For years, researchers have known that tracking has hard limits. Early work in the late 1980s suggested that observers could follow roughly four objects at a time, a figure that became something of a canonical number in cognitive psychology. Later discoveries complicated the picture considerably. When objects move slowly, people can track as many as eight or nine of them; crank up the speed, and even a single target can become a struggle. This smooth trade-off between the number of tracked objects and their speed was widely interpreted as evidence for a flexible attentional resource, one that can be divided among targets according to task demands. The harder the tracking becomes, the thinking went, the more of this precious resource each target consumes, leaving less available for the rest.

But there has always been an alternative explanation. Faster motion inevitably produces more close encounters between objects, moments when a target and a distractor pass near enough to be confused. According to this spatial interference account, tracking fails not because attention is spread too thin but because the visual system simply cannot disambiguate objects that come too close together. Similarly, adding more distractors to a display increases the likelihood of such confusions, and one influential proposal has suggested that distractors may even require active suppression, draining attention away from the targets. Distinguishing between these accounts has proved difficult, because behavioural performance alone cannot reveal what is happening to attention inside the brain. That is where the new study makes its mark.

Adamian and colleagues used a technique called steady-state visual evoked potentials, or SSVEPs, which exploits a clever property of the visual system. When objects flicker at distinct frequencies, the brain produces electrical responses at exactly those frequencies, measurable with electroencephalography. By tagging targets and distractors with different flicker rates, the researchers could separately track the neural processing of each stimulus type throughout the tracking period. Crucially, SSVEPs are highly sensitive to selective attention: attended objects produce stronger responses than ignored ones. The difference between target and distractor amplitudes therefore serves as a direct, continuous readout of how strongly attention is enhancing the tracked items in early visual cortex.

In the first experiment, seventeen participants tracked four red discs among eight identical discs while the objects moved at one of three speeds, ranging from a leisurely 3.8 degrees of visual angle per second to a demanding 10.9. The results on the behavioural side were exactly as expected: accuracy fell from 97 percent at the slowest speed to 82 percent at the fastest, a highly significant decline. The neural data, however, told a different story. During trials in which participants tracked successfully, the attentional enhancement of targets over distractors, quantified with a modulation index, remained statistically unchanged across all three speeds. Attention was not weakening as the task grew harder; it was holding its ground against mounting interference.

A control experiment ruled out a potential confound. Because targets and distractors flickered at different frequencies, participants might conceivably have used the flicker itself as a tracking cue rather than the initial outlines. Seventeen additional participants performed the task with all objects flickering at the same frequency or at two different frequencies. Performance did not depend on whether the frequencies differed, confirming that the frequency tagging did not inflate tracking success and that the SSVEP attention effects genuinely reflected attention to cued targets.

The second experiment asked a parallel question about clutter. Forty-one participants tracked four targets among displays containing eight, twelve, or sixteen objects, all moving at a constant moderate speed. Once again, behaviour deteriorated as the display grew denser, with accuracy dropping from 86 percent with eight objects to 77 percent with sixteen. And once again, the neural measure of attentional selection refused to follow. In the midline electrode cluster, reflecting earlier visual areas, the target-versus-distractor difference stayed flat. In the lateral cluster, associated with higher-order visual areas, the relative attentional enhancement actually increased slightly with more objects, the opposite of what a dilution account would predict. Whatever was making the task harder, it was not a weakening of attentional allocation to the targets.

The study also applied a novel source-localisation approach, using fMRI-derived templates to estimate which cortical areas generated the recorded signals. Attentional modulation appeared across most visual regions examined, from V2 through V4, the lateral occipital complex, and the motion-sensitive area hMT+, though intriguingly not in V1, whose tiny receptive fields may be unable to maintain stable responses to objects constantly moving through them. The overall drop in raw SSVEP amplitudes with speed and clutter, which affected targets and distractors alike, is best explained by low-level sensory factors rather than attentional withdrawal. Faster objects spend less time within small receptive fields, and denser displays intensify competitive interactions among stimuli crowding into shared receptive fields, in line with the classic biased competition model of attention.

The authors offer an evocative metaphor to tie the findings together, drawing on Pylyshyn’s notion of attention as a kind of glue binding targets to their mental representations. When the number of tracked targets increases, the same amount of glue must be spread more thinly, and each target receives weaker facilitation, which the team’s earlier work confirmed. But when speed or distractor number rises, the glue stays just as strong; instead, external forces tugging at the bonds, in the form of close encounters and spatial interference, grow more frequent and more violent, until the glue fails at discrete moments, producing the characteristic tracking errors of dropping a target or swapping it for a distractor. Attention allocation, in this view, is set entirely by top-down goals and does not react to bottom-up display characteristics.

The implications reach beyond the laboratory. The results suggest that training regimes or clinical assessments built around multiple object tracking should not assume that harder displays automatically mean weaker attention; performance and attentional allocation can dissociate in principled ways. They also favour target enhancement over distractor suppression as the primary mechanism of tracking, since attentional modulation was sensitive to the number of targets but stubbornly indifferent to the number of distractors. For a skill so central to driving, sport, and everyday navigation, the message is a reassuring one: when the world speeds up and crowds in, your brain does not loosen its grip on what matters. It simply fights harder against the chaos, and sometimes the chaos wins anyway.

Subject of Research: Neural mechanisms of attentional allocation in multiple object tracking under varying object speed and distractor number

Article Title: Object speed and distractor number affect performance but not attentional allocation in multiple object tracking

Article References: Adamian, N., Akalan, F., & K.Andersen, S. (2026). Object speed and distractor number affect performance but not attentional allocation in multiple object tracking. Attention, Perception, & Psychophysics, 88(7), Article 203. https://doi.org/10.3758/s13414-026-03343-3

Image Credits: AI Generated

DOI: 10.3758/s13414-026-03343-3

Keywords: multiple object tracking, visual attention, SSVEP, EEG, selective attention, motion perception, spatial interference, visual cortex, psychophysics, cognitive psychology, attentional allocation, biased competition