Influence of reinforcement and its omission on trial-by-trial changes of response bias in perceptual decision making.

Discrimination performance in perceptual choice tasks is known to reflect both sensory discriminability and nonsensory response bias. In the framework of signal detection theory, these aspects of discrimination performance are quantified through separate measures, sensitivity (d') for sensory discriminability and decision criterion (c) for response bias. However, it is unknown how response bias (i.e., criterion) changes at the single-trial level as a consequence of reinforcement history. We subjected rats to a two-stimulus two-response conditional discrimination task with auditory stimuli and induced response bias through unequal reinforcement probabilities for the two responses. We compared three signal-detection-theory-based criterion learning models with respect to their ability to fit experimentally observed fluctuations of response bias on a trial-by-trial level. These models shift the criterion by a fixed step (1) after each reinforced response or (2) after each nonreinforced response or (3) after both. We find that all three models fail to capture essential aspects of the data. Prompted by the observation that steady-state criterion values conformed well to a behavioral model of signal detection based on the generalized matching law, we constructed a trial-based version of this model and find that it provides a superior account of response bias fluctuations under changing reinforcement contingencies.

Benefits of multinomial processing tree models with discrete and continuous variables in memory research: an alternative modeling proposal to Juola et al. (2019).

Signal detection theory (SDT) and two-high threshold models (2HT) are often used to analyze accuracy data in recognition memory paradigms. However, when reaction times (RTs) and/or confidence levels (CLs) are also measured, they usually are analyzed separately or not at all as dependent variables (DVs). We propose a new approach to include these variables based on multinomial processing tree models for discrete and continuous variables (MPT-DC) with the aim to compare fits of SDT and 2HT models. Using Juola et al.'s (2019, Memory & Cognition, 47[4], 855-876) data we have found that including CLs and RTs reduces the standard errors of parameter estimates and accounts for interactions among accuracy, CLs, and RTs that classical versions of SDT and 2HT models do not. In addition, according to the simulations, there is an increase in the proportion of correct model selections when relevant DV are included. We highlight the methodological and substantive advantages of MPT-DC in the disentanglement of contributing processes in recognition memory.

Response uncertainty influences response bias in the sustained attention to response task: a signal detection theory perspective.

In the current investigation, we modified the high Go, low No-Go Sustained Attention to Response Task (SART) by replacing the single response on Go trials with a dual response to increase response uncertainty. In three experiments, a total of 80 participants completed either the original SART with no response uncertainty regarding the Go stimuli, or versions of the dual response SART in which response probabilities for the two possible responses to the Go stimuli varied from 0.9-0.1, 0.7-0.3, to 0.5-0.5. This resulted in a scale of increasing response uncertainty based on information theory to the Go stimuli. The probability of No-Go withhold stimuli was kept.11 in all experiments. Using the Signal Detection Theory perspective proposed by Bedi et al. (Psychological Research: 1-10, 2022), we predicted that increasing response uncertainty would result in a conservative response bias shift, noted by decreased errors of commission and slower response times to both Go and No-Go stimuli. These predictions were verified. The errors of commission in the SART may not be a measures of conscious awareness per se, but instead indicative of the level of participant trigger happiness-the willingness to respond quickly.

Борьба с ложной информацией при чрезвычайных ситуациях в области здравоохранения: практический инструментарий

В настоящем инструментарии описываются процедуры выявления и нейтрализации ложной информации в рамках следующих пяти этапов: выявление информационных сигналов, их проверка, оценка рисков, разработка мер реагирования и информационная работа с населением. Инструментарий можно рассматривать в качестве ценного источника информации для органов власти и других заинтересованных сторон; он помогает активно бороться с инфодемией и содействует распространению достоверных сведений и принятию обоснованных решений в области общественного здравоохранения.

On why we lack confidence in some signal-detection-based analyses of confidence.

Signal-detection theory (SDT) is one of the most popular frameworks for analyzing data from studies of human behavior - including investigations of confidence. SDT-based analyses of confidence deliver both standard estimates of sensitivity (d'), and a second estimate informed by high-confidence decisions - meta d'. The extent to which meta d' estimates fall short of d' estimates is regarded as a measure of metacognitive inefficiency, quantifying the contamination of confidence by additional noise. These analyses rely on a key but questionable assumption - that repeated exposures to an input will evoke a normally-shaped distribution of perceptual experiences (the normality assumption). Here we show, via analyses inspired by an experiment and modelling, that when distributions of experience do not conform with the normality assumption, meta d' can be systematically underestimated relative to d'. Our data highlight that SDT-based analyses of confidence do not provide a ground truth measure of human metacognitive inefficiency. We explain why deviance from the normality assumption is especially a problem for some popular SDT-based analyses of confidence, in contrast to other analyses inspired by the SDT framework, which are more robust to violations of the normality assumption.

Linking paranormal and conspiracy beliefs to illusory pattern perception through signal detection theory.

Previous research indicates that irrational beliefs (Paranormal beliefs & conspiracy theory endorsement) are associated with the perception of patterns in noise, but the previous findings do not conclusively describe this relationship. This study aims to disentangle the underlying parameters of this association by applying a signal detection theory approach, thus allowing to distinguish illusory pattern perception (false alarms) from perceptual sensitivity and response tendencies-while also taking base rate information into account. Results from a large sample (N = 723) indicate that paranormal beliefs relate to a more liberal response bias and a lower perceptual sensitivity, and that this relationship is driven by illusory pattern perception. Such a clear pattern could not be observed for conspiracy beliefs, for which the increase in false alarm rates was moderated by the base rate. The associations between irrational beliefs and illusory pattern perception were however less substantial compared to other sources of variance. Implications are discussed.

A zero-cost attention-based approach to promote cleaner streets: A Signal Detection Theory approach in Parisian streets.

In an effort to inform interventions targeting littering behaviour, we estimate how much a change in trash-bag colour increases trash can visibility in Paris. To that end, we applied standard Signal Detection techniques to test how much changing trash-bag colour affects subjects' trash can detection rates. In three pre-registered studies, we found that changing trash bag colour from grey to either red, green or blue considerably increases the perception of bins in British (tourist) and Parisian (resident) samples. We found that changing the bag colour from grey to blue increased visibility the most.

Incorporating Functional Response Time Effects into a Signal Detection Theory Model.

Signal detection theory (SDT; Tanner & Swets in Psychological Review 61:401-409, 1954) is a dominant modeling framework used for evaluating the accuracy of diagnostic systems that seek to distinguish signal from noise in psychology. Although the use of response time data in psychometric models has increased in recent years, the incorporation of response time data into SDT models remains a relatively underexplored approach to distinguishing signal from noise. Functional response time effects are hypothesized in SDT models, based on findings from other related psychometric models with response time data. In this study, an SDT model is extended to incorporate functional response time effects using smooth functions and to include all sources of variability in SDT model parameters across trials, participants, and items in the experimental data. The extended SDT model with smooth functions is formulated as a generalized linear mixed-effects model and implemented in the gamm4 R package. The extended model is illustrated using recognition memory data to understand how conversational language is remembered. Accuracy of parameter estimates and the importance of modeling variability in detecting the experimental condition effects and functional response time effects are shown in conditions similar to the empirical data set via a simulation study. In addition, the type 1 error rate of the test for a smooth function of response time is evaluated.

Recognition receiver operating characteristic asymmetry: Increased noise or information?

The recognition memory receiver operating characteristic (ROC) is typically asymmetric with a characteristic elevation of the left-hand portion. Whereas the unequal variance signal detection model (uvsd) assumes the asymmetry results because old item evidence is noisier than new item evidence, the dual process signal detection model (dpsd) assumes it results because old items convey more useful information than new items. To test these assumptions, the models were fit to old/new recognition data and their evidence parameters were used to predict performance on a novelty, three-alternative forced-choice (N3AFC) recognition task. Critically, under the uvsd model, increased old item variance (sigma) predicts poorer N3AFC performance, whereas under the dpsd model, increased recollection rates (Ro) predict better N3AFC performance. Hence, the asymmetry parameters of the two models make divergent predictions. In two experiments the dpsd model's predictions were supported, whereas the uvsd model yielded unpredicted (from that model's perspective) patterns. Through simulation it was also shown that the dpsd model predicted the uvsd model's mispredictions, which resulted because increases in old item noise markedly depress the upper portion of the ROC. Overall, the data demonstrate that increasing ROC asymmetry is not a function of increasingly noisier target evidence, but instead increasingly informative target evidence. These findings invalidate the uvsd model, which heretofore has been primarily supported by its post-hoc fitting ability, not its construct validity. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Drugs That Interact With Colchicine Via Inhibition of Cytochrome P450 3A4 and P-Glycoprotein: A Signal Detection Analysis Using a Database of Spontaneously Reported Adverse Events (FAERS).

BACKGROUND: Colchicine has a narrow therapeutic index. Its toxicity can be increased due to concomitant exposure to drugs inhibiting its metabolic pathway; these are cytochrome P450 3A4 (CYP3A4) and P-glycoprotein (P-gp). OBJECTIVE: To examine clinical outcomes associated with colchicine drug interactions using the spontaneous reports of the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). METHODS: We conducted a disproportionality analysis using FAERS data from January 2004 through June 2020. The reporting odds ratio (ROR) and observed-to-expected ratio (O/E) with shrinkage for adverse events related to colchicine's toxicity (ie, rhabdomyolysis/myopathy, agranulocytosis, hemorrhage, acute renal failure, hepatic failure, arrhythmias, torsade de pointes/QT prolongation, and cardiac failure) were compared between FAERS reports. RESULTS: A total of 787 reports included the combined mention of colchicine, an inhibitor of both CYP3A4 and P-gp drug, and an adverse event of interest. Among reports that indicated the severity, 61% mentioned hospitalization and 24% death. A total of 37 ROR and 34 O/E safety signals involving colchicine and a CYP3A4/P-gp inhibitor were identified. The strongest ROR signal was for colchicine + atazanavir and rhabdomyolysis/myopathy (ROR = 35.4, 95% CI: 12.8-97.6), and the strongest O/E signal was for colchicine + atazanavir and agranulocytosis (O/E = 3.79, 95% credibility interval: 3.44-4.03). CONCLUSION AND RELEVANCE: This study identifies numerous safety signals for colchicine and CYP3A4/P-gp inhibitor drugs. Avoiding the interaction or monitoring for toxicity in patients when co-prescribing colchicine and these agents is highly recommended.

ROC measures of memory accessibility.

Laming has recently proposed a way to measure the accessibility (as opposed to availability) of memories via recognition testing. His measure "Accessibility" is calculated by subtracting the hit rate and false alarm rate that fall at the point where the receiver-operating characteristic (ROC) curve's derivative is 1. I prove that, if one works within the framework of Unequal-Variance Signal Detection Theory (UVSDT), as Laming does, the measure "Accessibility" depends on the location of the response criterion (though always with a neutral likelihood ratio). Furthermore, I prove that the measure varies with the underlying variances of UVSDT regardless of which definition of bias (criterion or likelihood ratio) is used and, crucially, this holds even when the accuracy of discrimination performance or "sensitivity" (da) in UVSDT is constant. As such, from the standpoint of (at least) UVSDT, it is questionable whether or to what extent the new measure of "Accessibility" actually measures the accessibility of any memory.

Go-stimuli probability influences response bias in the sustained attention to response task: a signal detection theory perspective.

The sustained attention to response task (SART) is a popular measure in the psychology and neuroscience of attention. The underlying psychological cause for errors, in particular errors of commission, in the SART is actively disputed. Some researchers have suggested task-disengagement due to mind-wandering or mindlessness, and others have proposed strategic choices. In this study we explored an alternative perspective based on Signal Detection Theory, in which the high rate of commission errors in the SART reflects simply a shift in response bias (criterion) due to the high prevalence of Go-stimuli. We randomly assigned 406 participants to one of ten Go-stimuli prevalence rates (50%, 64%, 74%, 78%, 82%, 86%, 90%, 94%, 98% and 100%). As Go-stimuli prevalence increased reaction times to both Go and No-Go stimuli decreased, omission errors decreased and commission errors increased. These all were predicted from a hypothesized bias shift, but the findings were not compatible with some alternative theories of SART performance. These findings may have implications for similar tasks.

fullROC: An R package for generating and analyzing eyewitness-lineup ROC curves.

A police lineup is a procedure in which a suspect is surrounded by known-innocent persons (fillers) and presented to the witness for an identification attempt. The purpose of a lineup is to test the investigator's hypothesis that the suspect is the culprit, and the investigator uses the witness' identification decision and the associated confidence level to inform this hypothesis. Whereas suspect identifications provide evidence of guilt, filler identifications and rejections provide evidence of innocence. Despite the capacity of lineups to provide exculpatory information, past research has focused, almost exclusively, on inculpatory behaviors. We recently developed a method for incorporating all lineup outcomes in a single receiver operator characteristic (ROC) curve. The area under the full lineup ROC curve reflects the total capacity of a lineup procedure to discriminate guilty suspects from innocent suspects. Here, we introduce a Comprehensive R Archive Network (CRAN) package, fullROC, to support eyewitness researchers in using the full ROC approach to analyze lineup data. The fullROC package provides functions for adjusting identification rates, generating full ROC curves for lineup data, computing the area under the ROC curves (AUC), and statistically comparing the AUCs of different lineups. Using both simulated and empirical data, we illustrate the functionality of the fullROC CRAN package. In brief, the fullROC package provides a useful tool for eyewitness researchers to analyze lineup data using the full ROC method, which incorporates both the inculpatory and exculpatory information of eyewitness behaviors.

Does ROC asymmetry reverse when detecting new stimuli? Reinvestigating whether the retrievability of mnemonic information is task-dependent.

Recently, it has been suggested that the mnemonic information that underlies recognition decisions changes when participants are asked to indicate whether a test stimulus is new rather than old (Brainerd et al., 2021, Journal of Experimental Psychology: Learning Memory, and Cognition, advance online publication). However, some observations that have been interpreted as evidence for this assertion need not be due to mnemonic changes, but may instead be the result of conservative response strategies if the possibility of asymmetric receiver operating characteristics (ROCs) is taken into account. Conversely, recent findings in support of asymmetric ROCs rely on the assumption that the mnemonic information accessed by the decision-maker does not depend on whether an old or a new item is considered to be the target Kellen et al. (2021, Psychological Review 128[6], 1022-1050). Here, we aim to clarify whether there is such a difference in accessibility of mnemonic information by applying signal detection theory. To this end, we used two versions of a simultaneous detection and identification task in which we presented participants with two test stimuli at a time. In one version, the old item was the target; in the other, the new item was the target. This allowed us to assess differences in mnemonic information retrieved in the two tasks while taking possible ROC asymmetry into account. Results clearly indicate that there is indeed a difference in the accessibility of mnemonic information as postulated by (Brainerd et al., 2021, Journal of Experimental Psychology: Learning Memory, and Cognition, advance online publication).

Signal detection analysis of contingency assessment: Associative interference and nonreinforcement impact cue-outcome contingency sensitivity, whereas cue density affects bias.

In a signal detection theory approach to associative learning, the perceived (i.e., subjective) contingency between a cue and an outcome is a random variable drawn from a Gaussian distribution. At the end of the sequence, participants report a positive cue-outcome contingency provided the subjective contingency is above some threshold. Some researchers have suggested that the mean of the subjective contingency distributions and the threshold are controlled by different variables. The present data provide empirical support for this claim. In three experiments, participants were exposed to rapid streams of trials at the end of which they had to indicate whether a target outcome O1 was more likely following a target cue X. Interfering treatments were incorporated in some streams to impend participants' ability to identify the objective X-O1 contingency: interference trials (X was paired with an irrelevant outcome O2), nonreinforced trials (X was presented alone), plus control trials (an irrelevant cue W was paired with O2). Overall, both interference and nonreinforced trials impaired participants' sensitivity to the contingencies as measured by signal detection theory's d', but they also enhanced detection of positive contingencies through a cue density effect, with nonreinforced trials being more susceptible to this effect than interference trials. These results are explicable if one assumes interference and nonreinforced trials impact the mean of the associative strength distribution, while the cue density influences the threshold. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

A psychometric analysis of signal detection measures from ratings versus repeated and nonrepeated forced choices.

An analysis of the covariance and mean structure of signal detection measures for assessing recognition performance was conducted using data from ratings and repeated k-alternative forced choices (k-AFC). Measures were parameters of the unequal variance signal detection (UVSDT) and dual process signal detection (DPSDT) model and functions thereof, as well as area measures computed from the empirical receiver operating characteristic (ROC) curve. General sensitivity measures computed from UVSDT model parameters revealed reliabilities of about .70 based on 120 test trials. Doubling the number of test trials did not result in a substantial increase of reliability. Halving the number of test trials reduced the reliabilities to about .60. General sensitivity measures based on estimated parameters of the SDT models were slightly more favorable to measures based on the empirical ROC curve. General sensitivity measures resulting from different tasks exhibited similar reliabilities yet differed in size, with the measures from repeated k-AFC tasks being lower than those from the rating tasks. Considering the first selection of the k-AFC tasks only, assuming equal variance of the old and new familiarity distribution, resulted in sensitivity measures of similar size and reliability as those resulting from the rating tasks. Measures d' (familiarity-based sensitivity) and ρ (recollection probability) of the DPSDT model revealed reliabilities that were, in general, inacceptable low. This was particularly pronounced for the measures from the k-AFC tasks. The joint analysis of d' and ρ of the DPSDT model revealed that both measure the same latent construct. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

A Signal Detection Analysis of Olfactory Learning in 12-Month-Old 5xFAD Mice.

Although Alzheimer's disease is most often studied in terms of memory impairments, olfactory dysfunction begins in the early stages. We tested olfactory learning, sensitivity, and response bias using signal detection methods in 12-month-old male and female 5xFAD mice and their wildtype controls in the operant olfactometer. Odor detection was not reduced in the 5xFAD mice, but learning was, which was worse in female 5xFAD mice than in males. Female mice were more conservative in their response strategy. Signal detection analysis allows us to discriminate between cognitive and sensory deficits of male and female mouse models of AD.

Does scent attractiveness reveal women's ovulatory timing? Evidence from signal detection analyses and endocrine predictors of odour attractiveness.

Odour cues associated with shifts in ovarian hormones indicate ovulatory timing in females of many nonhuman species. Although prior evidence supports women's body odours smelling more attractive on days when conception is possible, that research has left ambiguous how diagnostic of ovulatory timing odour cues are, as well as whether shifts in odour attractiveness are correlated with shifts in ovarian hormones. Here, 46 women each provided six overnight scent and corresponding day saliva samples spaced five days apart, and completed luteinizing hormone tests to determine ovulatory timing. Scent samples collected near ovulation were rated more attractive, on average, relative to samples from the same women collected on other days. Importantly, however, signal detection analyses showed that rater discrimination of fertile window timing from odour attractiveness ratings was very poor. Within-women shifts in salivary oestradiol and progesterone were not significantly associated with within-women shifts in odour attractiveness. Between-women, mean oestradiol was positively associated with mean odour attractiveness. Our findings suggest that raters cannot reliably detect women's ovulatory timing from their scent attractiveness. The between-women effect of oestradiol raises the possibility that women's scents provide information about overall cycle fecundity, though further research is necessary to rigorously investigate this possibility.