiner, Java, & Richardson-Klavehn, 1996 ; Gardiner, Richardson-Klavehn, & Ramponi, 1997 ) remember-know-guess procedure (RKG version). It is shown that the RK version of the model fits remember-know data approximately as well as the one-dimensional signal detection model does. In contrast, the RKG version of the four-states model outperforms the corresponding detection model even if unequal variances for old and new items are allowed for.We show empirically that the two versions of the four-statesmodelmeasure the same state probabilities. However, the RKG version, requiring remember-know-guess judgments, provides parameter estimates with smaller standard errors and is therefore recommended for routine use.

scholarly journals A signal-detection analysis of eyewitness identification across the adult lifespan.

2017 ◽  
Vol 32 (3) ◽  
pp. 243-258 ◽  
Author(s):  
Melissa F. Colloff ◽  
Kimberley A. Wade ◽  
John T. Wixted ◽  
Elizabeth A. Maylor
Keyword(s):  
Signal Detection ◽  
Eyewitness Identification ◽  
Adult Lifespan ◽  
Detection Analysis ◽  
Signal Detection Analysis

Effects of Social Influence in a Recognition Task of Auditory Intensity: A Temporary Shift in Criterion

1976 ◽  
Vol 42 (1) ◽  
pp. 75-85 ◽  
Author(s):  
Michael D. Biderman ◽  
William D. McBrayer ◽  
Mary La Montagne
Keyword(s):  
Social Influence ◽  
Signal Detection ◽  
Principal Components ◽  
Recognition Task ◽  
Discriminative Ability ◽  
Auditory Intensity ◽  
Detection Model ◽  
Social Sensitivity ◽  
The Social ◽  
Orthogonal Components

The effects of responses of another person or a computer occurring prior to the subjects' responses in tasks of recognition of auditory intensity were interpreted in terms of a signal-detection model which assumed that subjects shifted their decision criteria temporarily on each trial. A parameter representing the amount of criterion shift reliably estimated sensitivity to social influence. When the social sensitivity parameter was estimated from the data, discriminative ability, defined as d', was unaffected by the presence of social influence. Principal components analyses suggested that social sensitivity and discriminative ability represented essentially orthogonal components of subjects' decision behavior.

scholarly journals The unequal variance signal-detection model of recognition memory: Investigating the encoding variability hypothesis

2020 ◽  
Vol 73 (8) ◽  
pp. 1242-1260
Author(s):  
Rory W Spanton ◽  
Christopher J Berry
Keyword(s):  
Recognition Memory ◽  
Signal Detection ◽  
Psychological Explanation ◽  
Memory Strength ◽  
Unequal Variance ◽  
Detection Model ◽  
The Mean ◽  
Encoding Variability ◽  
Strength Variance ◽  
Back Task

Despite the unequal variance signal-detection (UVSD) model’s prominence as a model of recognition memory, a psychological explanation for the unequal variance assumption has yet to be verified. According to the encoding variability hypothesis, old item memory strength variance (σo) is greater than that of new items because items are incremented by variable, rather than fixed, amounts of strength at encoding. Conditions that increase encoding variability should therefore result in greater estimates of σo. We conducted three experiments to test this prediction. In Experiment 1, encoding variability was manipulated by presenting items for a fixed or variable (normally distributed) duration at study. In Experiment 2, we used an attentional manipulation whereby participants studied items while performing an auditory one-back task in which distractors were presented at fixed or variable intervals. In Experiment 3, participants studied stimuli with either high or low variance in word frequency. Across experiments, estimates of σo were unaffected by our attempts to manipulate encoding variability, even though the manipulations weakly affected subsequent recognition. Instead, estimates of σo tended to be positively correlated with estimates of the mean difference in strength between new and studied items ( d), as might be expected if σo generally scales with d. Our results show that it is surprisingly hard to successfully manipulate encoding variability, and they provide a signpost for others seeking to test the encoding variability hypothesis.

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