What Is Brain Rot?

You open your phone to check one thing. Ten minutes later, you’re still scrolling. Not because you were looking for something in particular, but because stopping feels harder than continuing. 

This friction is one of the hallmarks of what we now call brain rot. And this form of digital engagement really does hit the brain differently than traditional media.

In controlled experiments, just 15 minutes of typical internet use was enough to shrink attention, and the effect persisted even after they went offline. Reading a print magazine for the same amount of time did not produce this effect [1].

This is critical because attention is a trainable system. The neural circuits that allocate attention are physically plastic, reshaping themselves with repeated exposure [2]. What you practice becomes easier, and what you neglect becomes harder. 

Short-form feeds like TikTok, Reels, and Shorts take advantage of that plasticity, training attention toward fast switching and constant novelty [3]. Over time, this makes sustained focus and mental effort harder to maintain. That mismatch between how attention is trained and how it’s actually needed is at the core of brain rot.

In this article, we’ll unpack what brain rot actually is, how digital environments reshape attention and memory at a structural level, and why the effects linger — along with what you can do about it.

What is Brain Rot?

Brain rot is what it feels like when attention becomes harder to sustain after long exposure to fast, low-effort digital content — especially endless scrolling and short-form feeds.

In 2024, brain rot was named Oxford’s Word of the Year, officially elevating an internet meme to formal English — and obligating lexicographers to produce a brain rot definition [4].

Oxford’s brain rot definition is:

“A perceived loss of intelligence or critical thinking skills, esp. (in later use) as attributed to the overconsumption of unchallenging or inane content or material. Now also: content or material that is perceived to have this effect. In recent use particularly associated with the overconsumption of such content posted online.”

The term brain rot is cultural. But the cognitive effects that it is pointing toward are empirical.

Brain rot reflects how the mind adapts to fast-paced digital interfaces — and that toll shows up clearly in attention, memory, and focus.

How Screen Time Affects the Brain

When it comes to brain rot, not all screen time affects the brain in the same way.

The biggest effects come from fast-switching digital environments — short-form video, infinite feeds, notifications, and interfaces that constantly reset what matters. Researchers refer to this pattern as media multitasking: rapid switching between streams of information, sometimes across apps, sometimes within a single feed [5].

Over time, the brain adapts to that rhythm.

Brain Rot Effect 1: Attention Loses Its Filter

One of the earliest and clearest effects is a loss of attentional filtering — the ability to keep irrelevant information out of mind. 

A well-known 2009 study from Stanford tested this directly [6].

Participants tracked two relevant items while ignoring others that were deliberately irrelevant. 

For light media multitaskers, added noise barely mattered. Their performance stayed stable even as distractions piled up. 

But for heavy media multitaskers, every distractor imposed a cost. Performance declined linearly with each additional irrelevant item.

This pattern maps onto the attentional demands of short-form feeds. Each clip is potentially important and nothing can be ignored for long.

Over time, attention becomes broad instead of selective.

Brain Rot Effect 2: What You Pay Attention To Doesn’t Stick

Once attention becomes more permissive, a second problem follows: information fails to stabilize into memory.

In a controlled experiment, researchers compared heavy and light media multitaskers on simple memory tasks. They briefly held information in mind and then had to recognize it moments later [7].

Light media multitaskers formed clear, stable representations. When they remembered something, they remembered it cleanly.

Heavy media multitaskers didn’t. Across tasks, their memories were less precise. 

And that loss of accuracy carried forward. The same individuals who formed blurrier memories in the moment were also worse at remembering the information later. Long-term memory suffered  — even for material they had paid attention to, and even when they felt confident they remembered it.

Light vs. heavy media multitaskers show differences in memory precision and retention. Participants were grouped as LMMs (light media multitaskers) or HMMs (heavy media multitaskers) based on habitual digital multitasking. Across tasks, LMMs formed more precise working-memory representations than HMMs, and that higher precision predicted better long-term memory for both task-relevant items and ignored distractors. From M.R. Uncapher, K. Thieu, A.D. Wagner, Psychon. Bull. Rev. 23 (2016) 483–490. Licensed by CC BY 4.0

This is where brain rot becomes particularly insidious. You might follow the content. You might even understand it. But less of it sticks.

Brain Rot Effect 3: Attention Becomes Restless

The final effect of brain rot is the one that we know all too well: the inexorable urge to scroll.

In a naturalistic study, researchers measured how often people switched between on-screen content during everyday computer use. Switches occurred about every 19 seconds, and nearly 75% of content was viewed for less than a minute [8].

That rhythm should sound all too familiar.

More revealing was when people switched. Physiological arousal didn’t spike after the switch. It built up beforehand — rising for roughly 12 seconds, peaking at the moment of switching, then dropping once new content appeared. 

The pattern was also asymmetric. Switching from work to entertainment showed a clear anticipatory arousal spike. Switching back did not. The feed pulls harder than it releases.

This is what fast-switching platforms train. Every TikTok, Reel, or Short resets relevance. Every swipe carries the promise that something better might be next. Over time, sustained focus feels almost painful, and attention just keeps moving because the brain has learned that relief follows the scroll.

To really understand why these digital environments leave a lasting imprint — and what we can do about it — we need to look at how training reshapes the brain.

Neuroplasticity and Brain Rot

The adult brain isn’t fixed. It remodels itself in response to repeated demands, a property known as neuroplasticity. 

Neuroplasticity unfolds through several overlapping processes. Synaptic plasticity adjusts how neurons communicate, structural plasticity reshapes brain architecture over time, and neurogenesis adds new neurons to existing circuits.

A classic demonstration comes from a juggling study [9]. Adults with no prior experience learned a simple three-ball cascade over three months. MRI scans showed measurable increases in gray matter localized to regions involved in visual motion processing. Mental practice left a physical mark.

Adult brain structure adapts to training, and reverses when training stops. MRI scans show localized increases in gray matter in visual-motion and control regions during weeks of juggling practice, followed by a measurable decline after practice ends. From J. Driemeyer, J. Boyke, C. Gaser, C. Büchel, A. May, PLoS One 3 (2008) e2669. Licensed under CC BY 4.0

These changes are remarkably specific. In musicians, for example, the brain allocates more cortical territory to the fingers used most [10].

This kind of sensory remapping isn’t limited to musicians. It shows up in digital interaction as well. Touchscreen users exhibit expanded cortical responses to stimulation of the thumb and index finger — a literal reshaping driven by everyday use [11].

But the most consequential training signal in modern digital life isn’t what our fingers are doing. It’s how our attention is being deployed. 

If sensory and motor systems remodel themselves through repeated use, then attention and control systems — which are just as plastic — should do the same. And repeated exposure to fast, reward-driven digital environments should leave a neural imprint.

We now have evidence that it does.

In one of the few longitudinal training studies in this area, researchers examined how excessive digital gaming affects brain structure over time. At baseline, heavy gamers showed reduced gray matter in the orbitofrontal cortex (OFC), a region involved in evaluating rewards and exercising restraint [12].

The researchers then took people with little gaming experience and randomly assigned them to six weeks of intensive daily gaming. Even over this short period, they showed a measurable reduction in OFC gray matter.

This is the missing link in understanding brain rot. Structural plasticity follows training. 

The remaining question isn’t whether digital environments shape the brain. It’s how that training signal is delivered. And in modern platforms, that signal is reward — specifically, dopamine.

How Dopamine Drives Brain Rot

Dopamine does not cause brain rot. But it’s very good at locking it in.

Contrary to popular myth, dopamine isn’t a “pleasure chemical.” It’s a learning signal. It tracks what might be worth paying attention to next, tuning behavior toward novelty, uncertainty, and predicted relevance [13].

Modern digital platforms lean directly on that system. Infinite feeds, personalization, and rapid switching repeatedly activate dopamine-driven learning in ways that reward constant exploration rather than sustained engagement [14]. 

This is how dopamine-driven learning entrenches brain rot over time.

Personalized short-video content selectively activates the brain’s reward-prediction system. Dopamine here reflects anticipation and predicted relevance rather than pleasure itself. From C. Su, H. Zhou, L. Gong, B. Teng, F. Geng, Y. Hu, NeuroImage 237 (2021) 118136. Licensed under CC BY 4.0

That’s because dopamine doesn’t reward what just happened. It flags what might be worth checking again.

Over time, attention tilts toward anticipation rather than completion — a defining pattern of brain rot.

2. Variable payoff keeps the system learning

If every swipe in your feed delivered the same reward, scrolling would lose its grip quickly.

Instead, most content is forgettable…but some of it is awesome. That uneven payoff is the point.

Decades of dopamine research show that dopamine neurons respond most strongly to unexpected rewards and prediction errors. Variable rewards keep the learning system engaged longer than consistent ones [16].

This mechanism, known as reward prediction error, is foundational to how habits form — for better and for worse [17].

This is the same learning principle that makes slot machines compelling. Applied to feeds, it trains the brain to keep sampling even when most samples disappoint.

3. Social metrics convert approval into a reward cue

Humans have always valued social approval. What’s new is that modern platforms quantify it.

Likes, views, shares, and follower counts turn social value into a visible signal the brain can track.

In an fMRI study, teenagers viewed Instagram-style photos that were identical except for the number of likes displayed [18]. Images with more likes were judged as more appealing and produced stronger activation in reward-related brain regions — even though nothing about the images themselves had changed!

For dopamine systems, these metrics act as cues. Over time, attention drifts toward what performs well, not what feels meaningful.

4. Autoplay removes the moment where control returns

Dopamine-driven behavior is easiest to regulate when environments provide natural stopping points.

Modern digital platforms systematically remove them.

In a large field experiment, researchers tested what happened when autoplay was disabled on Netflix. Viewers watched about 21 fewer minutes per day, and individual sessions were nearly 18 minutes shorter, just because the next episode no longer started automatically [19].

Without a stopping cue, reward-driven momentum carries behavior forward.

5. Micro-rewards train the impulse to check

Most digital use isn’t long engagement sessions. It’s dozens of quick check-ins.

Logging studies of real smartphone behavior show that the median user performs ~30–35 short checking sessions per day, most lasting under 30 seconds. In one dataset, 35% of phone interactions lasted just one second, and 90% ended within half a minute.

And checking doesn’t satisfy use. It expands it. 

The frequency of these micro-checks is positively correlated with total phone time, acting as a gateway into longer engagement. When researchers made it more likely that a quick check would reveal something new, checking behavior surged — in some cases rising nine-fold — and total phone use rose in parallel [20].

This is exactly the pattern dopamine systems learn from best: small rewards, delivered often, at low cost, with uncertain payoff.

Over time, the phone stops being something you decide to use and becomes something you reach for automatically.

How to Reduce Screen Time

The cognitive cost of screen time depends less on whether you use screens and more on how you use them.

Across 41 experimental studies, media multitasking was associated with a large drop in cognitive performance, with an average effect size of Cohen’s d = −0.71 [21].

In behavioral science, a d of 0.2 is considered small, 0.5 moderate, and 0.7 large. 

For perspective, a −0.71 effect size is similar in magnitude to the learning deficits researchers see when people try to learn after a full night without sleep [22].

But that effect size is not fixed.

Within the same meta-analysis, cognitive decrements ranged from near zero to severe depending on how digital environments are structured.

So what pushes performance toward that −0.71? 

The same features that drive brain rot in the first place.

Here are three evidence-based rules for reducing the cognitive cost of screen time without abandoning screens entirely.

Rule 1: Don’t Mix Mental Worlds

When people switch between unrelated tasks, performance drops hard (d ≈ −0.70). But when the tasks are related, the damage is a lot smaller (d ≈ −0.26).

The problem isn’t information overload. It’s forcing the brain to repeatedly abandon one mental frame and boot up another. Each reset burns cognitive fuel.

Practical takeaway: Writing while checking sources is fine. Writing while bouncing between email, TikTok, and group chats is not. If the content belongs to different mental realms, expect a steep cost.

Rule 2: Decide When You Switch

When people control the timing of pauses and switches, media multitasking produces little to no cognitive impairment (d ≈ −0.08). When timing is dictated by the system — via autoplay, notifications, or infinite feeds — performance collapses (d = −0.65 to −0.82).

That’s huge. Just losing control over pacing turns manageable screen use into brain rot training.

Practical takeaway: Turn off autoplay. Silence non-essential notifications. The moment where you decide whether to continue is a cognitive checkpoint, and it matters more than you think.

Rule 3: Keep Attention in One Place

When multitasking spans multiple devices — laptop, phone, tablet — cognitive performance drops sharply (d ≈ −0.70). However, when switching stays within the same screen or workspace, the effect is much smaller (d ≈ −0.26).

Each device introduces a new layout, new cues, and new habits. For the brain, it’s basically a full reset.

Practical takeaway: If you’re trying to focus, keep your smartphone out of reach. A second tab is not the same as a second screen.

Brain Rot FAQs

How to do a Digital Detox

The most effective digital detox involves temporarily removing the aspects of digital use that drive brain rot, not quitting outright [23].

Here’s why. A review of 139 studies on digital detox interventions found that structured reductions work better than total disconnection [24].

In one study, participants either gave up smartphones entirely for seven days, or reduced daily use by one hour. Both groups improved at first, but only the reduction group maintained benefits over time. The abstinence group largely reverted as soon as normal access returned [25].

So what does an evidence-based digital detox actually look like?

For a short window — maybe 3 to 7 days — the phone stays available for practical use: calls, texts, maps, work tools. What is eliminated is anything designed for endless sampling. Scrollable feeds are blocked. Short-form video is inaccessible. Autoplay is off.

Without constant novelty and forced switching, attention has no choice but to settle. Over time, that steadier state should start to feel normal again.

How to Increase Dopamine

Dopamine responsiveness improves most reliably through sustained effort toward meaningful goals [26].

Heavy exposure to high-novelty digital content raises the threshold for what feels rewarding. When that happens, ordinary tasks stop registering as motivating. This isn’t because dopamine is low, but rather because the system has adapted to constant stimulation.

Activities that restore dopamine signaling share a common structure: they require effort, unfold over time, and end with clear completion. 

Exercise fits that pattern better than almost anything else.

In animal models, just 30 days of exercise increased dopamine release capacity across the striatum, the brain’s core motivation and reward hub. The dopamine system itself became better at releasing dopamine when called upon, and the change persisted even after exercise stopped [27].

Other activities can move the needle similarly. Deep, uninterrupted reading rewards sustained comprehension. Learning a physical or technical skill reinforces dopamine signaling tied to progress and mastery, not quick hits. 

Sleep matters here too. Dopamine signaling is tightly coupled to circadian rhythms, and sleep loss blunts reward sensitivity [28]. If motivation feels absent across the board, sleep debt is often part of that equation [29].

Do Video Games Rot Your Brain?

Video games do not rot your brain — or at least not by default.

The problem shows up in games built around endless play, constant rewards, and minimal effort — especially mobile loot-driven games. Sessions have no clear end. Progress resets quickly. The incentive is to stay engaged, not to finish anything. In attentional terms, these games behave an awful lot like infinite social feeds.

But many games work quite differently.

Games that require navigating complex environments and committing to longer arcs of action place very different demands on attention [30].

For example, in controlled experiments, training on a complex 3D game (Super Mario 3D World) led to improvements in memory and spatial learning, while training on a simpler mobile game (Angry Birds) did not [31].

Strategy games reward planning and sustained focus. Narrative-driven games ask the player to stick with a single unfolding story, not unlike a novel. Skill-based games tie reward to error correction and improvement over time. These designs reinforce attention for staying with a task until something is completed or mastered.

Bottom line: the design of the game determines whether it reinforces brain rot.

References

[1] M. Peng, X. Chen, Q. Zhao, Z. Zhou, PLoS One 13 (2018) e0198543. 

[2] E.C. de Barros, IBRO Neurosci. Rep. 17 (2024) 415–422.

[3] N. Barton, M. Smyth, Memory 33 (2025) 788–801.

[4] Oxford English Dictionary, brain rot (n.), Oxford Univ. Press (2025), doi:10.1093/OED/7167742160.

[5] M.R. Uncapher, A.D. Wagner, Proc. Natl. Acad. Sci. U.S.A. 115 (2018) 9889–9896.

[6] E. Ophir, C. Nass, A.D. Wagner, Proc. Natl. Acad. Sci. U.S.A. 106 (2009) 15583–15587.

[7] M.R. Uncapher, K. Thieu, A.D. Wagner, Psychon. Bull. Rev. 23 (2016) 483–490.

[8] L. Yeykelis, J.J. Cummings, B. Reeves, J. Commun. 64 (2014) 167–192.

[9] B. Draganski, C. Gaser, V. Busch, G. Schuierer, U. Bogdahn, A. May, Nature 427 (2004) 311–312.

[10] C. Pantev, A. Engelien, V. Candia, T. Elbert, Ann. N. Y. Acad. Sci. 930 (2001) 300–314.

[11] A.D. Gindrat, M. Chytiris, M. Balerna, E.M. Rouiller, A. Ghosh, Curr. Biol. 25 (2015) 109–116.

[12] F. Zhou, C. Montag, R. Sariyska, B. Lachmann, M. Reuter, B. Weber, P. Trautner, K.M. Kendrick, S. Markett, B. Becker, Addict. Biol. 24 (2019) 100–109.

[13] K.M.J. Diederen, P.C. Fletcher, Neuroscientist 27 (2021) 30–46.

[14] E.S. Bromberg-Martin, M. Matsumoto, O. Hikosaka, Neuron 68 (2010) 815–834.

[15] C. Su, H. Zhou, L. Gong, B. Teng, F. Geng, Y. Hu, NeuroImage 237 (2021) 118136.

[16] W. Schultz, J. Neurophysiol. 80 (1998) 1–27.

[17] T.N. Lerner, A.L. Holloway, J.L. Seiler, Curr. Opin. Neurobiol. 67 (2021) 123–130.

[18] L.E. Sherman, A.A. Payton, L.M. Hernandez, P.M. Greenfield, M. Dapretto, Psychol. Sci. 27 (2016) 1027–1035.

[19] B. Schaffner, Y. Ulloa, R. Sahni, J. Li, A.K. Cohen, N. Messier, L. Gao, M. Chetty, Proc. ACM Hum.-Comput. Interact. 9 (2025) CSCW030.

[20] A. Oulasvirta, T. Rattenbury, L. Ma, E. Raita, Pers. Ubiquit. Comput. 16 (2012) 105–114.

[21] S.-H. Jeong, Y. Hwang, Hum. Commun. Res. 42 (2016) 599–618.

[22] C.R. Newbury, R. Crowley, K. Rastle, J. Tamminen, Psychol. Bull. 147 (2021) 1215–1240.

[23] T. Radtke, T. Apel, K. Schenkel, J. Keller, E. von Lindern, Mob. Media Commun. 10 (2022) 190–215.

[24] L. Marciano, S. Jindal, K. Viswanath, Pediatrics 154 (2024) e2024066142.

[25] J. Brailovskaia, J. Delveaux, J. John, V. Wicker, A. Noveski, S. Kim, H. Schillack, J. Margraf, J. Exp. Psychol. Appl. 29 (2023) 149–161.

[26] B.T. Sharpe, R.A. Spooner, Perspect. Public Health 145 (2025) 190–191.

[27] A. Marques, P. Marconcin, A.O. Werneck, G. Ferrari, É.R. Gouveia, M. Kliegel, M. Peralta, A. Ihle, Brain Sci. 11 (2021) 829.

[28] D. Tomasi, G.-J. Wang, N.D. Volkow, Transl. Psychiatry 6 (2016) e828.

[29] P.K. Parekh, C.A. McClung, Front. Psychiatry 6 (2016) 187.

[30] E.T. Smith, C. Basak, PLoS One 18 (2023) e0285925.

[31] G.D. Clemenson, C.E. Stark, J. Neurosci. 35 (2015) 16116–16125.