1 . “Assume you are wrong.” The advice came from Brian Nosek, a psychology professor, who was offering a strategy for pursuing better science.
To understand the context for Nosek’s advice, we need to take a step back to the nature of science itself. You see despite what many of us learned in elementary school, there is no single scientific method. Just as scientific theories become elaborated and change, so do scientific methods.
But methodological reform hasn’t come without some fretting and friction. Nasty things have been said by and about methodological reformers. Few people like having the value of their life’s work called into question. On the other side, few people are good at voicing criticisms in kind and constructive ways. So, part of the challenge is figuring out how to bake critical self-reflection into the culture of science itself, so it unfolds as a welcome and integrated part of the process, and not an embarrassing sideshow.
What Nosek recommended was a strategy for changing the way we offer and respond to critique. Assuming you are right might be a motivating force, sustaining the enormous effort that conducting scientific work requires. But it also makes it easy to interpret criticisms as personal attacks. Beginning, instead, from the assumption you are wrong, a criticism is easier to interpret as a constructive suggestion for how to be less wrong — a goal that your critic presumably shares.
One worry about this approach is that it could be demoralizing for scientists. Striving to be less wrong might be a less effective motivation than the promise of being right. Another concern is that a strategy that works well within science could backfire when it comes to communicating science with the public. Without an appreciation for how science works, it’s easy to take uncertainty or disagreements as marks against science, when in fact they reflect some of the very features of science that make it our best approach to reaching reliable conclusions about the world. Science is reliable because it responds to evidence: as the quantity and quality of our evidence improves, our theories can and should change, too.
Despite these worries, I like Nosek’s suggestion because it builds in cognitive humility along with a sense that we can do better. It also builds in a sense of community — we’re all in the same boat when it comes to falling short of getting things right.
Unfortunately, this still leaves us with an untested hypothesis (假说): that assuming one is wrong can change community norms for the better, and ultimately support better science and even, perhaps, better decisions in life. I don’t know if that’s true. In fact, I should probably assume that it’s wrong. But with the benefit of the scientific community and our best methodological tools, I hope we can get it less wrong, together.
1. What can we learn from Paragraph 3?| A．Reformers tend to devalue researchers’ work. |
| B．Scientists are unwilling to express kind criticisms. |
| C．People hold wrong assumptions about the culture of science. |
| D．The scientific community should practice critical self-reflection. |
| A．the enormous efforts of scientists at work | B．the reliability of potential research results |
| C．the public’s passion for scientific findings | D．the improvement in the quality of evidence |
| A．discouraging | B．ineffective | C．unfair | D．misleading |
| A．doubtful but sincere | B．disapproving but soft |
| C．authoritative and direct | D．reflective and humorous |
2 . You are what you eat-and what you eat may be encoded in your DNA. Studies have indicated that your genes play a role in determining the foods you find delicious or disgusting. But exactly how big a role they play has been difficult to figure out. “Everything has a genetic component even if it’s small,” says Joanne Cole, a geneticist at the University of Colorado School of Medicine. “We know that there is some genetic contribution to why we eat the foods we eat. Can we take the next step and actually locate the regions in the genome (基因)?”
New research led by Cole has gotten a step closer. Through a large-scale genomics analysis, her team has identified 481 genome regions that were directly linked to dietary patterns and food preferences. The findings, which have not yet been peer-reviewed, were presented last month at the American Society for Nutrition’s annual conference.
The team based the new study on a 2020 Nature Communications study by Cole and her colleagues that used data from the U.K. Biobank, a public database of the genetic and health information of 500,000 participants. By scanning genomes, the new analysis was able to identify 194 regions associated with dietary patterns and 287 linked to specific foods such as fruit, cheese, fish, tea and alcohol. Further understanding how genetics impact how we eat could reveal differences in nutritional needs or disease risks.
“One of the problems with a lot of these genomics studies is that they’re very small. They don’t have enough people to really be able to identify genes in ways that are credible. This study had a huge group of people, so it’s really powerful.” says Monica Dus, a geneticist at the University of Michigan. “The other thing that I thought was really great is that there are so many different features that they’re measuring related to diet including cholesterol (胆固醇), the body and socioeconomic backgrounds.” As the research advances, Dus says, such genome analysis could potentially assist health care providers and even policymakers to address larger issues that affect food access and health.
It’s definitely true that it may contribute to making sure there aren’t food deserts-areas which have limited access to fresh, healthy and affordable food or to making sure that there’s a higher minimum wage so that everyone can afford to eat, although the journey ahead remains lengthy and challenging.
1. How did researchers conduct the present study?| A．By involving a substantial number of participants. |
| B．By directly analyzing the data from a public database. |
| C．By building on a previous study based on large-scale data. |
| D．By identifying genome regions associated with dietary patterns. |
| A．Powerful participants were involved in the current study. |
| B．The methods employed for the previous studies were credible. |
| C．The genome analyses have helped address larger social issues. |
| D．Various features linked to diet were considered in the present study. |
| A．The benefits of latest large-scale diet-related genome analyses. |
| B．The contribution of genes to diet patters and food preferences. |
| C．The significance of a newly published diet-related genome discovery. |
| D．The introduction of a research on identifying diet-related genome regions. |
| A．National Geographic | B．Sports Illustrated for kids |
| C．Scientific American | D．The Wall Street Journal |
3 . We Need to Think about Conservation on a Different Timescale
Time, perceived by humans in days, months, and years, contrasts with nature’s grander scales of centuries and millennia, referred to as “deep time.” While paleontologists (古生物学者) are trained to think in deep time, conservationists are realizing the challenges it poses. Shortsightedness about time limits modern conservation, with efforts often overlooking past healthy conditions of ecosystems in the context of climate and biodiversity crises.
The shifting baseline syndrome (综合症), where standards in a place change gradually, makes conservation more complex. It involves evaluating ecosystems primarily on their recent past, often with negative consequences.
Recent shifts in California’s forest management practices, from stopping fires to embracing Indigenous knowledge of controlled burns, exemplify the importance of understanding historical ecosystem dynamics. To enhance conservation, adopting a deep-time approach is crucial.
Modern mathematical modeling, combined with long-term data, offers a pathway for preserving ecosystems. In California’s kelp (海带、海藻) forest, researchers identified an overlooked keystone species — the extinct Seller’s Sea Cow (大海牛). By examining past kelp forests, a deeper story impacting regeneration was revealed. The sea cow, a massive plant-cater, contributed to a diverse, vital undergrowth by trimming kelp and letting light reach the area.
The researchers put forward a novel approach to kelp forest restoration: selectively harvesting kelp, imitating the sea cow’s impact. This strategy, considering historical dynamics, challenges assumptions about recent ecosystems and offers new conservation methods.
Rather than only focusing on removing urchins (海胆) or reintroducing sea otters, the researchers suggest employing teams of humans to selectively harvest kelp, as the Steller’s sea cow once did, to encourage fresh growth. This sustainable harvest could benefit both the ecosystem and human consumption.
In short, assumptions based on the recent past may impede the understanding and protection of ecosystems. On the other hand, the application of controlled burns, similar modeling studies, and a deep-time perspective (视角) could significantly transform conservation efforts. Recognizing our role in an ongoing narrative spanning millions of years is essential, urging a comprehensive understanding of ecosystems through time. Embracing this role is crucial for shaping the future and establishing vital connections from the past to the future.
1. What is the “shifting baseline syndrome,” mentioned in the passage?| A．A syndrome that affects human beings’ perception of time. |
| B．A phenomenon where ecological standards shift in a place. |
| C．A psychological disorder common among conservationists. |
| D．A condition where ecosystems change gradually over time. |
| A．It promotes the prevention of wildfires. | B．It aids in mathematical modeling efforts. |
| C．It helps reveal historical ecosystem dynamics. | D．It enhances human consumption of ecosystems. |
| A．Reform. | B．Disrupt. | C．Quicken. | D．Deepen. |
| A．Shifting baseline syndrome has positive ecological changes. |
| B．Mathematical modeling with the latest data can be effective. |
| C．Deep-time perspective and historical dynamics are crucial. |
| D．Recent history is more preferred in ecosystem restoration. |
4 . Art Builds Understanding
Despite the long history of scholarship on experiences of art, researchers have yet to capture and understand the most meaningful aspects of such experiences, including the thoughts and insights we gain when we visit a museum, the sense of encounter after seeing a meaningful work of art, or the changed thinking after experiences with art. These powerful encounters can be inspiring, uplifting, and contribute to well-being and flourishing.
According to the mirror model of art developed by Pablo P. L. Tinio, aesthetic reception corresponds to artistic creation in a mirror-reversed fashion. Artists aim to express ideas and messages about the human condition or the world at large.
In addition, art making and art viewing are connected by creative thinking. Research in a lab at Yale University shows that an educational program that uses art appreciation activities builds creative thinking skills. It showed that the more time visitors spent engaging with art and the more they reflected on it, the greater the correspondence with the artists’ intentions and ideas.
Correspondence in feeling and thinking suggests a transfer — between creator and viewer — of ideas, concepts, and emotions contained in the works of art. Art has the potential to communicate across space and time.
| A．The viewers gain a new perspective on the story. |
| B．The theory of aesthetic cognitivism describes the value of art. |
| C．This helps to create connections and insights that otherwise would not happen. |
| D．To do so, they explore key ideas and continually expand them as they develop their work. |
| E．After spending more time with the work, the viewer begins to access the ideas of the artist. |
| F．For example, in one activity, people are asked to view a work of art from different perspectives. |
| G．Participants were more original in their thinking when compared to those who did not take part in the program. |
5 . Debate about artificial intelligence (AI) tends to focus on its potential dangers: algorithmic bias (算法偏见) and discrimination, the mass destruction of jobs and even, some say, the extinction of humanity. However, others are focusing on the potential rewards. Luminaries in the field such as Demis Hassabis and Yann LeCun believe that AI can turbocharge scientific progress and lead to a golden age of discovery. Could they be right?
Such claims are worth examining, and may provide a useful counterbalance to fears about large-scale unemployment and killer robots. Many previous technologies have, of course, been falsely hailed as panaceas (万灵药). But the mechanism by which AI will supposedly solve the world’s problems has a stronger historical basis.
In the 17th century microscopes and telescopes opened up new vistas of discovery and encouraged researchers to favor their own observations over the received wisdom of antiquity (古代), while the introduction of scientific journals gave them new ways to share and publicize their findings. Then, starting in the late 19th century, the establishment of research laboratories, which brought together ideas, people and materials on an industrial scale, gave rise to further innovations. From the mid-20th century, computers in turn enabled new forms of science based on simulation and modelling.
All this is to be welcomed. But the journal and the laboratory went further still: they altered scientific practice itself and unlocked more powerful means of making discoveries, by allowing people and ideas to mingle in new ways and on a larger scale. AI, too, has the potential to set off such a transformation.
Two areas in particular look promising. The first is “literature-based discovery” (LBD), which involves analyzing existing scientific literature, using ChatGPT-style language analysis, to look for new hypotheses, connections or ideas that humans may have missed. The second area is “robot scientists”. These are robotic systems that use AI to form new hypotheses, based on analysis of existing data and literature, and then test those hypotheses by performing hundreds or thousands of experiments, in fields including systems biology and materials science. Unlike human scientists, robots are less attached to previous results, less driven by bias—and, crucially, easy to replicate. They could scale up experimental research, develop unexpected theories and explore avenues that human investigators might not have considered.
The idea is therefore feasible. But the main barrier is sociological: it can happen only if human scientists are willing and able to use such tools. Governments could help by pressing for greater use of common standards to allow AI systems to exchange and interpret laboratory results and other data. They could also fun d more research into the integration of AI smarts with laboratory robotics, and into forms of AI beyond those being pursued in the private sector. Less fashionable forms of AI, such as model-based machine learning, may be better suited to scientific tasks such as forming hypotheses.
1. Regarding Demis and Yann’s viewpoint, the author is likely to be ______.| A．supportive | B．puzzled | C．unconcerned | D．doubtful |
| A．LBD focuses on testing the reliability of ever-made hypotheses. |
| B．Resistance to AI prevents the transformation of scientific practice. |
| C．Robot scientists form hypotheses without considering previous studies. |
| D．Both journals and labs need adjustments in promoting scientific findings. |
| A．Official standards have facilitated the exchange of data. |
| B．Performing scientific tasks relies on government funding. |
| C．Less popular AI forms might be worth paying attention to. |
| D．The application of AI in public sector hasn’t been launched. |
| A．Transforming Science. How Can AI Help? |
| B．Making Breakthroughs. What Is AI’s Strength? |
| C．Reshaping History. How May AI Develop Further? |
| D．Redefining Discovery. How Can AI Overcome Its Weakness? |
6 . Atomic shapes are so simple that they can’t be broken down any further. Mathematicians are trying to turn to artificial intelligence (AI) for help to build a periodic table of these shapes, hoping it will assist in finding yet-unknown atomic shapes.
Tom Coates at Imperial College London and his colleagues are working to classify atomic shapes known as Fano varieties, which are so simple that they can’t be broken down into smaller components. Just as chemists arranged element s in the periodic table by their atomic weight and group to reveal new insights, the researchers hope that organizing these atomic shapes by their various properties will help in understanding them.
The team has given each atomic shape a sequence of numbers based on its features such as the number of holes it has or the extent to which it bends around itself. This acts as a bar code (条形码) to identify it. Coates and his colleagues have now created an AI that can predict certain properties of these shapes from their bar code numbers alone, with an accuracy of 98 percent.
The team member Alexander Kasprzyk at the University of Nottingham, UK, says that the AI has let the team organize atomic shapes in a way that begins to follow the periodic table, so that when you read from left to right, or up and down, there seem to be general patterns in the geometry (几何) of the shapes.
Graham Nib lo at the University of Southampton, UK, stresses that humans will still need to understand the results provided by AI and create proofs of these ideas. “AI has definitely got unbelievable abilities. But in the same way that telescopes (望远镜) don’t put astronomers out of work, AI doesn’t put mathematicians out of work,” he says. “It just gives us new backing that allows us to explore parts of the mathematical landscape that are out of reach.”
The team hopes to improve the model to the point where missing spaces in its periodic table could point to the existence of unknown shapes.
1. What is the purpose of building a periodic table of shapes?| A．To gain deeper insights into the atomic shapes. |
| B．To create an AI to predict the unknown shapes. |
| C．To break down atomic shapes into smaller parts. |
| D．To arrange chemical elements in the periodic table. |
| A．Its holes. | B．Its bends. |
| C．Its atomic weight. | D．Its properties. |
| A．Design. | B．Help. | C．Duty. | D．Threat. |
| A．Thanks to AI, new atomic shapes have been discovered. |
| B．Mathematicians turn to AI to create more atomic shapes. |
| C．AI helps build a relationship between chemistry and maths. |
| D．A periodic table of shapes can be built with the help of AI. |
7 . Fresh fish should have a mild smell. Strong fishy smells are the first signs to go bad. How do the fishy smells come from?
It can be several days from when the fish are caught to when they reach the supermarket. In that time, bacteria that grow naturally in fish start to consume a substance called trimethylamine N-oxide(TMAO)in fish. These bacteria change TMAO into trimethylamine (TMA), the substance responsible for the fishy smells. Bacteria in fish can also change lysine(赖氨酸)into cadaverine(尸胺), a substance that’s associated with breaking down the fish once they are caught and giving off fishy smell.
Chemical reactions can also lead to fishy smells. This happens through the oxidation(氧化)of fat. Fish are an important source of omega-3 fatty acids. When these fats are exposed to oxygen, they oxidize and break down into the substance that you can smell.
To slow down the fishy smell, what is beyond question is that the less time between when the fish are caught and when they reach the kitchen, the better. But today, fish are often flown across the globe. To keep smell-producing bacteria at bay, the fish must be frozen or kept at the low temperature possible as soon as they are caught and cleaned.
Controlling fat oxidation can function as well, especially for fattier fish species. While freezing slows bacterial growth, it does not stop fat oxidation. This reaction will occur as long as oxygen is present. Fatty fish are usually not frozen because, despite the cold temperature, they’re going to oxidize pretty fast unless they are stored in a low oxygen container. That’s why those species are often canned.
It’s also important to remember that smell is not always an indicator of safety, especially in processed fish products. “What you might consider the fishy smell may be a delicacy in another culture,” said Carl A. Batt, a professor of food science at Cornell University.
1. Which of the following has the fishy smell?| A．Fish fat. | B．TMAO. | C．Cadaverine. | D．Lysine. |
| A．Drying them in the air. | B．Storing them in closed containers. |
| C．Carefully cleaning them. | D．Exposing them to rich oxygen. |
| A．Objective. |
| B．Negative. |
| C．Acceptable. |
| D．Unclear. |
| A．Topic—Example—Conclusion. | B．Topic—Comparison—Opinion. |
| C．Question——Cause——Solution. | D．Question—Effect—Opinion. |
8 . Fortunately, the days of being spread on thick baby oil and lying in the sun to get you skin yellowish-brown—or more likely burnt—are long over. Many sunscreens work by filtering (过滤) the sun’s ultraviolet (UV) rays to keep them from reaching skin cells and causing the DNA damage that can lead to wrinkles and skin cancer. But in recent years, the safety of some of those filtering chemical ingredients, particularly oxybenzone (氧苯铜), has been in question.
A 2019 study published in JAMA found evidence that oxybenzone is absorbed into the bloodstream, leading to concerns about whether it might affect functions of our body. Oxybenzone has also been detected in breast milk for newborn babies. Because of concerns about higher intake in children, doctors from the American Academy of Pediatrics advise against sunscreen with oxybenzone for kids.
The Environmental Working Group, an activist organization that monitors chemical safety, has called for a ban, but the U.S. Food and Drug Administration considers sunscreens with oxybenzone safe. “It’s uncertain,” says Deborah S. Sarnoff, president of the U.S. Skin Cancer Foundation. “Just because you’re absorbing the chemical doesn’t mean it’s dangerous.” Further study is required.
But oxybenzone is a risk to coral reefs. Hawaii and the U.S. Virgin Islands have banned the sale of sunscreens with oxybenzone. In a 2022 study published in Science, researchers found that some certain sea plants, when exposed to sunlight, turn oxybenzone into energy or something needed in a way that damages and kills corals.
Some companies have been trying to stop using oxybenzone gradually in stages, and many big brands offer oxybenzone-free options. For anyone who is pregnant or breastfeeding, or simply looking to avoid these chemical filters, Dr. Sarnoff recommends mineral sunscreens, which contain mainly physical barriers.
1. What is the advantage of sunscreen?| A．It gets your skin yellowish-brown. | B．It stops wrinkles and skin cancer. |
| C．It keeps UV rays from harming you. | D．It prevents skin cells from DNA damage. |
| A．They don’t want children to absorb more oxybenzone. |
| B．They don’t want oxybenzone to hurt babies’ functions. |
| C．They know oxybenzone can affect children’s bloodstream. |
| D．They know oxybenzone has been found in newborn babies. |
| A．Coral reefs in Hawaii were damaged or killed by sunscreens. |
| B．More research is needed to prove the danger of oxybenzone. |
| C．Some organizations have already banned the use of sunscreens. |
| D．Mineral sunscreens are much safer than those with oxybenzone. |
| A．The findings about sunscreens with oxybenzone. |
| B．Questions on safe use of oxybenzone raised by doctors. |
| C．Discussion on safety of oxybenzone between organizations. |
| D．Effects of sunscreens on humans and plants in recent studies. |
9 . In US emergency rooms (ER), the average wait time to see a doctor is more than two hours. There are more patients in need than there are doctors, nurses and other staff to help them. Many parents have suffered through hours in the ER with a sick, upset child, only to get sent home because their case is not considered urgent. What if there was another choice—like a house call from an intelligent machine?
Now, a new study shows that AI systems can assess (评估) a child’s medical chart and come up with a diagnosis (诊断), a determination of what is wrong with that patient.
The study took place at Guangzhou Women and Children’s Medical Center in southern China. First, a team of doctors reviewed 6, 183 medical charts. They summarized the information in these charts into a list of keywords linked to disease-related symptoms or signs, such as “fever”. Researchers then taught these keywords to the AI system. Once trained, the system scanned children’s charts for the key terms, checking if they were present or not in order to come to a conclusion. Finally, it offered diagnoses based on the charts, narrowing down from among 55 illness categories.
Dongxiao Zhu, an assistant professor of computer science at Wayne State University who did not take part in the study, however, sees this as “augmented intelligence (增强智能)” rather than “artificial intelligence”, because the system handled only 55 illness categories. Compare that to thousands of possibilities in the real world. The machine cannot yet get into the more complex aspects of a medical decision.
Zhu is also concerned about the amount of human work that went into the study—namely, the time and energy spent by human doctors. They spent hours grading the machine’s assessments and comparing them to their own. It’s no wonder that the process took four years. Considering that, it may be a while before you can skip the ER and see a robot-doctor instead.
1. What can we infer from Paragraph 1?| A．Patients pay too much for the ER. |
| B．American doctors aren’t responsible. |
| C．Children are treated urgently in the ER. |
| D．The emergency rooms are crowded with patients. |
| A．AI systems still have a long way to go. |
| B．AI systems diagnose disease like doctors. |
| C．AI systems will take over from doctors someday. |
| D．AI systems get into complex medical decisions. |
| A．By examining a patient first. |
| B．By reviewing many medical charts. |
| C．By scanning keywords about a disease. |
| D．By observing disease-related symptoms. |
| A．They need to be improved a lot. |
| B．They will replace real doctors soon. |
| C．They are suitable for complex disease. |
| D．They help doctors make a quick analysis. |
10 . When you ask people to judge others by their speech, a trend emerges: Listeners dislike disfluency. Slow talkers producing loads of ums and pauses(停顿)are generally perceived as less charming. But science tells us there may be even more to disfluency.
Disfluencies do not occur in arbitrary positions in sentences. Ums typically occur right before more difficult or low-frequency words. Imagine you’re having dinner with a friend at a restaurant,and there’re three items on the table: a knife, a glass, and a wine decanter(醒酒器). Your friend turns to you and says, “Could you hand me the...um...” What would you assume they want? Since it’s unlikely that they will hesitate before such common words as knife, and glass, chances are you’ll pick up the decanter and ask, “You mean this?”
This is exactly what we demonstrated through controlled eye-tracking studies in our lab. Apparently, listeners hear the um and predict that an uncommon word is most likely to follow.Such predictions, though, reflect more than just simple association between disfluencies and difficult words; listeners are actively considering from the speaker’s point of view. For example, when hearing a non-native speaker say the same sentence but with a thick foreign accent, listeners don’t show a preference for looking at low-frequency objects. This is probably because listeners assume non-native speakers may have as much trouble coming up with the English word for a common object, like a knife, as for unusual ones and can’t guess their intention.
In another experiment, listeners were presented with an atypical speaker who produced disfluencies before simple words and never before difficult words. Initially, participants displayed the natural predictive strategy: looking at uncommon objects. However, as more time went by, and they gained experience with this atypical distribution of disfluencies, listeners started to demonstrate the contrary predictive behavior: They tended to look at simple objects when hearing the speaker say um.
These findings represent further evidence that the human brain is a prediction machine: We continuously try to predict what will happen next, even though not all disfluencies are created equal.
1. What does the underlined word “arbitrary”mean in paragraph 2?| A．Random. | B．Strategic. | C．Obvious. | D．Consistent |
| A．They can be understood easily. | B．They actively put themselves in others’ shoes |
| C．Their vocabularies are limited. | D．Their disfluencies are a little less predictive. |
| A．Simple things are difficult in some cases. | B．Listeners can adjust predictions accordingly. |
| C．Distribution of disfluencies is changeable. | D．Disfluencies in communication can be avoided. |
| A．Pauses Coexist with Prediction. | B．Brains Are Powerful Prediction Machines. |
| C．Active Listeners Simplify Talks. | D．Disfluency Says More Than You Think. |
