8 November 2023: Neuromodulation and cognitive effort

Andrew Westbrook
Robert Wood Johnson Medical School
Rutgers University

Cognitive control is effortful, causing people to avoid demanding tasks, and undermining their goal-directed behavior. Yet the nature of effort costs, and the systems biasing decisions to exert or withhold effort are largely unknown. Striatal dopamine promotes physical effort for reward, by increasing sensitivity to reward benefits and decreasing sensitivity to effort costs. In the first part of my talk, I will discuss evidence that, as with physical action, striatal dopamine boosts motivation for cognitive effort as well. In a study combining [18F]-DOPA PET imaging of dopamine synthesis capacity with dopamine transport blocker methylphenidate, the D2 agent sulpiride, and placebo, we find that greater striatal dopamine signaling biases participants to choose harder working memory tasks to earn more money over easier tasks for less money. Moreover, gaze-informed drift diffusion modeling supports the inference that dopamine signaling increases willingness to exert effort by making people more sensitive to the benefits, and less sensitive to the costs of cognitive work.

In the second part of this talk, I consider a novel hypothesis about the nature of effort costs. Namely, that subjective cognitive effort is a phenomenological readout of divergence from criticality in the brain. Brains at rest exhibit emergent properties indicative of a dynamical system near a critical point – regulated by the balance of cortical excitation to inhibition. These properties are monotonically suppressed with increasing cognitive load, reflecting increasing divergence from criticality. Importantly, because criticality maximizes functional flexibility and information processing capacity, divergence implies computational costs. In my talk, I will discuss the rationale for the hypothesis linking subjective effort and criticality. I will also discuss a first study examining subjective cognitive effort and critical dynamics in EEG data while participants perform various levels of the N-back working memory task.

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25 October 2023: Modeling Decision-Making in Schizophrenia: Associations Between Computationally Derived Risk Propensity and Self-Reported Risk Perception

Emma Herms
Department of Psychological & Brain Sciences
Indiana University

Risk-taking is an integral part of decision-making. We take small risks throughout our daily life, from not feeding a parking meter to asking someone on a date. Taking risks in moderation can be advantageous – nothing ventured, nothing gained – and is an important part of learning. In contrast, taking too much or even too little risk may be disadvantageous. Thus, we are interested in the extent to which individuals with psychosis pursue risky rewards. A major limitation of task-based risky decision-making in psychosis-spectrum samples is that cognitive impairments are not typically controlled for, or explicitly examined (Purcell et al., 2021). In the current talk, I will present evidence from the Balloon Analogue Risk Task (BART), an uncertain-risk task where participants integrate information across trials to learn and weigh relative outcome probabilities. In this case, the likelihood that a balloon will explode. The BART is particularly interesting to examine because there is consistent evidence that individuals with psychosis pursue risky rewards less. However, given the complexities of the BART and confounds in psychosis samples, specific processes that contribute to this disadvantageous behavior remain poorly understood. Utilizing computational modeling, uncertain-risk decision-making behavior was parsed into subprocesses and examined for relationships with cognition, self-reported risk-specific processes, and non-risk specific personality traits to determine which of these external measures best explained group and individual differences in risk-taking.

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13 September 2023: Ecological Momentary Assessment of Cognitive Control and Mood in Mental Health

Mor Nahum
School of Occupational Therapy, Faculty of Medicine
The Hebrew University

Cognitive control, the mental operations underlying our goal-directed behaviors, have been suggested as a potential key mechanism in mental health, contribution to mental resilience, emotion regulation and reduction of depressive symptoms. However, although cognitive control ability has been shown to fluctuate, it is often captured in a one-time lab-based measurement. Here, we use ecological momentary assessment (EMA) of inhibitory control, a component of cognitive control, and mood in real life to assess their contribution to mental health. In Study 1, conducted in a group of 156 young adults during their basic combat training in IDF, we show that for those with higher levels of resilience, inhibitory control is associated with their momentary mood, such that better inhibitory control predicts better mood. In Study 2, which included a group of 106 participant with pre-clinical depression, we show that a one-time measurement of inhibitory control does not predict depressive symptoms. Instead, variability of inhibitory control across the EMA sessions predicted depressive symptoms one week later. In addition, reduced levels of inhibition predicted worse mood for those with higher baseline depressive symptoms. Finally, in Study 3, which included a group of 132 young adults with and without ADHD, we show that emotion dysregulation at baseline is not associated with a one-time measurement of inhibitory control. Instead, those with higher levels of emotion dysregulation have reduced mean EMA of inhibitory control and increased inhibition variability. These results suggest that the stability of cognitive control over time may be more relevant for prediction of mental health related outcomes. We discuss these results in light of cognitive models related to mental health and their potential clinical implications.

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24 May 2023: Removing thoughts via replacement, suppression, and clearing

Hyojeong Kim
Institute of Cognitive Science
University of Colorado Boulder

Failing to control the flow of our thoughts can impact our success in everyday life, and it is characteristic of multiple psychiatric disorders including depression and post-traumatic stress disorder. It has been demonstrated that thoughts can be removed from working memory (WM) in three differential methods (i.e., replacing a thought with something else, suppressing a specific thought, and clearing the mind of all thoughts), which recruit distinct neural regions involved in cognitive control (Banich et al., 2015). Our research has focused on how these different removal operations impact representational changes in WM and WM capacity as a result of the changes. In our removal task, participants were presented a target picture (a face, a fruit, or a scene) and then performed one of the removal operations based on a given instruction after the item offset. Employing multi-voxel pattern analysis (MVPA) on fMRI data, we were able to demonstrate that replacing, suppressing, and clearing are distinct neural processes that act upon WM information in different ways. By tracking the information contents of WM during the removal operations, we found that replacing an item was the quickest way to diminish information, followed by clearing and then suppressing. However, the representation of the removed item was mostly altered in WM when it was suppressed, followed by clearing and then replacing. Furthermore, these operations had different impacts on the encoding of new information. While maintaining an item interfered with the encoding of the next item as predicted, only suppressing an item, which displayed the most alteration of that item during removal, led to release from this proactive interference and even facilitated subsequent encoding (Kim et al., 2020). These results suggest that removal operations modulate WM representations in different ways, resulting in distinct consequences of representational alteration and WM capacity.

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10 May 2023: Interactions between neuromodulatory systems and cortex during initiation and switching of behaviour

Matthew Rushworth
Department of Experimental Psychology and Wellcome Trust Centre for Integrative Neuroimaging (WIN)
University of Oxford, UK

Voluntary actions are ones that appear to be initiated by people and other animals in the absence of any external instruction or cue.  However, the likelihood that voluntary actions are initiated depends on identifiable features of both the current and the recent environment, recent behaviour, and the consequences that will ensure if the action is made (for example, will it lead to reward or not).  These factors mediate their influence on the initiation of voluntary action via a distributed neural circuit spanning cortical regions such as the anterior cingulate and insula cortex and subcortical nuclei including habenula, basal forebrain, ventral tegmental area, substantia nigra, and raphe nucleus.  I review a series of recent studies conducted in non-human and human primates using a combination of neuroimaging, temporary inactivation via ultrasound stimulation, and pharmacological manipulation that begin to dissect these different influences on voluntary behaviour and to trace the anatomical pathways through which each operates on voluntary behaviour.  The timing of action initiation depends both on reward expectations and on recent behaviour and these influences are governed by changes in activity in the basal forebrain and anterior cingulate cortex, altered by disruption of activity in either of these areas, and by cholinergic manipulations.  However, whether or not an action is initiated also depends on the richness/sparseness of opportunities in the environment in general and this is tracked by activity in the raphe nucleus and altered by serotonergic manipulation.

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12 April 2023: Active Learning Impairments in Substance Use Disorders when Solving the Explore-Exploit Dilemma: Longitudinal Stability, Symptom Prediction, and Replication

Ryan Smith
Laureate Institute for Brain Research
University of Tulsa

Computational modeling is a promising approach for parsing behavioral processes and dysfunctions in individuals with substance use disorders (SUDs), but it is unclear how much these processes change during the recovery period. In this talk, I will describe a study in which we assessed baseline and 1-year follow-up data on a sample of healthy controls (HCs; N = 48) and treatment-seeking individuals with one or more SUDs (alcohol, cannabis, sedatives, stimulants, hallucinogens, and/or opioids; N = 83) who completed a standard ‘three-armed bandit’ task designed to assess explore-exploit behavior. I will also describe a pre-registered replication study with a new sample of 168 individuals with SUDs and 99 HCs. Computational models – based on the notion of active learning – were fit to behavior on the task. Relative to HCs, participants with SUDs were found at baseline to show slower learning rates in response to negative outcomes and less precise action selection. We then repeated these analyses when the same individuals returned and re-performed the task 1 year later to assess the stability of these baseline differences. We also examined whether baseline modelling measures could predict symptoms at follow-up. Bayesian analyses indicated that: (a) group differences in learning rates were stable over time (posterior probability = 1); (b) relationships between model parameters at baseline and follow-up were all significant and ranged from small to moderate (.25 < ICCs < .54); and (c) learning rates and/or information-seeking values at baseline were associated with substance use severity at 1-year follow-up in stimulant and opioid users (.36 < rs < .43, .002 < ps < .02). Differences in learning rates for losses replicated in the second sample, and model parameters could jointly differentiate specific substance disorders when combining samples. These findings suggest that processing dysfunctions involving learning to arbitrate between exploration and exploitation may show some stability throughout the recovery period. At the same time, individual computational differences at baseline had some predictive value for changes in substance use severity. Taken together, these results suggest active learning models may allow measurement of trait dysfunctions that could have predictive utility for substance use severity.

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29 March 2023: Intracranial recordings of value: unveiling the neurophysiological substrates of human decision-making with intracranial recordings

Ignacio Saez
Departments of Neuroscience, Neurology and Neurosurgery
Icahn School of Medicine at Mount Sinai

My research focuses on the study of the neural basis of human cognition, particularly decision-making, through the examination of invasive brain activity in neurosurgical patients. I combine human electrophysiological and electrochemical recordings during decision-making tasks with computational models of reward and choice. This experimental combination provides access to fast, highly detailed, high signal-to-noise, multi-areal neural activity from the human brain, which is highly suited to studying the relationship of distributed neural activity to aspects of cognition and the use of neurostimulation techniques to modulate behavior and treat cognitive aspects of disease. In this talk, I will cover the different methodologies we employ in the lab, as well as current research efforts in the study of decision-making and the combination of computational psychiatry and intracranial approaches. 

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15 March 2023: Navigating our uncertain social worlds

Oriel FeldmanHall
Department of Department of Cognitive, Linguistic and Psychological Sciences
Brown University

Interacting with others is one of the most inherently uncertain acts we embark on. There are a multitude of unknowns, including how to express ourselves, who to confide in, or whether to engage in risky behavior with our peers. All this uncertainty makes successfully navigating the social world a tremendous challenge. Combining behavioral and neuroscientific methods, we explore the social and emotional factors that shape and ultimately guide how humans learn to make adaptive decisions amongst this great uncertainty. In particular, we borrow models from the animal learning literature, and methods from computational neuroscience and machine learning, to examine how humans experience, process, and resolve this uncertainty to make more adaptive decisions.  

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1 March 2023: Motivation for emotional pleasure and pain in psychopathology

Yael Milgram
Department of Psychology
Harvard University

Emotion regulation deficits contribute to many mental health disorders. Most research sought to understand these deficits by investigating how people regulate emotions – specifically, which emotion regulation strategies people use and how effectively they implement them. However, emotion regulation strategies are used at the service of attaining desired emotional states. Therefore, people who struggle with psychopathology might differ not only in the strategies they use, but also in the emotional states they desire. In this talk, I will present evidence suggesting that some clinical populations differ from non-clinical populations in the degree to which they are motivated to experience painful and pleasant emotions, with an emphasis on Major Depressive Disorder. I will then present studies testing the implications of these differences for the use of emotion regulation strategies, emotional experiences, and mental health. Finally, I will offer a new perspective for understanding these findings, according to which motivation to experience painful emotions in psychopathology might reflect a form of emotional self-harm.

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