영어 모의고사 (자동 편성)

Working memory refers to the mental workspace in which information ①is temporarily 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 influential that it ②become widely known as the magic number seven. Subsequent research, however, revealed that Miller's estimate was somewhat generous, since it failed to account for the complexity of the material ③being remembered. Nelson Cowan later argued that when rehearsal strategies ④are prevented 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.

Millions of people now wear devices that continuously track their heart rate, sleep patterns, and daily activity, ①generating an intimate stream of health data that would have been unthinkable to collect just two decades ago. Users generally assume this information belongs to them, since it ②is drawn directly from their own bodies. In practice, however, the terms of service that few people read often ③grants the manufacturer broad rights to store, analyze, and even sell anonymized versions of that data to insurers or advertisers. This arrangement becomes especially troubling when health data influences decisions far beyond fitness, such as insurance premiums or employment screening. Advocates for reform argue that biological data deserves a category of protection ④stronger than ordinary consumer information, precisely because it can never truly ⑤be replaced or reset once it has been exposed.

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.

Military researchers have developed weapons systems ①capable of identifying and engaging targets without a human pressing the final button, a prospect that has ②alarmed arms control experts for over a decade. Advocates within defense agencies argue that autonomous systems can react faster than any human operator and may even reduce casualties by removing fear and fatigue from split-second decisions. Human rights organizations counter that removing a person from the final decision ③strip away moral responsibility at the exact moment it matters most, since a machine cannot be held accountable in any meaningful sense. Diplomatic negotiations toward an international treaty have repeatedly ④stalled, partly because military powers disagree on how to define autonomy precisely enough to regulate it. Meanwhile, the underlying technology continues to advance regardless of ⑤whether the diplomacy meant to govern it keeps pace.

As private companies develop the technology to mine asteroids for rare metals and water, a legal question once confined to science fiction has suddenly ①become urgent: who, if anyone, owns the resources of outer space? Existing international treaties state that no nation may claim sovereignty over celestial bodies, but they say remarkably little about ②whether a private company may extract and sell what it finds there. Some governments have already passed domestic laws ③granting their own companies rights to material mined in space, a move that other nations view as a unilateral attempt to settle a question that should ④decide collectively. Supporters of space mining argue that new resources could ease shortages on Earth and fund further exploration, while critics warn that treating space as an open frontier for extraction risks ⑤repeating the same environmental and political mistakes that colonization caused on Earth.

Economists traditionally measured a community's wealth in terms of factories, income, or infrastructure, yet sociologists increasingly point to a less ①visible resource: social capital, the networks of trust and reciprocity that allow people to cooperate for mutual benefit. Robert Putnam popularized the concept by documenting a ②decline in civic participation, noting that fewer Americans joined bowling leagues, parent associations, or local clubs even as bowling itself remained popular. Communities rich in social capital tend to enjoy ③lower crime and more effective local government, because neighbors who know and trust one another can coordinate informally without relying entirely on formal institutions. However, social capital is not uniformly beneficial; tightly bonded networks can also ④welcome outsiders and reinforce existing inequality. For this reason, researchers increasingly distinguish between bonding capital and bridging capital, which ⑤connects otherwise separate communities.

City governments increasingly market dense networks of surveillance cameras as a straightforward safety upgrade, promising ①faster emergency response and lower crime rates. In many cases, the technology delivers exactly what it promises, helping investigators identify suspects within hours rather than weeks. Yet the same infrastructure that catches criminals also creates a ②permanent record of where ordinary, law-abiding citizens go about their daily lives. Once a camera network exists, its purpose can quietly ③shrink, from tracking traffic accidents to monitoring political protests, often without new public debate or explicit approval. Civil liberties groups argue that a city should decide how much surveillance it wants before the cameras go up, not years ④afterward when the infrastructure is already impossible to remove. Convenience installed today, in other words, can quietly become ⑤control tomorrow.

Hospitals are increasingly adopting artificial intelligence tools that can scan medical images and flag potential tumors ①faster and, in some trials, more accurately than experienced radiologists. Patients understandably welcome any technology that catches disease earlier, yet the growing reliance on these tools raises an uncomfortable legal question that hospitals have only begun to address. When an AI system misses a diagnosis that a human doctor would likely have caught, responsibility becomes ②easy to assign. The doctor may argue that they simply trusted a tool the hospital required them to use, the hospital may argue that the software company guaranteed its accuracy, and the software company may argue that no algorithm can be expected to be ③perfect. Until legal systems ④clarify this chain of responsibility, patients harmed by an algorithmic error may find themselves with no clear party to hold ⑤accountable.

Suppose you meet a quiet, ①gentle man who speaks softly, tends a small garden, and organizes his bookshelf with meticulous care. Asked whether he is more likely a florist or a truck driver, most people ②instinctively choose florist, since his personality matches the stereotype so neatly. This judgment, however, ignores a crucial fact: truck drivers vastly ③trail florists in almost every country, which means that, statistically, he is far more likely to belong to the larger group regardless of how well he fits the flower-shop image. Relying on resemblance to a familiar category rather than on underlying numerical proportions, known among researchers as representativeness, leads people to overlook these base rates almost ④automatically. Even trained professionals, who should know better, often fall into the same trap when a vivid description feels more ⑤convincing than plain statistics.

Classical economic theory assumes that consumers evaluate every price change in the same rational way, weighing marginal benefit against marginal cost regardless of the absolute numbers involved. (A) In a well-known laboratory experiment, participants chose between two chocolates, one of higher quality priced at a small fee and one of lower quality offered for free. When both prices were merely reduced by the same amount, most participants still preferred the higher-quality option. Yet once the cheaper chocolate's price was lowered to zero, the majority abruptly switched their preference, even though the price gap between the two options had not actually changed. (B) Researchers explain this reversal by noting that a free item carries no perceived risk of loss, which makes it feel disproportionately attractive compared with any option that still requires payment, however small. (C) Behavioral economists, however, have discovered a striking exception: the moment a price drops to exactly zero, people's judgment shifts in a way that ordinary discounting cannot explain.