Posts tagged decision making
Articles and news from the latest research reports.
Scientists find brain region that helps you make up your mind
One of the smallest parts of the brain is getting a second look after new research suggests it plays a crucial role in decision making.
A University of British Columbia study published today in Nature Neuroscience says the lateral habenula, a region of the brain linked to depression and avoidance behaviours, has been largely misunderstood and may be integral in cost-benefit decisions.
“These findings clarify the brain processes involved in the important decisions that we make on a daily basis, from choosing between job offers to deciding which house or car to buy,” says Prof. Stan Floresco of UBC’s Dept. of Psychology and Brain Research Centre (BRC). “It also suggests that the scientific community has misunderstood the true functioning of this mysterious, but important, region of the brain.”
In the study, scientists trained lab rats to choose between a consistent small reward (one food pellet) or a potentially larger reward (four food pellets) that appeared sporadically. Like humans, the rats tended to choose larger rewards when costs—in this case, the amount of time they had to wait before receiving food–were low and preferred smaller rewards when such risks were higher.
Previous studies suggest that turning off the lateral habenula would cause rats to choose the larger, riskier reward more often, but that was not the case. Instead, the rats selected either option at random, no longer showing the ability to choose the best option for them.
The findings have important implications for depression treatment. “Deep brain stimulation – which is thought to inactivate the lateral habenula — has been reported to improve depressive symptoms in humans,” Floresco says. “But our findings suggest these improvements may not be because patients feel happier. They may simply no longer care as much about what is making them feel depressed.”
Background
Floresco, who conducted the study with PhD candidate Colin Stopper, says more investigation is needed to understand the complete brain functions involved in cost-benefit decision processes and related behaviour. A greater understanding of decision-making processes is also crucial, they say, because many psychiatric disorders, such as schizophrenia, stimulant abuse and depression, are associated with impairments in these processes.
The lateral habenula is considered one of the oldest regions of the brain, evolution-wise, the researchers say.
Scientists discover that ants, like humans, can change their priorities
All animals have to make decisions every day. Where will they live and what will they eat? How will they protect themselves? They often have to make these decisions as a group, too, turning what may seem like a simple choice into a far more nuanced process. So, how do animals know what’s best for their survival?
For the first time, Arizona State University researchers have discovered that at least in ants, animals can change their decision-making strategies based on experience. They can also use that experience to weigh different options.
The findings are featured today in the early online edition of the scientific journal Biology Letters, as well as in its Dec. 23 edition.
Co-authors Taka Sasaki and Stephen Pratt, both with ASU’s School of Life Sciences, have studied insect collectives, such as ants, for years. Sasaki, a postdoctoral research associate, specializes in adapting psychological theories and experiments that are designed for humans to ants, hoping to understand how the collective decision-making process arises out of individually ignorant ants.
“The interesting thing is we can make decisions and ants can make decisions – but ants do it collectively,” said Sasaki. “So how different are we from ant colonies?”
To answer this question, Sasaki and Pratt gave a number of Temnothorax rugatulus ant colonies a series of choices between two nests with differing qualities. In one treatment, the entrances of the nests had varied sizes, and in the other, the exposure to light was manipulated. Since these ants prefer both a smaller entrance size and a lower level of light exposure, they had to prioritize.
“It’s kind of like a humans and buying a house,” said Pratt, an associate professor with the school. “There’s so many options to consider – the size, the number of rooms, the neighborhood, the price, if there’s a pool. The list goes on and on. And for the ants it’s similar, since they live in cavities that can be dark or light, big or small. With all of these things, just like with a human house, it’s very unlikely to find a home that has everything you want.”
Pratt continued to explain that because it is impossible to find the perfect habitat, ants make various tradeoffs for certain qualities, ordering them in a queue of most important aspects. But, when faced with a decision between two different homes, the ants displayed a previously unseen level of intelligence.
According to their data, the series of choices the ants faced caused them to reprioritize their preferences based on the type of decision they faced. Ants that had to choose a nest based on light level prioritized light level over entrance size in the final choice. On the other hand, ants that had to choose a nest based on entrance size ranked light level lower in the later experiment.
This means that, like people, ants take the past into account when weighing options while making a choice. The difference is that ants somehow manage to do this as a colony without any dissent. While this research builds on groundwork previously laid down by Sasaki and Pratt, the newest experiments have already raised more questions.
“You have hundreds of these ants, and somehow they have to reach a consensus,” Pratt said. “How do they do it without anyone in charge to tell them what to do?”
Pratt likened individual ants to individual neurons in the human brain. Both play a key role in the decision-making process, but no one understands how every neuron influences a decision.
Sasaki and Pratt hope to delve deeper into the realm of ant behavior so that one day, they can understand how individual ants influence the colony. Their greater goal is to apply what they discover to help society better understand how humanity can make collective decisions with the same ease ants display.
“This helps us learn how collective decision-making works and how it’s different from individual decision-making,” said Pratt. “And ants aren’t the only animals that make collective decisions – humans do, too. So maybe we can gain some general insight.”
(Source: asunews.asu.edu)
New Study Decodes Brain’s Process for Decision Making
When faced with a choice, the brain retrieves specific traces of memories, rather than a generalized overview of past experiences, from its mental Rolodex, according to new brain-imaging research from The University of Texas at Austin.
Led by Michael Mack, a postdoctoral researcher in the departments of psychology and neuroscience, the study is the first to combine computer simulations with brain-imaging data to compare two different types of decision-making models.
In one model — exemplar — a decision is framed around concrete traces of memories, while in the other model — prototype — the decision is based on a generalized overview of all memories lumped into a specific category.
Whether one model drives decisions more than the other has remained a matter of debate among scientists for more than three decades. But according to the findings, the exemplar model is more consistent with decision-making behavior.
The study was published this month in Current Biology. The authors include Alison Preston, associate professor in the Department of Psychology and the Center for Learning and Memory; and Bradley Love, a professor at University College London.
In the study, 20 respondents were asked to sort various shapes into two categories. During the task their brain activity was observed using functional magnetic resonance imaging (fMRI), allowing researchers to see how the respondents associate shapes with past memories.
According to the findings, behavioral research alone cannot determine whether a subject uses the exemplar or prototype model to make decisions. With brain-imaging analysis, researchers found that the exemplar model accounted for the majority of participants’ decisions. The results show three different regions associated with the exemplar model were activated during the learning task: occipital (visual perception), parietal (sensory) and frontal cortex (attention).
While processing new information, the brain stores concrete traces of experiences, allowing it to make different kinds of decisions, such as categorization information (is that a dog?), identification (is that John’s dog?) and recall (when did I last see John’s dog?).
To illustrate, Mack says: Imagine having a conversation with a friend about buying a new car. When you think of the category “car,” you’re likely to think of an abstract concept of a car, but not specific details. However, abstract categories are composed of memories from individual experiences. So when you imagine “car,” the abstract mental picture is actually derived from experiences, such as your friend’s white sedan or the red sports car you saw on the morning commute.
“We flexibly memorize our experiences, and this allows us to use these memories for different kinds of decisions,” Mack says. “By storing concrete traces of our experiences, we can make decisions about different types of cars and even specific past experiences in our life with the same memories.”
Mack says this new approach to model-based cognitive neuroscience could lead to discoveries in cognitive research.
“The field has struggled with linking theories of how we behave and act to the activation measures we see in the brain,” Mack says. “Our work offers a method to move beyond simply looking at blobs of brain activation. Instead, we use patterns of brain activation to decode the algorithms underlying cognitive behaviors like decision making.”
(Source: utexas.edu)
While eating lunch, you notice an insect buzzing around your plate. Its color and motion could both influence how you respond. If the insect was yellow and black you might decide it was a bee and move away. Conversely, you might simply be annoyed at the buzzing motion and shoo the insect away. You perceive both color and motion, and decide based on the circumstances. Our brains make such contextual decisions in a heartbeat. The mystery is how.
In an article published Nov. 7 in the journal Nature, a team of Stanford neuroscientists and engineers delve into this decision-making process and report some findings that confound the conventional wisdom.
Until now, neuroscientists have believed that decisions of this sort involved two steps: one group of neurons that performed a gating function to ascertain whether motion or color was most relevant to the situation and a second group of neurons that considered only the sensory input relevant to making a decision under the circumstances.
But in a study that combined brain recordings from trained monkeys and a sophisticated computer model based on that biological data, Stanford neuroscientist William Newsome and three co-authors discovered that the entire decision-making process may occur in a localized region of the prefrontal cortex.
In this region of the brain, located in the frontal lobes just behind the forehead, they found that color and motion signals converged in a specific circuit of neurons. Based on their experimental evidence and computer simulations, the scientists hypothesized that these neurons act together to make two snap judgments: whether color or motion is the most relevant sensory input in the current context and what action to take.
“We were quite surprised,” said Newsome, the Harman Family Provostial Professor at the Stanford School of Medicine and lead author.
He and first author Valerio Mante, a former Stanford neurobiologist now at the University of Zurich and the Swiss Federal Institute of Technology, had begun the experiment expecting to find that the irrelevant signal, whether color or motion, would be gated out of the circuit long before the decision-making neurons went into action.
“What we saw instead was this complicated mix of signals that we could measure but whose meaning and underlying mechanism we couldn’t understand,” Newsome said. “These signals held information about the color and motion of the stimulus, which stimulus dimension was most relevant and the decision that the monkeys made. But the signals were profoundly mixed up at the single neuron level. We decided there was a lot more we needed to learn about these neurons and that the key to unlocking the secret might lie in a population level analysis of the circuit activity.”
To solve this brain puzzle the neurobiologists began a cross-disciplinary collaboration with Krishna Shenoy, a professor of electrical engineering at Stanford, and David Sussillo, co-first author on the paper and a postdoctoral scholar in Shenoy’s lab.
Sussillo created a software model to simulate how these neurons worked. The idea was to build a model sophisticated enough to mimic the decision-making process but easier to study than taking repeated electrical readings from a brain.
The general model architecture they used is called a recurrent neural network: a set of software modules designed to accept inputs and perform tasks similar to how biological neurons operate. The scientists designed this artificial neural network using computational techniques that enabled the software model to make itself more proficient at decision-making over time.
“We challenged the artificial system to solve a problem analogous to the one given to the monkeys,” Sussillo explained. “But we didn’t tell the neural network how to solve the problem.”
As a result, once the artificial network learned to solve the task, the scientists could study the model to develop inferences about how the biological neurons might be working.
The entire process was grounded in the biological experiments.
The neuroscientists trained two macaque monkeys to view a random-dot visual display that had two different features – motion and color. For any given presentation, the dots could move to the right or left, and the color could be red or green. The monkeys were taught to use sideways glances to answer two different questions depending on the currently instructed “rule” or context. Were there more red or green dots (ignore the motion)? Or were the dots moving to the left or right (ignore the color)?
Eye-tracking instruments recorded the glances, or saccades, that the monkeys used to register their responses. Their answers were correlated with recordings of neuronal activity taken directly from an area in the prefrontal cortex known to control saccadic eye movements.
The neuroscientists collected 1,402 such experimental measurements. Each time the monkeys were asked one or the other question. The idea was to obtain brain recordings at the moment when the monkeys saw a visual cue that established the context (either the red/green or left/right question) and what decision the animal made regarding color or direction of motion.
It was the puzzling mish-mash of signals in the brain recordings from these experiments that prompted the scientists to build the recurrent neural network as a way to rerun the experiment, in a simulated way, time and time again.
As the four researchers became confident that their software simulations accurately mirrored the actual biological behavior, they studied the model to learn exactly how it solved the task. This allowed them to form a hypothesis about what was occurring in that patch of neurons in the prefrontal cortex where perception and decision occurred.
“The idea is really very simple,” Sussillo explained.
Their hypothesis revolves around two mathematical concepts: a line attractor and a selection vector.
The entire group of neurons being studied received sensory data about both the color and the motion of the dots.
The line attractor is a mathematical representation for the amount of information that this group of neurons was getting about either of the relevant inputs, color or motion.
The selection vector represented how the model responded when the experimenters flashed one of the two questions: red or green, left or right?
What the model showed was that when the question pertained to color, the selection vector directed the artificial neurons to accept color information while ignoring the irrelevant motion information. Color data became the line attractor. After a split second these neurons registered a decision, choosing the red or green answer based on the data they were supplied.
If question was about motion, the selection vector directed motion information to the line attractor, and the artificial neurons chose left or right.
“The amazing part is that a single neuronal circuit is doing all of this,” Sussillo says. “If our model is correct, then almost all neurons in this biological circuit appear to be contributing to almost all parts of the information selection and decision-making mechanism.”
Newsome put it like this: “We think that all of these neurons are interested in everything that’s going on, but they’re interested to different degrees. They’re multitasking like crazy.”
Other researchers who are aware of the work but were not directly involved are commenting on the paper.
“This is a spectacular example of excellent experimentation combined with clever data analysis and creative theoretical modeling,” said Larry Abbott, Co-Director of the Center for Theoretical Neuroscience and the William Bloor Professor, Neuroscience, Physiology & Cellular Biophysics, Biological Sciences at Columbia University.
Christopher Harvey, a professor of neurobiology at Harvard Medical School, said the paper “provides major new hypotheses about the inner-workings of the prefrontal cortex, which is a brain area that has frequently been identified as significant for higher cognitive processes but whose mechanistic functioning has remained mysterious.”
The Stanford scientists are now designing a new biological experiment to ascertain whether the interplay between selection vector and line attractor, which they deduced from their software model, can be measured in actual brain signals.
“The model predicts a very specific type of neural activity under very specific circumstances,” Sussillo said. “If we can stimulate the prefrontal cortex in the right way, and then measure this activity, we will have gone a long way to proving that the model mechanism is indeed what is happening in the biological circuit.”
Virginia Tech to Host Neuroscience Workshop in Switzerland
Neuroscientists will discuss cognition, computation, decisions
Nearly two dozen of the world’s leading neuroscientists will gather in Switzerland next month to share their latest findings on the mysteries of how the brain processes information and makes decisions.
The Virginia Tech Carilion Research Institute European–U.S. Workshop on the Neuroscience of Cognition, Computation, and Decisions will be held at Virginia Tech’s Center for European Studies and Architecture at Riva San Vitale in Ticino on Oct. 16 to Oct. 18.
“We have two principal goals for this intensive workshop,” said Michael Friedlander, associate provost for health sciences at Virginia Tech and executive director of the Virginia Tech Carilion Research Institute. “First, we want to identify new and powerful integrated approaches to bridge multiple levels of understanding brain function. We are also hoping to lay the foundations for pioneering innovative and disruptive approaches to transcending disciplines and technologies across teams of leading European brain researchers and Virginia Tech Carilion Research Institute neuroscientists.”
The workshop will convene 10 neuroscientists from the institute and 13 neuroscientists from prominent brain-research institutions in five European countries, includinbg the Centre National de la Recherche Scientifique and École Polytechnique in France; the Central Institute of Mental Health Mannheim, Freie Universität Berlin, the Max Planck Institute for Biological Cybernetics, the Max Planck Institute for Human Development, and the University of Heidelberg in Germany; the International School for Advanced Studies in Trieste, Italy; École Polytechnique Fédérale de Lausanne, ETH Zürich, and the University of Zurich in Switzerland; and University College London in the United Kingdom.
Workshop participants will address emerging views of how neuronal and synaptic networks in the brain assemble, process, store, and access information and how large-scale networks of interconnected neurons perform in humans and other mammals. The participants will also consider the functional architecture that underlies the brain’s decision-making capacity, the neural basis of social interactions, the effects of the environment on information processing, and the consequences of a range of disorders on the function of the human brain.
Participants will share their newest discoveries in multiple sessions of several speakers each, followed by in-depth discussions to identify congruent perspectives and converging insights from multiple disciplines.
The discoveries will represent a broad array of technological and conceptual approaches, including analysis of detailed structural and functional properties of individual neurons and synaptic networks obtained with powerful electrophysiological, genetic, and optical imaging methods; functional brain imaging and behavioral studies in individuals and groups of interacting humans; and computational analysis and modeling of brain function and behavior.
Additional experts will address economics and game theory applications to human brain function and behavior in health and in disease; analysis of development, aging, and educational interventions on brain function; and the modulation of brain function acutely and over time in health and in various disorders that affect behavior, neural information processing, and decision-making.
“This workshop is taking place at a confluence of important national and international milestones in brain research in both Europe and the United States,” Friedlander said. “The Blue Brain Project in Europe represents a major international coalition to support large-scale, detailed analysis of the circuitry of the brain, while in the United States, President Barack Obama’s BRAIN Initiative will support innovative new approaches to high-resolution, large-scale functional mapping of the brain. We’re hoping to harness the wisdom of experts on both continents to develop new approaches and better technologies for diagnosing and treating neurological and psychiatric disorders that affect people worldwide.”
Neural and Behavioral Evidence for an Intrinsic Cost of Self-Control
The capacity for self-control is critical to adaptive functioning, yet our knowledge of the underlying processes and mechanisms is presently only inchoate. Theoretical work in economics has suggested a model of self-control centering on two key assumptions: (1) a division within the decision-maker between two ‘selves’ with differing preferences; (2) the idea that self-control is intrinsically costly. Neuroscience has recently generated findings supporting the ‘dual-self’ assumption. The idea of self-control costs, in contrast, has remained speculative. We report the first independent evidence for self-control costs. Through a neuroimaging meta-analysis, we establish an anatomical link between self-control and the registration of cognitive effort costs. This link predicts that individuals who strongly avoid cognitive demand should also display poor self-control. To test this, we conducted a behavioral experiment leveraging a measure of demand avoidance along with two measures of self-control. The results obtained provide clear support for the idea of self-control costs.
Old memories recombine to give a taste of the unknown
Ever tried beetroot custard? Probably not, but your brain can imagine how it might taste by reactivating old memories in a new pattern.
Helen Barron and her colleagues at University College London and Oxford University wondered if our brains combine existing memories to help us decide whether to try something new.
So the team used an fMRI scanner to look at the brains of 19 volunteers who were asked to remember specific foods they had tried.
Each volunteer was then given a menu of 13 unusual food combinations – including beetroot custard, tea jelly, and coffee yoghurt – and asked to imagine how good or bad they would taste, and whether or not they would eat them.
"Tea jelly was popular," says Barron. "Beetroot custard not so much."
When each volunteer imagined a new combination, they showed brain activity associated with each of the known ingredients at the same time. It is the first evidence to suggest that we use memory combination to make decisions, says Barron.
(Source: newscientist.com)
In longterm relationships, the brain makes trust a habit
After someone betrays you, do you continue to trust the betrayer? Your answer depends on the length of the relationship, according to research by sociologist Karen Cook of Stanford University and her colleagues. The researchers found that those who have been deceived early in a relationship use regions of the brain associated with controlled, careful decision making when deciding if they should continue to trust the person who deceived them. However, those betrayed later in a relationship use areas of the brain associated with automatic, habitual decision making, increasing the likelihood of forgiveness. The study appears in the Proceedings of the National Academy of Sciences.
Cook and her team wanted to understand why some people choose to reconcile after they’ve become victims of betrayal, but others don’t. They hypothesized that if the relationship formed recently, the victim will engage in conscious, deliberate problem solving when deciding how to respond to the deceit. On the other hand, if the relationship has existed for a long time, the victim will take trustworthy behavior for granted and consider a breach of trust an exception to the rule.
To test their hypothesis, the team performed an online experiment, using subjects recruited through an internet survey provider. Each subject received eight dollars and could either keep the money or give it to an unseen partner. If the subject gave the money away, its value would triple. The partner would then decide whether to keep it all or give half back to the subject.
Unbeknownst to the subject, the partner was really a computer, sometimes programmed to betray the subject early in the game and sometimes programmed to betray the subject later. Cook’s team found that after an early betrayal, the subject would be more likely to keep the money than after a late betrayal.
When the team repeated the experiment in a laboratory, with subjects hooked up to fMRI scanners, the anterior cingulate cortex, associated with conscious learning, planning and problem solving, and the lateral frontal cortex, associated with feelings of uncertainty, became more active after early betrayal. In contrast, the lateral temporal cortex, associated with habituated decision making, became more active after late betrayal.
As with the first experiment, an early betrayal increased the likelihood of the subject holding onto the money in later rounds. Early betrayal also increased the amount of time taken to make a decision, suggesting that victims of early betrayal were putting more conscious thought into their decisions than victims of late betrayal were.
The researchers hope their study will increase understanding of why some victims of deceit continue to forgive those who deceived them.
Sleep deprivation linked to junk food cravings
A sleepless night makes us more likely to reach for doughnuts or pizza than for whole grains and leafy green vegetables, suggests a new study from UC Berkeley that examines the brain regions that control food choices. The findings shed new light on the link between poor sleep and obesity.
Using functional magnetic resonance imaging (fMRI), UC Berkeley researchers scanned the brains of 23 healthy young adults, first after a normal night’s sleep and next, after a sleepless night. They found impaired activity in the sleep-deprived brain’s frontal lobe, which governs complex decision-making, but increased activity in deeper brain centers that respond to rewards. Moreover, the participants favored unhealthy snack and junk foods when they were sleep deprived.
“What we have discovered is that high-level brain regions required for complex judgments and decisions become blunted by a lack of sleep, while more primal brain structures that control motivation and desire are amplified,” said Matthew Walker, a UC Berkeley professor of psychology and neuroscience and senior author of the study published today (Tuesday, Aug. 6) in the journal Nature Communications.
Moreover, he added, “high-calorie foods also became significantly more desirable when participants were sleep-deprived. This combination of altered brain activity and decision-making may help explain why people who sleep less also tend to be overweight or obese.”
Previous studies have linked poor sleep to greater appetites, particularly for sweet and salty foods, but the latest findings provide a specific brain mechanism explaining why food choices change for the worse following a sleepless night, Walker said.
“These results shed light on how the brain becomes impaired by sleep deprivation, leading to the selection of more unhealthy foods and, ultimately, higher rates of obesity,” said Stephanie Greer, a doctoral student in Walker’s Sleep and Neuroimaging Laboratory and lead author of the paper. Another co-author of the study is Andrea Goldstein, also a doctoral student in Walker’s lab.
In this newest study, researchers measured brain activity as participants viewed a series of 80 food images that ranged from high-to low-calorie and healthy and unhealthy, and rated their desire for each of the items. As an incentive, they were given the food they most craved after the MRI scan.
Food choices presented in the experiment ranged from fruits and vegetables, such as strawberries, apples and carrots, to high-calorie burgers, pizza and doughnuts. The latter are examples of the more popular choices following a sleepless night.
On a positive note, Walker said, the findings indicate that “getting enough sleep is one factor that can help promote weight control by priming the brain mechanisms governing appropriate food choices.”
Switching between habitual and goal-directed actions — a ‘2 in 1’ system in our brain
"Pressing the button of the lift at your work place, or apartment building is an automatic action – a habit. You don’t even really look at the different buttons; your hand is almost reaching out and pressing on its own. But what happens when you use the lift in a new place? In this case, your hand doesn’t know the way, you have to locate the buttons, find the right one, and only then your hand can press a button. Here, pushing the button is a goal-directed action." It is with this example that Rui Costa, principal investigator at the Champalimaud Neuroscience Programme (CNP), explains how critical it is to be able to shift between habits and goal-direct actions, in a fast and accurate way, in everyday life.
To unravel the circuit that underlies this capacity, the capacity to “break habits”, was the goal of this study, carried out by Christina Gremel and Rui Costa, at NIAAA, National Institutes of Health, USA and the Champalimaud Foundation, in Portugal, that is published today (Date) in Nature Communications.
"We developed a task where mice would shift between making the same action in a goal-directed or habitual manner. We could then, for the first time, directly examine brain areas controlling the capacity to break habits," explains the study’s lead author Christina Gremel from NIAAA. Evidence from previous studies has shown that two neighbouring regions of the brain are necessary for these different functions – the dorsal medial striatum is necessary for goal-directed actions and the dorsal lateral striatum is necessary for habitual actions. What was not known, and this new study reveals, is that a third region, the orbital frontal cortex (OFC), is critical for shifting between these two types of actions. As explained by Rui Costa, "when neurons in the OFC were inhibited, the generation of goal-directed actions was disrupted, while activation of these neurons, by means of a technique called optogenetics, selectively increased goal-directed actions."
For Costa, the results of this study suggest “something quite extraordinary – the same neural circuits function in a dynamic way, enabling the learning of automatic and goal-directed actions in parallel.”
These results have important implications for understanding neuropsychiatric disorders where the balance between habits and goal-directed actions is disrupted, such as obsessive-compulsive disorder.
The neural bases of behaviour, and their connection to neuropsychiatric disorders, are at the core of ongoing work by neuroscientists and clinicians at the Champalimaud Foundation.
(Source: eurekalert.org)