̽��ֱ�� of Cambridge - Columbia ̽��ֱ��

For the brain, context is key to new theory of movement and memory

Anonymous — Wed, 24 Nov 2021 16:00:00 +0000

How is it that a chef can control their knife to fillet a fish or peel a grape and can wield a cleaver just as efficiently as a paring knife? Even those of us less proficient in the kitchen learn to skilfully handle an astonishing number of different objects throughout our lives, from shoelaces to tennis rackets.

This ability to continuously acquire new skills, without forgetting or degrading old ones, comes naturally to humans but is a major challenge even for today’s most advanced artificial intelligence systems.

Now, scientists from the ̽��ֱ�� of Cambridge and Columbia ̽��ֱ�� (USA) have developed and experimentally verified a new mathematical theory that explains how the human brain achieves this feat. Called the COntextual INference (COIN) model, it suggests that identifying the current context is key to learning how to move our bodies.

̽��ֱ��model describes a mechanism in the brain that is constantly trying to figure out the current context. ̽��ֱ��theory suggests that these continuously changing beliefs about context determine how to use existing memories — and whether to form new ones. ̽��ֱ��results are reported in the journal Nature.

“Imagine playing tennis with a different racket than usual or switching from tennis to squash,” said co-senior author Dr Daniel Wolpert from Columbia ̽��ֱ��. “Our theory explores how your brain adjusts to these situations and whether to treat them as distinct contexts.”

According to the COIN model, the brain maintains a repertoire of motor memories, each associated with the context in which it was created, such as playing squash versus tennis. Even for a single swing of the racket, the brain can draw upon many memories, each in proportion to how much the brain believes it is currently in the context in which that memory was created.

This goes against the traditional view that only one memory is used at a time. To improve performance on the next swing, the brain also updates all memories, once again depending on its belief about the current context. When the context of the movement is judged to be new (the first time we play squash after years of tennis, for example), the brain automatically creates a new memory for that context. This ensures that we do not overwrite previously established memories, such as the memory for playing tennis.

This research may lead to better physical therapy strategies to help people with injuries use their bodies again. Often the improvements seen in the setting of a physical therapist's office do not transfer to improvements in the real world.

“With a better understanding of how context affects motor learning, you can think about how to nudge the brain to generalise what it learns to contexts outside of the physical therapy session,” said first author Dr James Heald. “A better understanding of the basic mechanisms that underlie the context dependence of memory and learning could have therapeutic consequences in this area.”

“What I find exciting is that the principles of the COIN model may also generalise to many other forms of learning and memory, not just memories underlying our movement,” said co-senior author Professor Máté Lengyel from Cambridge’s Department of Engineering. “For example, the spontaneous recurrence of seemingly forgotten memories, often triggered by a change in our surroundings, has been observed both in motor learning and in post-traumatic stress disorder.”

COINing a new model

Practice with a tennis racket, and the brain forms motor memories of how you moved your arm and the rest of your body that improve your serve over time. But learning isn’t as simple as just making better memories to make movements more precise, the researchers said. Otherwise, a tennis player’s serves might improve to the point at which they never hit a ball out of bounds. ̽��ֱ��real world and our nervous systems are complex, and the brain has to deal with a lot of variability.

How does the brain distinguish this noise — these random fluctuations — from new situations? And how does it understand that a slightly lighter tennis racket can still be operated using previous tennis racket memories? But that a table tennis paddle is an entirely different kind of object that requires starting from scratch?

̽��ֱ��answer, according to the COIN model, may be Bayesian inference, a mathematical technique used to deal with uncertainty. This method statistically weighs new evidence in light of prior experience in order to update one's beliefs in a changeable world. In the COIN model, a context is a simplifying assumption that, in a given set of circumstances, certain actions are more likely to lead to some consequences than others. ̽��ֱ��new theory's acceptance of the role that uncertainty plays in motor learning is similar to how quantum physics views the universe in terms of probabilities instead of certainties, the scientists noted.

Getting a handle on the theory

̽��ֱ��researchers put the COIN model to the test on data from previous experiments, as well as new experiments, in which volunteers interacted with a robotic handle. Participants learned to manipulate the handle to reach a target while the handle pushed back in different ways.

Volunteers who spent time learning to operate the handle as it pushed to the left, for instance, had more trouble operating the handle when it changed behaviour and pushed to the right, as compared to volunteers who started with a handle pushing to the right. ̽��ֱ��COIN model explained this effect, called anterograde interference.

“ ̽��ֱ��longer you learn one task, the less likely you are to move into a new context with the second task,” said Wolpert. “You’re still forming a motor memory of the second task, but you’re not using it yet because your brain is still stuck back in the first context.”

̽��ֱ��model also predicted that a learned skill can re-emerge even after subsequent training seems to have erased it. Called spontaneous recovery, this re-emergence is seen in many other forms of learning besides motor learning. For example, spontaneous recovery has been linked with challenges in treating post-traumatic stress disorder, where contexts can trigger traumatic memories to spontaneously recur.

Scientists usually explain spontaneous recovery by invoking two different learning mechanisms. In one, memories learned quickly are forgotten quickly, and in the other, memories learned slowly are forgotten slowly, and can thus reappear. In contrast, the COIN model suggests there is just one mechanism for learning instead of two separate ones, and that memories that apparently vanished may be ready to pop back with the right trigger: the belief that the context has re-emerged. ̽��ֱ��researchers confirmed this in their lab with new experiments.

Máté Lengyel is a Fellow of Churchill College. ̽��ֱ��research was supported by the European Research Council, the Wellcome Trust, the Royal Society, the National Institutes of Health, and the Engineering and Physical Sciences Research Council.

Reference:
James B Heald, Máté Lengyel and Daniel M Wolpert. ‘Contextual inference underlies the learning of sensorimotor repertoires.’ Nature (2021). DOI: 10.1038/s41586-021-04129-3

Adapted from a Columbia ̽��ֱ�� press release.

Mathematical model could help in physical therapy and shed light on learning more generally.

̽��ֱ��COIN model may also generalise to many other forms of learning and memory, not just memories underlying our movement

Máté Lengyel

Chino Rocha via Unsplash

Tennis match

̽��ֱ��text in this work is licensed under a Creative Commons Attribution 4.0 International License. Images, including our videos, are Copyright © ̽��ֱ�� of Cambridge and licensors/contributors as identified. All rights reserved. We make our image and video content available in a number of ways – as here, on our main website under its Terms and conditions, and on a range of channels including social media that permit your use and sharing of our content under their respective Terms.

Yes

Study identifies our ‘inner pickpocket’

sc604 — Tue, 21 May 2019 07:00:00 +0000

̽��ֱ��researchers, from the ̽��ֱ�� of Cambridge, the Central European ̽��ֱ��, and Columbia ̽��ֱ��, found that one of the reasons that successful pickpockets are so efficient is that they are able to identify objects they have never seen before just by touching them. Similarly, we are able to anticipate what an object in a shop window will feel like just by looking at it.

In both scenarios, we are relying on the brain’s ability to break up the continuous stream of information received by our sensory inputs into distinct chunks. ̽��ֱ��pickpocket is able to interpret the sequence of small depressions on their fingers as a series of well-defined objects in a pocket or handbag, while the shopper’s visual system is able to interpret photons as reflections of light from the objects in the window.

Our ability to extract distinct objects from cluttered scenes by touch or sight alone and accurately predict how they will feel based on how they look, or how they look based on how they feel, is critical to how we interact with the world.

By performing clever statistical analyses of previous experiences, the brain can immediately both identify objects without the need for clear-cut boundaries or other specialised cues, and predict unknown properties of new objects. ̽��ֱ��results are reported in the open-access journal eLife.

“We’re looking at how the brain takes in the continuous flow of information it receives and segments it into objects,” said Professor Máté Lengyel from Cambridge’s Department of Engineering, who co-led the research. “ ̽��ֱ��common view is that the brain receives specialised cues: such as edges or occlusions, about where one thing ends and another thing begins, but we’ve found that the brain is a really smart statistical machine: it looks for patterns and finds building blocks to construct objects.”

Lengyel and his colleagues designed scenes of several abstract shapes without visible boundaries between them, and asked participants to either observe the shapes on a screen or to ‘pull’ them apart along a tear line that passed either through or between the objects.

Participants were then tested on their ability to predict the visual (how familiar did real jigsaw pieces appear compared to abstract pieces constructed from the parts of two different pieces) and haptic properties of these jigsaw pieces (how hard would it be to physically pull apart new scenes in different directions).

̽��ֱ��researchers found that participants were able to form the correct mental model of the jigsaw pieces from either visual or haptic (touch) experience alone, and were able to immediately predict haptic properties from visual ones and vice versa.

“These results challenge classical views on how we extract and learn about objects in our environment,” said Lengyel. “Instead, we’ve show that general-purpose statistical computations known to operate in even the youngest infants are sufficiently powerful for achieving such cognitive feats. Notably, the participants in our study were not selected for being professional pickpockets -- so these results also suggest there is a secret, statistically savvy pickpocket in all of us.”

̽��ֱ��research was funded in part by the Wellcome Trust and the European Research Council.

Reference:
Gábor Lengyel et al. ‘Unimodal statistical learning produces multimodal object-like representations.’ eLife (2019). DOI: 10.7554/eLife.43942

Researchers have identified how the human brain is able to determine the properties of a particular object using purely statistical information: a result which suggests there is an ‘inner pickpocket’ in all of us.

These results suggest there is a secret, statistically savvy pickpocket in all of us

Máté Lengyel

Yes

Changes in ocean 'conveyor belt' predicted abrupt climate changes

sc604 — Wed, 20 Mar 2019 00:00:01 +0000

In the Atlantic Ocean, a giant ‘conveyor belt’ carries warm waters from the tropics into the North Atlantic, where they cool and sink and then return southwards in the deep ocean. This circulation pattern is known as the Atlantic Meridional Overturning Circulation (AMOC) and it’s an important player in the global climate, regulating weather patterns in the Arctic, Europe, and around the world.

Evidence increasingly suggests that this oceanic current system is slowing down, and some scientists fear it could have major effects, such as causing temperatures to dive in Europe and warming the waters off the eastern coast of the USA, potentially harming fisheries and exacerbating hurricanes.

A new study published in Nature Communications provides insight into how quickly these changes could take effect if the ocean current system continues weakening.

An international team of scientists studied one of the key sections of the AMOC –where North Atlantic water sinks from the surface to the bottom of the ocean. They confirmed that changes in the ocean conveyor belt preceded abrupt and major climatic changes during the transition out of the last ice age, referred to as the last deglaciation. ̽��ֱ��study is the first to determine the time lags between past changes to the AMOC and major climate changes.

“Our reconstructions indicate that there are clear climate precursors provided by the ocean state — like warning signs, so to speak,” said lead author Francesco Muschitiello from the ̽��ֱ�� of Cambridge’s Department of Geography, who completed the work while a postdoc at Columbia ̽��ֱ��.

Until now, it has been difficult to resolve whether past changes in the ocean conveyor belt occurred before or after the abrupt climate shifts that punctuated the last deglaciation in the Northern Hemisphere. To overcome the usual challenges, the team pieced together data from a sediment core drilled from the bottom of the Nordic Seas, a lake sediment core from southern Scandinavia, and ice cores from Greenland.

Scientists typically rely on radioactive carbon (carbon 14) dating to determine the ages of sediments. This relationship is tricky in ocean sediments, though, because carbon 14 is created in the atmosphere, and it takes time for the carbon to make its way through the ocean. By the time it reaches the organisms at the bottom of the water column, the carbon 14 could already be hundreds or thousands of years old. So the team needed a different way to date the sediment layers in the marine core.

̽��ֱ��researchers solved this puzzle by measuring carbon 14 levels from a nearby lake sediment core and matching it to the marine core layers. Next, they compared the real age of the marine sediments to the deep ocean carbon 14 measurement, giving them a record of ocean circulation patterns in this region over time. ̽��ֱ��final piece of the puzzle was to analyse ice cores from Greenland, to study changes in temperature and climate over the same time period.

Comparing the data from the three cores revealed that the AMOC weakened in the time leading up to the planet’s last major cold snap around 13,000 years ago. ̽��ֱ��ocean circulation began slowing down about 400 years before the cold snap, but once the climate started changing, temperatures over Greenland plunged quickly by about 6 degrees.

A similar pattern emerged near the end of that cold snap, transitioning out of the ice age; the current started strengthening roughly 400 years before the atmosphere began to heat up dramatically, when Greenland warmed up rapidly — its average temperature climbed by about 8 degrees over just a few decades, causing glaciers to melt and sea ice to drop off considerably in the North Atlantic.

For now it’s not fully clear why there was such a long delay between the AMOC changes and climatic changes over the North Atlantic.

It’s also difficult to pinpoint what these patterns from the past could signify for Earth’s future. Recent evidence suggests that the AMOC began weakening again 150 years ago. However, current conditions are quite different from the last time around, says Muschitiello: the global thermostat was much lower back then, winter sea ice stretched farther south than New York Harbour, and the ocean structure would have been much different. In addition, the past weakening of the AMOC was much more dramatic than today’s trend so far.

“It is clear that there are some precursors in the ocean, so we should be watching the ocean. ̽��ֱ��mere fact that AMOC has been slowing down, that should be a concern based on what we have found,” said Muschitiello.

̽��ֱ��study should also help to improve the physics behind climate models, which generally assume the climate alters abruptly at the same time as AMOC intensity changes. ̽��ֱ��model refinements, in turn, could make climate predictions more accurate. As Svensson puts it: “As long as we do not understand the climate of the past, it is very difficult to constrain the climate models needed to make realistic future scenarios.”

Reference:
Francesco Muschitiello et al. 'Deep-water circulation changes lead North Atlantic climate during deglaciation.' Nature Communications (2019). DOI: 10.1038/s41467-019-09237-3

Adapted from Columbia ̽��ֱ�� story.

A bold response to the world’s greatest challenge
̽��ֱ�� ̽��ֱ�� of Cambridge is building on its existing research and launching an ambitious new environment and climate change initiative. Cambridge Zero is not just about developing greener technologies. It will harness the full power of the ̽��ֱ��’s research and policy expertise, developing solutions that work for our lives, our society and our biosphere.

A new study is the first to measure the time lags between changing ocean currents and major climate shifts.

There are clear climate precursors provided by the ocean state — like warning signs, so to speak

Francesco Muschitiello

Muschitiello et al

A simplified diagram of the Atlantic Meridional Overturning Circulation

Yes

Opinion: What can we learn about you from just one click?

sc604 — Tue, 14 Nov 2017 12:03:31 +0000

Whether you like it or not, almost every step you take online is recorded: the websites you visit, the purchases you make, the songs you listen to, the messages you post or read on social sites, and the pages you follow on Facebook. These digital footprints provide a treasure trove of data that can reveal not only what you like and how you see the world, but also who you are as a person.

In our research entitled “Psychological targeting as an effective approach to digital mass persuasion” published in the Proceedings of the National Academy of Science, we show that these digital footprints can be used to influence effectively the behaviour of large groups of people. By targeting consumers with persuasive messages that are tailored to their core psychological profiles (e.g. the degree to which they are extroverted or introverted) it is possible to significantly increase the likelihood that people will take a specific action, such as clicking on an ad or purchasing the promoted product.

̽��ֱ��basic principle behind this form of personalised persuasion is not new: marketing practitioners have long used behavioural and demographic data to target consumers with customised messages. What is new, however, is the ability to identify and target audiences based on psychological traits that reflect people’s preferences and needs at a much deeper and instinctual level. Prior targeting might have focused on demographic or behavioural attributes such as ‘women ages 18-45’ who searched for the term ‘Soccer World Cup on Google between 2-4pm’. Psychological targeting, however, can focus on a person’s fundamental character traits and psychological needs, which are known to explain and predict preferences in a broad variety of contexts.

Psychological targeting in action

Across three studies, we targeted more than 3.5 million users on Facebook. As of now, Facebook advertising does not allow marketers to directly target users based on their psychological traits. However, it does so indirectly by offering the possibility to target based on Facebook ‘Likes’. While previous research has shown that one can accurately predict people’s psychological traits after getting their permission to access to their Facebook profiles, we leveraged inherent features of the Facebook advertising platform to target our ads at consumer segments of different psychological profiles. For example, if liking ‘Socialising’ on Facebook correlates with the personality trait of extroversion, and liking ‘Stargate’ goes hand in hand with introversion, then targeting users associated with each of these Likes allows us to separately target extroverted and introverted audiences.

Then, we sent out persuasive appeals in the form of Facebook ads that either aligned with or ran counter to the users’ psychological profiles. Finally, we measured users’ reactions to the ads by counting which ad users clicked on (i.e. clicks) and whether users purchased the product promoted in the ad (i.e. conversions).

In one of the experiments, for instance, we chose an online beauty retailer and created customised ads that could be targeted toward either extroverts or introverts, as identified according to their Facebook Likes. We found that matching the content of persuasive messages to individuals’ psychological characteristics resulted in up to 40% more clicks and up to 50% more purchases than their mismatching or un-personalised messages. Extroverts responded more positively to advertising messages when the beauty retailer’s ad was focused on extroverted preferences and interests (e.g. showing a group of women in a social situation, dancing, and having fun, accompanied by ad copy saying: ‘Dance like no one’s watching (but they totally are)’). Meanwhile, introverts responded more positively to those ads that focused on introverted preferences (e.g. a single woman by herself in a quiet environment, enjoying her ‘me-time,’ accompanied by ad copy saying: ‘Beauty doesn't have to shout’).

Implications: the good and the bad

̽��ֱ��ability to influence the behaviour of large groups of people by tailoring persuasive messages to their psychological needs could be used to help people make better decisions, and lead healthier and happier lives. Human nature regularly encourages us to act in ways that focus on short-term benefits and neglect negative long-term consequences: just ask anyone who has ever tried to diet how difficult it is to resist the temptation of a chocolate bar and instead eat an apple. ̽��ֱ��same can be said about saving money: putting money aside for a rainy day is certainly less enjoyable in the moment than spending it on the new pair of shoes that caught your eye in a store window. So, how can psychological targeting help people overcome their human limitations?

Let’s take the example of saving money. Similar to the way psychological targeting can convince people to buy a product, it can also be used to convince people to save more. When targeting people identified as extroverts, ads could encourage them to imagine spending their savings on an exciting summer holiday with their friends in a vibrant and exhilarating city that allows them to pursue outgoing and social activities. Conversely, when targeting introverts, ads could highlight the ability to invest one’s savings in making their home a more comfortable refuge to escape the hectic outside world. In both cases, psychological targeting could help people to see the benefits of saving, and eventually save more.

On the other hand, psychological targeting could be used to exploit weaknesses in people’s character and persuade them to take action against their best interest. For example, online casinos could target ads at individuals who have psychological traits associated with pathological gambling. In fact, psychological targeting has been covered extensively in the context of its ability to influence the outcome of elections. While the veracity of these claims remains uncertain, our findings illustrate how psychological mass persuasion could be used to manipulate people to behave in ways that are neither in their best interest nor in the best interest of society.

Next steps: fuelling a critical debate

Our findings show that psychological targeting works. ̽��ֱ��technology is not science fiction; it exists today. To us, the most important discussion we need to have now is not what may or may not have happened in the past, but what we as individuals and as a society can and should do moving forward. Key questions that need to be answered in a critical public discourse are:

How do we as consumers and society-at-large want to use this new technology? In what settings do we want to facilitate its application, and when do we want to restrict it? For which purposes should we use it, for which should we not? Under which agreements should we be allowed to implement it, and with which required a degree of transparency?

̽��ֱ��reason we started this research was to provide empirical evidence for the effectiveness of psychological targeting. Our hope is that these findings can support the public debate on this topic by showing both the general public and key decision makers – such as elected officials and business leaders – just how important and timely this topic is. Our belief is that by having an open and transparent discussion, solutions and checks and balances can be developed in the form of policies, regulations and technological counter-measures, which will ensure that psychological targeting serves as a driver for good rather than evil.

Adapted from a story published on Columbia ̽��ֱ��’s website.

How effective is psychological targeting in advertising? Dr Sandra Matz, a former PhD student at Cambridge now based at Columbia ̽��ֱ��, and her co-authors, including Dr David Stillwell from the Cambridge Psychometrics Centre, have published a new study which demonstrates that companies only need one Facebook ‘like’ to effectively target potential customers.

Official White House Photo by Lawrence Jackson

Members of the audience take pictures as President Barack Obama participates in a town hall meeting moderated by CEO Mark Zuckerberg at Facebook headquarters in Palo Alto, Calif. April 20, 2011

̽��ֱ��text in this work is licensed under a Creative Commons Attribution 4.0 International License. For image use please see separate credits above.

Yes

Modelling how the brain makes complex decisions

sc604 — Thu, 04 Feb 2016 10:17:10 +0000

Researchers have constructed the first comprehensive model of how neurons in the brain behave when faced with a complex decision-making process, and how they adapt and learn from mistakes.

̽��ֱ��mathematical model, developed by researchers from the ̽��ֱ�� of Cambridge, is the first biologically realistic account of the process, and is able to predict not only behaviour, but also neural activity. ̽��ֱ��results, reported in ̽��ֱ��Journal of Neuroscience, could aid in the understanding of conditions from obsessive compulsive disorder and addiction to Parkinson’s disease.

̽��ֱ��model was compared to experimental data for a wide-ranging set of tasks, from simple binary choices to multistep sequential decision making. It accurately captures behavioural choice probabilities and predicts choice reversal in an experiment, a hallmark of complex decision making.

Our decisions may provide immediate gratification, but they can also have far-reaching consequences, which in turn depend on several other actions we have already made or will make in the future. ̽��ֱ��trouble that most of us have is how to take the potential long-term effects of a particular decision into account, so that we make the best choice.

There are two main types of decisions: habit-based and goal-based. An example of a habit-based decision would be a daily commute, which is generally the same every day. Just as certain websites are cached on a computer so that they load faster the next time they are visited, habits are formed by ‘caching’ certain behaviours so that they become virtually automatic.

An example of a goal-based decision would be a traffic accident or road closure on that same commute, forcing the adoption of a different route.

“A goal-based decision is much more complicated from a neurobiological point of view, because there are so many more variables – it involves exploring a branching set of possible future situations,” said the paper’s first author Dr Johannes Friedrich of Columbia ̽��ֱ��, who conducted the work while a postdoctoral researcher in Cambridge’s Department of Engineering. “If you think about a detour on your daily commute, you need to make a separate decision each time you reach an intersection.”

Habit-based decisions have been thoroughly studied by neuroscientists and are fairly well-understood in terms of how they work at a neural level. ̽��ֱ��mechanisms behind goal-based decisions however, remain elusive.

Now, Friedrich and Dr Máté Lengyel, also from Cambridge’s Department of Engineering, have built a biologically realistic solution to this computational problem. ̽��ֱ��researchers have shown mathematically how a network of neurons, when connected appropriately, can identify the best decision in a given situation and its future cumulative reward.

“Constructing these sorts of models is difficult because the model has to plan for all possible decisions at any given point in the process, and computations have to be performed in a biologically plausible manner,” said Friedrich. “But it’s an important part of figuring out how the brain works, since the ability to make decisions is such a core competence for both humans and animals.”

̽��ֱ��researchers also found that for making a goal-based decision, the synapses which connect the neurons together need to ‘embed’ the knowledge of how situations follow on from each other, depending on the actions that are chosen, and how they result in immediate reward.

Crucially, they were also able to show in the same model how synapses can adapt and re-shape themselves depending on what did or didn’t work previously, in the same way that it has been observed in human and animal subjects.

“By combining planning and learning into one coherent model, we’ve made what is probably the most comprehensive model of complex decision-making to date,” said Friedrich. “What I also find exciting is that figuring out how the brain may be doing it has already suggested us new algorithms that could be used in computers to solve similar tasks,” added Lengyel.

̽��ֱ��model could be used to aid in the understanding of a range of conditions. For instance, there is evidence for selective impairment in goal-directed behavioural control in patients with obsessive compulsive disorder, which forces them to rely instead on habits. Deep understanding of the underlying neural processes is important as impaired decision making has also been linked to suicide attempts, addiction and Parkinson's disease.

Reference:
Johannes Friedrich and Máté Lengyel. ‘Goal-Directed Decision Making with Spiking Neurons.’ ̽��ֱ��Journal of Neuroscience (2016). DOI: 10.1523/JNEUROSCI.2854-15.2016

Researchers have built the first biologically realistic mathematical model of how the brain plans and learns when faced with a complex decision-making process.

By combining planning and learning into one coherent model, we’ve made what is probably the most comprehensive model of complex decision-making to date

Johannes Friedrich

Seung Lab

EyeWire Candy Neurons

̽��ֱ��text in this work is licensed under a Creative Commons Attribution 4.0 International License. For image use please see separate credits above.

Yes

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