We are looking for an outstanding applicant to develop large-scale circuit models for decision making within a collaborative consortium that includes the Allen Institute for Neural Dynamics, New York University, and the University of Chicago. This ambitious NIH-funded project requires the creativity and expertise to integrate multimodal data sets (e.g., connectivity, large-scale neural recordings, behavior) into a comprehensive modeling framework. The successful applicant will join Jorge Jaramillo’s Distributed Neural Dynamics and Control Lab at the Grossman Center at the University of Chicago. Throughout the course of the postdoctoral training, there will be opportunities to visit the other sites in Seattle (Karel Svoboda) and New York (Adam Carter, Xiao-Jing Wang) for additional training and collaboration opportunities. Appointees will join as Grossman Center Postdoctoral Fellows at the University of Chicago and will have access to state-of-the-art facilities and additional opportunities for collaboration with exceptional experimental labs within the Department of Neurobiology, as well as other labs from the departments of Physics, Computer Sciences, and Statistics. The Grossman Center offers competitive postdoctoral salaries in the vibrant and international city of Chicago, and a rich intellectual environment that includes the Argonne National Laboratory and the Data Science Institute. Postdoctoral fellows will also have the possibility to work in additional projects with other Grossman Center faculty members.

We are looking for an outstanding applicant to develop large-scale circuit models for decision making within a collaborative consortium that includes the Allen Institute for Neural Dynamics, New York University, and the University of Chicago. This ambitious NIH-funded project requires the creativity and expertise to integrate multimodal data sets (e.g., connectivity, large-scale neural recordings, behavior) into a comprehensive modeling framework. The successful applicant will join Jorge Jaramillo’s Distributed Neural Dynamics and Control Lab at the Grossman Center at the University of Chicago. Throughout the course of the postdoctoral training, there will be opportunities to visit the other sites in Seattle (Karel Svoboda) and New York (Adam Carter, Xiao-Jing Wang) for additional training and collaboration opportunities. Appointees will join as Grossman Center Postdoctoral Fellows at the University of Chicago and will have access to state-of-the-art facilities and additional opportunities for collaboration with exceptional experimental labs within the Department of Neurobiology, as well as other labs from the departments of Physics, Computer Sciences, and Statistics. The Grossman Center offers competitive postdoctoral salaries in the vibrant and international city of Chicago, and a rich intellectual environment that includes the Argonne National Laboratory and the Data Science Institute. Postdoctoral fellows will also have the possibility to work in additional projects with other Grossman Center faculty members.

Developing agents capable of modeling complex environments and human behaviors within them is a key goal of artificial intelligence research. Progress towards this goal has exciting potential for applications in video games, from new tools that empower game developers to realize new creative visions, to enabling new kinds of immersive player experiences. This talk focuses on recent advances of my team at Microsoft Research towards scalable machine learning architectures that effectively capture human gameplay data. In the first part of my talk, I will focus on diffusion models as generative models of human behavior. Previously shown to have impressive image generation capabilities, I present insights that unlock applications to imitation learning for sequential decision making. In the second part of my talk, I discuss a recent project taking ideas from language modeling to build a generative sequence model of an Xbox game.

Developing agents capable of modeling complex environments and human behaviors within them is a key goal of artificial intelligence research. Progress towards this goal has exciting potential for applications in video games, from new tools that empower game developers to realize new creative visions, to enabling new kinds of immersive player experiences. This talk focuses on recent advances of my team at Microsoft Research towards scalable machine learning architectures that effectively capture human gameplay data. In the first part of my talk, I will focus on diffusion models as generative models of human behavior. Previously shown to have impressive image generation capabilities, I present insights that unlock applications to imitation learning for sequential decision making. In the second part of my talk, I discuss a recent project taking ideas from language modeling to build a generative sequence model of an Xbox game.

On Wednesday, November 30th, at noon ET / 6PM CET, we will host Alexandre Boutet and Erik H. Middlebrooks. Alexandre Boutet, MD, PhD, is a neuroradiology fellow at the University of Toronto, and will tell us about “How to become an integral part of the functional neurosurgery team as a radiologist”. Erik H. Middlebrooks, MD, is a Professor and Consultant of Neuroradiology and Neurosurgery and the Neuroradiology Program Director at Mayo Clinic. Beside his scientific presentation about “Bridging the Gap from Research to Clinical Decision Making in Epilepsy Neuromodulation”, he will also give us a glimpse at the “Person behind the science”. The talks will be followed by a shared discussion. You can register via talks.stimulatingbrains.org to receive the (free) Zoom link!

In this talk, we will examine human behavior from the perspective of the choices we make every day. We will study the role of the brain in enabling these decisions and discuss some simple computational models of decision making and the neural basis. Towards the end, we will have a short, interactive session to engage in some easy decisions that will help us discover our own biases.

Sex-based modulation of cognitive processes could set the stage for individual differences in vulnerability to neuropsychiatric disorders. While value-based decision making processes in particular have been proposed to be influenced by sex differences, the overall correct performance in decision making tasks often show variable or minimal differences across sexes. Computational tools allow us to uncover latent variables that define different decision making approaches, even in animals with similar correct performance. Here, we quantify sex differences in mice in the latent variables underlying behavior in a classic value-based decision making task: a restless two-armed bandit. While male and female mice had similar accuracy, they achieved this performance via different patterns of exploration. Male mice tended to make more exploratory choices overall, largely because they appeared to get ‘stuck’ in exploration once they had started. Female mice tended to explore less but learned more quickly during exploration. Together, these results suggest that sex exerts stronger influences on decision making during periods of learning and exploration than during stable choices. Exploration during decision making is altered in people diagnosed with addictions, depression, and neurodevelopmental disabilities, pinpointing the neural mechanisms of exploration as a highly translational avenue for conferring sex-modulated vulnerability to neuropsychiatric diagnoses.

The application of neuroimaging methods to marketing has recently gained lots of attention. In analyzing consumer behaviors, the inclusion of neuroimaging tools and methods is improving our understanding of consumer’s preferences. Human emotions play a significant role in decision making and critical thinking. Emotion classification using EEG data and machine learning techniques has been on the rise in the recent past. We evaluate different feature extraction techniques, feature selection techniques and propose the optimal set of features and electrodes for emotion recognition.Affective neuroscience research can help in detecting emotions when a consumer responds to an advertisement. Successful emotional elicitation is a verification of the effectiveness of an advertisement. EEG provides a cost effective alternative to measure advertisement effectiveness while eliminating several drawbacks of the existing market research tools which depend on self-reporting. We used Graph theoretical principles to differentiate brain connectivity graphs when a consumer likes a logo versus a consumer disliking a logo. The fusion of EEG and sentiment analysis can be a real game changer and this combination has the power and potential to provide innovative tools for market research.

During primate evolution, prefrontal cortex (PFC) expanded substantially relative to other cortical areas. The expansion of PFC circuits likely supported the increased cognitive abilities of humans and anthropoids to sample information about their environment, evaluate that information, plan, and decide between different courses of action. What quantities do these circuits compute as information is being sampled towards and a decision is being made? And how can they be related to anatomical specialisations within and across PFC? To address this, we recorded PFC activity during value-based decision making using single unit recording in non-human primates and magnetoencephalography in humans. At a macrocircuit level, we found that value correlates differ substantially across PFC subregions. They are heavily shaped by each subregion’s anatomical connections and by the decision-maker’s current locus of attention. At a microcircuit level, we found that the temporal evolution of value correlates can be predicted using cortical recurrent network models that temporally integrate incoming decision evidence. These models reflect the fact that PFC circuits are highly recurrent in nature and have synaptic properties that support persistent activity across temporally extended cognitive tasks. Our findings build upon recent work describing economic decision making as a process of attention-weighted evidence integration across time.

Nearly every aspect of life involves decisions between options that differ in both their expected rewards and the potential costs (such as delay to reward delivery or risk of harm) that accompany those rewards. The ability to choose adaptively when faced with such decisions is critical for well-being and overall quality of life. In neuropsychiatric conditions such as substance use disorders, however, decision making is often compromised, which can prolong and exacerbate their severity and co-morbidities. In this seminar, Dr. Setlow will discuss research in rodent models investigating behavioral and biological mechanisms of cost-benefit decision making. In particular, he will focus on factors (including sex) that contribute to differences in cost-benefit decision making across the population, how variability in decision making is related to substance use, and how substance use can produce long-lasting changes in decision preference.

The dream of a systems neuroscientist is to be able to unravel neural mechanisms that give rise to behavior. It is increasingly appreciated that behavior involves the concerted distributed activity of multiple brain regions so the focus on single or few brain areas might hinder our understanding. There have been quite a few technological advancements in this domain. Functional ultrasound imaging (fUSi) is an emerging technique that allows us to measure neural activity from medial frontal regions down to subcortical structures up to a depth of 20 mm. It is a method for imaging transient changes in cerebral blood volume (CBV), which are proportional to neural activity changes. It has excellent spatial resolution (~100 μm X 100 μm X 400 μm); its temporal resolution can go down to 100 milliseconds. In this talk, I will present its use in two model systems: marmoset monkeys and rats. In marmoset monkeys, we used it to delineate a social – vocal network involved in vocal communication while in rats, we used it to gain insights into brain wide networks involved in evidence accumulation based decision making. fUSi has the potential to provide an unprecedented access to brain wide dynamics in freely moving animals performing complex behavioral tasks.

Decision-making in noisy environments with constant sensory evidence involves integrating sequentially-sampled evidence, a strategy formalized by diffusion models which is supported by decades behavioral and neural findings. By contrast, it is unknown whether this strategy is also used during decision-making when the underlying sensory evidence is expected to change. Here, we trained monkeys to identify the dominant color of a dynamically refreshed checkerboard pattern that doesn't become informative until after a variable delay. Animals' behavioral responses were briefly suppressed after an abrupt change in evidence, and many neurons in the frontal eye field displayed a corresponding dip in activity at this time, similar to the dip frequently observed after stimulus onset. Generalized drift-diffusion models revealed that behavior and neural activity were consistent with a brief suppression of motor output without a change in evidence accumulation itself, in contrast to the popular belief that evidence accumulation is paused or reset. These results suggest that a brief interruption in motor preparation is an important strategy for dealing with changing evidence during perceptual decision making.

The world around us is complex, but at the same time full of meaningful regularities. We can detect, learn and exploit these regularities automatically in an unsupervised manner i.e. without any direct instruction or explicit reward. For example, we effortlessly estimate the average tallness of people in a room, or the boundaries between words in a language. These regularities and prior knowledge, once learned, can affect the way we acquire and interpret new information to build and update our internal model of the world for future decision-making processes. Despite the ubiquity of passively learning from the structured information in the environment, the mechanisms that support learning from real-world experience are largely unknown. By combing sophisticated cognitive tasks in human and rats, neuronal measurements and perturbations in rat and network modelling, we aim to build a multi-level description of how sensory history is utilised in inferring regularities in temporally extended tasks. In this talk, I will specifically focus on a comparative rat and human model, in combination with neural network models to study how past sensory experiences are utilized to impact working memory and decision making behaviours.

Analogy offers a new interpretation of a common concern in ethics: whether decision making includes or excludes a consideration of moral issues. This is often discussed as the moral awareness of decision makers and considered a motivational concern. The possible new interpretation is that moral awareness is in part a matter of expertise. Some failures of moral awareness can then be understood as stemming from novicehood. Studies of analogical transfer are consistent with the possibility that moral awareness is in part a matter of expertise, that as a result motivation is less helpful than some prior theorizing would predict, and that many adults are not as expert in the domain of ethics as one might hope. The possibility of expert knowledge of ethical principles leads to new questions and opportunities.

Microbes have shaped the evolution of eukaryotes and contribute significantly to the physiology and behavior of animals. Some of these traits are inherited by the progenies. Despite the vast importance of microbe-host communication, we still do not know how bacteria change short term traits or long-term decisions in individuals or communities. In this seminar I will present our work on how commensal and pathogenic bacteria impact specific neuronal phenotypes and decision making. The traits we specifically study are the degeneration and regeneration of neurons and survival behaviors in animals. We use the nematode Caenorhabditis elegans and its dietary bacteria as model organisms. Both nematode and bacteria are genetically tractable, simplifying the detection of specific molecules and their effect on measurable characteristics. To identify these molecules we analyze their genomes, transcriptomes and metabolomes, followed by functional in vivo validation. We found that specific bacterial RNAs and bacterially produced neurotransmitters are key to trigger a survival behavioral and neuronal protection respectively. While RNAs cause responses that lasts for many generations we are still investigating whether bacterial metabolites are capable of inducing long lasting phenotypic changes.

Did we eat spaghetti for lunch because we saw our colleague eat spaghetti? What drives a risk decision? How can our breakfast impact our decisions throughout the day? Research from different disciplines such as economics, psychology and neuroscience have attempted to investigate the motives and modulators of human decision making. Human decisions can be flexibly modulated by the different experiences we have in our daily lives, at the same time, bodily processes, such as metabolism can also impact economic behavior. These modulations can occur through our social networks, through the impact of our own behavior on the social environment, but also simply by the food we have eaten. Here, I will present a series of recent studies from my lab in which we shed light on the psychological, neural and metabolic motives and modulators of human decision making.

As Socrates said that "I know that I know nothing," our mind's function to be aware of our ignorance is essential for abstract and conceptual reasoning. However, the biological mechanism to enable such a hierarchical thought, or meta-cognition, remained unknown. In the first part of the talk, I will demonstrate our studies on the neural mechanism for metacognition on memory in macaque monkeys. In reality, awareness of ignorance is essential not only for the retrospection of the past but also for the exploration of novel unfamiliar environments for the future. However, this proactive feature of metacognition has been understated in neuroscience. In the second part of the talk, I will demonstrate our studies on the neural mechanism for prospective metacognitive matching among uncertain options prior to perceptual decision making in humans and monkeys. These studies converge to suggest that higher-order processes to self-evaluate mental state either retrospectively or prospectively are implemented in the primate neural networks.

During primate evolution, prefrontal cortex (PFC) expanded substantially relative to other cortical areas. The expansion of PFC circuits likely supported the increased cognitive abilities of humans and anthropoids to plan, evaluate, and decide between different courses of action. But what do these circuits compute as a decision is being made, and how can they be related to anatomical specialisations within and across PFC? To address this, we recorded PFC activity during value-based decision making using single unit recording in non-human primates and magnetoencephalography in humans. At a macrocircuit level, we found that value correlates differ substantially across PFC subregions. They are heavily shaped by each subregion’s anatomical connections and by the decision-maker’s current locus of attention. At a microcircuit level, we found that the temporal evolution of value correlates can be predicted using cortical recurrent network models that temporally integrate incoming decision evidence. These models reflect the fact that PFC circuits are highly recurrent in nature and have synaptic properties that support persistent activity across temporally extended cognitive tasks. Our findings build upon recent work describing economic decision making as a process of attention-weighted evidence integration across time.

The idea that behavior results from a dynamic interplay between automatic and controlled processing underlies much of decision science, but has also generated considerable controversy. In this talk, I will highlight behavioral and neural data showing how recently-developed computational models of decision making can be used to shed new light on whether, when, and how decisions result from distinct processes operating at different timescales. Across diverse domains ranging from altruism to risky choice biases and self-regulation, our work suggests that a model of prioritized attentional sampling and evidence accumulation may provide an alternative explanation for many phenomena previously interpreted as supporting dual process models of choice. However, I also show how some features of the model might be taken as support for specific aspects of dual-process models, providing a way to reconcile conflicting accounts and generating new predictions and insights along the way.

Foraging research aims at describing, understanding, and predicting resource-gathering behaviour. Optimal Foraging Theory (OFT) is a sub-discipline that emphasises that these aims can be aided by segmenting foraging behaviour into discrete problems that can be formally described and examined with mathematical maximization techniques. Examples of such segmentation are found in the isolated treatment of issues such as patch residence time, prey selection, information gathering, risky choice, intertemporal decision making, resource allocation, competition, memory updating, group structure, and so on. Since foragers face these problems simultaneously rather than in isolation, it is unsurprising that OFT models are ‘always wrong but sometimes useful’. I will argue that a progressive optimal foraging research program should have a defined strategy for dealing with predictive failure of models. Further, I will caution against searching for brain structures responsible for solving isolated foraging problems.

My research focuses, broadly speaking, on how emotions impact decision making. Specifically, I am interested in affective biases, a phenomenon known to be important in depression. Using a rodent decision-making task, combined with computational modelling I have investigated how different antidepressant and pro-depressant manipulations that are known to alter mood in humans alter judgement bias, and provided insight into the decision processes that underlie these behaviours. I will also highlight how the combination of behaviour and modelling can provide a truly translation approach, enabling comparison and interpretation of the same cognitive processes between animal and human research.

In many societal problems, individuals exhibit a conflict between keeping resources (e.g., money, time or attention) to themselves or sharing them with another individual or group. The reasons motivating decisions in favor of others welfare can thereby vary from purely altruistic to completely strategic. Be it the stranger making an effort returning a lost valet to its rightful owner or a co-worker pitching in her fair share in a joint project. Actions like that create an environment that makes living together a pleasant experience. Hence, understanding how decisions determining the welfare of oneself and others are made is important for facilitating this behavior by building institutions that maximize the rate of cooperation in a society. To shed new light on such decision making processes I will present recent evidence from a set of process tracing experiments utilizing eye-tracking and economic games. Experiments will focus on the role of social preferences in the choice construction process and will identify mechanisms (i.e., search and processing depth, information weighting, and ignorance) through which they guide choice behavior. I will in particular focus on the differences and commonalitiesbetween strategic and altruistic decisions. Specifically, investigating to which extent people direct attention towards certain components of the decision situation in a context-dependent manner.

Machine learning researchers frequently focus on human-level performance, in particular in games. However, in these applications human (or human-level) behavior is commonly reduced to a simple dot on a performance graph. Cognitive science, in particular theories of learning and decision making, could hold the key to unlock what is behind this dot, thereby gaining further insights into human cognition and the design principles of intelligent algorithms. However, cognitive experiments commonly focus on relatively simple paradigms such as restricted multi-armed bandit tasks. In this talk, I will argue that cognitive science can turn its lens to more complex scenarios to study exploration in real-world domains and online games. I will show in one large data set of online food delivery orders and across many online games how current cognitive theories of learning and exploration can describe human behavior in the wild, but also how these tasks demand us to expand our theoretical toolkit to describe a rich repertoire of real-world behaviors such as empowerment and fun.

Uncertainty plays a critical role in reinforcement learning and decision making. However, exactly how subjective uncertainty influences behaviour remains unclear. Multi-armed bandits are a useful framework to gain more insight into this. Paired with computational tools such as Kalman filters, they allow us to closely characterize the interplay between trial-by-trial value, uncertainty, learning, and choice. In this talk, I will present recent research where we also measured participants visual fixations on the options in a multi-armed bandit task. The estimated value of each option, and the uncertainty in these estimations, influenced what subjects looked at in the period before making a choice and their subsequent choice, as additionally did fixation itself. Uncertainty also determined how long participants looked at the obtained outcomes. Our findings clearly show the importance of uncertainty in learning and decision making.

Interval timing is a fundamental component action, and is susceptible to motor-related temporal distortions. Previous studies have shown that movement biases temporal estimates, but have primarily considered self-modulated movement only. However, real-world encounters often include situations in which movement is restricted or perturbed by environmental factors. In the following experiments, we introduced viscous movement environments to externally modulate movement and investigated the resulting effects on temporal perception. In two separate tasks, participants timed auditory intervals while moving a robotic arm that randomly applied four levels of viscosity. Results demonstrated that higher viscosity led to shorter perceived durations. Using a drift-diffusion model and a Bayesian observer model, we confirmed these biasing effects arose from perceptual mechanisms, instead of biases in decision making. These findings suggest that environmental perturbations are an important factor in movement-related temporal distortions, and enhance the current understanding of the interactions of motor activity and cognitive processes. https://www.biorxiv.org/content/10.1101/2020.10.26.355396v1

Deep learning is an emerging computational approach inspired by the human brain’s neural connectivity that has transformed machine-based image analysis. By using histopathology as a model of an expert-level pattern recognition exercise, we explore the ability for humans to teach machines to learn and mimic image-recognition and decision making. Moreover, these models also allow exploration into the ability for computers to independently learn salient histological patterns and complex ontological relationships that parallel biological and expert knowledge without the need for explicit direction or supervision. Deciphering the overlap between human and unsupervised machine reasoning may aid in eliminating biases and improving automation and accountability for artificial intelligence-assisted vision tasks and decision-making. Aleksandar Ivanov Title:

Model-based approaches to control and decision making have long held the promise of being more powerful and data efficient than model-free counterparts. However, success with model-based methods has been limited to those cases where a perfect model can be queried. The game of Go was mastered by AlphaGo using a combination of neural networks and the MCTS planning algorithm. But planning required a perfect representation of the game rules. I will describe new algorithms that instead leverage deep neural networks to learn models of the environment which are then used to plan, and update policy and value functions. These new algorithms offer hints about how brains might approach planning and acting in complex environments.

Ideas about optimization are at the core of how we approach biological complexity. Quantitative predictions about biological systems have been successfully derived from first principles in the context of efficient coding, metabolic and transport networks, evolution, reinforcement learning, and decision making, by postulating that a system has evolved to optimize some utility function under biophysical constraints. Yet as normative theories become increasingly high-dimensional and optimal solutions stop being unique, it gets progressively hard to judge whether theoretical predictions are consistent with, or "close to", data. I will illustrate these issues using efficient coding applied to simple neuronal models as well as to a complex and realistic biochemical reaction network. As a solution, we developed a statistical framework which smoothly interpolates between ab initio optimality predictions and Bayesian parameter inference from data, while also permitting statistically rigorous tests of optimality hypotheses.

The mammalian claustrum, by virtue of its dense interconnectivity with cortex and other brain structures, has been hypothesized to mediate functions ranging from decision making to consciousness. I will be presenting experimental evidence for the existence of a claustrum in reptiles, its role in generating brain dynamics characteristic of sleep, and discuss our neuroetholgical approach towards understanding fundamental aspects of sleep and claustrum function.