The research, led by Ylka Kolken and Peter De Weerd of Maastricht University together with Nitzan Censor of Tel Aviv University and colleagues, revisits one of the most influential paradigms in visual neuroscience: the texture discrimination task, or TDT, first devised by Dov Karni and Dov Sagi in 1991. In the classic version of this task, participants fixate on the center of a screen and judge whether a briefly flashed letter is a T or an L, while simultaneously reporting the orientation of a faint, texture-defined target in the periphery. Performance is measured by the stimulus onset asynchrony, or SOA, the time gap between the target and a masking pattern that participants need to reach a fixed accuracy level of 81.6 percent. Lower SOA thresholds mean sharper perception.

Over the past decade, a series of studies from Censor’s group had already shaken the field by showing that after a single initial training session, just five near-threshold reminder trials per day could match the gains produced by hundreds of daily practice trials. The proposed mechanism was memory reconsolidation: when a memory is reactivated, it briefly becomes labile and can be modified or strengthened before being stored again. Brief reminders, the argument went, could repeatedly reopen the consolidation window, allowing learning to proceed efficiently without massive repetition. But a nagging question remained: under what circumstances do reminders actually work?

Kolken and colleagues suspected the answer lay in attention. The dual-task design of the standard TDT forces participants to divide attention between a central letter and a peripheral texture, and decades of work have shown that divided attention carries a steep cost. In a classic experiment, Sabine Kastner and colleagues found that peripheral discrimination performance plummeted from 86 percent to 45 percent correct when a demanding central task was performed simultaneously. If learning in the dual-task TDT is therefore suboptimal, starved of attentional resources, then reminders might only be beneficial when the original encoding was weakened by that divided attention. To test this, the team compared learning in the standard dual-task TDT with a single-task version in which the central letter discrimination was removed entirely, leaving only the peripheral target behaviorally relevant.

The experiment followed a clean three-arm design. In both task versions, participants were assigned to a full training condition with daily sessions across five days, a reminder condition with training on days one and five plus five tiny reminder trials on the intermediate days, or a control condition with no practice whatsoever between the first and fifth days. In the dual-task TDT, with thirty participants evenly split across conditions, the results replicated the reminder literature precisely. Full training and reminders produced nearly identical, substantial reductions in SOA threshold, while the control group barely improved. Bayesian analyses confirmed that the reminder and full-training groups were statistically indistinguishable, and both clearly outperformed the controls.

Then came the surprise. When the central fixation task was removed and fixation was enforced by an eye tracker that aborted any trial in which gaze strayed more than 1.5 degrees from center, day-one thresholds collapsed to around 62 milliseconds, compared with roughly 180 milliseconds in the dual-task version. The difficulty of the classic task, it turned out, was mainly attentional rather than sensory. Because this near-ceiling performance left no room for improvement, the researchers made the stimulus harder, coarsening the texture spacing and shrinking the target from three elements to two, and ran the full three-condition experiment again with thirty new participants.

The outcome upended expectations. In the single-task TDT, all three groups, including the controls who did nothing between day one and day five, improved by similar amounts. A block-by-block analysis ruled out rapid within-session learning as the explanation: performance at the start of day five was significantly better than performance at the end of day one, a gain of roughly 36 milliseconds in SOA threshold that reflected genuine between-session consolidation. In other words, a single session of fully attended training was enough to set in motion a learning process that unfolded over several days entirely offline, with no reminders and no additional practice. Full daily training offered no advantage whatsoever over doing nothing at all.

The team also ruled out a seductive alternative explanation: cheating with the eyes. Because attention and gaze are tightly linked, participants in the dual-task TDT might have quietly shifted their fixation toward the anticipated peripheral target, adopting a compromise gaze position to boost performance. Eye-tracking recordings during the dual-task version showed this was not happening. Ninety percent of trials fell within 1.2 degrees of the fixation point on day one, average gaze position did not shift toward the target between days one and five, and there was no consistent direction of drift across participants. The T/L task, long assumed to guarantee fixation, apparently did exactly that.

The broader implication is that attentional context, the configuration determining how many spatial locations must be attended, is a key factor shaping which route perceptual learning takes. When attention is divided, as in the classic dual-task TDT, the initial memory trace is weak and needs to be actively reactivated through reminders to consolidate into lasting improvement. When attention is fully available, a single session produces a trace strong enough to consolidate on its own, unfolding over multiple days without any external prompting. This framework may also explain why a recent study by Zhu and Zhang found no reminder benefit in a dual-task TDT, and why Chen and Op de Beeck found reminder gains to be smaller than full training for complex-object learning: in both cases, the attentional resources available for encoding may have differed from the original reminder studies.

The findings carry real practical weight. Training programs for vision rehabilitation, sports, and skill learning almost universally assume that daily practice blocks are essential, implicitly assuming that offline learning cannot extend beyond a single day. This study suggests that assumption deserves scrutiny: improvements can continue for days after a single well-attended session, and the true time course of this latent consolidation, whether it proceeds gradually or concentrates in the first night of sleep, remains an open question the authors flag for future work with intermediate test points. The study is not without limitations, including the inability to fully record eye movements in the single-task condition and a residual trend toward lower single-task thresholds, but the core message stands. Practice may still make perfect, but the brain, given the right conditions, appears remarkably capable of finishing the job on its own.

Subject of Research: Attentional context determines whether reminder trials or offline consolidation drive visual perceptual learning

Article Title: No practice and still perfect: Different routes for promoting offline gains in perceptual learning

Article References: Kolken, Y., Roberts, M. J., Naseri, P., De Martino, F., Censor, N., & De Weerd, P. (2026). No practice and still perfect: Different routes for promoting offline gains in perceptual learning. iScience, 29(11), Article 117839. https://doi.org/10.1016/j.isci.2026.117839

Image Credits: AI Generated

DOI: 10.1016/j.isci.2026.117839

Keywords: visual perceptual learning, texture discrimination task, memory reconsolidation, attention, offline learning, memory consolidation, eye tracking, divided attention, perceptual thresholds, neuroscience, skill learning, iScience

The idea behind a-tDCS is elegantly simple. A weak electrical current, in this case just one milliampere, is passed through an electrode placed over the scalp, gently shifting the resting membrane potential of neurons beneath it. When delivered over the primary motor cortex (M1), the brain region that directly controls voluntary movement, anodal stimulation is thought to depolarize neuronal membranes and make the region more excitable. Since decades of research have shown that repeated activation of task-specific cortical neurons during practice drives synaptic strengthening and cortical reorganization, the theoretical logic follows that boosting M1 excitability during practice should amplify the circuits being trained, leading to faster learning and better retention. Indeed, previous studies pairing a-tDCS with physical training have reported larger motor-evoked potentials, faster reaction times, and fewer errors than training alone.

The Indiana University team, however, has accumulated a mixed track record with the technique. In their own laboratory, M1 stimulation failed to accelerate learning of a simple choice reaction time task or dart throwing at randomly selected targets, yet it did enhance performance on a tweezer dexterity task and on a rhythm-timing videogame that required pressing a single key with precise timing. Those inconsistencies raised an important question: what specific combination of task demands makes M1 stimulation effective? To find out, the researchers designed a new experiment using a Guitar Hero-style rhythm game, a task superficially similar to their earlier successful paradigm but with a few crucial differences that, as it turned out, may have made all the difference.

Forty healthy adults, averaging about 22 years of age and with widely varying levels of gaming experience, were recruited for the study. Crucially, participants were excluded if they had ever played a stringed instrument or used a guitar-shaped game controller, ensuring that everyone started from a comparable baseline of ignorance. The task used an open-source rhythm game called Clone Hero, played with a wireless guitar controller. Colored notes scrolled up a virtual fretboard, and participants had to hold down the correct fret buttons with the index through pinky fingers of their left hand while strumming with their right thumb at exactly the right moment. Some passages required two fret buttons to be pressed simultaneously, and the continuous scrolling rhythm demanded moment-to-moment timing precision.

Each participant visited the laboratory twice, at the same time of day. On the first visit, they completed a familiarization trial, a three-song pre-test block, a 20-minute practice block during which stimulation was delivered, and a three-song post-test immediately afterward. They returned 24 hours later for a retention test. Half the participants received real a-tDCS: a 35-square-centimeter electrode over the motor cortical hotspot corresponding to their non-dominant hand, with a return electrode over the ipsilateral supraorbital region, delivering one milliampere for the full 20-minute practice period. The other half received sham stimulation, which included identical 30-second ramps of current at the beginning and end to mimic the tingling sensation, but no current in between. The study was single-blind, meaning participants did not know which group they were in. The researchers also used finite-element modeling software to estimate the current density reaching the gray matter beneath the electrode, confirming values comparable to those used in their previous studies.

Performance was quantified with three game metrics: accuracy, the percentage of notes hit correctly; best continuous streak, the longest unbroken run of successful notes; and overstrums, a count of erroneous strum attempts. The results on these measures told a clear story about practice and an equally clear story about stimulation. Across all participants, accuracy improved dramatically from pre-test through practice, post-test, and the 24-hour follow-up, with the statistical analysis showing an enormous effect of time on accuracy. Best streaks lengthened and overstrums declined in parallel, and gains were not merely maintained but in some cases continued to grow at the retention session, a classic signature of offline consolidation. But when the a-tDCS and sham groups were compared, there were no differences on any measure at any time point, and no time-by-group interactions emerged. Even Bayesian analyses, which quantify the evidence for or against group differences, returned values hovering near one, indicating no meaningful evidence in either direction.

The null result is particularly striking because the study was powered to detect a moderate-to-large effect. An a priori power analysis indicated that 15 to 18 participants per group would suffice to detect a group-by-time interaction of the anticipated size, and the researchers collected 20 per group to buffer against unexpected variability. Yet the observed data showed the two groups nowhere near being statistically different, and, complicating the interpretation, also too variable to be declared statistically equivalent. Two one-sided tests for equivalence produced confidence intervals far wider than the predefined equivalence bounds, reflecting the noisy, trial-to-trial fluctuations inherent in the task. In rhythm games, a single lapse in attention can derail an entire long sequence of notes, even in otherwise skilled performers, and that volatility swamped any signal the stimulation might have produced.

So why did stimulation fail here when it worked on a superficially similar task before? The researchers point to the specific computational demands of the Guitar Hero-style game. Unlike the earlier rhythm task, which required pressing a single arrow key in time with the beat, this game is bimanual: one hand strums while the other, the one whose cortical representation was targeted, presses frets. It also demands simultaneous double-note presses and continuous integration of visual input, finger selection, and strum timing. These features likely shift the burden of learning away from M1-dependent, use-dependent plasticity and toward the cerebellum and fronto-striatal circuits, which handle error-based prediction and trial-by-trial correction. In other words, boosting the excitability of M1 may have been stimulating the wrong node of a distributed learning network. Early performance gains in timing-heavy tasks are often cerebellar in origin, and no amount of cortical excitation in the motor strip can substitute for that.

The study also highlights practical limitations that plague the broader tDCS literature. While one milliampere reliably increases M1 excitability, recent guidelines emphasize that current flow patterns depend on electrode montage, that baseline excitability varies between individuals, and that neuroanatomical variability moderates behavioral outcomes. The heterogeneous sample, which included participants ranging from non-gamers to heavy gamers and was not stratified by other fine-motor experience such as keyboard typing, may have introduced response variability that masked group-level effects. There is also the possibility that 20 minutes of practice was simply too short to engage the slower consolidation processes where M1 excitability changes exert their strongest influence, though the preserved gains at 24 hours show that consolidation did occur in both groups equally. And because the task is bimanual, stimulating only the fret-hand hemisphere ignores the strumming hand entirely; a bilateral montage might behave differently.

For a field that has been criticized for inconsistent replication, the study is a valuable datapoint. It demonstrates, with careful methodological controls, adequate statistical power, and a well-characterized stimulation protocol, that enhancing M1 excitability alone is insufficient to modify learning of a complex, dexterous, timing-based task. The message is not that brain stimulation is useless, but that its effects are contingent: on the stimulation site, on the neural systems the task actually engages, and on the type of learning being measured. Future work, the authors suggest, should target other nodes of the motor learning network, such as the cerebellum or prefrontal regions, or combine stimulation sites across hemispheres. In the meantime, aspiring Guitar Hero champions would be better off logging practice hours than strapping an electrode to their heads. The brain, it turns out, learns what it practices, and it cannot easily be hacked from the outside.