영어 모의고사 (자동 편성)

Psychologists studying human judgment often describe the mind as ①operating through two distinct systems rather than a single unified process. System 1 works quickly and automatically, ②generating impressions and snap judgments without any noticeable sense of effort, much as a driver instinctively brakes when a ball suddenly rolls into the street. System 2, by contrast, is slow, deliberate, and demanding, engaged only when a problem ③require careful calculation or conscious reasoning. Most daily decisions are handled almost entirely by System 1, which allows people ④to function efficiently without exhausting their limited mental resources. This efficiency comes at a cost, however, because System 1 relies on simple shortcuts that can produce systematic errors, and System 2 is frequently too lazy or too occupied to double-check them. As a result, a judgment often feels most confident precisely at the moment when it has skipped the careful scrutiny that could have caught its own mistake, leaving the thinker unaware of how easily the impression was ⑤formed.

Generative AI systems learn to write, draw, and compose by studying enormous collections of existing human work, much of ①which was created without the artists' knowledge or consent. Supporters of the technology argue that this process ②resembles how a human student learns by studying the masters, absorbing style and technique rather than copying specific pieces. Critics respond that the comparison ③breaks down at scale: a single AI model can absorb millions of works in days and then generate outputs that directly compete with the very artists whose work ④trained it. Courts around the world are now ⑤ask to decide whether training an algorithm on copyrighted material counts as fair use or as infringement.

Imagine rewriting a bacterium's code the way a programmer edits a line of software, and much of what synthetic biology ①promises begins to make sense. Researchers in this field assemble standardized genetic parts into circuits that can be inserted into a cell, instructing it to perform a task that no natural organism would ②carry out. Because these circuits are built from interchangeable parts rather than designed from scratch each time, a circuit tested in one lab can often be reused, with ③minor adjustments, in an entirely different application. Critics note that biological systems ④always behave as predictably as electronic circuits, since a gene inserted into a new cell can interact with existing pathways in unexpected ways, occasionally producing results no engineer intended. Even so, as the catalog of standardized parts keeps expanding, engineers are gaining ⑤confidence to design increasingly complex systems.

Imagine watching a fair coin land on heads five times in a row and feeling an increasingly strong intuition that the next flip is somehow due to produce tails. This intuition, though psychologically ①compelling, rests on a basic misunderstanding of probability, since each flip of a fair coin remains entirely ②independent of every flip that came before it. The coin possesses no memory of its previous outcomes, and its probability of landing on tails on the sixth flip stays ③fixed at fifty percent regardless of how many heads preceded it. Casinos have long profited from this misconception, watching gamblers place larger bets after a losing streak simply because they ④mistakenly believe that a win has become statistically overdue. Economists have even documented the same error among trained professionals, including loan officers who grow more likely to approve a risky application simply because they recently rejected several applications ⑤sporadically.

Millions of people now wear devices that continuously track their heart rate, sleep patterns, and daily activity. Users generally assume this information belongs to them, since it is drawn directly from their own bodies. In practice, however, the terms of service that few people read often grant the manufacturer broad rights to store, analyze, and even sell anonymized versions of that data. Advocates for reform argue that biological data deserves a category of protection stronger than ordinary consumer information, precisely because _______________.

The debate over nudging often centers on a single, deceptively simple question: does saving people from their own cognitive errors amount to genuine assistance or subtle manipulation? (A) Supporters respond that every choice architecture, whether intentionally designed or not, already influences behavior in some direction, so deliberately choosing a beneficial default is preferable to leaving the arrangement to chance or to the interests of profit-driven designers. (B) Opponents of nudging argue that any attempt to steer behavior, however gently, treats citizens as incapable of making sound decisions on their own and therefore undermines the very autonomy it claims to respect. (C) This rebuttal shifts the debate away from whether influence should exist at all and toward the more practical question of who should be trusted to design it and for what purpose.

Why do people often overvalue objects they have partly built themselves, even when the finished product is objectively inferior to something they could have simply purchased? (A) This puzzle, which researchers named after a furniture retailer famous for selling items that require home assembly, challenges the standard economic assumption that value is determined solely by an object's inherent qualities. (B) In a series of experiments, participants who assembled a plain storage box were willing to pay nearly as much for their own imperfect creation as they were for a professionally built version, despite being fully aware of its flaws. (C) Labor, it seems, functions as a kind of psychological investment: once effort has been poured into a task, individuals become reluctant to judge the outcome objectively, because doing so would require admitting that their effort was partly wasted. This tendency helps explain why consumers often prefer products that demand some degree of personal assembly, even when a comparable finished item is available at a lower price and requires no time investment whatsoever.

Hospitals are increasingly adopting artificial intelligence tools that can scan medical images and flag potential tumors faster than experienced radiologists. (A) The doctor may argue that they simply trusted a tool the hospital required them to use, while the hospital may blame the software company instead. (B) Yet the growing reliance on these tools raises an uncomfortable legal question: when an AI system misses a diagnosis, who is responsible? (C) Until legal systems clarify this chain of responsibility, patients harmed by an algorithmic error may find themselves with no clear party to hold accountable.

주어진 문장: This insight helps explain why memory athletes can recall astonishing amounts of information despite possessing the same underlying cognitive architecture as everyone else. Working memory refers to the mental workspace in which information is temporarily held and manipulated while a person performs a cognitive task. ( ① ) Such tasks include solving an arithmetic problem, following a rapid conversation, or holding a phone number in mind before dialing it. ( ② ) 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 became widely known as the magic number seven. ( ③ ) Subsequent research, however, revealed that Miller's estimate was somewhat generous, since it failed to account for the complexity of the material being remembered. ( ④ ) Nelson Cowan later argued that when rehearsal strategies are 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 inflated by clever strategies. ( ⑤ ) Contemporary psychologists therefore distinguish between the fixed limit of working memory and the flexible techniques that allow individuals to transcend that limit.

The secret lies not in the cotton plant itself but in a borrowed piece of bacterial DNA. A cotton plant that manufactures its own insecticide sounds like science fiction, yet it has been growing in fields around the world for decades. ( ① ) Scientists achieved this by isolating a gene from a soil bacterium that produces a protein toxic to specific insect pests but harmless to humans. ( ② ) That gene is then inserted into the plant's own genome so that every cell manufactures the protective protein. ( ③ ) Other genetically modified crops carry a gene that allows them to survive direct exposure to a particular herbicide, letting farmers spray a field to kill weeds without harming the crop growing among them. ( ④ ) Supporters point to yield increases and reduced pesticide spraying documented across millions of hectares, while critics worry about weeds and insects evolving resistance to these built-in defenses over time, much as bacteria evolve resistance to antibiotics. ( ⑤ ) Regulatory agencies in most countries now require extensive testing before a genetically modified crop reaches the market, evaluating everything from its nutritional content to its potential effects on nearby ecosystems.