pwc-archive/papers-with-abstracts huggingface dataset:
A rodent paradigm for studying perceptual decisions under asymmetric reward
Authors: Xiaoyue Zhu, Jeffrey C. Erlich
Date: 2021-12-22 00:00:00

Many real-life decisions involve both perceptual processes and weighing the consequences of different actions. However, the neural mechanisms underlying perceptual decisions have typically been examined separately from those underlying economic decisions. Here, we trained rats to make choices informed by both perceptual and value cues on a trial-by-trial basis. As in typical perceptual tasks, subjects were rewarded for correctly categorizing a tone relative to a learned threshold. To add an economic component, a light indicated, on each trial, whether correct responses to one side gave higher rewards than correct responses to the other side. As such, on trials with some perceptual uncertainty, it could be worthwhile to choose the unlikely option, if it had higher expected value. We found that, despite subjects sensitivity to the frequency of the cue and the reward sizes, their behavior was not optimal: subjects tended to shift their choices in a stimulus-independent way following light flashes. Moreover, subjects tended to under-shift, which could be interpreted as being over-confident in their perceptual beliefs or as being risk-averse.
Parallel locomotor control strategies in mice and flies
Authors: Ana I. Gonçalves, Jacob A. Zavatone-Veth, Megan R. Carey, Damon A. Clark
Date: 2021-12-22 00:00:00

Our understanding of the neural basis of locomotor behavior can be informed by careful quantification of animal movement. Classical descriptions of legged locomotion have defined discrete locomotor gaits, characterized by distinct patterns of limb movement. Recent technical advances have enabled increasingly detailed characterization of limb kinematics across many species, imposing tighter constraints on neural control. Here, we highlight striking similarities between coordination patterns observed in two genetic model organisms: the laboratory mouse and Drosophila. Both species exhibit continuously-variable coordination patterns with similar low-dimensional structure, suggesting shared principles for limb coordination and descending neural control.
Skilled motor control implies a low entropy of states but a high entropy of actions
Authors: Nicola Catenacci Volpi, Martin Greaves, Dari Trendafilov, Christoph Salge, Giovanni Pezzulo, Daniel Polani
Date: 2021-12-22 00:00:00

The mastery of skills such as playing tennis or balancing an inverted pendulum implies a very accurate control of movements to achieve the task goals. Traditional accounts of skilled action control that focus on either routinization or perceptual control make opposite predictions about the ways we achieve mastery. The notion of routinization emphasizes the decrease of the variance of our actions, whereas the notion of perceptual control emphasizes the decrease of the variance of the states we visit, but not of the actions we execute. Here, we studied how participants managed control tasks of varying levels of complexity, which consisted in controlling inverted pendulums of different lengths. We used information-theoretic measures to compare the predictions of alternative theoretic accounts that focus on routinization and perceptual control, respectively. Our results indicate that the successful performance of the control task strongly correlates with the decrease of state variability and the increase of action variability. As postulated by perceptual control theory, the mastery of skills consists in achieving stable control goals by flexible means.
A dynamical scan path model for task-dependence during scene viewing
Authors: Lisa Schwetlick, Daniel Backhaus, Ralf Engbert
Date: 2021-12-21 00:00:00

In real-world scene perception human observers generate sequences of fixations to move image patches into the high-acuity center of the visual field. Models of visual attention developed over the last 25 years aim to predict two-dimensional probabilities of gaze positions for a given image via saliency maps. Recently, progress has been made on models for the generation of scan paths under the constraints of saliency as well as attentional and oculomotor restrictions. Experimental research demonstrated that task constraints can have a strong impact on viewing behavior. Here we propose a scan path model for both fixation positions and fixation durations, which includes influences of task instructions and interindividual differences. Based on an eye-movement experiment with four different task conditions, we estimated model parameters for each individual observer and task condition using a fully Bayesian dynamical modeling framework using a joint spatial-temporal likelihood approach with sequential estimation. Resulting parameter values demonstrate that model properties such as the attentional span are adjusted to task requirements. Posterior predictive checks indicate that our dynamical model can reproduce task differences in scan path statistics across individual observers.
Imagery agnosia and its phenomenology
Authors: Włodzisław Duch
Date: 2021-12-20 00:00:00

Lack of vivid sensory imagery has recently become an active subject of research, under the name of aphantasia. Extremely vivid imagery, or hyperphantasia, is at the other end of the spectrum of individual differences. While most research has focused on visual imagery in this paper I argue that from a neuropsychological perspective this phenomenon is much more widespread, and should be categorized as imagery sensory agnosia. After over twenty years of learning to play music phenomenology of auditory imagery agnosia is described from the first-person perspective. Reflections on other forms of imagery agnosia and deficits of autobiographical memories are presented and a hypothesis about putative brain processes that can account for such phenomena is discussed. Extreme individual differences in imagery and in autobiographical memory have implications for many fields of study, from consciousness research to education.
Predictive Coding Theories of Cortical Function
Authors: Linxing Preston Jiang, Rajesh P. N. Rao
Date: 2021-12-19 00:00:00
Tasks: Bayesian Inference

Predictive coding is a unifying framework for understanding perception, action and neocortical organization. In predictive coding, different areas of the neocortex implement a hierarchical generative model of the world that is learned from sensory inputs. Cortical circuits are hypothesized to perform Bayesian inference based on this generative model. Specifically, the Rao-Ballard hierarchical predictive coding model assumes that the top-down feedback connections from higher to lower order cortical areas convey predictions of lower-level activities. The bottom-up, feedforward connections in turn convey the errors between top-down predictions and actual activities. These errors are used to correct current estimates of the state of the world and generate new predictions. Through the objective of minimizing prediction errors, predictive coding provides a functional explanation for a wide range of neural responses and many aspects of brain organization.
Statistical inference on representational geometries
Authors: Heiko H. Schütt, Alexander D. Kipnis, Jörn Diedrichsen, Nikolaus Kriegeskorte
Date: 2021-12-16 00:00:00
Tasks: valid

Neuroscience has recently made much progress, expanding the complexity of both neural-activity measurements and brain-computational models. However, we lack robust methods for connecting theory and experiment by evaluating our new big models with our new big data. Here, we introduce new inference methods enabling researchers to evaluate and compare models based on the accuracy of their predictions of representational geometries: A good model should accurately predict the distances among the neural population representations (e.g. of a set of stimuli). Our inference methods combine novel 2-factor extensions of crossvalidation (to prevent overfitting to either subjects or conditions from inflating our estimates of model accuracy) and bootstrapping (to enable inferential model comparison with simultaneous generalization to both new subjects and new conditions). We validate the inference methods on data where the ground-truth model is known, by simulating data with deep neural networks and by resampling of calcium imaging and functional MRI data. Results demonstrate that the methods are valid and conclusions generalize correctly. These data analysis methods are available in an open-source Python toolbox (rsatoolbox.readthedocs.io).
The human behavioural immune system is a product of cultural evolution
Authors: Edwin S. Dalmaijer, Thomas Armstrong
Date: 2020-08-30 00:00:00
Tasks: Nutrition

Disgust is a basic emotion that serves to avoid contaminants, and is central to the behavioural immune system. While disgust-motivated avoidance occurs in bonobos and chimpanzees, humans show uniquely high levels of contamination sensitivity. Current theory postulates that human disgust is primarily a genetic adaptation, side-lining social transmission and denying parental modelling. Here, we test whether this strong view is warranted by simulating 100000 years of cultural and biological evolution in various scenarios. We modelled disgust as a trait that governed the extent to which individuals forewent potentially contaminated nutrition. This lowered their risk of gastrointestinal illness, at the cost of increasing starvation risk and birth-interval. As environmental levels and costs of contamination rose, so did relative fitness of high-disgust individuals. Evolutionary shifts in disgust as polygenic trait occurred as a consequence of germ-cell mutations, but were most prominent in populations with high initial genetic variance. Crucially, cultural transmission between generations operated at a higher rate, even if parental modelling was eliminated. This study serves not only as evidence of cultural evolution shaping the behavioural immune system, but also as an illustration of emerging theories that paint affective and cognitive mechanisms as socially transmitted rather than biologically determined functions.
Finding Structure in Silence: The Role of Pauses in Aligning Speaker Expectations
Authors: Maja Linke, Michael Ramscar
Date: 2021-12-15 00:00:00
Tasks: Open-Ended Question Answering

The intelligibility of speech relies on the ability of interlocutors to dynamically align their expectations about the rates at which informative changes in signals occur. Exactly how this is achieved remains an open question. We propose that speaker alignment is supported by the statistical structure of spoken signals and show how pauses offer a time-invariant template for structuring speech sequences. Consistent with this, we show that pause distributions in conversational English and Korean provide a memoryless information source. We describe how this can facilitate both the initial structuring and maintenance of predictability in spoken signals over time, and show how the properties of this signal change predictably with speaker experience. These results indicate that pauses provide a structuring signal that interacts with the morphological and rhythmical structure of languages, allowing speakers at all stages of lifespan development to distinguish signal from noise and maintain mutual predictability in time.
The impact of aging on human brain network target controllability
Authors: Giulia Bassignana, Giordano Lacidogna, Paolo Bartolomeo, Olivier Colliot, Fabrizio De Vico Fallani
Date: 2021-12-14 00:00:00

Understanding how few distributed areas can steer large-scale brain activity is a fundamental question that has practical implications, which range from inducing specific patterns of behavior to counteracting disease. Recent endeavors based on network controllability provided fresh insights into the potential ability of single regions to influence whole brain dynamics through the underlying structural connectome. However, controlling the entire brain activity is often unfeasible and might not always be necessary. The question whether single areas can control specific target subsystems remains crucial, albeit still poorly explored. Furthermore, the structure of the brain network exhibits progressive changes across the lifespan, but little is known about the possible consequences in the controllability properties. To address these questions, we adopted a novel target controllability approach that quantifies the centrality of brain nodes in controlling specific target anatomo-functional systems. We then studied such target control centrality in human connectomes obtained from healthy individuals aged from 5 to 85. Main results showed that the sensorimotor system has a high influencing capacity, but it is difficult for other areas to influence it. Furthermore, we reported that target control centrality varies with age and that temporal-parietal regions, whose cortical thinning is crucial in dementia-related diseases, exhibit lower values in older people. By simulating targeted attacks, such as those 19 occurring in focal stroke, we showed that the ipsilesional hemisphere is the most affected one regardless of the damaged area. Notably, such degradation in target control centrality was more evident in younger people, thus supporting early-vulnerability hypotheses after stroke.
Relating Blindsight and AI: A Review
Authors: Joshua Bensemann, Qiming Bao, Gaël Gendron, Tim Hartill, Michael Witbrock
Date: 2021-12-09 00:00:00

Processes occurring in brains, a.k.a. biological neural networks, can and have been modeled within artificial neural network architectures. Due to this, we have conducted a review of research on the phenomenon of blindsight in an attempt to generate ideas for artificial intelligence models. Blindsight can be considered as a diminished form of visual experience. If we assume that artificial networks have no form of visual experience, then deficits caused by blindsight give us insights into the processes occurring within visual experience that we can incorporate into artificial neural networks. This article has been structured into three parts. Section 2 is a review of blindsight research, looking specifically at the errors occurring during this condition compared to normal vision. Section 3 identifies overall patterns from Section 2 to generate insights for computational models of vision. Section 4 demonstrates the utility of examining biological research to inform artificial intelligence research by examining computation models of visual attention relevant to one of the insights generated in Section 3. The research covered in Section 4 shows that incorporating one of our insights into computational vision does benefit those models. Future research will be required to determine whether our other insights are as valuable.
COVID 19: Open source model for rapid reduction of R to below 1 in high R0 scenarios
Authors: Mark R Baker, Elizabeth L Hawthorne, Jessica R Rogge
Date: 2021-12-24 00:00:00

We present an open source model that allows quantitative prediction of the effects of testing on the rate of spread of COVID-19 described by R, the reproduction number, and on the degree of quarantine, isolation and lockdown required to limit it. The paper uses the model to quantify the outcomes of different test types and regimes, and to identify strategies and tests that can reduce the rate of spread and R value by a factor of between 1.67 and 33.3, reducing it to between 60% and 3% of the initial value.
Analysis of animal-related electric outages using species distribution models and community science data
Authors: Mei-Ling E. Feng, Olukunle O. Owolabi, Toryn L. J. Schafer, Sanhita Sengupta, Lan Wang, David S. Matteson, Judy P. Che-Castaldo, Deborah A. Sunter
Date: 2021-12-22 00:00:00

Animal-related outages (AROs) are a prevalent form of outages in electrical distribution systems. Animal-infrastructure interactions vary across focal species and regions, underlining the need to study the animal-outage relationship in more species and diverse systems. Animal activity has been used as an indicator of reliability in the electrical grid system and to describe temporal patterns in AROs. However, these ARO models have been limited by a lack of available estimates of species activity, instead approximating activity based on seasonal and weather patterns in animal-related outage records and characteristics of broad taxonomic groups, e.g., squirrels. We highlight publicly available resources to fill the ecological data gap that is limiting joint analyses between ecology and energy sectors. Species distribution models (SDMs), a common technique to model the distribution of a species across geographic space and time, paired with data sourced from eBird, a community science database for bird observations, provided us with species-specific estimates of activity to model spatio-temporal patterns of AROs. These flexible, species-specific estimates can allow future animal-indicators of grid reliability to be investigated in more diverse regions and ecological communities, providing a better understanding of the variation that exists in animal-outage relationship. AROs were best modeled by accounting for multiple outage-prone species activity patterns and their unique relationships with seasonality and habitat availability. Different species were important for modeling outages in different landscapes and seasons depending on their distribution and migration behavior. We recommend that future models of AROs include species-specific activity data that account for the diverse spectrum of spatio-temporal activity patterns that outage-prone animals exhibit.
Beyond Low Earth Orbit: Biological Research, Artificial Intelligence, and Self-Driving Labs
Authors: Lauren M. Sanders, Jason H. Yang, Ryan T. Scott, Amina Ann Qutub, Hector Garcia Martin, Daniel C. Berrios, Jaden J. A. Hastings, Jon Rask, Graham Mackintosh, Adrienne L. Hoarfrost, Stuart Chalk, John Kalantari, Kia Khezeli, Erik L. Antonsen, Joel Babdor, Richard Barker, Sergio E. Baranzini, Afshin Beheshti, Guillermo M. Delgado-Aparicio, Benjamin S. Glicksberg, Casey S. Greene, Melissa Haendel, Arif A. Hamid, Philip Heller, Daniel Jamieson, Katelyn J. Jarvis, Svetlana V. Komarova, Matthieu Komorowski, Prachi Kothiyal, Ashish Mahabal, Uri Manor, Christopher E. Mason, Mona Matar, George I. Mias, Jack Miller, Jerry G. Myers Jr., Charlotte Nelson, Jonathan Oribello, Seung-min Park, Patricia Parsons-Wingerter, R. K. Prabhu, Robert J. Reynolds, Amanda Saravia-Butler, Suchi Saria, Aenor Sawyer, Nitin Kumar Singh, Frank Soboczenski, Michael Snyder, Karthik Soman, Corey A. Theriot, David Van Valen, Kasthuri Venkateswaran, Liz Warren, Liz Worthey, Marinka Zitnik, Sylvain V. Costes
Date: 2021-12-22 00:00:00

Space biology research aims to understand fundamental effects of spaceflight on organisms, develop foundational knowledge to support deep space exploration, and ultimately bioengineer spacecraft and habitats to stabilize the ecosystem of plants, crops, microbes, animals, and humans for sustained multi-planetary life. To advance these aims, the field leverages experiments, platforms, data, and model organisms from both spaceborne and ground-analog studies. As research is extended beyond low Earth orbit, experiments and platforms must be maximally autonomous, light, agile, and intelligent to expedite knowledge discovery. Here we present a summary of recommendations from a workshop organized by the National Aeronautics and Space Administration on artificial intelligence, machine learning, and modeling applications which offer key solutions toward these space biology challenges. In the next decade, the synthesis of artificial intelligence into the field of space biology will deepen the biological understanding of spaceflight effects, facilitate predictive modeling and analytics, support maximally autonomous and reproducible experiments, and efficiently manage spaceborne data and metadata, all with the goal to enable life to thrive in deep space.
Evolutionary trend of SARS-CoV-2 inferred by the homopolymeric nucleotide repeats
Authors: Changchuan Yin
Date: 2022-01-01 00:00:00

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of the current global COVID-19 pandemic, in which millions of lives have been lost. Understanding the zoonotic evolution of the coronavirus may provide insights for developing effective vaccines, monitoring the transmission trends, and preventing new zoonotic infections. Homopolymeric nucleotide repeats (HP), the most simple tandem repeats, are a ubiquitous feature of eukaryotic genomes. Yet the HP distributions and roles in coronavirus genome evolution are poorly investigated. In this study, we characterize the HP distributions and trends in the genomes of bat and human coronaviruses and SARS-CoV-2 variants. The results show that the SARS-CoV-2 genome is abundant in HPs, and has augmented HP contents during evolution. Especially, the disparity of HP poly-(A/T) and ploy-(C/G) of coronaviruses increases during the evolution in human hosts. The disparity of HP poly-(A/T) and ploy-(C/G) is correlated to host adaptation and the virulence level of the coronaviruses. Therefore, we propose that the HP disparity can be a quantitative measure for the zoonotic evolution levels of coronaviruses. Peculiarly, the HP disparity measure infers that SARS-CoV-2 Omicron variants have a high disparity of HP poly-(A/T) and ploy-(C/G), suggesting a high adaption to the human hosts.
Etude de classification des bacteriophages
Authors: Dung Nguyen, Alix Boc, Abdoulaye Banire Diallo, Vladimir Makarenkov
Date: 2022-01-01 00:00:00
Tasks: Classification

Phages are one of the most present groups of organisms in the biosphere. Their identification continues and their taxonomies are divergent. However, due to their evolution mode and the complexity of their species ecosystem, their classification is not complete. Here, we present a new approach to the phages classification that combines the methods of horizontal gene transfer detection and ancestral sequence reconstruction.
The Sackin Index of Simplex Networks
Authors: Louxin Zhang
Date: 2021-12-31 00:00:00

A phylogenetic network is a simplex (or 1-component tree-child) network if the child of every reticulation node is a network leaf. Simplex networks are a superclass of phylogenetic trees and a subclass of tree-child networks. Generalizing the Sackin index to phylogenetic networks, we prove that the expected Sackin index of a random simplex network is asymptotically $\Omega(n^{7/4})$ in the uniform model.