A signal detection theory meta-analysis of psychological inoculation against misinformation.

Challenging the dominant view of learning as abstract content internalization, Situated Learning Theory (SLT) reframed learning as a socially mediated, context-bound process and gained influence by aligning with late-twentieth-century commitments to authenticity, participation, and identity. This paper examines the inferential structure of Situated Learning Theory as a theoretical framework, arguing that despite its descriptive and sociocultural appeal, it cannot sustain empirical scrutiny, with references to curricular, policy, professional development, and workplace contexts serving only to illustrate the downstream consequences of adopting a theory without explicit inferential constraints. Key terms such as authentic context and community of practice remain analytically vague and difficult to operationalize. The analysis applies formal tools from Signal Detection Theory, Bayesian model comparison, and information theory to identify three criteria that any viable learning theory must satisfy: (1) a rule for distinguishing learning from non-learning, (2) a mechanism for penalizing predictive error through likelihood updating, and (3) a basis for comparing competing explanations by parsimony and explanatory yield. These standards reveal that SLT offers no testable boundary conditions, no criterion for identifying failure, and no mechanism for theoretical revision. Like the Ptolemaic system that expanded epicycles to preserve its assumptions, SLT accommodates all outcomes but predicts none. This critique does not dispute the interpretive or descriptive contributions of sociocultural research, but evaluates SLT’s scientific standing in contexts where theories are expected to support discrimination, prediction, and revision. As a result, the paper positions its critique not as a terminal verdict, but as an invitation to recast SLT into a form that can be tested, refined, and applied in instructional contexts—preserving its sociocultural insights while restoring empirical accountability. This approach redefines theory advancement in education as the capacity to generate, test, and refine explanations of learning across contexts.

On the uncertainty associated with using a signal detection theory model to analyze data from forensic black-box studies.

The reliability of multisensory integration in enhancing behavioral performance.

Inattentional noise leads to subjective color uniformity across the visual field.

ABSTRACT Introduction Metacognition can be negatively affected after moderate to severe traumatic brain injury (TBI). This study utilized functional magnetic resonance imaging (fMRI) to identify patterns of neural activation associated with metacognitive confidence judgments after moderate to severe TBI. Method Twenty-four adults with chronic moderate to severe TBI and 10 non-injured adults (nonTBIs) were scanned while performing a meta-memory recognition task. Metacognitive accuracy was quantified using a signal detection theory approach. Activation present during the metamemory task, as well as group differences in activation correlated to metacognitive accuracy were identified. Results Adults with TBI did not differ in their metacognitive accuracy from nonTBIs; however, differences in neural recruitment were noted. Adults with TBI demonstrated stronger relationships between metacognitive performance and activation in the left angular gyrus and left supramarginal gyrus, while nonTBIs showed stronger associations in the left superior parietal lobule, left lateral occipital cortex, left precuneus, and left occipital fusiform gyrus. Conclusions The findings suggest that different neural resources may be used after TBI to facilitate metacognitive processing and to modulate the direction of confidence accuracy. Particularly, greater activation in the angular gyrus may reflect strategies to rely on monitoring processes and enhanced memory to facilitate metacognitive processing post-injury.

In military operations, the ability to detect, identify, and respond to auditory alerts in complex and dynamic environments is crucial for safety and mission success. Typical alert designs, however, often fail to account for characteristics of noisy and cognitively demanding conditions, so that the levels of alerts required to support desired levels of performance are minimized. To redress those shortcomings, we developed a pair of alerts, one having consonant harmony ("friendly"), the other dissonant harmony ("enemy"). Those alerts were placed strategically within the spectrum of the masker to minimize masking while maintaining high levels of detection and discrimination performance. The detectability and discriminability of the "friendly" and "enemy" alerts was assessed as a function of signal-to-noise-masker ratio (S/N) while employing a masker consisting of continuous military "truck noise." Both of the alerts occupied a narrow spectral region within the masker around 500-Hz. Subjects (n = 20) performed an auditory detection/discrimination task in isolation or with a simultaneous visual "N-Back task." The N-Back task was also run in isolation. The auditory task employed a free-response vigilance paradigm with underlying temporal "trials" that were unknown to the subjects. They experienced temporal uncertainty regarding when an alert might be added to the masker. This approach afforded measures of "hit" and "false-alarm" rates and the computation of bias-free measures of sensitivity (d'). Trials were blocked by S/N with values of S/N visited via descending and ascending series. Stimuli were presented at an overall level of 70 dB SPL (in the absence of alerts) via Sennheiser HD 280 headphones. Values of d' (sensitivity) indicated that high levels of detection performance were obtained despite the harmonic "friendly" and inharmonic "enemy" alerts occupying a common spectral locus. That outcome likely occurred because subjects discriminated the alerts on the basis of perceived consonance or dissonance. Values of ß (response bias) revealed that subjects adopted conservative response criteria. Turning to discrimination performance, differences between obtained values of p(c) and p(c)max also indicated that subjects did not adopt neutral criteria. In the presence of a simultaneous, visual N-Back task (dual-task condition), auditory detection and discrimination performance was not degraded. In contrast, N-Back performance was poorer in the dual-task condition than when it was measured in isolation. The results establish "proof of concept" regarding our approach to evaluating detection and discrimination of auditory alerts within a situationally realistic vigilance paradigm. The findings reveal the advantages of employing a Theory of Signal Detection (TSD)-based free-response paradigm to evaluate human performance in such a setting. In addition, the results highlight the potential advantages of employing alerts tailored to the specific spectral profile of the ambient acoustic environment. Overall, our findings can be applied to enhance both the performance and evaluation of practitioners who must respond appropriately to critical alerts in high-consequence settings. The potential enhancements extend beyond military applications, for example, to situations in which clinicians must monitor multiple metrics of patient status in environments with potentially distracting auditory and visual information.

To interact with the world effectively, the human visual system must extract meaningful features from visual scenes. One key feature are edges, luminance or texture discontinuities in two-dimensional (2D) images that often correspond to object boundaries in three-dimensional scenes. Edge sensitivity has traditionally been studied with well-controlled stimuli and binary choice tasks, but it is unclear how well these insights transfer to real-world behavior. Recent studies have extended this approach using natural images but typically retained binary button presses. In this study, we extend the approach further and ask observers (N = 20) to trace edges in natural scenes, presented with or without 2D visual noise. To quantify edge detection performance, we use a signal detection theory–inspired approach. Participants' edge traces in the noise-free condition serve as an individualized “ground-truth” or signal, used to categorize edge traces from noise conditions into hits, false alarms, misses, and correct rejections. Observers produce remarkably consistent edge traces across conditions. Noise interference patterns mirror results from traditional edge sensitivity studies, especially for edges with spectral properties similar to natural scenes. This suggests that insights from controlled paradigms can transfer to naturalistic ones. We also examined edge traces to identify which image features drive edge perception, using interindividual variability as a pointer to relevant features. We conclude that line drawings are a powerful tool to investigate edge sensitivity and potentially other aspects of visual perception, enabling nuanced exploration of real-world visual behavior with few experimental trials.

In recent years, researchers have become increasingly interested in cotton, a precious cash crop everywhere from Pakistan to China to Texas, is crucial for the textile industry and agricultural economies around the world. But its production is continuously threatened by a diversity of plant diseases, which can be triggered by pathogens such as bacteria, fungi and viruses. The conventional diagnostic approaches to confirm these diseases are generally not sensitive, time-consuming, and at the same time laborious. In recent times, network biology approaches have emerged as a robust platform in predicting disease associations and simulating diseases interplay in plants. The purpose of this paper is to investigate the application of complex network theory in cotton plant disease detection and control, particularly for the integration with machine learning and artificial intelligence. However, as many techniques are promising, only a few real-time interpretable field deployable models exist up to now to be used in local agricultural systems, especially in developing countries such as Pakistan. From this perspective, I propose that lightweight hybrid models should be developed which integrating network theory with spatiotemporal environment data, and open-access database would get higher availability. I believe there is a big opportunity in having a mobile-based decision support system, which is based on the graph learning model and can be used by farmers. This survey recommends that although substantial contributions have been achieved, there are still research opportunities for integrating complex networks into realistic and scalable systems regarding plant disease detection. This paper aims to explore the application of complex network theory in cotton plant disease detection and management, focusing on integrating with machine learning and artificial intelligence. However, despite many promising methods, there is still a lack of real-time, interpretable, and field-deployable models that can be used in local agricultural systems, particularly in developing countries like Pakistan. Based on this analysis, I recommend the development of lightweight hybrid models that combine network theory with spatiotemporal environmental data, and the use of open-access datasets to enable broader adoption. I also see great potential in integrating mobile-based decision support systems powered by graph learning models for use by farmers directly. This review concludes that while significant progress has been made, the integration of complex networks into practical, scalable solutions for plant disease detection remains an open and valuable research direction.

Teachers shape classroom friendships through everyday instructional decisions, such as grouping strategies and seat arrangement. However, teachers’ perceptions of peer relationships may not align with students’ perceptions, which would hinder the effectiveness of instructional designs. The current study aims to explore the degree of teacher–student agreement in perceiving classroom friendships from the lens of signal detection theory and to explore variations in agreement across individuals and dyads. This study included a total of 5,852 dyads (i.e., student pairs) formed by 155 students in four tenth-grade classrooms. Results showed that the teacher–student agreement was at a mid-low level overall (Jaccard index = .32). Specifically, teachers only recognized 41% of friendships identified by students, although they correctly rejected 96% of non-friend relationships. Differences in agreement could be explained by characteristics of individual students and similarities within dyads regarding gender, unsociability with peers, and relatedness to teachers. Findings of this study provide in-depth understandings of the alignment and discrepancy between students’ and teachers’ perspectives on the classroom ecology, which suggest that researchers should be cautious in informant selection for peer relationship assessment. Educational practitioners should also enhance their awareness and provide individualized support to improve adolescents’ classroom social experiences.

Assessing decision thresholds in primary school students using signal detection theory: validating an adapted version of the beads task.

The dark side of the white coat: startle potentiation and memory bias in patients with illness-related anxiety and controls - A cross-sectional psychophysiological study.

How do people know when they are right? Confidence judgments - the ability to assess the correctness of one's own decisions - are a key aspect of human metacognition. This self-evaluative act plays a central role in learning, memory, consciousness, and group decision-making. In this paper, I reframe metacognition as a structured exchange of information between stimulus, decision-maker (the actor), and confidence judge (the rater), akin to a multi-agent communication system. Within this framework, the actor aims to resolve stimulus uncertainty, while the rater seeks to infer the accuracy of the actor's response. Applying techniques from information theory, I develop three novel measures of metacognitive efficiency: meta- , meta- , and meta- . These indices are derived from entropy and divergence principles, and quantify how effectively confidence judgments transmit information about both external stimuli and internal decisions. Simulations show that these measures possess several advantages over traditional signal detection theory metrics such as meta- and the M-ratio, including more interpretable scaling, robustness to performance imbalances, and sensitivity to structural constraints. By formalizing metacognitive sensitivity as an information-processing problem, this framework offers a unified, theoretically grounded approach to studying confidence and sheds light on the sources of metacognitive inefficiency across individuals and contexts.

Using implicit bias to enhance student learning of signal detection theory.

Abstract Signal detection theory suggests animals adjust signals to minimize response errors while receivers respond to signals that are easily detected and discriminated from background noise. Anthropogenic noise occurs in the same frequencies that most vertebrate animals use to communicate, probably increasing communication errors such as missed signal detection. Previous research focused on signal modification in the presence of noise, whereas here we assess communication error rates and whether communication errors are influenced by anthropogenic noise. We measured background noise and conducted playback experiments with northern cardinals (Cardinalis cardinalis) at 29 sites along a relatively quiet to loud anthropogenic noise gradient ranging from 51 to 74 dB (±6). Through playback experiments, we observed that males committed communication errors 45% of the time with 55% correct vocal responses, but had 84% correct responses to signals with movement behaviour, therefore compensating for a lack of vocal responses. Contrary to our predictions, there was not an association between anthropogenic noise and communication error rates. Future research should consider how and when noise affects receiver errors for a more holistic understanding of communication in the presence of background noise.

Towards developing fake and satire news detection policies using component-based SEM and interpersonal detection theory

Estimating quantitative cognitive models from data is a staple of modern psychological science, but can be difficult and inefficient. Particle Metropolis within Gibbs (PMwG) is a robust and efficient sampling algorithm that supports model estimation in a hierarchical Bayesian framework. This tutorial shows how cognitive modeling can proceed efficiently using pmwg, a new open-source package for the R language. We step through implementing the pmwg package with simple signal detection theory models, to more complex cognitive models in which two tasks are jointly modeled together. Through this process, we also address questions of model adequacy and model selection, which must be solved in order to answer meaningful psychological questions. PMwG, and the pmwg package, has the potential to move the field of psychology ahead in new and interesting directions, and to resolve questions that were once too hard to answer with previously available sampling methods.

The first major laboratory studies of vigilance by Mackworth in 1948 and later revealed a decline in the probability of detecting brief targets as the time on task increases. Whether referred to as a vigilance decrement or something else (e.g., a failure of sustained attention), because such failures have great applied significance (e.g., in road safety, radiology, air-traffic control, civil defense, etc.), understanding the vigilance decrement and discovering ways to avoid it are important goals for psychological science. The purpose of this historical review is to provide a picture of the extensive scientific literature exploring the nature(s) of the vigilance decrement, with an emphasis, but not exclusionary focus, on the signal detection theory framework. Beginning in the early 1960s, researchers started to interpret this decline in target detections using signal detection theory, wherein a decrease in detections can be attributed to a decrease in sensitivity of the observer to the difference between targets and non-targets, a conservative shift in the observer's response criterion, or, of course, both. Some early investigators suggested that which of these two causes of the decline in detections is operating may depend on the rate at which events (targets and non-targets combined) are presented: When the event rate is slow, criterion shifts dominate detection failures, whereas declines in sensitivity become more pronounced as event rates increase. Nevertheless, the contribution of sensitivity declines has been recently challenged. One source of the challenge is the relatively low false-alarm rate in so many studies on the vigilance decrement. Another is the possibility that for a variety of reasons, the observer in a relatively long vigil may stop attending to the source of the task-relevant signals. Some recommendations are offered based on our reading of the ~75 years of vigilance research.

One of the key aspects of Shannon theory is that it provides guidance for designing the most efficient systems, such as minimizing errors and clarifying the limits of coding. This theory has seen great developments in the 50 years since 1948. It has played a vital role in enabling the development of modern ultra-fast, stable, and highly dependable information and communication systems. Shannon theory is supported by statistical communication theories such as detection and estimation theory. The theory of communication systems that transmit Shannon information using quantum media is called quantum Shannon information theory, and research began in the 1960s. The theoretical formulation comparable to conventional Shannon theory has been completed. Its important role is to suggest that application of quantum effects will surpass existing communication performance. It would be meaningless if performance, efficiency, and utility were to deteriorate due to quantum effects, even if a certain new function is given. This paper suggests that there are various limitations to utilizing quantum Shannon information theory to benefit real-world communication systems and presents a theoretical framework for achieving the ultimate goal. Finally, we present the perfect secure cipher that overcomes the Shannon impossibility theorem without degrading communication performance and sensors as an example.

Outlier detection is a critical task in the intelligent operation and maintenance (O&M) of transportation equipment, as it helps ensure the safety and reliability of systems like high-speed trains, aircraft, and intelligent vehicles. Nearest neighbor-based detectors generally offer good interpretability, but often struggle with complex data scenarios involving diverse data distributions and various types of outliers, including local, global, and cluster-based outliers. Moreover, these methods typically rely on predefined contamination, which is a critical parameter that directly determines detection accuracy and can significantly impact system reliability in O&M environments. In this paper, we propose a novel chain-based theory for outlier detection with the aim to provide an interpretable and transparent solution for fault detection. We introduce two methods based on this theory: Cascaded Chain Outlier Detection (CCOD) and Parallel Chain Outlier Detection (PCOD). Both methods identify outliers through sudden increases in chaining distances, with CCOD being more sensitive to local data distributions, while PCOD offers higher computational efficiency. Experimental results on synthetic and real-world datasets demonstrate the superior performance of our methods compared to existing state-of-the-art techniques, with average improvements of 11.3% for CCOD and 14.5% for PCOD.