青少年、人工智能与风险决策科学，与Valerie Reyna博士

你可能认为做决定最聪明的方式是收集所有事实，仔细权衡，然后选择最理性的选项。但心理学研究提出了不同的观点：理解情境的要旨，而非精确复述事实，往往能带来最佳选择。今天我们将与一位研究决策心理的专家探讨要旨的重要性，以及它如何解释从青少年冒险倾向到健康错误信息传播等现象。那么，为何我们的大脑依赖模糊思维？这何时是件好事？

You might think that the smartest way to make a decision is to gather all the facts, weigh them carefully, and then choose the most rational option. But psychological research suggests something different: Understanding the gist of a situation, not necessarily being able to recount precise facts, often leads to the best choices. Today we're going to talk to a psychologist who studies decision making about the importance of gist and why it can help explain everything from teenagers' propensity for risk taking to the spread of health misinformation. So why does our brain lean on fuzzy thinking? And when is that a good thing?

青少年、成人和人工智能如何理解风险？我们如何设计顺应人类自然决策模式的系统和信息？欢迎收听《心理学访谈》，这是美国心理学会的旗舰播客节目，探讨心理科学与日常生活的联系。我是主持人金·米尔斯。今天的嘉宾是博士。

How do teens, adults, and artificial intelligence agents make sense of risk? And how can we design systems and messages that work with our natural decision making styles rather than against them? Welcome to Speaking of Psychology, the flagship podcast of the American Psychological Association that examines the links between psychological science and everyday life. I'm Kim Mills. My guest today is Doctor.

瓦莱丽·雷娜，康奈尔大学洛伊丝与梅尔文·图赫曼人类发展讲席教授。她研究人类生命周期中的判断、决策与记忆，是模糊痕迹理论的创立者——该决策模型已广泛应用于法律、医学和公共卫生领域。她近期研究聚焦青少年风险决策、医疗法律决策及人工智能决策。雷娜博士。

Valerie Rayna, the Lois and Melvin Tuchman Professor of Human Development at Cornell University. She studies judgment, decision making and memory across the lifespan. She is the developer of fuzzy trace Theory, a decision making model that has been widely applied in law, medicine and public health. Her recent work has focused on understanding risky decision making in adolescents, medical and legal decision making, and AI decision making. Doctor.

雷娜博士是美国心理学会会士，曾获多项荣誉，包括实验心理学与认知科学分会终身成就奖。雷娜博士，感谢您今天参与节目。

Rayna is an APA fellow and has won many awards for her work, including the Lifetime Achievement Award from APA's Division of Experimental Psychology and Cognitive Science. Doctor. Rayna, thank you for joining me today.

很荣幸受邀，感谢您精彩的介绍。

It's a pleasure to be here, and thank you for that lovely summary.

让我们从您研究的核心开始。您创立的模糊痕迹理论刚才已提及，能否为我们讲解该理论如何帮助人们决策？

Let's start with the core of your work. You developed something called fuzzy trace theory, which I just mentioned. Can you tell us about that and how it helps people make decisions?

如您所言，心理表征可分为情境要旨——即该情境的核心意义，与我们所谓的逐字表征。后者指对经历进行字面精确的记忆再现，并试图基于此做出决策。显然，若思维过于字面化，知识迁移将十分困难。例如医生诊断时，虽新患者与既往病例表面细节不同，但核心要旨相通。

Well, as you mentioned, there's a distinction between mentally representing the gist of a situation, which is the meaningful bottom line of that situation, and what we call the verbatim representation. And by that, we mean a very literal and precise representation as though you were memorizing experience and then trying to go on the basis of that to make decisions. And as you can readily imagine, if you're a very literal thinker, it's very hard to transfer your knowledge. So you learn about one particular patient if you're a doctor, but the new patient is like that patient. The gist is the same, but the verbatim superficial details might be different.

生活就是这样。你在童年和青少年时期积累经验，然后踏入社会，现在你正试图将这些信息应用到新情境中。如果你能抓住要点，理解面前选项的核心含义，你就能更出色、更有准备地应对。同时，你也不会陷入那些最终证明对真正重要之事无关紧要的细枝末节中。

And life is like that. You gain experience as a child and as an adolescent, and you go out into the world, and now you're trying to transfer that information to new situations. And if you get the gist, if you get the bottom line meaning of the options before you, you're better able and better equipped to do that. And you also don't get mired in in the minutiae, the the details that turn out not to be core to what really matters.

能否再举几个现实生活中的例子，说明人们关注要点而非纠缠于所有事实时如何做出更好决策？

Could you give us a few more real life examples of how people make better decisions when they focus on gist instead of getting bogged down in all these facts?

当然。我想再次强调，事实很重要。从我们的角度看，了解事实和掌握知识极其重要。但关键在于从这些细节中提炼出对当前情境的核心意义。

Sure. And again, I wanna underline. Facts are important. Knowing facts and having knowledge is extremely important in our perspective. But it's then extracting from those details what the core meaning is for this situation.

所以这不是一种无视事实的方法，而是基于丰富事实的方法，但你必须将其浓缩为本质含义。举个具体例子：疫情期间，当社区新冠感染率仅为1%后来到5%时，许多流行病学家反应激烈，公众却大多觉得这个数字微不足道。

So it's not it's not a fact free kind of approach. It's a fact rich kind of approach, but then you have to boil that down to what does it mean. So a concrete example would be during COVID, a lot of epidemiologists were jumping up and down when the community prevalence rate of COVID infection was a very tiny one percent and then five percent, people were like, oh my goodness. That's huge. And I think most of us members of the public were thinking, gee, that seems like a tiny number.

人们会疑惑：为何如此激动？这明明是个很小的数字。如果仅从表面理解这些事实，会误判风险程度。但当社区传播率达到1%时，实际影响已经非常重大。

Why is everyone so excited? It is a tiny number. Literally, it's a small percentage. So if you just took the facts and you took them at face value, it would signal that there isn't much of a risk. But by the time the community spread of COVID is at one percent, it's huge.

这甚至可能引发灾难性后果。更关键的是，增长是指数级的——意味着它会急速攀升。这种'急速上升'的感知就是要点，而'微小数字代表大问题'这个认知也是要点。

It's potentially catastrophic. And moreover, the increase is exponential, meaning that it goes up, up, up very fast. So that sense of up, up, up very fast, that's a gist. And the fact that a tiny number is a big problem, that's a gist.

那么如果我想买车或买罐花生酱，比起检查所有可选产品，基于要点（GIST）做决策会更好吗？

So if I wanna buy a car or even a jar of peanut butter, do I make a better decision if I'm working on GIST instead of trying to check out every available product that's out there?

例如，当你查看营养标签时，人们往往被细节淹没。虽然人们试图让它易于理解，比如标注每日建议摄入量的百分比。但这究竟意味着什么？如何将这么多克糖与那么多克碳水化合物或蛋白质进行比较？对大多数人来说，要抓住其中的要点极其困难。

For example, if you look at nutrition labels, people are swamped by detail. And folks try to make it comprehensible. You know, it's a percentage of your recommended daily value. Well, what does that mean? And how do you compare this many grams of sugar to that many grams of, you know, this much carbohydrates to protein to it is extremely hard for most people to grasp the gist.

这是健康食品吗？还是不健康？吃了有好处还是有害？而营养师会告诉你，食物本身没有好坏之分。

Is this a healthy food? Is it not? Is it good to eat? Is it bad to eat? And nutritionist will then tell you there are no good or bad foods.

这取决于你如何食用它们。所以现在，盯着标签上那些细节的人完全困惑了。他们试图理解信息的要点。这才是关键——如果你关注营养，就需要知道这是否是值得购买的正确选择。

It depends on how you're eating them. So now the, you know, the person who's staring at those details on the label is completely perplexed. They're trying to get the gist of the information. So that's the key. It to know whether it's the right thing to buy if you're interested in nutrition.

如果你在意口味，那也很重要。要知道，奖励敏感性和奖励动机在决策中很重要，但同样重要的是把握事实的要点。对于这种奖励，我有哪些选择？我认为营养的例子很好地说明了人们被事实淹没，却未必抓住事实的核心。

If you're interested in taste, that matters too. You know, reward sensitivity and reward motivation is important in decision making, but also getting the gist of the facts. What are my options for this reward? I think the nutrition example is probably a real good one of being overwhelmed with the facts and not necessarily getting the point of the facts.

那么，让我们谈谈年轻人与决策，因为众所周知青少年以做出糟糕决定而闻名，很多人将此归因于他们觉得自己无敌。但你的研究表明这并非全部真相。那么基于要点的推理如何帮助解释青少年为何会采取那些看似不明智的风险行为？

Well, let's talk about young people and decision making, because we know that teens are famous for making bad decisions, and a lot of people chalk that up to the idea that they feel invincible. But your research suggests that's not really the full story. So how does gist based reasoning help explain what's really happening when teens take otherwise unwise risks?

关于青少年感觉无敌的观点很有趣，因为事实证明这是错误的。这是个迷思。他们并不认为自己无敌。实际上，他们对许多行为的风险评估比成年人对自己的评估更高。所以他们有时知道自己正在参与风险行为。

That notion that adolescents feel invincible is interesting because it turns out to be false. It's a myth. They don't think they're invincible. They in fact estimate their own risks as higher for many of these behaviors than adults estimate their risk. So they know sometimes they're engaging in risky behavior.

这就更令人好奇：那他们为什么还要这么做？问题在于成年人和孩子都有所谓的乐观偏见。他们认为自己面临的风险比其他人略低，但研究显示——不仅是我自己的研究，文献综述和元分析都支持模糊痕迹理论这一反直觉的预测——青少年经常权衡风险与回报，并以经济学家认为理性的方式将它们结合起来。问题在于，像感染HIV等可能改变人生的极端坏事，其概率非常小。如果你只是计算概率，即使是从已感染的伴侣那里传播的概率，数值上其实相当低。

So then it's even more fascinating as to then why do they do it? The issue is that both adults and kids have what's called an optimism bias. They think they're less a little less at risk than maybe the other person is, but they're weighing, in fact, what the research shows, and not only my own research, but reviews of the literature and meta analyses are consistent with this very counterintuitive prediction of fuzzy trace theory, which is that adolescents are often weighing the risk and reward, and they're combining them the way an economist would say you should to be rational. The problem is you have very small probabilities of extremely bad things like HIV infection or those kinds of things that are can be life altering. So if you're just counting up, looking at the probability, the probability of transmission even from an infected partner is actually numerically quite small.

但这完全没抓住重点。关键在于你不该那么做，因为你可能会被感染，而那些被感染的人正是这样被感染的。所以问题不在于这只是个小概率事件，你需要计算然后说，好吧，我要冒个明知的风险。那是个不健康的选择。青少年越这样做（他们似乎确实如此），就会陷入越多麻烦，导致糟糕的结果，包括健康问题和其他公共卫生问题。

But that entirely misses the point. Namely that you shouldn't do that because you might get infected and of the people who were infected, that's how they got infected. So it's not a question of it's just a small probability and you have to calculate and say, well, I'm gonna take an informed risk. That is an unhealthy choice. And the more that adolescents do that, which they seem to do, the more trouble they will get into and have bad outcomes, bad health outcomes and other kinds of public health outcomes.

所以如果我理解正确，问题不在于我是个青少年所以觉得自己无敌，而是虽然我是青少年，但觉得这事不会发生在我身上。可能会发生在她身上，但不会是我。是这种想法吗？

So if I heard you right, it's not that I'm an adolescent and I think I'm invincible, but it's I'm an adolescent, but it's not gonna happen to me. It's gonna happen to her maybe, but not me. Is that the thinking?

这是部分原因。顺便说，这确实存在。再次强调，这就是为什么字面思维未必是好事——因为从字面上看这确实成立。青少年并非完全活在另一个现实中，这其实就是现实。

That's part of it. And by the way, that's true. Again, if so many of these this is why literal is not necessarily good thinking because literally that's true. It's not that adolescents totally are, you know, in another reality. That's actually reality.

这些灾难性后果发生的概率通常很小。但如果你反复冒险，风险会随时间累积。这是我们用于青少年过早性行为干预的核心观点之一：我们讨论过累积风险最终几乎必然发生。比如如果你每个月都进行无保护性行为，一年之内几乎百分之百会有人怀孕。

The probability of these very catastrophic outcomes happening are often quite small. Now they accumulate over time if you repeatedly engage in them. And that's one of the gist we used in an intervention we had for teens on premature, sexual activity. We talked about cumulative risk ultimately being essentially certain. So if you have unprotected sex every month for a year, at some point during the year, virtually a hundred percent of people, someone will become pregnant.

所以这变成了全有或全无的概要判断。但每次都是小概率事件。因此他们确实不太可能遭遇这些糟糕后果——这从字面上看是事实，但我们会说这不是正确的看待方式。正确的方式是统观全局，认识到这就像玩俄罗斯轮盘赌。

So it becomes an all or none, something nothing gist. But each time is a small probability. So they are in fact unlikely to have these bad outcomes. That is in fact literally true, but that's not the way to look at it, we would say. The way to look at it is to put the whole picture together and to say, this is sort of like playing Russian roulette.

俄罗斯轮盘赌里，弹膛只有一颗子弹，所以概率很小。但后果太严重，即使你可能想'为了一百万美元我愿意玩'，也不该冒这个险。按照常规经济理论，如果金额足够高，冒这个风险是理性的。但我们会说金额和子弹数量都是字面细节，这仍然不是个好主意。

In Russian roulette, you have only one bullet in the chamber, so you have a small probability. But the outcome is so bad, you shouldn't take that risk even though you might think, well, for a million dollars, I'd play Russian roulette. It's rational according to regular economic theories to take that risk if the amount of dollars is high enough. But we would say the amount of dollars and the number of bullets are verbatim details, and it's still not a good idea.

所以这就是你参与开发的干预计划的一部分？教导青少年用不同方式思考？

So this is part of the intervention program then that you have helped to develop that teaches teens to reason differently?

确实如此。我们随后实施了这一方案。我们采用了一个非常有效的项目，但其中有些细节我们认为并不适合引导年轻人形成正确的心理关注点。这项干预的初衷是，能否让这些青少年像成年人一样思考？成年人看待这种情况的反应会是：你在开玩笑吗？

Exactly. We then implemented that. We took a a really effective program, but that had some details in it that we thought were just not the right psychological focus to reach young people. And the the ambition of the of the intervention was, can we get these adolescents to think more like adults? The way adults look at that situation is, are you kidding?

要知道，我们当然——没人会拿俄罗斯轮盘赌开玩笑。这毫无意义。如果涉及的是金钱问题，那还有点道理——但这又有什么关系呢？你明白吗？

You know, we of course, no one would ever play Russian with that. That makes no sense. If you're talking about dollars, that's kind of a little you know? What does that got to do with it? You know?

或是子弹数量的问题。但如果年轻人以近乎超理性的方式思考，我们能否引导他们更多地从要旨（GIST）角度考虑？我们成功做到了这一点，实验组与对照组之间产生了显著差异。更重要的是，要旨认知具有持久性。

Or the number of bullets. But if young people think in that almost hyperrational way, can we get them to think more in terms of GIST? And we were able to do that. We were able to have a significant change between the treatment groups and the control group. And what's more about GIST is GIST endures.

当你在健康课上教授年轻人知识时，你传授的是一堆事实。而当他们日后在生活中应用这些信息时，留存下来的是这些事实的要旨，而非逐字细节。比如HPV感染率具体是多少？很少有人记得。但他们确实记得感染率比想象中高得多。

When you teach young people something in a health class, you teach them a bunch of facts. What remains later on when they're in life trying to apply that information is the gist of those facts, not the verbatim details. What was the percentage of HPV infection? Very few people remember that. But they do remember that it's a lot higher than you might think.

对吧？特别是在疫苗接种普及前，感染率远超人们想象。正是这类要旨会被长期保留，并持续影响行为。我们的研究证实了这种长期行为影响。

Right? This is like especially pre vaccination. It was a lot higher than you think. So that kind of gist is what's retained and what influences behavior over the longer term. And that's what we showed, that it influenced behavior over the longer term.

那么转向基于要旨的思考方式，是否是人类从青春期到成年自然发展过程中的必然演变？就像你自然而然就掌握了这种思维方式？

And is moving to gist based thinking a natural evolution in human development that as you move from adolescence to adulthood that it sort of you get it. It just becomes the way that you work?

众多领域的研究表明，确实存在这种从字面思维向要旨思维的转变。这种转变似乎与特定领域的经验积累有关。即使是成年人，若初入某个领域（比如医学生比资深心脏病专家更依赖字面信息），也会经历这个过程。我们的研究证实：无论是青少年在风险决策中积累生活经验，还是医学生成长为专科医师的过程，随着领域经验的增加，这种思维转变就会发生。

The research in many, many domains suggests that there is exactly that kind of shift from more literal verbatim thinking to more just thinking. It seems to depend on experience in a domain. So you can actually have that experience as an adult if you start out as a novice in a domain like a medical would be more verbatim than, you know, an experienced cardiologist. And we showed that in a study we did. So that as you gain experience in a domain, whether if you're a child, you're gaining experience in life with these kinds of risky decisions we're talking about, Or if you're a medical student and then eventually you become an experienced, subspecialist, you're gaining experience in that domain.

在这两种情况下，都显示出从逐字逐句的精确思维转向更注重本质要点的思考方式。

In both those cases, you show a shift from more verbatim literal thinking to more just bottom line thinking.

让我们再深入探讨一下医学与公共卫生领域。模糊痕迹理论及基于要点的思维方式如何解释错误信息为何如此容易传播？

Let's talk a little bit more about medicine and public health. How does fuzzy trace theory and gist based thinking help explain why misinformation spreads so easily?

这是个引人入胜的问题，也是我们这个时代最重要的问题之一。最初应对错误信息的方法就是简单地提供事实——无论是青少年健康领域还是当前领域都是如此。但研究表明，仅提供事实似乎并不能显著改变人们的认知，未必能纠正错误信息。

That's a fascinating question and one of the most important questions of our time. The approach of initial, ways to deal with misinformation was to simply give people facts. And that's that was true in the health domain with teenagers, and that's true in this domain. And what people showed is that giving facts alone did not seem to change people that much. It didn't seem to necessarily redress the misinformation.

虽然有一定效果，但令人惊讶的是其成效远不及预期。部分原因在于人们如何理解这些事实：首先是动机因素——他们是否信任信息提供者？无论是AI、社交媒体还是专业医生，信息来源的可信度至关重要。

It does to some degree, but it's surprisingly not as effective as it should be. And one of the reasons for that is what people are taking away from the facts. First of all, there are motivational things. Do they trust the person giving them the facts? The trustworthiness of all of these sources of information whether it's AI or social media or expert doctors is really important.

假设你信任信息传达者，那么你是否能抓住信息要点？能否真正理解事实背后的原因？这正是要点思维的关键所在。这不单关乎事实真伪或你的相信意愿，也不取决于是否符合你的政治立场等因素。

But given that you trust who's communicating to you, do you then get the point of the information? Are you able to really understand the why behind the facts? And that's where the gist comes in. So it's not just whether a fact is true or false or whether you're motivated to believe it or not. It doesn't fit your political persuasion or whatever.

这些因素固然存在，但真正重要的是：这对你而言是否合理？是否符合事实的本质？当我们从旁观者视角审视时，会疑惑：为何有人相信这类信息？

Those are obviously, those are true. Those are factors. But really what's what matters a lot is does this make sense to you? Does this fit the gist of the facts? And when we look at these facts from, you know, from an outside perspective, we say, well, why does that person believe something like that?

其实从他们的背景知识和经验来看，这些错误信息是自洽的——它们为这个复杂世界提供了合理解释，特别是在缺乏相关背景知识（如科学素养）的情况下。因此在该个体的认知语境中，这个『事实』显得合理，这正是人们倾向于相信的原因。

Well, from their background knowledge and their experience, that make that misinformation makes sense. It makes sense of a world that's very complicated, a world where they may not have the relevant background knowledge, like scientific literacy and so on. So that fact in that context for that individual makes sense, and that's why people tend to believe it.

那么科学家和公共卫生传播者如何运用您的理念，更有效地向公众传达他们的信息呢？

So how can scientists and public health communicators use your ideas to more effectively get their messages out to the public?

实际上已有大量研究尝试将我们的理论构想转化为实用工具。2016年一篇综述分析了约94项研究，其中部分属于干预性研究。关键是要提炼这些信息的核心要点。我们常会召集由专家科学家、临床医师和经验丰富的患者组成的评审小组——这里说的'专家患者'是指亲身经历过化疗、放疗等治疗过程的病患。

There have actually been a number of studies attempting to do just that, to take our theoretical ideas and to implement them in very practical tools for people. There was a review in 2016 of about 94 studies, some of which were intervention studies. So you have to take this information and really decide and extract what the gist of it is. So often we'll meet with, a panel of expert, scientists, maybe expert clinicians, and expert patients. And by expert patients, I mean someone who's been through it, who knows what it's like to experience these kinds of therapies like chemotherapy and radiation and medication.

以类风湿性关节炎药物为例，这些生物制剂明明能有效缓解疼痛并改善长期预后，但研发出来后为何使用率不高？这就是我们要解决的问题。

So just to take one example out of the, the the different implementations, We looked at rheumatoid arthritis drugs. These drugs are called biologics. And the question was, why aren't more people taking these drugs when they were developed? Because they would probably be a very good therapeutic option for people. They would both reduce their pain and increase their long term medical outcomes.

通过与专家对话，我们从这些技术性极强的药物资料中提炼核心价值，设计出干预方案，并通过在线工具进行简短呈现。

It would improve those. So we design an intervention by talking to experts and really trying to extract from all these technical details about these medications, and they are very technical. What's the bottom line meaning of this? And then present the information. And we did this in a very short intervention, an online tool.

我们追踪真实患者在使用该在线工具前后的选择变化。结果显示，他们的决策更符合自身价值观——比如减轻疼痛、陪伴家人等这些最根本的生活诉求。

We had real patients, and we looked at their, you know, choices before and their choices after this online tool. And afterwards, their choices were much more value concordant, which means they had certain values. They wanted to not have pain. They wanted to be there for their family. They had values like that that are very core simple values.

当患者理解了干预措施的精髓后，他们的用药选择会发生转变，从而更好地支撑这些核心价值。

And getting the gist of the intervention caused them to shift their choices about medication so that they lined up and supported those values better.

我们稍事休息。回来后我将与雷纳博士探讨虚假记忆现象——概要思维如何导致人们记住从未发生过的事。您研究过虚假记忆吧？就是人们坚信记得某些虚构经历的现象。当人们产生这种错误认知时，大脑究竟发生了什么？

We're going to take a short break. When we return, I'll talk with doctor Raina about false memories and how gist thinking sometimes leads people to remember things that never really happened. You've looked at false memories, right? That phenomenon where people think they remember something that never really happened. What is going on in people's brains when they think that they've experienced something that they never did?

通常，他们大脑中发生的是记住事件的要点而非具体细节。人们主观地编码现实，但却是并行处理的。就像同时录制实际对话和要点摘要的双轨录音。因此他们处于双重思维状态，大脑同时接收字面信息和核心要义。

Well, the typical thing that's going on in their brain is that they're remembering the gist of what happened and not what happened. So people subjectively encode reality, but they do so in parallel. It's as though there's a tape recording of the actual words in parallel with this gist that's being recorded at the same time. So they're of two minds. So into their brain goes both of these things, the verbatim and the gist.

根据提问方式的不同，你可能得到精确答复或概要回答。这与你提问时提供的检索线索有关。我最喜欢的例子是医生场景：实习医生问患者'你在服用布洛芬吗？'患者回答没有——尽管急诊室最近刚给他开过这种药。

And depending on how you ask a question, you might get the verbatim out, you might get the gist out. That has to do with the retrieval cue that you ask the nature of the question. So one of my favorite examples is with doctors where they would say, okay, the resident doctor, the doctor in training goes in and asks the patient, are you, taking ibuprofen? And the patient says no. Now they had been in the emergency room recently in which they were prescribed ibuprofen.

实习医生看着病历问'你在吃布洛芬吗？'患者说没有。随后主治医师进来换了个问法：'你在服用止痛药吗？'患者回答是的。医生继续追问：'具体是什么药？'

So the resident is looking at the chart and saying, you taking ibuprofen? Patient says no. The resident exits that comes back in with the attending who's the senior physician and says, are you taking anything for pain? And the patient says, yes. And then the doctor says, what are you taking?

'是布洛芬吗？'患者思考片刻后恍然大悟：'啊对，是的。'患者对药物学名给出了错误否定，但当触及核心要义'是否服用止痛药'时，答案就变成了肯定——结果正是同一种药物。

Are you taking ibuprofen? And the patient thinks for a minute and says, why, yes, I am. So the patient falsely says no to the verbatim technical name of the drug, but says, when you say the gist, hey, are you taking anything for pain? The answer is yes. And it turns out to be that drug.

当我指出这点时住院医生们非常高兴，因为他们常因此类问题被纠正。关键在于你给患者提问时提供的记忆检索线索。多数人长期记忆更擅长保留要义（比如'我在吃止痛药'），这才是我们事后回忆时认定的真实经历——尽管医生当初说的是另一个表述。

The residents were very happy with me when I pointed this out because they get corrected a lot about things like that. And it has to do with how you ask the question of the patient, the retrieval cue in the question. So most of us, most of the time, remember better the gist, I'm taking something for pain over a long term. And that's what ends up getting that's what we remember later as having in fact experienced. But that's what the doctor said, you know, they said that and that's what they remember.

你会错误地记住某些经历，因为你基于自身理解来解读事件。后期你会坚信这就是事实。我们在数据验证中发现一个反直觉现象：这种倾向从童年到成年是递增的。你变得更依赖要义记忆，意味着错误记忆概率反而上升——精确记忆能力不是增强而是减弱了。

So you will falsely remember experiencing something because you're interpreting events in light of what you're what you understand about them. And later on, you believe that that's reality. And one of the surprising implications of that, which we tested in data and found to be true is, you notice I said your tendency to do that increases from childhood to adulthood. You become more gist based, which means your probability of having false memories goes up. So you don't become more accurate in a verbatim sense, you become less accurate.

即使在实验室环境下，成年人出现错误记忆的概率也高于儿童。我们称之为'发展性逆转'现象，它颠覆了'成年人理应比儿童更可靠'的常规认知——实际上由于更依赖要义记忆，成年人的记忆准确度反而更低。

So your tendency to to have false memories even in a laboratory context is higher if you're an adult than if you're a child. And we show that that's called developmental reversals because it reverses the usual expectation that, of course, adults are more confident than children, but they're in fact less accurate because they're more gist based.

我最喜欢且可能更为显著的关于人们虚假记忆的经历之一，发生在我作为纽约市记者期间，当时自由女神像正在翻新。我和同事们采访了许多坚持声称自己年轻时曾登上自由女神像火炬部分的人。当然，火炬部分已关闭数十年，这些人根本不可能上去过，但他们却坚称自己曾在那里，并在脑海中形成了攀登火炬的影像——尽管我们知道这绝无可能。

One of my favorite and probably more salient experiences of people with false memories came I was a reporter in New York City around the time that the Statue Of Liberty was being refurbished. And we interviewed my colleagues and I interviewed a lot of people who insisted that they had gone up into the torch of the Statue Of Liberty when they were youngsters. And of course, the torch had been closed for decades. So there was no way that these people ever went up into the torch, but they insisted that they had been up there. And they had mental images of going up in the torch when we know that they could it just didn't happen.

我们可以在实验室里系统地重现这类体验。正因如此，我们能在科学允许的范围内相当自信——当然科学总有保留条款。但这种自信源于我们能在实验室复现那种生动的、现象学上具体的体验。如果让你反复检索逐字记忆，它会强化并变得更鲜活，然后你就会开始润色它。

And we can systematically create those experiences in the laboratory. That's why we can be fairly confident as much as you can be in science, which means there's always a caveat. But we're fairly because we can recreate a vivid phenomenologically concrete experience like that in the lab. So if we have you retrieve your verbatim memory over and over, it strengthens and becomes more vivid to you. And then you begin to embellish it.

为什么？因为这合乎逻辑。这是事件的叙事，是事件的缘由。我本可能在那里，因为...

Why? Because it makes sense. It's a narrative of the event. It's the why of the event. I would have been there because.

一旦你添加了这些细节，它在脑海中就会变得如此可信且清晰，让你误以为自己亲身经历过。

And once you add that, it becomes so believable and so sharp in your mind, you think you experienced it.

在你近期研究中，你探讨了AI代理如何做决策。能向听众介绍一下吗？你们研究了哪些决策类型？AI的决策方式与人类有何异同？

In your recent work, you've looked at how AI agents make decisions. Can you tell our listeners about that research? I mean, what types of decisions have you looked at? How does AI decision making compare with the way that humans make decisions?

多年前我开始研究时，曾写过计算机与人类决策方式的比较，讨论过人机不匹配问题。因为当时的观点认为计算机更擅长逐字思考，而人类更依赖要点思维。将两者结合时就会出现兼容性问题，所以我们早年讨论的是计算机辅助决策。

Well, when I started out, and years ago I wrote comparisons between how a computer makes a decision versus a human being. And I talked about human computer mismatches. Because the idea in those days was that computers were more verbatim thinkers, but people were more gist based thinkers. So when you put them together, there was an incompatibility. So way back when we talked about a, you know, computer assisted decision making.

这些计算机程序被开发出来后，比如会根据患者症状输出一长串诊断清单，但医生们并不轻易采纳。部分使用者接受，但多数持抗拒态度。部分原因就在于这种不匹配——基于证据公式化生成的症状清单，计算机试图辅助决策，但其决策方式与人类医生的思维模式截然不同。

And these computer programs would be developed and, you know, they would output this, for example, enormous list of diagnoses given a patient's symptoms, and doctors did not adopt them readily. Some did, but mostly they resisted them. And part of that reason is because there's this mismatch. Here's a list of symptoms and this very formulaically based on evidence, computer was trying to assist in making the decisions. But it wasn't they weren't making the decisions the way human doctors were making decisions.

那么哪种更基于要旨。后来机器学习模型出现了，我研究过这两种，现在仍在研究。它们仍有些算法化和字面化。你可以讨论第二类错误和第一类错误，比如患者确实患病时，计算机程序是否检测到？或者计算机误报说患者有病而实际没有？

So which was more gist based. Now machine learning's, models eventually came along, and I've studied those two and I'm studying those now. Those are still somewhat algorithmic and literal. And, you know, you can talk about, okay, type two error and type one error, which is like, okay, if the patient has the disease, does the computer program say it does? Or does it false alarm when the computer says you have the disease, but the patient doesn't?

是否存在漏诊？患者实际患病但计算机未检测到。你可以统计所有这些数据并汇总，机器学习有各种统计指标、精确率和召回率。但归根结底，这仍是种非常算法化、机械化的决策方式，缺乏要旨基础。它能汇总海量患者数据，但决策方式与人类不同。

Does it have a miss? The patient actually has the disease and the computer misses it. You can talk about all those statistics and add them all up, but there's all kinds of machine learning summaries, statistics, precision, and recall. But at the end of the day, that's still a very kind of algorithmic, mechanical, not very gist based way of making decisions. It can summarize enormous quantities of data about patients, but it doesn't make the decision in the same way a human being does.

我们一直在比较机器学习模型与人类决策者（包括医生）。但最近出现了LLMs的发展，如ChatGPT这类人工智能体。这确实是质的飞跃。我们发表了初步发现，例如约一年前的论文显示当时的CHADGPT（3.5版本）就像青少年，既不完全像成人那样基于要旨，也不完全字面化。

So we've been looking at comparing machine learning models to human decision makers, including physicians. But most recently, we have the development of these LLMs, things like ChatGPT and those kinds of artificial intelligence agents. And there has really been a qualitative change. And we published the initial findings of that, for example, in a paper about a year or so ago showing that CHADGPT at the time, this was a 3.5 version, was starting it was sort of like an adolescent. It wasn't, you know, completely just based like adult humans, and it wasn't entirely literal.

它表现出某些过渡性模式，宛如青少年。但它会犯些有趣的错误，显示出认知偏差的萌芽、框架效应及类似成人的非理性行为。所以它处于过渡阶段。此后，我们认为ChatGPT的思维变得更加基于要旨。

It showed some of the patterns that were transitional as though it were a teenager. But it was making some of these errors that are kind of interesting. It was showing cognitive biases, the beginning of cognitive biases and framing and, you know, irrational behavior of the sort of people adults in fact show. So it was transitional. Since then, chat GPT has become even more, we think, more gess based than it's thinking.

仿佛正在经历发育模式：曾是儿童，后成青少年，现为成人。现在它可能会有虚假记忆和认知偏差。至少目前数据似乎暗示如此。

It's as though it's experiencing this developmental pattern. It once was a child, and then it was an adolescent, and now it's an adult. And now it will probably have false memories and cognitive biases. At least that's what the data seem to suggest so far.

你是否乐观认为我们能纠正这些错误？那些幕后操控AI的人类会进步而非退步吗？

Are are you optimistic that, we're gonna be able to correct for these errors, that the human beings who are sort of running the show behind the AI that we're gonna get better and not get worse?

这很有趣，因为问题在于：什么才是真正的错误？这确实是个问题。从严格字面的经济理性理论角度看，这些AI体和成熟的成年人确实在犯错。对吧？

Well, this is interesting because the question is, what's really an error? Right? That is a problem. It is certainly the case that from a strictly literal economic theory of rationality perspective, these AI agents and adult mature humans are making errors from that perspective. Right?

因此，理论上存在一个你愿意在玩俄罗斯轮盘赌时冒险的金额，或者可以说，这个数字必须非常高。或者你可能会说，这是个例外，因为涉及生死。好吧，那么换成HIV感染呢？理应存在一个值得冒险的回报水平。

So there should be amount of money you should be willing to risk on playing Russian roulette, or you could you know, it just has to be a very high number. Or you could say, well, that's an exception because it involves death. Okay. Well, then there's HIV infection. There ought to be a level of reward that makes that worth it.

对吧？这会是理性选择，但不知为何感觉不太对。所以要点视角认为，这些认知偏差揭示了人类某些高级特质。我们现在探讨的是：何时基于要点的决策才是正确的，即便它违背了字面指南或细节？我想许多临床工作者会对此产生共鸣，无论是心理还是医学临床领域。

Right? That would be the rational choice, but somehow that doesn't seem like the right choice. So the gist perspective would say these cognitive biases illustrate something advanced about people. So what we're trying to look at now is when does a gist based decision actually the right decision despite violating the literal guidelines or the literal details? And I think many clinicians would resonate with that, both psychology clinicians and medical clinicians.

具体而言，有时患者不符合指南标准，而临床医生的判断是正确的；有时不符合只是医生判断失误，可能因为他们未能跟进最新进展。但有经验的临床医生知道，指南存在例外，且存在明智的例外。这正是要点决策可能优于字面机器学习模型或刻板指南之处。

Namely that sometimes the patient doesn't fit the guideline and the clinician is right about that. Sometimes they don't fit and it's just the clinician is wrong because they're maybe not having kept up with everything. But sometimes the experienced clinician, there are exceptions to guidelines, and there are intelligent exceptions. And that's where the gist would actually perform better than the literal machine learning model or the literal guidelines.

那么你接下来的研究方向是什么？还有哪些重大问题待解答？

So what's next for you? What are the big questions you're still trying to answer?

你刚才提到的AI领域就涉及部分问题。我正试图深入理解AI决策的本质——短时间内发生了惊人变化，这是个真正的移动靶。但这是我想弄清的课题之一。

Well, you've hit on some of them, in terms of the AI. I'm trying to really get my arms around the nature of AI decision making. It's been there's been a remarkable change in a short period of time. So it's a real moving target. But that's one of the things I wanna understand.

我既想推进关于人类的认知理论，也想推动人工智能理论发展。因此需要从人类视角和人工视角双重理解智能。我对这些研究在公共卫生、医疗等现实决策中的影响很感兴趣。最近我还研究了辩诉交易，这类似经典决策场景：一边是确定的认罪协议，另一边是开庭审判的赌博——开庭存在无罪释放的概率，但也有定罪风险。

I wanna move the theory forward about humans, but also move the theory forward about AI artificial intelligence as well. So understand intelligence from a human perspective and from an artificial perspective. And I am interested in the implications of this for real world decisions, things like public health and medicine. And I've recently, done some work on plea bargaining, which resembles these very classic decision scenarios in which you have a sure thing, namely the plea versus a gamble, which is going to trial. And going to trial is a gamble because there's some probability always of acquittal, but there's also a probability of being convicted.

除此之外还存在不确定性。这就构成了确定选项与可能无罪结果的赌博之间的经典困境。我们最近将理论扩展至此，还将探索人工智能在此领域的应用：AI能否助力法律体系？能否帮助医疗系统产出更优、更公平的结果？在非人类决策时，能否保持人文关怀的价值？

And there's uncertainty in addition to that. So you have a classic dilemma there between a sure thing and a gamble in which you have the possibility of acquittal. So we recently extended the theory to that, and we're gonna be looking at artificial intelligence approaches to that as well. Will artificial intelligence help the system, the legal system? Will artificial intelligence help the medical system to produce better outcomes, fairer outcomes, outcomes that retain humane values even if it's not humans making the decisions?

在这种情况下，你如何判断AI是否做出了正确决定？我的意思是，你要么选择庭审要么放弃，无法两者兼得。

In instances like that, how will you know whether AI made the correct decision? I mean, because either you go to trial or you don't, and you can't do both things.

确实如此。这是个真正的挑战。而且我们也没有关于辩诉交易结果的良好数据。这些并非公开保存的记录。要知道，如今我们在医学领域取得了很大进展，但研究医疗结果时同样面临类似难题。

Exactly. That is a real challenge. And we don't have good data about the outcomes of plea bargaining too. They're not public records that are kept. You know, we've now made a lot of progress in medicine, and and it's difficult because some of these same problems exist in studying medical outcomes.

要知道，那位患者可能病情更严重，所以才导致更差的结果。这在大学附属医院很常见——他们会接收最复杂的病例。如果仅看医疗结果，他们的表现可能不如社区医院，因为所有疑难病例都是从社区医院转诊过来的。

You know, that patient could have been sicker. That's why they had a worse outcome. That's often true, for example, of university hospitals. They'll get the most complicated patients. So if you just were to look at their medical outcomes, they might not be as good as a community hospital because that's all the hard patients were transferred from the community possible to the university hospital.

这就像比较苹果和橘子。你说得对，如果只有现实数据，确实很难判断决策优劣。结果数据有帮助，但还不够。你需要建立理论：这个结果是如何产生的？能否用某种已知正确结果的基准来衡量？

So it's apples and oranges. So you're right. That's a very if you only had real life data, you have a real difficulty judging what a good decision is. Outcomes are good, but they're not enough. You need to have a theory of how did that was that outcome reached, and can you really benchmark it against something where you kinda have a sense of what the right outcome is?

我们目前正深入进行相关研究，并获得重要机构资助。NIH和NSF正在资助的项目中，我们与医生合作识别例外病例——可以输入客观症状，研究诊疗指南外的特例，探讨某些情况下是否把握核心要义比拘泥细节更重要。而庭审结果研究更困难，正如你所说，现有数据库更匮乏。这就像医学曾经的发展阶段——如今急诊室会收集各类结果数据，但过去并非如此。

So we we're immersed right now in in work, and and it's funded by some major agencies. Missed and and NSF are funding work right now where we have some idea with the doctors, you know, patients that are exceptions, where we can put objective symptoms in and and and look at exceptions to guidelines and really study those and and try to approach this from a is the gist the right answer in some cases as opposed to the verbatim details. With trial outcomes, it's harder because there's less of a database out there of of outcomes just exactly as you said. I mean, that's where we were with medicine at one point. We you know, now in emergency rooms, they collect data on outcomes of various kinds, for example, that they didn't use to collect.

医学领域过去确实缺乏数据收集。临床试验主要是二十世纪才出现的现象，医学临床指南也非自古就有。我认为法律领域也可能迎来类似发展——或许可以像退伍军人医院那样匿名收集案件结果数据。重点不在于识别个人，而是通过收集结果来优化系统，使其更公平，让所有参与者受益。

They didn't collect, you know, a lot of data in medicine. Clinical trials are a phenomenon mainly of the twentieth century. It wasn't forever that we had clinical guidelines in medicine. So I see that kind of future possible in law where we would collect data maybe anonymously like we do in veterans hospitals about patient outcomes. We don't necessarily identify people because the point of collecting those outcomes is to better the system, to make the system more fair, to make the system better for everyone involved.

如果开展这类工作，我们将获得更好的基准。但现阶段，我们正在研究各种方法，通过假设性决策与实际决策的对比来进行评估。

So if we do that kind of thing, we'll have a better benchmark. But in the meantime, we're looking at various ways to look at hypothetical decisions and compare them to actual decisions.

嗯，拉纳博士，这次谈话非常有趣。感谢您今天参与我们的节目。

Well, Doctor. Rana, this has been very interesting. I want to thank you for joining me today.

我也非常愉快。非常感谢您的邀请。

It's been such a pleasure. Thank you so much.

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我代表美国心理学会，我是金·米尔斯。

For the American Psychological Association, I'm Kim Mills.