When Markets Move Faster Than Our Brains

Imagine this: it’s 3 a.m., and you keep receiving notifications about “the AI boom”. Your tech stocks have been climbing for weeks; your investment app now shows a significant unrealised gain. Social media is full of people saying this is a “once‑in‑a‑lifetime opportunity”. Part of you thinks it can’t go up forever. Another part whispers, but what if it does, and I miss out? You tell yourself to be cautious and stick to your initial plan to sell. Nevertheless, here you are, still scrolling social media, still debating whether to lock in profit or double down.

Sounds familiar? If so, you’re not alone. It can happen to new and long‑time investors, as well as experienced traders.

Let us shift scene for a second. It’s right before the market opens, and crude oil has jumped 4% overnight. Traders on your feed have boasted huge wins, but you missed it. Seeing the price swings on the pre‑market chart and yesterday’s loss still in the back of your mind, you feel a powerful urge to “make it back” by jumping in on the open.

Different situations, but they feel similar in a way. Your plans suggest caution, but your emotions and what others seem to be doing push you the other way. Signals from data, advice, social cues, and gut instinct pull you in different directions at once. You bounce between complete paralysis (doing nothing) and the urge to act now.

Implementation requirement: In fast-moving settings, it helps to create temporal buffers between information arrival and decision execution. In other words, create a short pause between what you learn and what you do with it; implement mandatory cooling periods; establish predefined decision rules that can be executed without real-time deliberation.

These temporal buffers can help investors feel more in control and confident, knowing they have time to think before acting.

The goal is not to slow anyone down; it’s to respect how the brain actually works. Allowing a brief pause between stimuli and responses gives the mind enough time to think clearly before speed takes over. Accommodating neurobiological processing limitations should not be viewed as a drag on performance, but as a way to sharpen it.

2 . Neural Mechanisms of Financial Decision-making

To understand why stress, speed, and ambiguity so easily distort our decisions, we should look at the brain regions involved in financial and risk-related choices.⁵ Neurofinance – basically neuroscience applied to financial decision-making - adds a biological layer to this picture. Researchers have mapped which brain regions activate during different types of financial decisions using various brain imaging techniques.⁶

Let’s first look at the prefrontal cortex (PFC). The PFC handles planning, analysis, and impulse control. It’s what enables us to think in terms of months or years rather than minutes and is critical for deliberate, systematic thinking. However, under acute stress or time pressure, PFC activity declines and connections to other brain regions weaken, making it harder to maintain long-term plans.⁷ Meanwhile, the amygdala might already be reacting to potential losses and negative surprises, treated as potential threats, before conscious deliberation kicks in.⁴

Another region linked to money choices is the nucleus accumbens (NAcc). The NAcc is part of the ventral striatum, central to reward anticipation. Greater risk-taking behaviours and speculative choices are often associated with greater NAcc activation.⁸

The final region worth looking at is the insula. Involved in processing pain, disgust, and anticipated regret, its heightened activation is associated with risk aversion and avoidance of options that could lead to regret.

These systems constantly interact. Under rapid feedback, social signals, or ambiguous risk, reward circuitry can override deliberative control, while fear-driven responses make losses feel intolerable.

3. Social Amplification: Why We Don't Decide Alone

We almost never decide in a vacuum. Our choices are shaped and amplified by what we believe others think and by the stories circulating in our groups. Remember that 3 a.m. scroll ⁹ through AI hype and headlines about rising tech stocks? At that moment, you were forming views not only about underlying fundamentals but also about other people’s expectations (“I think they expect prices to climb”).

Several concepts help clarify this process, and you may have encountered some of them. The first is the Theory of Mind, the capacity to attribute mental states (e.g., beliefs, intentions, desires) to others. It’s encompassed by “mental representation” whereby we build internal models of the world, including representations of other people’s thoughts. “Mindreading” is an everyday term often used to describe the ability to infer others' mental states.

When this capacity becomes layered, we stop reasoning only about facts or even about others’ beliefs, and start reasoning about others’ beliefs about others. Decisions shift from “What is true?” to “What do people think others will do?”. This type of layering is also described as recursive. In other words, recursive mindreading involves embedding one mental representation within another: I believe that you think that she expects prices to increase, and so forth. Each additional layer adds a further level of meta-representation. In financial contexts, these recursive structures can generate what might be called recursive belief loops: decisions are no longer based solely on fundamentals, but on expectations about how others will interpret and act upon those fundamentals.

Let’s get back to our 3 a.m. scenario. When this capacity becomes layered or recursive, it means that you are no longer simply forming a view about reality (“AI stocks are rising”), nor even just about another person’s belief (“She thinks AI stocks will rise”). Instead, you begin stacking beliefs inside beliefs.

At the first level, you interpret facts. At the second level, you interpret someone else’s interpretation of those facts. At the third level, you interpret how someone else interprets another person’s interpretation.

For example:

• Level 1: I think AI stocks are overvalued

• Level 2: I think other investors believe AI stocks will continue rising

• Level 3: I think other investors believe that most investors expect prices to rise

Each additional layer increases strategic complexity. The decision is no longer about whether prices should rise, but about whether enough people expect them to rise, and hence about whether others expect that expectation to hold.

This layering process is what makes social markets reflexive. Prices can move not only because fundamentals change, but because beliefs about others’ beliefs shift. The more recursive the reasoning becomes, the further decisions may drift from underlying value and toward collective expectation management. By stacking others’ mental models, we are prone to creating chains of expectations that can amplify trends, narratives, and volatility. It’s easy to see how this can drive bubbles even when individuals see the risks, isn’t it? ¹⁰

Recent moves in Gold and Silver offer a contemporary illustration ¹¹. When prices accelerate, commentary quickly shifts from macro drivers — inflation expectations, central bank demand, geopolitical risk — to narratives about positioning and flows. Market participants are no longer asking only whether gold or silver is fundamentally attractive, but also whether enough others will increase their allocations. Rising prices themselves become signals.

Visible buyers and strong inflows, coupled with repeated media coverage, can increase the perceived legitimacy of the move. If many investors are acting, the action appears safer and more justified (a mechanism known as “social proof”). Herding can follow. Not because everyone shares the same conviction about intrinsic value. As a matter of fact, you might reduce your discomfort of standing apart by aligning your position with the crowd. Indeed, even sceptical participants may join, reasoning that the trend can persist as long as others continue to expect it to.

In such instances, price dynamics reflect more than supply and demand for a metal. They reflect layered expectations about collective behaviour. Gold and silver need not be mispriced for these mechanisms to operate. Once beliefs about others’ beliefs begin to dominate decision-making, momentum can feed on visibility itself. Not only do markets move on information, but also on interpretations of how that information will be interpreted.

Neuroscience complements this picture: reward circuits respond to social opportunity signals, and exclusion or being "left out" carries a real emotional cost in the brain¹². Whether in markets, corporate strategy, or social movements, social cognition turns isolated decisions into collective phenomena.

Conclusion

Late-night AI euphoria, the will to make losses back when markets open and momentum in gold or silver are not isolated episodes. These three examples reveal common structural patterns in high-speed, socially amplified environments. Our neural systems fire up before complete conscious processing completes. This translates into rapid feedback that activates threat and reward circuits. Recursive social reasoning transforms price moves into signals about collective belief. Visible participation generates social proof that can draw even sceptics into alignment.

None of this requires irrationality - or anything beyond the standard operating constraints of human cognition. It requires only bounded cognition interacting with visibility, acceleration and interdependence. Markets become reflexive not because participants abandon reason, but because reasoning itself becomes socially layered.

Understanding this architecture is the first step. Before discussing tools, checklists, or structural safeguards, it is necessary to recognise the terrain: neural constraints, speed mismatches, and social amplification are structural features of modern decision environments. Any durable approach to investing or leadership must begin there.

in Minds & Values will focus on the intersection of neuroscience, finance, psychology, and strategy so that high-stakes choices become less about resisting impulses in the moment and more about designing environments where the right action becomes the easy one. In upcoming articles, this will become more concrete: practical tools you can actually use, from decision checklists and pre-commitment protocols to portfolio review rituals, meeting structures, and leadership practices that reduce bias and protect clarity under pressure.

Awareness of this architecture is useful but insufficient. It must be translated into process design i.e. creating decision frameworks that work with neural mechanisms, not against them. Implementing strategies that accommodate brain responses can improve decision quality in financial contexts.

1 . The Speed Mismatch between Neural Architecture & Modern Times

Today’s markets and news move faster than we can process. Our brains, however, were not designed to handle endless streams of alerts and rapid algorithm-driven feedback loops.¹ Our neural circuitry – that once kept our ancestors safe from danger and immediate physical threats – still fires up when we see a spiky price chart, spooky headlines on social media or on the news. Such quick emotional responses aren’t random; they follow patterns we all share:

Rapid threat detection: subcortical brain structures, including the amygdala, activate well before consciousness arises.² This activation leads to defensive responses that may not be appropriate in the face of rapid price moves or ambiguous communications, which would require more deliberative than reactive processing.

Short-term pattern recognition: we are wired to detect repeating or unusual patterns and outliers in limited information – a trait commonly referred to as survival and evolutionary, which tends to lead us to over-extrapolate from recent market swings or short performance history.³

Asymmetric emotional weighting: the idea that losses hurt us more than equivalent gains is supported by research (known as loss aversion) – though this effect varies with context and magnitude. Neuroimaging studies suggest that gains and losses involve both overlapping and distinct biological correlates.⁴

Current financial markets illustrate a clear mismatch between these neural systems and modern environments. In fact, algorithms and AI-driven systems propagate information and execute orders at sub-millisecond speeds. In contrast, we (human decision-makers) require seconds to minutes to read and interpret data, not to mention the substantially longer time needed to recover from arousal or stress states. During rapid market movements, this gap between event occurrence and human comprehension widens. Algorithmic systems continue operating while human neural processing (particularly the deliberative functions that help us step back and think clearly) remains engaged with information that may already be outdated.

The structural principle generalises beyond finance. Whenever operational systems accelerate substantially beyond the pace we humans can process at the sensory, cognitive, or motor level, expect elevated stress, bias activation, and increased error rates.

References

AInvest. (2026, February 2). Gold’s $60B ETF Inflow vs. Silver’s Overbought Flip: The Flow Tells the Story. AInvest. https://www.ainvest.com/news/gold-60b-etf-inflow-silver-overbought-flip-flow-tells-story-2602/

Arnsten, A. (2015). Stress weakens prefrontal networks: molecular insults to higher cognition. Nature Neuroscience, 18, 1376-1385. https://doi.org/10.1038/nn.4087.

Baddeley, M. (2009). Herding, social influence and economic decision-making: socio-psychological and neuroscientific analyses. Philosophical Transactions of the Royal Society B Biological Sciences, 365(1538), 281–290. https://doi.org/10.1098/rstb.2009.0169

Barberis, N., Shleifer, A., & Vishny, R. (1998). A model of investor sentiment. Journal of Financial Economics, 49(3), 307–343. https://doi.org/10.1016/s0304-405x(98)00027-0

Beverungen, A., & Lange, A.-C. (2018). Cognition in high-frequency trading: The costs of consciousness and the limits of automation. Theory, Culture & Society, 35(6), 89–111. https://doi.org/10.1177/0263276418758906

Bikhchandani, S., Hirshleifer, D., & Welch, I. (1992). A theory of fads, fashion, custom, and cultural change as informational cascades. Journal of Political Economy, 100(5), 992–1026. https://doi.org/10.1086/261849

Breiter, H. C., Aharon, I., Kahneman, D., Dale, A., & Shizgal, P. (2001). Functional imaging of neural responses to expectancy and experience of monetary gains and losses. Neuron, 30(2), 619–639. https://doi.org/10.1016/s0896-6273(01)00303-8

CFA Institute. (2024, April 29). How data can lead investment managers astray. https://www.cfainstitute.org/insights/articles/how-data-can-lead-investment-managers-astray

Eisenberger, N. I., & Lieberman, M. D. (2004). Why rejection hurts: A common neural alarm system for physical and social pain. Trends in Cognitive Sciences, 8(7), 294–300. https://doi.org/10.1016/j.tics.2004.05.010

Filip, A. M., & Pochea, M. M. (2023). Intentional and spurious herding behavior: A sentiment driven analysis. Journal of Behavioral and Experimental Finance, 38, 100810. https://doi.org/10.1016/j.jbef.2023.100810

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Notes

¹ Eisenberger and Lieberman (2004) show that social exclusion recruits a “neural alarm system” that overlaps with physical pain. Contemporary work on financial microstructure notes that high‑frequency markets operate at sub‑millisecond latencies, well below the c.400–500 ms needed for human visual responses (O’Hara, 2015), while Beverungen and Lange (2018) analyse how algorithmic trading unfolds at temporal scales beneath conscious awareness. Together with models of informational cascades in financial and social settings (Bikhchandani, Hirshleifer, & Welch, 1992; Huber et al., 2014; Palmer, 2026), these findings support the interpretation that our neurocognitive “operating system” is overwhelmed by our contemporary decision environments (rather than being flawed).

² See LeDoux (1996) and Wu, Sacchet & Knutson (2012)

³ See Mattson (2014) on superior pattern processing (SPP) as the evolved human cognitive edge, enabling rapid motif detection in uncertain environments; and CFA Institute (2025) on apophenia in investing, where sparse data leads to illusory trends and poor decisions. Evidence also suggests humans systematically overestimate how representative small samples are of larger populations. Tversky and Kahneman (1971) termed this phenomenon "belief in the law of small numbers." In financial contexts, this manifests as excessive extrapolation from recent price trends or brief performance histories (Barberis, Shleifer, & Vishny, 1998; Lakonishok, Shleifer, & Vishny, 1994).

⁴ Suggested further readings include Breiter et al. (2001) on functional magnetic resonance imaging (fMRI) experiments with monetary losses and gains; Mellers & Ritov (2009) on how beliefs mediate how pleasurable or painful gains and losses are experienced; Sokol-Hessner et al. (2012) on emotional regulation, reducing behavioural loss aversion and dampening amygdala responses to losses.

⁵ I’d greatly encourage you to check the interactive brain model by Wellcome Trust, Matt Wimsatt and Jackson Simpson available at: https://www.brainfacts.org/3d-brain.

⁶ See Frydman & Camerer (2016) and Kuhnen & Knitsen (2005).

⁷ See Arnsten (2015) on the effect of stress on cognition and Sussman & Sekuler (2022) on how time pressure negatively impacts executive functions.

⁸ NAcc’s heightened activation influence seems to be context- and content-dependent. See Telzer et al. (2012) on how strong NAcc responses to prosocial or socially meaningful rewards can instead reduce risky behaviour over time

⁹ Filip & Pochea (2023) underlined the increased reliance on social media and apps as tools for financial decision-making.

¹⁰ For more insights on how price predictions drive bubbles, see how market players’ expectations play out in asset pricing models (Hommes et al., 2007). See O’Grady et al. (2015) on recursive mindreading.

¹¹ See articles published by CNBC (Taylor, 2026), AIInvest (2026) as examples of media coverage of the recent moves in previous metals.

¹² See Baddeley (2009) for an introduction to neuroscientific approaches and insights into economic and financial decision-making.