영어 모의고사 (자동 편성)

When people recall precisely where they were and what they were doing at the moment they learned of some momentous public event, the resulting memory often feels extraordinarily vivid, as though it ①had been etched into the mind with photographic precision; researchers have termed such recollections flashbulb memories. Proponents of this concept once ②argued that the emotional intensity and surprise ③accompanying such events ④triggers a distinct neural mechanism that preserves details with unusual fidelity, effectively exempting them from the gradual decay that afflicts ordinary memories. Subsequent longitudinal studies, however, have complicated this picture considerably: when researchers compared accounts gathered within days of an event to accounts gathered years later, they discovered that details had frequently been altered, omitted, or even fabricated, even though the individuals reporting them expressed unwavering confidence in their accuracy. This persistent mismatch between subjective certainty and objective consistency implies that flashbulb memories, far from being flawless recordings, are reconstructed narratives ⑤shaped by rehearsal, media exposure, and the passage of time.

Emissions trading, a market-based mechanism for ①curbing greenhouse gases, allows governments to set an overall limit on carbon output while letting firms trade permits within that limit. Under this system, ②known as cap-and-trade, companies that cut emissions more cheaply than required can sell surplus allowances to firms facing higher reduction costs. Proponents argue that this flexibility lets society reach a given environmental target at the lowest overall cost, since abatement shifts toward whoever can ③achieve it most economically. Critics, however, ④contends that trading schemes frequently allocate too many permits, a flaw that has historically depressed carbon prices and blunted incentives to invest in cleaner technology. For example, early phases of several regional markets granted allowances so generously that permit prices collapsed, ⑤leaving polluters with little pressure to change their practices.

When engineers design the software ①that controls a self-driving car, they inevitably face a question that philosophers have debated for decades. If a crash becomes unavoidable, should the vehicle protect its own passenger at all costs, or should it be programmed ②minimizing total harm, even if that means steering toward the person inside? Surveys reveal an uncomfortable contradiction: most people say a car should be programmed to save the greater number of lives, yet the same people admit ③they would never buy a vehicle designed to sacrifice its own owner. This gap between the ideal and the personal choice suggests that ethical programming cannot ④be settled by public opinion alone. Manufacturers, regulators, and philosophers must instead agree on transparent rules before autonomous vehicles ⑤are allowed to make such decisions on the road.

Every time a smartphone connects to a cell tower or a Wi-Fi network, it quietly records a data point that can later ①be pieced together into a detailed map of where its owner has been. Most users agree to this tracking without ②reading the fine print, assuming that a single company's database poses little threat. The real danger, however, emerges when location data from multiple sources ③combining. A pattern that reveals a person's home, workplace, and daily route can expose far more than any single app ever intended to share. Companies often argue that anonymized data cannot be traced back to individuals, yet researchers have repeatedly ④shown that a handful of location points is enough to identify a specific person. As a result, privacy experts increasingly insist that location data ⑤deserves the same legal protection as medical records.

A few years ago, spotting a manipulated video ①required a trained eye and specialized software. Today, freely available applications allow almost anyone ②to swap a face or fabricate a voice convincingly enough to fool casual viewers within minutes. This shift has quietly undermined one of the oldest assumptions in human communication: that seeing and hearing something ③are a reasonably reliable way to confirm it happened. Journalists now face the burden of ④verifying footage that once would have been accepted as self-evident proof. Perhaps more troubling than any single fake video is the broader effect on public trust, in which genuine footage can ⑤be dismissed as fabricated simply because deepfakes exist.

Working memory refers to the mental workspace in which information is ①temporarily held and manipulated while a person performs a cognitive task. Such tasks include solving an arithmetic problem, following a rapid conversation, or holding a phone number in mind before dialing it. In the mid-twentieth century, the psychologist George Miller proposed that this workspace could accommodate roughly seven discrete items. His claim proved so ②influential that it became widely known as the magic number seven. Subsequent research, however, revealed that Miller's estimate was somewhat ③conservative, since it failed to account for the complexity of the material being remembered. Nelson Cowan later argued that when rehearsal strategies are ④prevented and attention is tightly controlled, genuine capacity hovers closer to four items rather than seven. This discrepancy suggests that what earlier studies had measured was not raw storage capacity itself but a hybrid quantity ⑤inflated by clever strategies. Contemporary psychologists therefore distinguish between the fixed limit of working memory and the flexible techniques that allow individuals to transcend that limit.

Imagine a brilliant scientist named Mary who has spent her entire life inside a black-and-white room, learning everything physical science could ever teach about color, including the exact wavelengths of light and the precise neural processes triggered when a person sees red. Despite this ①complete physical knowledge, Mary herself has never actually seen a color; every book, screen, and object in her world has been carefully restricted to shades of gray. Suppose that one day Mary is finally released from the room and shown a ripe red tomato for the first time. Philosopher Frank Jackson argued that Mary would learn something entirely ②new at that moment, namely what it actually feels like to see red, even though she already possessed every physical fact about color there was to know beforehand. If Mary genuinely gains new knowledge upon leaving the room, then physical facts alone ③cannot capture everything there is to know about conscious experience, ④confirming that something was indeed missing from her exhaustive scientific education all along, a conclusion that some physicalists find entirely ⑤unsurprising.

Generative AI systems learn to write, draw, and compose by studying enormous collections of existing human work. Supporters argue that this resembles how a human student learns by studying the masters. Critics respond that the comparison breaks down at scale: _______________. Courts around the world are now being asked to decide whether training an algorithm on copyrighted material counts as fair use or as infringement.

Military researchers have developed weapons systems capable of identifying and engaging targets without a human pressing the final button. Advocates argue that autonomous systems can react faster than any human operator. Human rights organizations counter that removing a person from the final decision strips away moral responsibility, since _______________. Diplomatic negotiations toward an international treaty have repeatedly stalled, partly because military powers disagree on how to define autonomy precisely enough to regulate it.

For this reason, many economists argue that the real challenge is not the robots themselves, but the speed at which society is expected to adapt to them. Factories that once employed hundreds of workers on an assembly line now run with a fraction of that staff, as robotic arms handle tasks that used to require human hands. ( ① ) Economists have long debated whether this shift destroys jobs or simply transforms them. ( ② ) Historical evidence offers some comfort: previous waves of automation eventually created new categories of work that no one had predicted. ( ③ ) However, critics point out that earlier transitions unfolded over generations, giving workers time to retrain, whereas today's robots are being deployed at a pace that leaves little room for adjustment. ( ④ ) A factory worker displaced this year cannot simply wait for a new industry to emerge decades from now. ( ⑤ )