Framework Of Detection Theory Research Articles

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.

Adaptive decision-making depends on pupil-linked arousal in rats performing tactile discrimination tasks.

Perceptual decision-making is a dynamic cognitive process and is shaped by many factors, including behavioral state, reward contingency, and sensory environment. To understand the extent to which adaptive behavior in decision-making is dependent on pupil-linked arousal, we trained head-fixed rats to perform perceptual decision-making tasks and systematically manipulated the probability of Go and No-go stimuli while simultaneously measuring their pupil size in the tasks. Our data demonstrated that the animals adaptively modified their behavior in response to the changes in the sensory environment. The response probability to both Go and No-go stimuli decreased as the probability of the Go stimulus being presented decreased. Analyses within the signal detection theory framework showed that while the animals' perceptual sensitivity was invariant, their decision criterion increased as the probability of the Go stimulus decreased. Simulation results indicated that the adaptive increase in the decision criterion will increase possible water rewards during the task. Moreover, the adaptive decision-making is dependent on pupil-linked arousal as the increase in the decision criterion was the largest during low pupil-linked arousal periods. Taken together, our results demonstrated that the rats were able to adjust their decision-making to maximize rewards in the tasks, and that adaptive behavior in perceptual decision-making is dependent on pupil-linked arousal.NEW & NOTEWORTHY Perceptual decision-making is a dynamic cognitive process and is shaped by many factors. However, the extent to which changes in sensory environment result in adaptive decision-making remains poorly understood. Our data provided new experimental evidence demonstrating that the rats were able to adaptively modify their decision criterion to maximize water reward in response to changes in the statistics of the sensory environment. Furthermore, the adaptive decision-making is dependent on pupil-linked arousal.

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.

Quantifying body ownership information processing and perceptual bias in the rubber hand illusion

Bodily illusions have fascinated humankind for centuries, and researchers have studied them to learn about the perceptual and neural processes that underpin multisensory channels of bodily awareness. The influential rubber hand illusion (RHI) has been used to study changes in the sense of body ownership — that is, how a limb is perceived to belong to one's body, which is a fundamental building block in many theories of bodily awareness, self-consciousness, embodiment, and self-representation. However, the methods used to quantify perceptual changes in bodily illusions, including the RHI, have mainly relied on subjective questionnaires and rating scales, and the degree to which such illusory sensations depend on sensory information processing has been difficult to test directly. Here, we introduce a signal detection theory (SDT) framework to study the sense of body ownership in the RHI. We provide evidence that the illusion is associated with changes in body ownership sensitivity that depend on the information carried in the degree of asynchrony of correlated visual and tactile signals, as well as with perceptual bias and sensitivity that reflect the distance between the rubber hand and the participant's body. We found that the illusion's sensitivity to asynchrony is remarkably precise; even a 50 ms visuotactile delay significantly affected body ownership information processing. Our findings conclusively link changes in a complex bodily experience such as body ownership to basic sensory information processing and provide a proof of concept that SDT can be used to study bodily illusions.

Revealing the influence of bias in a letter acuity identification task: A noisy template model

In clinical testing of visual acuity, it is often assumed that performance reflects sensory abilities and observers do not exhibit strong biases for or against specific letters, but this assumption has not been extensively tested. We re-analyzed single-letter identification data as a function of letter size, spanning the resolution threshold, for 10 Sloan letters at central and paracentral visual field locations. Individual observers showed consistent letter biases across letter sizes. Preferred letters were named much more often and others less often than expected (group averages ranged from 4% to 20% across letters, where the unbiased rate was 10%). In the framework of signal detection theory, we devised a noisy template model to distinguish biases from differences in sensitivity. When bias varied across letter templates the model fitted very well - much better than when sensitivity varied without bias. The best model combined both, having substantial biases and small variations in sensitivity across letters. The over- and under-calling decreased at larger letter sizes, but this was well-predicted by template responses that had the same additive bias for all letter sizes: with stronger inputs (larger letters) there was less opportunity for bias to influence which template gave the biggest response. The neural basis for such letter bias is not known, but a plausible candidate is the letter-recognition machinery of the left temporal lobe. Future work could assess whether such biases affect clinical measures of visual performance. Our analyses so far suggest very small effects in most settings.

Tears for pears: Influence of children's neophobia on categorization performance and strategy in the food domain.

Preschoolers’ neophobic dispositions mainly target fruits and vegetables. They received a great deal of attention in the past decades as these dispositions represent the main psychological barrier to dietary variety. Recently, children’s food neophobia has been found to be negatively correlated with their categorization performance (i.e., the accuracy to discriminate between food categories). We investigated categorization strategies among neophobic children, tendencies to favor one type of error over the other (misses over false alarms), in order to compensate for their poor categorization performance. To capture children’s categorization strategies, we used the Signal Detection Theory framework. A first experiment assessed 120 3-to-6-years old children’ sensitivity to discriminate between foods and nonfoods as well as their decision criterion (i.e., response strategy). In a second experiment, we manipulated the influence of food processing. The hypothesis was that food processing acts as a sign of human interventions that decreases uncertainty about edibility and thus promotes feelings of safety in the food domain. 137 children were tested on a food versus nonfood categorization task contrasting whole and sliced stimuli. In both experiments, increased levels of food neophobia were significantly associated with poorer categorization sensitivity and with a more conservative decision criterion (i.e., favoring “it is inedible” errors). Additionally, results from Experiment 2 revealed that food processing did not influence neophobic children, whereas their neophilic counterparts adopted a more liberal decision criterion for sliced stimuli than for whole stimuli. These findings are the first demonstration of a relationship between a decision criterion and food neophobia in young children. These results have strong implications for theories of food neophobia and laid the groundwork for designing novel types of food education interventions.

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" 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.

Toward a more comprehensive modeling of sequential lineups

Sequential lineups are one of the most commonly used procedures in police departments across the USA. Although this procedure has been the target of much experimental research, there has been comparatively little work formally modeling it, especially the sequential nature of the judgments that it elicits. There are also important gaps in our understanding of how informative different types of judgments can be (binary responses vs. confidence ratings), and the severity of the inferential risks incurred when relying on different aggregate data structures. Couched in a signal detection theory (SDT) framework, the present work directly addresses these issues through a reanalysis of previously published data alongside model simulations. Model comparison results show that SDT modeling can provide elegant characterizations of extant data, despite some discrepancies across studies, which we attempt to address. Additional analyses compare the merits of sequential lineups (with and without a stopping rule) relative to showups and delineate the conditions in which distinct modeling approaches can be informative. Finally, we identify critical issues with the removal of the stopping rule from sequential lineups as an approach to capture within-subject differences and sidestep the risk of aggregation biases.

Metacognition in functional cognitive disorder.

Functional cognitive disorder is common but underlying mechanisms remain poorly understood. Metacognition, an individual’s ability to reflect on and monitor cognitive processes, is likely to be relevant. Local metacognition refers to an ability to estimate confidence in cognitive performance on a moment-to-moment basis, whereas global metacognition refers to long-run self-evaluations of overall performance. Using a novel protocol comprising task-based measures and hierarchical Bayesian modelling, we compared local and global metacognitive performance in individuals with functional cognitive disorder. Eighteen participants with functional cognitive disorder (mean age = 49.2 years, 10 males) were recruited to this cross-sectional study. Participants completed computerized tasks that enabled local metacognitive efficiency for perception and memory to be measured using the hierarchical meta-d’ model within a signal detection theory framework. Participants also completed the Multifactorial Memory Questionnaire measuring global metacognition, and questionnaires measuring anxiety and depression. Estimates of local metacognitive efficiency were compared with those estimated from two control groups who had undergone comparable metacognitive tasks. Global metacognition scores were compared with the existing normative data. A hierarchical regression model was used to evaluate associations between global metacognition, depression and anxiety and local metacognitive efficiency, whilst simple linear regressions were used to evaluate whether affective symptomatology and local metacognitive confidence were associated with global metacognition. Participants with functional cognitive disorder had intact local metacognition for perception and memory when compared with controls, with the 95% highest density intervals for metacognitive efficiency overlapping with the two control groups in both cognitive domains. Functional cognitive disorder participants had significantly lower global metacognition scores compared with normative data; Multifactorial Memory Questionnaire-Ability subscale (t = 6.54, P < 0.0001) and Multifactorial Memory Questionnaire-Satisfaction subscale (t = 5.04, P < 0.0001). Mood scores, global metacognitive measures and metacognitive bias were not significantly associated with local metacognitive efficiency. Local metacognitive bias [β = −0.20 (SE = 0.09), q = 0.01] and higher depression scores as measured by the Patient Health Questionnaire-9 [β = −1.40 (SE = 2.56), q = 0.01] were associated with the lower global metacognition scores. We show that local metacognition is intact, whilst global metacognition is impaired, in functional cognitive disorder, suggesting a decoupling between the two metacognitive processes. In a Bayesian model, an aberrant prior (impaired global metacognition), may override bottom-up sensory input (intact local metacognition), giving rise to the subjective experience of abnormal cognitive processing. Future work should further investigate the interplay between local and global metacognition in functional cognitive disorder.

Information-theoretic signal detection theory.

Signal detection theory (SDT), the standard mathematical framework by which we understand how stimuli are classified into distributions such as signal or noise, is an essential part of the modern psychologist's toolkit. This article introduces some mathematical tools derived from information theory which allow surprisingly simple approximations to key quantities in SDT. The main idea is a lower bound on the probability of correct classification of a stimulus, as a function of information-theoretic properties of the generating distribution. This bound depends on three distinct factors, each of which can be quantified information-theoretically: (a) The prior uncertainty in the choice of generating distribution; (b) the inherent separability of the classes; and (c) the discrepancy between the observer's model of the class distributions and the "true" model. The bound is only a loose substitute for the conventional method for computing proportion correct (via integration) but generalizes readily to multiple dimensions and larger numbers of stimulus categories, where direct integration is computationally difficult. Moreover, unlike most conventional SDT formulae, this bound does not require Gaussian distributions. Most importantly, the information-theoretic signal detection theory (IT-SDT) framework sheds light on the way classification performance depends on the discrepancy between the observer's assumptions and those actually governing the environment. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

A cognitive model of delusion propensity through dysregulated correlation detection

BackgroundWe present a novel account of delusion propensity that integrates the roles of working memory (WM), decision criteria, and information gathering biases. This framework emphasises the role of aberrant correlation detection, which leads to the spurious perception of relationships between one's experiences. The frequency of such outcomes is moderated by the scaling of one's decision criteria which, for reasons discussed, must also account for WM capacity. The proposed dysregulated correlation detection account posits that propensity for delusional ideation is influenced by disturbances in this mechanism. MethodsHypotheses were tested using a novel task that required participants (N = 92) to identify correlation between binary manipulations of simple shapes, presented as sequential pairs. Decision criteria and correlation detection were assessed under a Signal Detection Theory framework, while WM capacity was assessed through the Automated Operation Span Task and delusion propensity was measured using the Peters Delusion Inventory. Structural equation modeling was conducted to evaluate the proposed model. ResultsConsistent with the central hypothesis, an interaction between decision criteria and WM was found to contribute significantly to delusion propensity through its effect on correlation detection accuracy. Greater delusion propensity was observed among participants with more liberal decision criteria, which was also in accordance with hypotheses. At the same time, the total effect of WM on delusion propensity was not found to be significant. ConclusionsThese findings provide preliminary support for the proposed dysregulated correlation detection account of propensity for delusional ideation.

How personal characteristics impact phishing susceptibility: The mediating role of mail processing

In the phishing email literature, recent researchers have given much attention to individual differences in phishing susceptibility from the perspective of the Big Five personality traits. Although the effectiveness and advantages of the phishing susceptibility measures in the signal detection theory (SDT) framework have been verified, the cognitive mechanisms that lead to individual differences in these measures remain unknown. The current study proposed and examined a theoretical path model to explore how the Big Five personality traits, related knowledge and experience and the cognitive processing of emails (i.e., mail elaboration) influence users’ susceptibility to phishing emails. A sample of 414 Chinese participants completed the 44-item Big Five Personality Inventory (BFI-44), Mail Elaboration Scale (MES), Web Experience Questionnaire, Experience with Electronic Mail Scale, Knowledge and Technical Background Test and a demographic questionnaire. The phishing susceptibility measures were calculated after the participants finished an email legitimacy task in a role-playing scenario. The results showed that the general profile of the “victim personality” included low conscientiousness, low openness and high neuroticism, and Internet experience and computer and web knowledge played an important role. All of these factors have significant indirect effects on phishing susceptibility by influencing mail elaboration. Moreover, the probabilities of checking for further information or deleting the email reflect the sensitivity of email judgment. These findings reveal the mediating role of cognitive processing between individual factors and phishing susceptibility. The theoretical implications of this study for the phishing susceptibility literature and its applications to phishing risk interventions or training programs are discussed.

Synergy of spatial frequency and orientation bandwidth in texture segregation.

Defining target textures by increased bandwidths in spatial frequency and orientation, we observed strong cue combination effects in a combined texture figure detection and discrimination task. Performance for double-cue targets was better than predicted by independent processing of either cue and even better than predicted from linear cue integration. Application of a texture-processing model revealed that the oversummative cue combination effect is captured by calculating a low-level summary statistic (), which describes the differential contrast energy to target and reference textures, from multiple scales and orientations, and integrating this statistic across channels with a winner-take-all rule. Modeling detection performance using a signal detection theory framework showed that the observers’ sensitivity to single-cue and double-cue texture targets, measured in units, could be reproduced with plausible settings for filter and noise parameters. These results challenge models assuming separate channeling of elementary features and their later integration, since oversummative cue combination effects appear as an inherent property of local energy mechanisms, at least for spatial frequency and orientation bandwidth-modulated textures.

Somatosensory Illusions Elicited by Sham Electromagnetic Field Exposure: Experimental Evidence for a Predictive Processing Account of Somatic Symptom Perception.

According to the predictive processing theory of somatic symptom generation, body sensations are determined by somatosensory input and central nervous predictions about this input. We examined how expectations shape predictions and consequently bodily perceptions in a task eliciting illusory sensations as laboratory analogue of medically unexplained symptoms. Using the framework of signal detection theory, the influence of sham Wi-Fi on response bias (c) and somatosensory sensitivity (d') for tactile stimuli was examined using the somatic signal detection task (SSDT). A healthy student sample (n = 83) completed the SSDT twice (sham Wi-Fi on/off) in a randomized order after watching a film that promoted adverse health effects of electromagnetic fields. When expecting a Wi-Fi signal to be present, participants showed a significantly more liberal response bias c (p = .010, ηp2 = 0.08) for tactile stimuli in the SSDT as evidence of a higher propensity to experience somatosensory illusions. No significant alteration of somatosensory sensitivity d' (p = .76, ηp2 < 0.002) was observed. Negative expectations about the harmfulness of electromagnetic fields may foster the occurrence of illusory symptom perceptions via alterations in the somatosensory decision criterion. The findings are in line with central tenets of the predictive processing account of somatic symptom generation. This account proposes a decoupling of percept and somatosensory input so that perception becomes increasingly dependent on predictions. This biased perception is regarded as a risk factor for somatic symptom disorders.

Human-algorithm teaming in face recognition: How algorithm outcomes cognitively bias human decision-making.

In face recognition applications, humans often team with algorithms, reviewing algorithm results to make an identity decision. However, few studies have explicitly measured how algorithms influence human face matching performance. One study that did examine this interaction found a concerning deterioration of human accuracy in the presence of algorithm errors. We conducted an experiment to examine how prior face identity decisions influence subsequent human judgements about face similarity. 376 volunteers were asked to rate the similarity of face pairs along a scale. Volunteers performing the task were told that they were reviewing identity decisions made by different sources, either a computer or human, or were told to make their own judgement without prior information. Replicating past results, we found that prior identity decisions, presented as labels, influenced volunteers’ own identity judgements. We extend these results as follows. First, we show that the influence of identity decision labels was independent of indicated decision source (human or computer) despite volunteers’ greater distrust of human identification ability. Second, applying a signal detection theory framework, we show that prior identity decision labels did not reduce volunteers’ attention to the face pair. Discrimination performance was the same with and without the labels. Instead, prior identity decision labels altered volunteers’ internal criterion used to judge a face pair as “matching” or “non-matching”. This shifted volunteers’ face pair similarity judgements by a full step along the response scale. Our work shows how human face matching is affected by prior identity decision labels and we discuss how this may limit the total accuracy of human-algorithm teams performing face matching tasks.

Target Detection in Passive Radar Under Noisy Reference Channel: A New Threshold-Setting Strategy

In the detection theory framework, it is customary to assign a bound to the false alarm probability and to attempt to maximize the detection probability subject to this constraint. In the problem of moving target detection in passive radar with a noisy reference channel, we formulate a detection problem as a composite hypothesis-testing problem and solve it with the likelihood ratio test (LRT) principle, which is known as generalized LRT in the electrical engineering works of literature. In such a problem, we show that any uncertainty in the value of the direct signal-to-noise ratio of the reference channel, abbreviated as $\text{DNR}_r$ , can result in excessive false alarm probability of the proposed noisy-reference-channel-based detector in the low- $\text{DNR}_r$ regime. To facilitate efficient operation under uncertainty in $\text{DNR}_r$ , we propose a new threshold-setting strategy to adjust the level of the proposed detector. Through extensive Monte–Carlo simulations, we examine the above problem and investigate the efficiency of the proposed threshold-setting strategy. Besides, we apply the framework of the kernel theory to the target detection problem of a noisy and ideal reference channel passive radar to propose two new detectors. As such, we replace the inner products of the proposed tests with appropriate polynomial kernel functions allowing for richer feature space to be deployed in the detection, achieving better detection performance. In this case, our detection performance results show that the kernelized detectors offer more that 1-dB signal-to-noise ratio gain as compared to their conventional counterparts.

Email phishing and signal detection: How persuasion principles and personality influence response patterns and accuracy

Phishing is a social engineering tactic where a malicious actor impersonates a trustworthy third party with the intention of tricking the user into divulging sensitive information. Previous social engineering research in a real-world setting has shown an interaction between the personality of the target and the persuasion principle used. This study investigated whether this interaction is present in the realm of email phishing. Additionally, a signal detection theory framework was used to evaluate how the various persuasion principles influence accuracy, sensitivity (d’), and response criterion placement. A personality inventory and an email identification task (phishing or legitimate) were used. These data support previous findings that high extroversion is predictive of increased susceptibility to phishing attacks. The various persuasions principles elicited diverse response criterions and sensitivities, though all investigated persuasion principles resulted in a liberal decision criterion, except one. These findings are interpreted and discussed.

Modeling Closed Captioning Subjective Quality Assessment by Deaf and Hard of Hearing Viewers

Closed Captioning (CC) is a service primarily designed for deaf and hard of hearing (D/HoH) viewers. The CC translates spoken speech into text for television or film screen display. The quality assessment methods for live captioning are limited to quantitative measures, while the viewers are still dissatisfied with the current quality. One method to improve the current quality assessment procedure is to include D/HoH viewers in the evaluation procedure for their subjective assessment input. However, it could be costly and impractical to perform evaluations for the entire broadcasted shows. Therefore, it would be helpful to model subjective assessments that could replicate and predict human decisions. In this article, we report on a model of probabilities of D/HoH viewer assessment decisions for CC quality factors based on actual user preferences. An online survey was designed and conducted to collect assessment data for 22 error variation samples from four quality factors: delay, speed, missing words, and paraphrasing of captions. The results are analyzed using the signal detection theory framework to create decision probability models for D/HoH viewers.

An ecological framework of neophobia: from cells to organisms to populations.

Neophobia is the fear of novel stimuli or situations. This phenotype has recently received much ecological attention, primarily in the context of decision making. Here, we explore neophobia across biological levels of organisation, first describing types of neophobia among animals and the underlying causes of neophobia, highlighting high levels of risk and uncertainty as key drivers. We place neophobia in the framework of Error Management Theory and Signal Detection Theory, showing how increases in overall risk and uncertainty can lead to costly non-responses towards novel threats unless individuals lower their response threshold and become neophobic. We then discuss how neophobic behaviour translates into population and evolutionary consequences before introducing neophobia-like processes at the cellular level, where some phenomena such as allergy and autoimmunity can parallel neophobic behaviour. Finally, we discuss neophobia attenuation, considering how a sudden change in the environment from dangerous to safe can lead to problematic over-responses (i.e. the 'maladaptive defensive carry-over' hypothesis), and discuss treatment methods for such over-responses. We anticipate that bridging the concept of neophobia with a process-centered perspective can facilitate a transfer of insight across organisational levels.

Acoustic noise and vision differentially warp the auditory categorization of speech.

Speech perception requires grouping acoustic information into meaningful linguistic-phonetic units via categorical perception (CP). Beyond shrinking observers' perceptual space, CP might aid degraded speech perception if categories are more resistant to noise than surface acoustic features. Combining audiovisual (AV) cues also enhances speech recognition, particularly in noisy environments. This study investigated the degree to which visual cues from a talker (i.e., mouth movements) aid speech categorization amidst noise interference by measuring participants' identification of clear and noisy speech (0 dB signal-to-noise ratio) presented in auditory-only or combined AV modalities (i.e., A, A+noise, AV, AV+noise conditions). Auditory noise expectedly weakened (i.e., shallower identification slopes) and slowed speech categorization. Interestingly, additional viseme cues largely counteracted noise-related decrements in performance and stabilized classification speeds in both clear and noise conditions suggesting more precise acoustic-phonetic representations with multisensory information. Results are parsimoniously described under a signal detection theory framework and by a reduction (visual cues) and increase (noise) in the precision of perceptual object representation, which were not due to lapses of attention or guessing. Collectively, findings show that (i) mapping sounds to categories aids speech perception in "cocktail party" environments; (ii) visual cues help lattice formation of auditory-phonetic categories to enhance and refine speech identification.