scholarly journals A Hierarchical Unequal-Variance Signal Detection Model for Binary Data

2021 ◽  
Author(s):  
Martin Lages
Keyword(s):  
Signal Detection ◽  
Binary Data ◽  
Population Level ◽  
Unequal Variance ◽  
Binary Responses ◽  
Detection Model ◽  
Sampling Distributions ◽  
Equal Variance ◽  
Gaussian Signal ◽  
Multiple Conditions

Gaussian signal detection models with equal variance are typically used for detection and discrimination data whereas models with unequal variance rely on data with multiple response categories or multiple conditions. Here a hier- archical signal detection model with unequal variance is suggested that requires only binary responses from a sample of participants. Introducing plausible constraints on the sampling distributions for sensitivity and response criterion makes it possible to estimate signal variance at the population level. This model was applied to existing data from memory and reasoning tasks and the results suggest that parameters can be reliably estimated, allowing a direct comparison of signal detection models with equal- and unequal-variance.

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.

scholarly journals Evidence for the Contribution of A Threshold Retrieval Process to Semantic Memory

2017 ◽  
Vol 70 (10) ◽  
pp. 2026-2047 ◽  
Author(s):  
Maria Kempnich ◽  
Josephine A. Urquhart ◽  
Akira R. O'Connor ◽  
Chris J.A. Moulin
Keyword(s):  
Signal Detection ◽  
Semantic Memory ◽  
Target Identification ◽  
Dual Process ◽  
Semantic Retrieval ◽  
Retrieval Process ◽  
Operating Characteristics ◽  
Semantic Task ◽  
Unequal Variance ◽  
Detection Model

It is widely held that episodic retrieval can recruit two processes: a threshold context retrieval process (recollection) and a continuous signal strength process (familiarity). Conversely the processes recruited during semantic retrieval are less well specified. We developed a semantic task analogous to single-item episodic recognition to interrogate semantic recognition receiver-operating characteristics (ROCs) for a marker of a threshold retrieval process. We fitted observed ROC points to three signal detection models: two models typically used in episodic recognition (unequal variance and dual-process signal detection models) and a novel dual-process recollect-to-reject (DP-RR) signal detection model that allows a threshold recollection process to aid both target identification and lure rejection. Given the nature of most semantic questions, we anticipated the DP-RR model would best fit the semantic task data. Experiment 1 (506 participants) provided evidence for a threshold retrieval process in semantic memory, with overall best fits to the DP-RR model. Experiment 2 (316 participants) found within-subjects estimates of episodic and semantic threshold retrieval to be uncorrelated. Our findings add weight to the proposal that semantic and episodic memory are served by similar dual-process retrieval systems, though the relationship between the two threshold processes needs to be more fully elucidated.

Fechner's Law: Where Does the Log Transform Come From?

2010 ◽  
Vol 23 (2) ◽  
pp. 155-171 ◽  
Author(s):  
Donald Laming
Keyword(s):  
General Theory ◽  
Signal Detection ◽  
Degrees Of Freedom ◽  
Weber’S Law ◽  
Sensory Discrimination ◽  
Weber's Law ◽  
Detection Model ◽  
Physical Magnitude ◽  
Equal Variance ◽  
Internal Sensation

AbstractThis paper looks at Fechner's law in the light of 150 years of subsequent study. In combination with the normal, equal variance, signal-detection model, Fechner's law provides a numerically accurate account of discriminations between two separate stimuli, essentially because the logarithmic transform delivers a model for Weber's law. But it cannot be taken to be a measure of internal sensation because an equally accurate account is provided by a χ2 model in which stimuli are scaled by their physical magnitude. The logarithmic transform of Fechner's law arises because, for the number of degrees of freedom typically required in the χ2 model, the logarithm of a χ2 variable is, to a good approximation, normal. This argument is set within a general theory of sensory discrimination.

scholarly journals ROC Asymmetry Is Not Diagnostic of Unequal Residual Variance in Gaussian Signal Detection Theory

2021 ◽  
Author(s):  
Maximilian M. Rabe ◽  
D. Stephen Lindsay ◽  
Reinhold Kliegl
Keyword(s):  
Signal Detection ◽  
Signal Detection Theory ◽  
Discrimination Performance ◽  
Statistical Error ◽  
Detection Theory ◽  
Formal Proof ◽  
Residual Variance ◽  
Operating Characteristics ◽  
Equal Variance ◽  
Gaussian Signal

Signal detection theory (SDT) is used to analyze yes/no judgment accuracy in many research domains of psychology. SDT yields separate estimates for response bias/criterion (c) and for sensitivity/discriminability (d'). Discrimination performance can be displayed in Receiver Operating Characteristics (ROCs) plotting hit and false alarm rates at various levels of confidence. We provide formal proof and simulations showing that asymmetric ROCs in Gaussian SDT are not exclusively diagnostic of unequal residual variance but may as well result from equal-variance models with c and d' systematically varying across subjects and/or items. Falsely attributing zROC slopes to unequal residual variance while neglecting true group-level variability introduces systematic and unsystematic statistical error. We show that ordinal regression models minimize such errors while estimating all SDT parameters and statistical criteria in a single model.

scholarly journals A direct test of the unequal-variance signal detection model of recognition memory

2007 ◽  
Vol 14 (5) ◽  
pp. 858-865 ◽  
Author(s):  
Laura Mickes ◽  
John T. Wixted ◽  
Peter E. Wais
Keyword(s):  
Recognition Memory ◽  
Signal Detection ◽  
Direct Test ◽  
Unequal Variance ◽  
Detection Model

scholarly journals Pre-experimental Familiarization Increases Hippocampal Activity for Both Targets and Lures in Recognition Memory: An fMRI Study

2011 ◽  
Vol 23 (12) ◽  
pp. 4164-4173 ◽  
Author(s):  
Greig I. de Zubicaray ◽  
Katie L. McMahon ◽  
Lydia Hayward ◽  
John C. Dunn
Keyword(s):  
Signal Detection ◽  
Dual Process ◽  
Memory Strength ◽  
High Confidence ◽  
Unequal Variance ◽  
Detection Model ◽  
Parahippocampal Cortex ◽  
Hippocampal Activity ◽  
Model Account ◽  
List Discrimination

In the present study, items pre-exposed in a familiarization series were included in a list discrimination task to manipulate memory strength. At test, participants were required to discriminate strong targets and strong lures from weak targets and new lures. This resulted in a concordant pattern of increased “old” responses to strong targets and lures. Model estimates attributed this pattern to either equivalent increases in memory strength across the two types of items (unequal variance signal detection model) or equivalent increases in both familiarity and recollection (dual process signal detection [DPSD] model). Hippocampal activity associated with strong targets and lures showed equivalent increases compared with missed items. This remained the case when analyses were restricted to high-confidence responses considered by the DPSD model to reflect predominantly recollection. A similar pattern of activity was observed in parahippocampal cortex for high-confidence responses. The present results are incompatible with “noncriterial” or “false” recollection being reflected solely in inflated DPSD familiarity estimates and support a positive correlation between hippocampal activity and memory strength irrespective of the accuracy of list discrimination, consistent with the unequal variance signal detection model account.

Constructing and model-fitting receiver operator characteristics using continuous data.

2018 ◽  
Author(s):  
Josephine Ann Urquhart ◽  
Akira O'Connor
Keyword(s):  
Signal Detection ◽  
Least Squares Method ◽  
Model Fitting ◽  
Simulated Data ◽  
Continuous Variable ◽  
Continuous Data ◽  
Model Parameters ◽  
Operating Characteristics ◽  
Unequal Variance ◽  
Discrete Discrimination

Receiver operating characteristics (ROCs) are plots which provide a visual summary of a classifier’s decision response accuracy at varying discrimination thresholds. Typical practice, particularly within psychological studies, involves plotting an ROC from a limited number of discrete thresholds before fitting signal detection parameters to the plot. We propose that additional insight into decision-making could be gained through increasing ROC resolution, using trial-by-trial measurements derived from a continuous variable, in place of discrete discrimination thresholds. Such continuous ROCs are not yet routinely used in behavioural research, which we attribute to issues of practicality (i.e. the difficulty of applying standard ROC model-fitting methodologies to continuous data). Consequently, the purpose of the current article is to provide a documented method of fitting signal detection parameters to continuous ROCs. This method reliably produces model fits equivalent to the unequal variance least squares method of model-fitting (Yonelinas et al., 1998), irrespective of the number of data points used in ROC construction. We present the suggested method in three main stages: I) building continuous ROCs, II) model-fitting to continuous ROCs and III) extracting model parameters from continuous ROCs. Throughout the article, procedures are demonstrated in Microsoft Excel, using an example continuous variable: reaction time, taken from a single-item recognition memory. Supplementary MATLAB code used for automating our procedures is also presented in Appendix B, with a validation of the procedure using simulated data shown in Appendix C.

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