Biology subjects

Loewenstein, Y.

Publications and source records attributed to Loewenstein, Y..

16 recordsLinked to original sources

bioRxiv2026

Stochastic Procrastination and Optimal Task Division

Procrastination is widespread, costly, and persistent despite strong intentions to change. We show how procrastination can arise in stationary environments, where there are no deadlines, changing incentives, or new information to justify delay. With hyperbolic discounting, stochastic delay, i.e., acting with a fixed probability each period, emerges as the only self-consistent policy: the only policy that resolves belief-policy inconsistency. We further show that standard reinforcement learning algorithms converge to this Pareto-inefficient solution. We extend the self-consistent model to divisible and continuous-action tasks. In these settings, allowing a voluntary break after completing a subtask never increases, and often reduces, expected delay; enforcing such breaks can reduce delay further, often substantially. Optimal task division equalizes completion probabilities across subtasks, implying that early stages of a larger task should be easier when the goal is to reduce overall time to completion. The model therefore gives a formal rationale and exact quantification for familiar remedies such as task division, starting easy, and enforced breaks, while explaining why procrastination can persist even when intentions, incentives, and information remain unchanged.

neuroscience↗

bioRxiv2026

Predictive abstract task representations support few-shot learning in the rat brain

Few-shot learning reveals core mechanisms of flexible cognition and adaptive decision- making in animal behavior by showing how animals rapidly infer rules, categories, or action strategies from sparse experience. Few-shot learning often depends on abstract representations that capture task structure and generalize across similar instances of the same task. The orbitofrontal cortex (OFC) is known to encode abstract representations, but whether these representations can adapt to new contingencies at the rapid timescale of few-shot learning is unknown. We trained rats on a sequence-learning task with recurring structure but changing contingencies. Rats demonstrated few-shot learning, reaching near-optimal performance within 2-3 rewards after each contingency change, exploiting the tasks recurring structure to rapidly generalize across sequence blocks. We recorded OFC activity using Neuropixels probes during task performance. The firing rate of OFC units in the few seconds preceding each poke encoded the task state and predicted the selected action. Remarkably, this predictive activity also encoded the abstract notion of role: the function of an action under the current contingencies. Role representations in the OFC changed on the same rapid timescale as the behavioral changes. We suggest that few-shot learning in this task is supported by the rapid remapping of roles onto actions.

neuroscience↗

bioRxiv2024

Spatiotemporal dynamics of locomotor decisions in Drosophila melanogaster

Decision-making in animals often involves choosing actions while navigating the environment, a process markedly different from static decision paradigms commonly studied in laboratory settings. Even in decision-making assays in which animals can freely locomote, decision outcomes are often interpreted as happening at single points in space and single moments in time, a simplification that potentially glosses over important spatiotemporal dynamics. We investigated locomotor decision-making in Drosophila melanogaster in Y-shaped mazes, measuring the extent to which their future choices could be predicted through space and time. We demonstrate that turn-decisions can be reliably predicted from flies locomotor dynamics, with distinct predictability phases emerging as flies progress through maze regions. We show that these predictability dynamics are not merely the result of maze geometry or wall-following tendencies, but instead reflect the capacity of flies to move in ways that depend on sustained locomotor signatures, suggesting an active, working memory-like process. Additionally, we demonstrate that fly mutants known to have sensory and information-processing deficits exhibit altered spatial predictability patterns, highlighting the role of visual, mechanosensory, and dopaminergic signaling in locomotor decision-making. Finally, highlighting the broad applicability of our analyses, we generalize our findings to other species and tasks. We show that human participants in a virtual Y-maze exhibited similar decision predictability dynamics as flies. This study advances our understanding of decision-making processes, emphasizing the importance of spatial and temporal dynamics of locomotor behavior in the lead-up to discrete choice outcomes.

animal behavior and cognition↗

bioRxiv2024

Idiosyncratic choice bias and feedback-induced bias differ in their long-term dynamics

A well-known observation in repeated-choice experiments is that a tendency to prefer one response over the others emerges if the feedback consistently favors that response. Choice bias, a tendency to prefer one response over the others, however, is not restricted to biased-feedback settings and is also observed when the feedback is unbiased. In fact, participant-specific choice bias, known as idiosyncratic choice bias (ICB), is common even in symmetrical experimental settings in which feedback is completely absent. Here we ask whether feedback-induced bias and ICB share a common mechanism. Specifically, we ask whether ICBs reflect idiosyncrasies in choice-feedback associations prior to the measurement of the ICB. To address this question, we compare the long-term dynamics of ICBs with feedback-induced biases. We show that while feedback effectively induced choice preferences, its effect is transient and diminished within several weeks. By contrast, we show that ICBs remained stable for at least 22 months. These results indicate that different mechanisms underlie the idiosyncratic and feedback-induced biases.

neuroscience↗

bioRxiv2024

On the Geometry of Somatosensory Representations in the Cortex

It is well-known that cortical areas specializing in the processing of somatosensory information from different parts of the body are arranged in an orderly manner along the cortex. It is also generally accepted that in the cortex, somatosensory information is initially processed in the primary somatosensory cortex and from there, it is hierarchically processed in other cortical regions. Previous studies have focused on the organization of representation at a level of a single or few cortical regions, identifying multiple body maps. However, the question of the large-scale organization of these different maps, and their relation to the hierarchical organization has received little attention. This is primarily because the highly convoluted shape of the cortical surface makes it difficult to characterize the relationship between cortical areas that are centimeters apart. Here, we used functional MRI to characterize cortical responses to full-body light touch stimulation. Our results indicate that the organization of both body representation and hierarchy is radial, with a small number of extrema that reign over a large number of cortical regions. Quantitatively computing the local relationship between the gradients of body and hierarchy maps, we show that the interaction between these two radial geometries, body representation and hierarchy in S1 are approximately orthogonal. However, this orthogonality is restricted to S1. Similar organizational patterns in the visual and auditory systems suggest that radial topography may be a common feature across sensory systems. Significance statementThe sensation of touch on our skin is represented in the brain as a map, where body parts are organized sequentially from head to toe. In the cerebral cortex, multiple body maps are distributed across numerous regions, processing signals at different hierarchical levels. Is there a large-scale organization of these body maps in the cerebral cortex? We show that all previously known body maps and their hierarchies are organized with a radial geometry. Similar radial geometry may also characterize the visual and auditory systems, indicating that radial geometry is a common organizational principle of sensory processing in the cortex.

neuroscience↗

bioRxiv2023

Towards Choice Engineering

Effectively shaping human and animal behavior has been of great practical and theoretical importance for millennia. Here we ask whether quantitative models of choice can be used to achieve this goal more effectively than qualitative psychological principles. We term this approach, which is motivated by the effectiveness of engineering in the natural sciences, choice engineering. To address this question, we launched an academic competition, in which the academic participants were instructed to use either quantitative models or qualitative principles to design reward schedules that maximally bias choice in a repeated, two-alternative task. We found that a choice engineering approach was the most successful method for shaping behavior in our task. This is a proof of concept that quantitative models are ripe to be used in order to engineer behavior. Finally, we show that choice engineering can be effectively used to compare models in the cognitive sciences, thus providing an alternative to the standard statistical methods of model comparison that are based on likelihood or explained variance.

animal behavior and cognition↗

bioRxiv2023

Homeostasis of a representational map in the neocortex

Cortical function in general and the processing of sensory stimuli in particular are remarkably robust against the continuous loss of neurons during aging, and even the accelerated loss during prodromal stages of neurodegeneration1,2. Population activity of neurons in sensory cortices represents the environment in form of a map, which is structured in an informative way for guiding behavior. Here, we used the mouse auditory cortex as a model and tested in how far the structure of the representational map is protected by homeostatic network mechanisms against the removal of neurons. We combined longitudinal two-photon calcium imaging of population responses evoked by a diverse set of sound stimuli with a targeted microablation of functionally characterized neurons. Unilateral microablation of 30 - 40 selected highly sound-responsive neurons in layer 2/3 led to a temporary disturbance of the representational map in the spared population that, however, recovered in subsequent days. At the level of individual neurons, we observed that the recovery of the spared network was predominantly driven by neurons unresponsive to the sounds before microablation which strengthened the correlation structure of the local network after gaining responsiveness. In contrast, selective microablation of inhibitory neurons induced a prolonged disturbance of the representational map that was primarily characterized by a destabilization of sound responses across trials. Together, our findings provide a link between the tuning and plasticity of individual neurons and the structure of a representational map at the population level which reveals homeostatic network mechanisms safeguarding sensory processing in neocortical circuits.

neuroscience↗

bioRxiv2023

Sex-specific declines in cholinergic-targeting tRNA fragments in the nucleus accumbens in Alzheimer's disease

IntroductionFemales with Alzheimers disease (AD) suffer accelerated dementia and loss of cholinergic neurons compared to males, but the underlying mechanisms are unknown. Seeking causal contributors to both these phenomena, we pursued changes in tRNA fragments (tRFs) targeting cholinergic transcripts (CholinotRFs). MethodsWe analyzed small RNA-sequencing data from the nucleus accumbens (NAc) brain region which is enriched in cholinergic neurons, compared to hypothalamic or cortical tissues from AD brains; and explored small RNA expression in neuronal cell lines undergoing cholinergic differentiation. ResultsNAc CholinotRFs of mitochondrial genome origin showed reduced levels that correlated with elevations in their predicted cholinergic-associated mRNA targets. Single cell RNA seq from AD temporal cortices showed altered sex-specific levels of cholinergic transcripts in diverse cell types; inversely, human-originated neuroblastoma cells under cholinergic differentiation presented sex-specific CholinotRF elevations. DiscussionOur findings support CholinotRFs contributions to cholinergic regulation, predicting their involvement in AD sex-specific cholinergic loss and dementia.

bioinformatics↗

bioRxiv2021

Paradoxical relationship between speed and accuracy in olfactory figure-background segregation

In natural settings, many stimuli impinge on our sensory organs simultaneously. Parsing these sensory stimuli into perceptual objects is a fundamental task faced by all sensory systems. Similar to other sensory modalities, increased odor backgrounds decrease the detectability of target odors by the olfactory system. The mechanisms by which background odors interfere with the detection and identification of target odors are unknown. Here we utilized the framework of the Drift Diffusion Model (DDM) to consider possible interference mechanisms in an odor detection task. We consider effects of background odors on both signal and noise in the decision-making dynamics, and show that these produce different predictions about decision accuracy and speed. To test these predictions, we trained mice to detect target odors that are embedded in random background mixtures in a two-alternative choice task. Trial duration was independent of behavioral reaction times to avoid motivating rapid responses. We found that the behavioral data is most consistent with background odors acting by adding noise to the decision-making dynamics. The added noise decreases the correct rate, but also decreases decision times, thereby creating a paradoxical relationship between speed and accuracy of target detection, where mice make faster and less accurate decisions in the presence of background odors.

neuroscience↗

bioRxiv2020

Features Of Hippocampal Astrocytic Domains And Their Spatial Relation To Excitatory And Inhibitory Neurons

The mounting evidence for the involvement of astrocytes in neuronal circuits function and behavior stands in stark contrast to the lack of detailed anatomical description of these cells and the neurons in their domains. To fill this void, we imaged >30,000 astrocytes in cleared hippocampi, and employed converging genetic, histological and computational tools to determine the elaborate structure, distribution and neuronal content of astrocytic domains. First, we characterized the spatial distribution of >19,000 astrocytes across CA1 lamina, and analyzed the detailed morphology of thousands of reconstructed domains. We then determined the excitatory content of CA1 astrocytes, averaging above 13 pyramidal neurons per domain and increasing towards CA1 midline. Finally, we discovered that somatostatin neurons are found in close proximity to astrocytes, compared to parvalbumin and VIP inhibitory neurons. This resource expands our understanding of fundamental hippocampal design principles, and provides the first quantitative foundation for neuron-astrocyte interactions in this region.

neuroscience↗

bioRxiv2019

Stability of excitatory structural connectivity predicts the probability of CA1 pyramidal neurons to become engram neurons

Neurons undergoing activity-dependent plasticity represent experience and are functional for learning and recall thus they are considered cellular engrams of memory. Although increase in excitability and stability of structural synaptic connectivity have been implicated in the formation and persistance of engrams, the mechanisms bringing engrams into existence are still largely unknown. To investigate this issue, we tracked the dynamics of structural excitatory synaptic connectivity of hippocampal CA1 pyramidal neurons over two weeks using deep-brain two-photon imaging in live mice. We found that neurons that will prospectively become part of an engram display higher stability of connectivity than neurons that will not. A novel experience significantly stabilizes the connectivity of non-engram neurons. Finally, the density and survival of dendritic spines negatively correlates to freezing to the context but not to the tone in a trace fear conditioning learning paradigm.

neuroscience↗

bioRxiv2019

The \"creatures\" of the human cortical somatosensory system

Penfields description of the \"homunculus\", a \"grotesque creature\" with large lips and hands and small trunk and legs depicting the representation of body-parts within the primary somatosensory cortex (S1), is one of the most prominent contributions to the neurosciences. Since then, numerous studies have identified additional body-parts representations outside of S1. Nevertheless, it has been implicitly assumed that S1s homunculus is representative of the entire somatosensory cortex. Therefore, the distribution of body-parts representations in other brain regions, the property that gave Penfields homunculus its famous \"grotesque\" appearance, has been overlooked. We used whole-body somatosensory stimulation, functional MRI and a new cortical parcellation to quantify the organization of the cortical somatosensory representation. Our analysis showed first, an extensive somatosensory response over the cortex; and second, that the proportional representation of body-parts differs substantially between major neuroanatomical regions and from S1, with, for instance, much larger trunk representation at higher brain regions, potentially in relation to the regions functional specialization. These results extend Penfields initial findings to the higher level of somatosensory processing and suggest a major role for somatosensation in human cognition.

neuroscience↗

bioRxiv2018

Idiosyncratic choice bias in decision tasks naturally emerges from neuronal network dynamics

Idiosyncratic tendency to choose one alternative over others in the absence of an identified reason is a common observation in two-alternative forced-choice experiments. It is tempting to account for it as resulting from the (unknown) participant-specific history and thus treat it as a measurement noise. Here we quantify idiosyncratic choice biases in a perceptual discrimination task and a motor task. We report substantial and significant biases in both cases that cannot be accounted for by the experimental context. Then, we present theoretical evidence that even in idealized experiments, in which the settings are symmetric, idiosyncratic choice bias is expected to emerge from the dynamics of competing neuronal networks. We thus argue that idiosyncratic choice bias reflects the microscopic dynamics of choice and therefore is virtually inevitable in any comparison or decision task.

neuroscience↗

bioRxiv2017

Learning Fast And Slow: Deviations From The Matching Law Can Reflect An Optimal Strategy Under Uncertainty

Behavior which deviates from our normative expectations often appears irrational. A classic example concerns the question of how choice should be distributed among multiple alternatives. The so-called matching law predicts that the fraction of choices made to any option should match the fraction of total rewards earned from the option. This choice strategy can maximize reward in a stationary reward schedule. Empirically, however, behavior often deviates from this ideal. While such deviations have often been interpreted as reflecting noisy, suboptimal, decision-making, here we instead suggest that they reflect a strategy which is adaptive in nonstationary and uncertain environments. We analyze the results of a dynamic foraging task. Animals exhibited significant deviations from matching, and animals turned out to be able to collect more rewards when deviation was larger. We show that this behavior can be understood if one considers that animals had incomplete information about the environments dynamics. In particular, using computational models, we show that in such nonstationary environments, learning on both fast and slow timescales is beneficial. Learning on fast timescales means that an animal can react to sudden changes in the environment, though this inevitably introduces large fluctuations (variance) in value estimates. Concurrently, learning on slow timescales reduces the amplitude of these fluctuations at the price of introducing a bias that causes systematic deviations. We confirm this prediction in data - monkeys indeed solved the bias-variance tradeoff by combining learning on both fast and slow timescales. Our work suggests that multi-timescale learning could be a biologically plausible mechanism for optimizing decisions under uncertainty.

neuroscience↗

bioRxiv2017

Striatal Action-Value Neurons Reconsidered

It is generally believed that during economic decisions, striatal neurons represent the values associated with different actions. This hypothesis is based on a large number of studies, in which the neural activity of striatal neurons was measured while the subject was learning to prefer the more rewarding action. Here we show that these publications are subject to at least one of two critical confounds and that most are subject to both. First, we show that even weak temporal correlations in the neuronal data may result in an erroneous identification of action-value representations. We demonstrate this by erroneously identifying action-value representations, both in simulations and in the neural activity recorded in unrelated experiments. Second, we show that the experiments and analyses designed to dissociate action-value representation from the representation of other decision variables cannot do so. Specifically, we show that neurons representing policy may be erroneous identified as representing action-values. We suggest different solutions to identifying action-value representation that are not subject to these confounds. Applying one of these solutions to previously identified action-value neurons in the basal ganglia we fail to detect action-value representations there. Thus, we conclude that the claim that striatal neurons encode action-values must await new experiments and analyses.

neuroscience↗

bioRxiv2016

Distinct sources of deterministic and stochastic components of action timing decisions in rodent frontal cortex

The selection and timing of actions are subject to determinate influences such as sensory cues and internal state as well as to effectively stochastic variability. Although stochastic choice mechanisms are assumed by many theoretical models, their origin and mechanisms remain poorly understood. Here we investigated this issue by studying how neural circuits in the frontal cortex determine action timing in rats performing a waiting task. Electrophysiological recordings from two regions necessary for this behavior, medial prefrontal cortex (mPFC) and secondary motor cortex (M2), revealed an unexpected functional dissociation. Both areas encoded deterministic biases in action timing, but only M2 neurons reflected stochastic trial-by-trial fluctuations. This differential coding was reflected in distinct timescales of neural dynamics in the two frontal cortical areas. These results suggest a two-stage model in which stochastic components of action timing decisions are injected by circuits downstream of those carrying deterministic bias signals.

neuroscience↗