Yael Niv

Computational models of addiction often rely on a model-free reinforcement learning (RL) formulation, owing to the close associations between model-free RL, habitual behavior and the dopaminergic system. However, such formulations typically do not capture key recurrent features of addiction phenomena such as craving and relapse. Moreover, they cannot account for goal-directed aspects of addiction that necessitate contrasting, model-based formulations. Here we synthesize a growing body of evidence and propose that a latent-cause framework can help unify our understanding of several recurrent phenomena in addiction, by viewing them as the inferred return of previous, persistent "latent causes"...

BACKGROUND: Ventricular assist devices (VADs) play a critical and increasing role in treating end-stage heart failure in pediatric patients. A growing number of patients are supported by VADs as a bridge to heart transplantation. Experience with VADs in the pediatric population is limited, and experience in Israel has not been published. OBJECTIVES: To describe this life-saving technology and our experience with VAD implantation in children with heart failure, including characteristics and outcomes...

Positive and negative affective states are respectively associated with optimistic and pessimistic expectations regarding future reward. One mechanism that might underlie these affect-related expectation biases is attention to positive- versus negative-valence features (e.g., attending to the positive reviews of a restaurant versus its expensive price). Here we tested the effects of experimentally induced positive and negative affect on feature-based attention in 120 participants completing a compound-generalization task with eye-tracking...

Events associated with aversive or rewarding outcomes are prioritized in memory. This memory boost is commonly attributed to the elicited affective response, closely linked to noradrenergic and dopaminergic modulation of hippocampal plasticity. Herein we review and compare this 'affect' mechanism to an additional, recently discovered, 'prediction' mechanism whereby memories are strengthened by the extent to which outcomes deviate from expectations, that is, by prediction errors (PEs). The mnemonic impact of PEs is separate from the affective outcome itself and has a distinct neural signature...

Although online samples have many advantages for psychiatric research, some potential pitfalls of this approach are not widely understood. Here we detail circumstances in which spurious correlations may arise between task behaviour and symptom scores. The problem arises because many psychiatric symptom surveys have asymmetric score distributions in the general population, meaning that careless responders on these surveys will show apparently elevated symptom levels. If these participants are similarly careless in their task performance, this may result in a spurious association between symptom scores and task behaviour...

COVID-19 patients are oftentimes over- or under-treated due to a deficit in predictive management tools. This study reports derivation of an algorithm that integrates the host levels of TRAIL, IP-10, and CRP into a single numeric score that is an early indicator of severe outcome for COVID-19 patients and can identify patients at-risk to deteriorate. 394 COVID-19 patients were eligible; 29% meeting a severe outcome (intensive care unit admission/non-invasive or invasive ventilation/death). The score's area under the receiver operating characteristic curve (AUC) was 0...

Cognitive tasks are capable of providing researchers with crucial insights into the relationship between cognitive processing and psychiatric phenomena. However, many recent studies have found that task measures exhibit poor reliability, which hampers their usefulness for individual-differences research. Here we provide a narrative review of approaches to improve the reliability of cognitive task measures. Specifically, we introduce a taxonomy of experiment design and analysis strategies for improving task reliability...

Realistic and complex decision tasks often allow for many possible solutions. How do we find the correct one? Introspection suggests a process of trying out solutions one after the other until success. However, such methodical serial testing may be too slow, especially in environments with noisy feedback. Alternatively, the underlying learning process may involve implicit reinforcement learning that learns about many possibilities in parallel. Here we designed a multi-dimensional probabilistic active-learning task tailored to study how people learn to solve such complex problems...

How do biological systems learn continuously throughout their lifespans, adapting to change while retaining old knowledge, and how can these principles be applied to artificial learning systems? In this Forum article we outline challenges and strategies of 'lifelong learning' in biological and artificial systems, and argue that a collaborative study of each system's failure modes can benefit both.

How does rumination affect reinforcement learning-the ubiquitous process by which we adjust behavior after error in order to behave more effectively in the future? In a within-subject design ( n =49), we tested whether experimentally manipulated rumination disrupts reinforcement learning in a multidimensional learning task previously shown to rely on selective attention. Rumination impaired performance, yet unexpectedly this impairment could not be attributed to decreased attentional breadth (quantified using a "decay" parameter in a computational model)...

Across species, animals have an intrinsic drive to approach appetitive stimuli and to withdraw from aversive stimuli. In affective science, influential theories of emotion link positive affect with strengthened behavioural approach and negative affect with avoidance. Based on these theories, we predicted that individuals' positive and negative affect levels should particularly influence their behaviour when innate Pavlovian approach/avoidance tendencies conflict with learned instrumental behaviours. Here, across two experiments - exploratory Experiment 1 ( N  = 91) and a preregistered confirmatory Experiment 2 ( N  = 335) - we assessed how induced positive and negative affect influenced Pavlovian-instrumental interactions in a reward/punishment Go/No-Go task...

The search for an 'ideal' approach to investigate the functional connections in the human brain is an ongoing challenge for the neuroscience community. While resting-state functional magnetic resonance imaging (fMRI) has been widely used to study individual functional connectivity patterns, recent work has highlighted the benefits of collecting functional connectivity data while participants are exposed to naturalistic stimuli, such as watching a movie or listening to a story. For example, functional connectivity data collected during movie-watching were shown to predict cognitive and emotional scores more accurately than resting-state-derived functional connectivity...

There is no single way to represent a task. Indeed, despite experiencing the same task events and contingencies, different subjects may form distinct task representations. As experimenters, we often assume that subjects represent the task as we envision it. However, such a representation cannot be taken for granted, especially in animal experiments where we cannot deliver explicit instruction regarding the structure of the task. Here, we tested how rats represent an odor-guided choice task in which two odor cues indicated which of two responses would lead to reward, whereas a third odor indicated free choice among the two responses...

[This corrects the article DOI: 10.3389/fnbeh.2013.00164.].

Cigarette smoking constitutes a leading global cause of morbidity and preventable death1 , and most active smokers report a desire or recent attempt to quit2 . Smoking-cessation-induced weight gain (SCWG; 4.5 kg reported to be gained on average per 6-12 months, >10 kg year-1 in 13% of those who stopped smoking3 ) constitutes a major obstacle to smoking abstinence4 , even under stable5,6 or restricted7 caloric intake. Here we use a mouse model to demonstrate that smoking and cessation induce a dysbiotic state that is driven by an intestinal influx of cigarette-smoke-related metabolites...

RNA-binding proteins (RBPs) play a key role in post-transcriptional regulation via binding to coding and non-coding RNAs. Recent development in experimental technologies, aimed to identify the targets of RBPs, has significantly broadened our knowledge on protein-RNA interactions. However, for many RBPs in many organisms and cell types, experimental RNA-binding data is not available. In this chapter we describe a computational approach, named RBPmap, available as a web service via https://rbpmap.technion.ac.il/ and as a stand-alone version for download...

Mood is an integrative and diffuse affective state that is thought to exert a pervasive effect on cognition and behavior. At the same time, mood itself is thought to fluctuate slowly as a product of feedback from interactions with the environment. Here we present a new computational theory of the valence of mood-the Integrated Advantage model-that seeks to account for this bidirectional interaction. Adopting theoretical formalisms from reinforcement learning, we propose to conceptualize the valence of mood as a leaky integral of an agent's appraisals of the Advantage of its actions...

The orbitofrontal cortex (OFC) has been implicated in goal-directed planning and model-based decision-making. One key prerequisite for model-based decision-making is learning the transition structure of the environment-the probabilities of transitioning from one environmental state to another. In this work, we investigated how the OFC might be involved in learning this transition structure, by using fMRI to assess OFC activity while humans experienced probabilistic cue-outcome transitions. We found that OFC activity was indeed correlated with behavioral measures of learning about transition structure...

Understanding the brain requires us to answer both what the brain does, and how it does it. Using a series of examples, I make the case that behavior is often more useful than neuroscientific measurements for answering the first question. Moreover, I show that even for "how" questions that pertain to neural mechanism, a well-crafted behavioral paradigm can offer deeper insight and stronger constraints on computational and mechanistic models than do many highly challenging (and very expensive) neural studies...

Much of traditional neuroeconomics proceeds from the hypothesis that value is reified in the brain, that is, that there are neurons or brain regions whose responses serve the discrete purpose of encoding value. This hypothesis is supported by the finding that the activity of many neurons covaries with subjective value as estimated in specific tasks, and has led to the idea that the primary function of the orbitofrontal cortex is to compute and signal economic value. Here we consider an alternative: That economic value, in the cardinal, common-currency sense, is not represented in the brain and used for choice by default...