The research tackles a long-standing question in sensorimotor science. When we reach for an object, our grip force rises in anticipation of its weight, a phenomenon known as anticipatory grip force control. If the prediction is right, the lift feels smooth and effortless. If it is wrong, the object either slips from our fingers or jerks upward with comical force. Decades of work have shown that these predictions are built from two ingredients: visual cues, most obviously the apparent size or volume of the object, and the memory of recent lifting experience. A large-looking bottle is expected to be heavy; a bottle that felt heavy last time is expected to feel heavy again.

That second ingredient turns out to be surprisingly dominant. In the Durham experiments, when participants lifted objects with no informative visual cues at all, their grip force behaviour was strongly biased by whatever they had lifted on the previous trial. This serial dependence, the tendency of the most recent experience to colour the next prediction, has been documented in perception before, but the new results show it exerting a powerful pull on motor planning itself. Left to its own devices, the lifting system essentially assumes the world has not changed since the last interaction.

The team measured this bias using peak grip force rate, or PGFR, the speed at which grip force climbs during the initial phase of a lift. PGFR is a well-established window into the brain’s internal heaviness predictions because it reflects what the motor system expected before tactile information arrived. Participants performed a precision-grip lifting task in which objects came in three heaviness levels, and the researchers tracked how PGFR shifted across four experimental conditions: a no-cue baseline, a familiar cue, and two kinds of novel cues.

The familiar cue was visual volume, the everyday signal that bigger means heavier. The novel cues were deliberately arbitrary: patterns of line orientations printed on the surfaces of the objects, with no natural relationship to weight. Crucially, the researchers also split the novel cues by presentation format. In the intrinsic condition, the line patterns appeared physically on the objects themselves, much as a label might. In the extrinsic condition, participants saw diagrammatic depictions of the same objects and patterns on a screen, meaning the weight information was available only through an interpretive step rather than being directly attached to the thing being lifted.

Before running the study, the authors had reasoned that the familiar volume cue might carry more predictive weight than an arbitrary pattern, simply because a lifetime of experience has calibrated the size-weight relationship. They also suspected that cues printed on the object might outperform cues shown on a screen, since intrinsic presentation requires no mapping between a display and the physical world. Neither prediction survived contact with the data. The familiar volume cue did not exert a stronger influence than the novel cues, and the intrinsic presentation offered no measurable advantage over the extrinsic one.

What did matter was simply having a cue at all. All three visual cue conditions significantly reduced the serial dependence bias compared with the no-cue baseline. In other words, any reliable visual signal about weight, whether natural or arbitrary, on-object or on-screen, was enough to loosen the grip of recent experience and let the motor system plan each lift on its own terms. The computational modelling accompanying the behavioural data, with individual cue-weighting parameters fitted for each participant, supported the same conclusion: novel cues were integrated into the heaviness prediction with weights comparable to those of the familiar cue.

The speed of this learning is the most striking aspect of the findings. Participants did not need weeks of training to make an arbitrary line orientation meaningful. The sensorimotor system, often portrayed as conservative and heavily reliant on long-term calibration, absorbed the new cue rapidly and deployed it in service of grip force planning without any apparent performance penalty. This suggests that the machinery of motor prediction is less wedded to specific cue types than theories emphasising learned cue familiarity might imply, and more open to whatever statistical regularities the environment currently offers.

The equivalence of intrinsic and extrinsic presentation carries equally interesting implications. Much of the emerging field of sensory augmentation assumes that information delivered through artificial channels, such as a display or a wearable device, will be harder to incorporate than information arriving through the body’s native senses. These results suggest that, at least for weight prediction in a controlled lifting task, the brain treats a diagrammatic on-screen cue as a perfectly serviceable predictor. The interpretive distance between a screen and an object did not dilute the cue’s influence on motor planning.

The practical implications extend beyond the laboratory. In industrial settings, medical environments, logistics and teleoperation, workers routinely handle objects whose weight cannot be judged by appearance, from sealed containers to virtual or robotic proxies. The findings suggest that equipping such environments with arbitrary visual markers, even simple line patterns shown on a monitor, could rapidly improve the safety and efficiency of manual handling by steadying grip force against the misleading pull of recent experience. For designers of augmented reality interfaces, the message is encouraging: the human motor system appears ready to trust whatever weight information you show it, almost immediately, and without demanding that the information be painted onto the object itself. As the authors note, the work highlights the flexibility of sensorimotor control and may inform manual interactions in environments with limited or unconventional weight predictors, a description that fits an increasing share of the technological world we now lift, carry and manipulate every day.

Subject of Research: How novel visual cues are learned to update internal predictions of object weight during lifting

Article Title: Learning a novel visual cue to update internal predictions of object weight

Article References: Learning a novel visual cue to update internal predictions of object weight. (n.d.). https://doi.org/10.1186/s12915-026-02741-1

Image Credits: AI Generated

DOI: 10.1186/s12915-026-02741-1

Keywords: grip force, motor planning, visual cues, object weight, serial dependence, sensorimotor control, anticipatory control, precision grip, sensory augmentation, psychophysics, BMC Biology, Durham University

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 theoretical backdrop for the work is the increasingly popular Bayesian account of serial dependence. According to this framework, the brain behaves like an intuitive statistician, combining noisy sensory evidence with expectations derived from recent experience to produce decisions that are optimal under uncertainty. When the current stimulus is encoded poorly, its internal representation is noisy, and the perceptual system should lean more heavily on the previous stimulus, producing stronger attractive biases. Conversely, when the previous stimulus, often called the inducer, is itself noisy, its influence on the current decision should weaken because a unreliable source of prior information deserves less weight. This elegant logic has generated a large body of findings, mostly documenting attractive biases in tasks such as orientation and face estimation. Yet the framework makes a prediction that has been far less explored: in some tasks, the strategy that actually minimizes error is not attraction but repulsion, and uncertainty should then modulate repulsive biases in predictable ways.

Yildirim and Boduroglu designed an experiment precisely to test that prediction. Participants viewed three tilted gratings in rapid succession, each flashed for roughly 83 milliseconds, and later reported the orientation of each one from memory. The critical manipulation lay in the timing. The first two targets, T1 and T2, were separated by either three or seven intervening stimuli, while the third target, T3, always followed T2 at a lag of seven. At the shorter lag, T2 fell inside the temporal window of the well-known attentional blink, the brief period after detecting one target during which attentional resources for the next are depleted. As a result, participants’ representations of T2 were measurably worse at Lag 3 than at Lag 7, as indexed by larger absolute estimation errors and lower precision. This created a natural experiment in representational noise: the same stimulus, T2, sometimes served as a well-encoded target and sometimes as a degraded one, and in both cases it also acted as the inducer for the subsequent report of T3.

The results overturned a straightforward reading of the Bayesian account. When T2’s representation was noisier, produced by the attentional blink at the short lag, the repulsive serial bias away from T1 actually grew stronger, not weaker. Reduced attentional resources amplified the tendency for participants’ estimates of T2 to be pushed away from the orientation they had just seen on T1. This is the first demonstration, the authors report, that diminished attention increases repulsive serial bias in working memory. In other words, when the current representation is unreliable, the system does not simply lean harder on the past; instead, in this task regime, it actively differentiates the present from the past, as if exaggerating the contrast between the two to keep them from being confused.

Equally striking was what did not change. The noisiness of the inducer, that is, the fidelity of T2, had no measurable effect on the bias observed in estimates of T3. Whether T2 had been encoded under the attentional blink or with full attentional resources, the repulsive pull of T2 on T3 estimates remained statistically indistinguishable. Under a strict Bayesian reading, a degraded inducer should contribute a weaker prior and therefore a smaller bias. The absence of such modulation suggests that the brain may treat the stored memory trace of a stimulus as a fixed reference point for differentiation, regardless of how faithfully that stimulus was originally captured, or that the adaptive mechanism at work here is not the classic Bayesian weighting scheme at all.

Perhaps the most intriguing findings concern the temporal reach of the repulsive effect. Serial dependence is usually studied between adjacent items, and many researchers have assumed that its influence decays rapidly with intervening stimuli. But in this experiment, estimates of T3 were repelled not only by T2 but also by T1, an item separated from T3 by a lag of ten stimuli in the rapid stream. The bias therefore extended well beyond the immediate perceptual history, implying that multiple past representations can coexist in working memory and each exert a distinct repulsive influence on a current report. This long-range repulsion is difficult to reconcile with simple low-level adaptation accounts, in which sensory neurons tuned to the previous orientation become fatigued and shift subsequent percepts away, because such aftereffects are typically short-lived and confined to the immediately preceding stimulus.

The study also uncovered evidence of repulsion operating in the opposite direction of time. Estimates of T2 were pushed away from the orientation of T3, the stimulus that followed it. Since T3 had not yet been presented when T2 appeared, this backward bias could not reflect perception at encoding. Instead, the authors argue, it emerged during the maintenance period, while T2’s representation was being held in working memory and before T3’s report was required. This implies that repulsive differentiation is an active, ongoing computation performed on stored representations rather than a passive residue of earlier sensory processing. When a new item enters working memory, existing representations appear to be reorganized to maximize their separation from the newcomer, a form of online uncertainty minimization that unfolds during the retention interval itself.

Taken together, these findings complicate the popular narrative that serial dependence is a single, attractively inclined mechanism serving perceptual stability. The authors conclude that the pattern they observed reflects adaptive uncertainty minimization in working memory that goes beyond standard Bayesian accounts of serial dependence. When a target representation is noisy, the optimal strategy for distinguishing successive items in this task is to push them apart, and the visual system appears to implement exactly that strategy, amplifying repulsion under attentional deprivation. At the same time, the insensitivity of downstream biases to inducer fidelity, the long-range influence of T1 on T3, and the backward repulsion from T3 onto T2 collectively suggest that the underlying mechanism is more like an active separation process operating across the entire contents of working memory than a passive weighting of previous percepts as priors.

The work, which was supported by the Scientific and Technological Research Council of Turkey through a 1001 program grant and doctoral and international fellowships to the first author, carries implications well beyond the laboratory. Visual working memory underlies everyday activities as varied as navigating a crowded street, comparing two paint swatches, or tracking a moving vehicle through traffic, and all of these depend on keeping successive percepts distinct. The discovery that attentional lapses strengthen the active separation of memories offers a new lens on the errors people make under cognitive load, and it may inform models of perceptual decision making, visual search, and even clinical conditions characterized by attentional dysfunction. For now, the study stands as a vivid reminder that memory is not a passive recording but a dynamic negotiation between past and present, one in which the brain sometimes decides that the best way to remember what it just saw is to remember it as deliberately different from what came before.

Subject of Research: Repulsive serial dependence in visual working memory and how target fidelity shapes serial biases

Article Title: Repulsive serial dependence in visual working memory modulated by target fidelity

Article References: Yildirim, B., & Boduroglu, A. (2026). Repulsive serial dependence in visual working memory modulated by target fidelity. BMC Biology. https://doi.org/10.1186/s12915-026-02739-9

Image Credits: AI Generated

DOI: 10.1186/s12915-026-02739-9

Keywords: visual working memory, serial dependence, serial bias, attentional blink, orientation estimation, Bayesian inference, perceptual decision making, attention, visual adaptation, working memory maintenance, repulsive bias, target fidelity