Sabina Orso and Andrea Pavan asked twenty-eight volunteers to watch a classic laboratory illusion called a random-dot kinematogram: a circle of 200 white dots, each drifting coherently in one direction at about six degrees per second for half a second. On every trial, the direction changed unpredictably across the full 360-degree range. Participants then indicated the direction they had just seen by moving a mouse cursor along a circular ring, a setup that allows errors to be measured to the fraction of a degree. The critical twist was timing. On different trials, participants waited 0, 1, 3 or 6 seconds after the dots vanished before giving their answer. If the pull of the previous motion only appears once memory gets involved, the immediate-report condition should look clean, while the longer delays should accumulate bias. If instead the bias is baked into perception itself, it should be there from the very first moment.

The answer the experiment produced was neither of the two predicted patterns. Using a derivative-of-Gaussian model, a standard mathematical tool in serial-dependence research that fits a peaked curve to how response errors depend on the angular difference between consecutive directions, the researchers found a significant attractive bias at every single delay, including the supposedly pristine zero-second condition. Participants did not wait for a retention interval to start being influenced by history; the previous motion direction was already pulling their reports the instant they responded. Peak bias estimates ranged from 0.80 degrees at 0 seconds to 2.04 degrees at 6 seconds, and all of them were statistically greater than zero after correction for multiple comparisons.

Yet the delay effect was real, and it was strange. The strength of the bias did not climb steadily with time as a purely memory-based account would predict. Instead it followed a non-monotonic trajectory: moderate immediately after the stimulus, larger at 1 second, dipping at 3 seconds, and peaking at 6 seconds. Statistical modeling confirmed that the interaction between delay and the serial-dependence term significantly improved the fit of the model, meaning the magnitude of the history effect genuinely shifted across retention intervals. A tidy theory in which immediate responses are dominated by repulsion, an aftereffect that pushes perception away from the previous stimulus to sharpen sensitivity to change, while delayed responses reveal attraction, simply did not survive contact with the data. Attraction was present from the start; delay modulated it rather than creating it.

There is an important caveat here, one the authors are careful to underline. Even the zero-second condition was not a pure window into perception, because responding inevitably involves post-perceptual decision and motor stages. So the study cannot cleanly isolate a perceptual mechanism. What it can say is that a simple dissociation between immediate sensory repulsion and delayed mnemonic attraction is wrong, and that recent motion history is already shaping reports when no additional retention interval exists at all. Between those two poles lies a messier, more interesting picture in which multiple processes with different time courses combine to produce the final bias.

A complementary analysis drove that point home even harder. The parametric Gaussian-based model imposes a smooth shape on the data, so the researchers also performed a folded-bias analysis that collapses positive and negative direction changes and examines bias at each absolute angular distance separately. This revealed that attraction was not spread evenly across the angular range. It was strongest at small-to-intermediate differences, particularly around 10 to 20 degrees, and at some intermediate and larger distances the errors actually flipped into local repulsive deflections. In other words, the fitted attraction curve is best read as a compact summary of a composite phenomenon, not as evidence of a single uniform attractive mechanism stretching across all direction changes.

The study also contained an uncomfortable self-check. When the researchers added a predictor based on the direction participants had responded on the previous trial, rather than the stimulus they had seen, the response history explained a large share of the variance in current errors, and the delay-dependent stimulus effect lost its statistical significance. Because stimulus history and response history were moderately correlated in the design, the two cannot be fully pulled apart. The honest conclusion is that part of the measured serial dependence may reflect decisional, motor or response-based carryover, and future experiments will need designs that deliberately decorrelate what people saw from what they said to determine how much of the effect is truly stimulus-driven.

Two exploratory analyses hinted at further layers of complexity. One suggested that motion history may bias judgments not only toward the exact previous direction but also toward the opposite direction, consistent with the idea that the visual system encodes motion along a broader axis rather than as a single vector, and with neuroimaging work showing that motion direction can be represented as a bimodal probability distribution in visual cortex. Because the stimulus sequence was optimized for the main direction-based question, with small transitions overrepresented and exact 180-degree reversals excluded, the authors treat this axis effect as hypothesis-generating rather than confirmed. A second exploratory analysis of response times found no simple relationship between how fast people answered and how similar consecutive directions were, offering no easy converging support for a similarity-based decisional account.

The Bologna team was also candid about an inescapable confound in serial-dependence research: because small direction changes were intentionally overrepresented, participants could in principle have learned the transition statistics and used them to guide responses, a strategy resembling regression to the mean. Control checks showed that target directions were broadly distributed and that positive and negative transitions were balanced, which limits but does not eliminate this concern. The authors note too that serial dependence is not automatically helpful; recent large-scale evidence suggests it can even worsen perceptual decisions in some contexts. Whether the bias is adaptive depends on the task, not on some benevolent design feature of the brain.

What the study ultimately delivers is a more honest map of a phenomenon that has often been drawn too simply. Recent motion history biases our judgments of motion direction from the very first moment we report them, the strength of that bias shifts in a non-monotonic way as the retention interval stretches from zero to six seconds, and the overall effect appears to be a composite of perceptual, memory-related and response-related influences whose relative weights change over time. Raw data, analysis code and experiment scripts are publicly available, inviting the field to build on the result. For now, one thing is clear: the continuous, stable visual world you experience is stitched together, in part, from what you saw just before, and the thread connecting those moments is woven over seconds, not switched on by a clock.

Subject of Research: Serial dependence in visual motion direction perception and its modulation by retention interval

Article Title: Retention interval modulates motion-history biases in visual motion perception

Article References: Retention interval modulates motion-history biases in visual motion perception. (n.d.). https://doi.org/10.3758/s13414-026-03345-1

Image Credits: AI Generated

DOI: 10.3758/s13414-026-03345-1

Keywords: serial dependence, motion perception, visual psychophysics, random-dot kinematograms, visual short-term memory, retention interval, perceptual stability, derivative-of-Gaussian, motion axis, adaptation, response bias, decision-making

The heart of the problem lies in the distinction between two psychological quantities that are easily confused. In an old–new recognition task, participants judge whether each test item was previously studied. Researchers usually want to know about sensitivity, the participant’s true ability to discriminate studied targets from unstated lures. But performance is also shaped by bias, the participant’s tendency to say “old” regardless of the evidence. A liberal responder says “old” often and racks up both hits and false alarms; a conservative responder says “old” rarely. A valid sensitivity measure must ignore this bias entirely. If two experimental conditions genuinely have identical memory accuracy but differ in how willing participants are to respond, the correct conclusion is that no sensitivity difference exists, and a statistical test should produce a significant result only about 5 percent of the time, matching the conventional alpha level.

Previous simulation work had already raised alarms. Rotello and colleagues showed in 2008 that common single-point measures, which compute sensitivity from a single pair of hit rate and false alarm rate, systematically confuse bias with sensitivity. Yet those findings, cited only around 110 times, failed to change practice. In a review of 253 recognition memory papers published across 40 Springer Nature journals between 2018 and 2024, the new study found that percent correct and corrected recognition together accounted for nearly a quarter of reported measures, with d′ close behind. Not one of the dominant measures had ever been validated in a way that escaped circularity, because in real experiments the true sensitivity is unknowable except through the very measures being tested.

The theoretical foundation of the crisis is a mismatch between two models. Every dataset is generated by some underlying population of memory signals, described by a data-generating model. Three major families compete to describe recognition memory: continuous Gaussian signal detection models, including the unequal-variance signal detection model, or UVSD; discrete threshold models such as the double-high-threshold model, which underlie corrected recognition; and mixture models like the dual-process signal detection model, which combines an all-or-none recollection process with a continuous familiarity process. Meanwhile, every sensitivity measure embodies a measurement model, a set of assumptions about the relationship between hits and false alarms that can be visualized as a receiver operating characteristic curve. When the measurement model does not match the data-generating model, two operating points that truly reflect identical sensitivity can appear to fall on different curves, and bias masquerades as a genuine memory difference.

The simulations confronted this problem directly. Across 1,962 parameter combinations, each replicated 10,000 times, the researchers sampled memory signals from distributions corresponding to four data-generating models, created two conditions that were identical in sensitivity but differed in decision criteria, and then tested each condition-pair with t-tests for five different measures: corrected recognition, A′, d′, the geometric area under the ROC curve, and da. Whenever a measure was valid, false positive rates should hover near 5 percent. The single-point measures failed almost everywhere outside the narrow case where their own assumptions happened to hold: d′ survived only under equal-variance Gaussian distributions, and corrected recognition only under rectangular distributions. Crucially, under unequal-variance Gaussians, which most experts consider the most plausible account of recognition data, all of these measures produced false discovery rates far above 5 percent, climbing steadily as sample size grew and reaching a staggering 100 percent in the largest, longest simulated experiments.

That last number deserves a pause. A 100 percent Type I error rate means that every single experiment would find a significant sensitivity difference where none exists, and every replication would confirm it. This inverts the usual hope that large samples and many trials protect science from error. With invalid measures, more data simply amplify the confound, producing results of enormous psychometric reliability and near-zero validity. The authors draw a pointed parallel to the replication crisis: highly replicable findings can still be false discoveries, and decades of research effort have been spent chasing effects, from the “revelation effect” to the apparent memory advantage of emotional words, that later analyses attributed to bias rather than sensitivity. Similar measurement failures have been documented in eyewitness lineup research, reasoning, social psychology, and child welfare.

The solution the researchers champion is da, a signal detection measure that, like d′, transforms hit and false alarm rates into z-scores and takes their difference, but that abandons the indefensible equal-variance assumption. Instead, da measures the distance between target and lure distributions in units of their root-mean-square standard deviation, using the zROC slope, denoted S, to estimate the actual variance ratio for each participant. Empirical studies consistently find that target distributions are roughly 25 percent wider than lure distributions, with slopes averaging about 0.8 but varying widely across individuals, from around 0.5 to above 1. The new version of da exploits this by collecting confidence ratings, which yield multiple operating points per participant, and estimating S by linear regression on each participant’s zROC curve, requiring at least three usable points per condition. Only one parametric assumption remains: that the underlying distributions are Gaussian.

The simulations vindicated this choice. For both equal- and unequal-variance Gaussian data, da held Type I error rates at approximately 5 percent across every sample size, number of trials, criterion placement, and distributional separation tested. Under the dual-process mixture model, da also stayed near 5 percent in typical scenarios, because the ROC curve implied by that model closely resembles the UVSD curve, though errors rose toward 16 percent in extreme parameterizations with very large recollection contributions. Only for rectangular threshold distributions, where the Gaussian assumption is simply wrong, did da break down, as theory predicts. The one caveat concerned precision: with few trials and many participants, noisy estimates of S could push error rates to about 10 percent, and experiments that excluded many participants for having too few operating points showed similar inflation. The remedy, the authors argue, is straightforward: run enough trials per participant, closer to 128 than to 64, so that the slope estimate converges on the true variance ratio.

Even the seemingly assumption-free alternative fared poorly. The geometric area under the empirical ROC curve, popular in machine learning and often promoted as non-parametric, was computed by connecting adjacent operating points with straight lines and summing triangles and trapezoids, which omits area whose size depends on where the points fall. Because that omitted area shifts with bias, the measure was biased too, reaching near-total false positive rates under large criterion shifts with big samples. The researchers also decline to endorse full mathematical modeling as a universal fix, noting that in a blinded expert challenge, experienced modelers reached strikingly inconsistent conclusions about which experiments had manipulated sensitivity, bias, or neither, and that modelers in the literature overwhelmingly choose threshold models whose fit is inferior to continuous alternatives. A simple scalar measure available to every researcher, they argue, beats a fragile modeling pipeline.

The implications reach beyond recognition memory to the entire family of single-interval tasks, from perceptual discrimination and attentional cueing to lexical decision and metacognitive judgment, wherever binary judgments against a criterion are made. For the existing recognition literature, the news is sobering: a substantial share of published sensitivity effects based on binary responses and standard indices may be uninterpretable, though the authors offer limited escape routes, such as mirror effects, in which higher hit rates coexist with lower false alarm rates in a way bias cannot explain, and designs where within-list criterion shifts are implausible. For future work, the prescription is sharper. Collect confidence ratings, compute da with an individually estimated variance ratio, and let reviewers and editors retire measures whose false positives replicate perfectly forever. In the ongoing struggle to make psychology’s discoveries both replicable and true, the study suggests, the battle begins with choosing the right ruler.

Subject of Research: Validation of bias-independent sensitivity measures for recognition memory using Monte Carlo simulations

Article Title: Reject common measures like d′ and Corrected Recognition, embrace da: Simulation explorations of single- and multi-point recognition measures of sensitivity

Article References: Levi, A., Danino, R., Rotello, C. M., & Goshen-Gottstein, Y. (2026). Reject common measures like d′ and Corrected Recognition, embrace da: Simulation explorations of single- and multi-point recognition measures of sensitivity. Behavior Research Methods, 58(10), Article 287. https://doi.org/10.3758/s13428-026-03130-w

Image Credits: AI Generated

DOI: 10.3758/s13428-026-03130-w

Keywords: recognition memory, sensitivity, response bias, signal detection theory, Monte Carlo simulations, measurement crisis, da measure, ROC curves, unequal variance model, Type I error, Behavior Research Methods, replication crisis

The research builds on a well-known puzzle in clinical psychology. Earlier work by Carol Thompson and colleagues, published in Nursing Research in 2008, showed that when nurses are placed under time pressure, their ability to detect high-risk patient conditions measurably declines. The hit rate for spotting deterioration dropped from 86 percent without time pressure to 68 percent under it, and the key signal detection measure of detectability, known as d-prime, fell regardless of how many years of experience the nurse had. But that landmark study left an important question unanswered: was the decline in accuracy simply the result of a speed-accuracy trade-off, the well-documented tendency of people to sacrifice precision when forced to act quickly, or was time pressure fundamentally altering the perceptual machinery of clinical judgement itself?

To answer that question, the Japanese team designed an ambitious laboratory experiment that treated nursing judgement like a psychophysical signal-detection problem. Twenty-one registered nurses, fourteen of them women with an average of 6.6 years of hospital experience, were recruited through snowball sampling. All had worked on hospital wards or in intensive care units, and all were right-handed. The researchers created eighty twenty-second video stimuli that mimicked the displays of bedside medical monitors, presenting seven vital-sign parameters drawn from the National Early Warning Score 2, or NEWS2, the widely used triage tool that aggregates respiratory rate, blood oxygen, systolic blood pressure, pulse, consciousness level, temperature, and oxygen supplementation into a single risk score.

Half of the eighty datasets represented low-risk patients with aggregate NEWS2 scores between zero and four, while the other half represented high-risk patients scoring seven or eight, the range associated with a substantially elevated probability of cardiac arrest, unplanned intensive care admission, or death within twenty-four hours. Three experienced nurses vetted every stimulus for face validity before testing began. Seated eighty centimeters from a laptop in a quiet, carefully controlled room, with display luminance held at 10.4 candelas per square meter, participants watched each simulated patient and pressed one of two mouse buttons to judge whether urgent intervention was warranted, responding as quickly and accurately as possible and then rating their confidence on a five-point scale.

The headline finding is deceptively simple: nurses assessed the risk of acute deterioration in approximately five seconds, with a mean reaction time of 5,169 milliseconds and an overall accuracy of about 76 percent. That speed is plausible, the authors note, because previous research found clinicians needed roughly six seconds to choose among four diagnostic options after reading a detailed patient case; here the information load was lighter and there were only two response options. Crucially, the measured detectability value of 1.71 landed squarely within the range reported by prior signal detection studies of nursing risk judgement, which have produced d-prime values between roughly 0.98 and 1.75, lending the new paradigm immediate credibility.

But the deeper discovery emerged when the researchers broke performance down by individual NEWS2 score. Repeated-measures analysis of variance revealed that stimuli with a score of zero, meaning every parameter was perfectly normal, produced both faster reactions and higher accuracy than every other score. From this pattern, Hishiya and Ishihara inferred a two-stage architecture for nursing risk perception. The first stage, which they call the normal-abnormal discrimination phase, is a rapid triage of whether anything is wrong at all, completed in under five seconds and apparently mastered by anyone holding a nursing license. The second stage, the risk evaluation phase, kicks in only once an abnormality has been detected, and its duration, estimated as the reaction time difference between score-zero stimuli and everything else, proved to be the real engine of individual performance.

The correlations here are striking. Nurses who took longer in this risk evaluation phase showed significantly lower detectability, with a correlation coefficient of minus 0.52, and a significantly more conservative response bias, with a correlation of plus 0.65. In signal detection terms, d-prime measures how well a perceiver separates true signals from noise, while the criterion C captures the liberal or conservative leaning of their decisions. A liberal nurse over-flags danger, triggering unnecessary interventions that can divert scarce staff from patients who genuinely need them; a conservative nurse under-flags, and a deteriorating patient may slip through unnoticed. The finding that the time spent evaluating abnormal stimuli predicts both accuracy and bias suggests that this evaluative stage, not basic abnormality detection, is where clinical judgement quality is forged, and where two of the twenty-one participants even showed the phases running in parallel rather than in sequence.

Notably, the study found no evidence of a speed-accuracy trade-off across individuals. The correlation between d-prime and overall reaction time was weakly negative and statistically non-significant, which the authors interpret as evidence that the accuracy losses previously observed under time pressure reflect genuine changes in perceptual-behavioral information processing caused by the situation, rather than a deliberate slowing-down trade. This distinction matters enormously for patient safety: if time pressure corrupts the processing pipeline itself rather than merely shifting a strategic dial, then interventions must target the processing environment, not just train nurses to be more careful when rushed.

Equally intriguing were the physiological results. The researchers attached silver-silver chloride electrodes to participants’ palms and forearms and recorded skin potential level, a measure of sympathetic nervous system arousal, sampled continuously at 1,000 hertz throughout the task. While arousal levels did not correlate with accuracy or speed, a striking pattern emerged around experience: nurses who reported frequent subjective involvement with acute deterioration showed significantly lower normalized skin potential than their colleagues with rare exposure. In other words, nurses who regularly face patient crises appear to run their judgement machinery at a calmer physiological register, echoing earlier findings that expert nurses maintain stable heart rates during clinical decisions while novices do not, and hinting at a low-arousal, low-cognitive-load strategy honed by repeated real-world exposure to emergencies.

Perhaps the most socially resonant discovery involves personality. Among the five basic traits measured with the Ten Item Personality Inventory, only agreeableness correlated with response bias, and it did so positively and significantly, with a coefficient of 0.57. Agreeable nurses leaned conservative, more inclined to judge ambiguous patients as low-risk. The authors speculate a gently uncomfortable possibility: highly agreeable nurses may weigh the busyness of their colleagues, consciously or not, and hesitate to escalate cases that would demand scarce human resources. Previous work has already linked organizational culture to risk-detection bias, and together these findings point toward a practical intervention already envisaged by the researchers: giving individual nurses structured feedback on their own decision-making tendencies, quantified through the same signal detection lens used in this study.

The research is not without limits, which its authors candidly acknowledge. The sample of twenty-one nurses is small, the stimuli omitted patient background details such as illness history and medication, and high-risk cases appeared in half the trials, far more often than the five to ten percent prevalence seen in real hospitals, a mismatch known in vision science to shift detection accuracy and decision criteria. Yet the study stands as a proof of concept for something genuinely new in nursing science: a way to probe the unconscious, intuitive machinery of clinical judgement without relying on what nurses can articulate about their own thinking. Since nursing judgement is frequently intuitive, built on rapid pattern recognition that practitioners themselves cannot fully explain, behavioral and psychophysical tools like reaction time analysis and signal detection theory may expose what subjective self-report never can. If follow-up studies manipulate time pressure, multitasking, and realistic case prevalence, the five-second risk radar documented here could become the foundation for a new generation of nursing education, staffing policy, and patient safety science, one calibrated not to what nurses say they do, but to what their minds demonstrably do in the critical first seconds of a crisis.

Subject of Research: The information processing mechanisms underlying nurses' clinical risk judgement, examined with signal detection theory and reaction time analysis

Article Title: Exploring information processing underlying risk judgement: Implications from signal detection theory and reaction time analysis

Article References: Hishiya, R., & Ishihara, M. (2026). Exploring information processing underlying risk judgement: Implications from signal detection theory and reaction time analysis. Heliyon, 12(14), Article e45422. https://doi.org/10.1016/j.heliyon.2026.e45422

Image Credits: AI Generated

DOI: 10.1016/j.heliyon.2026.e45422

Keywords: nursing, risk judgement, signal detection theory, reaction time, clinical decision-making, patient deterioration, NEWS2, skin potential level, response bias, detectability, time pressure, cognitive processing