Low-rank Gallus gallus domesticus chicks are better at transitive inference reasoning

A form of deductive reasoning, transitive inference, is thought to allow animals to infer relationships between members of a social group without having to remember all the interactions that occur. Such an ability means that animals can avoid direct confrontations which could be costly. Here we show that chicks perform a transitive inference task differently according to sex and rank. In female chicks, low-ranking birds performed better than did the highest ranked. Male chicks, however, showed an inverted U-shape of ability across rank, with the middle ranked chicks best able to perform the task. These results are explained according to the roles the sexes take within the group. This research directly links the abilities of transitive inference learning and social hierarchy formation and prompts further investigation into the role of both sex and rank within the dynamics of group living.

Perceptual Inference, Learning, and Attention in a Multisensory World

Adaptive behavior in a complex, dynamic, and multisensory world poses some of the most fundamental computational challenges for the brain, notably inference, decision-making, learning, binding, and attention. We first discuss how the brain integrates sensory signals from the same source to support perceptual inference and decision-making by weighting them according to their momentary sensory uncertainties. We then show how observers solve the binding or causal inference problem—deciding whether signals come from common causes and should hence be integrated or else be treated independently. Next, we describe the multifarious interplay between multisensory processing and attention. We argue that attentional mechanisms are crucial to compute approximate solutions to the binding problem in naturalistic environments when complex time-varying signals arise from myriad causes. Finally, we review how the brain dynamically adapts multisensory processing to a changing world across multiple timescales. Expected final online publication date for the Annual Review of Neuroscience, Volume 44 is July 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Bias Adaptive Statistical Inference Learning Agents for Learning from Human Feedback

We present a novel technique for learning behaviors from ahuman provided feedback signal that is distorted by system-atic bias. Our technique, which we refer to as BASIL, modelsthe feedback signal as being separable into a heuristic evalu-ation of the utility of an action and a bias value that is drawnfrom a parametric distribution probabilistically, where thedistribution is defined by unknown parameters. We presentthe general form of the technique as well as a specific algo-rithm for integrating the technique with the TAMER algo-rithm for bias values drawn from a normal distribution. Wetest our algorithm against standard TAMER in the domain ofTetris using a synthetic oracle that provides feedback undervarying levels of distortion. We find our algorithm can learnvery quickly under bias distortions that entirely stymie thelearning of classic TAMER.

An intelligent approach framework for decision-making risk analysis of Chinese hydropower corporations

An intelligent decision analytic framework for dealing with complex decision-making risk system is presented and Bayesian network (BN) approach is utilized to evaluate the influence of multilevel uncertainty in various risks (e.g., social, natural, economic, intracompany risks) on decision-making deviation of Chinese hydropower corporations. The technique of fuzzy probability is approached to calculate intricate parameters to the question of inference learning through the sensitivity and influence power analysis, the results of back inference show that there exists the risk transformation mechanism from external uncertain risks (e.g., social risks, ecological environment factors) to hydropower corporations’ internal uncertainties closely relating to economic uncertainties through strategic planning. The study concerning identification and intelligent analysis of uncertain risks in decision-making process illustrates the feasibility and validity of applying BN and its pragmatic implications on hydropower corporations strategic planning and guidance in operational management.

CogSci2020

Classification and inference learning

Predictability of the antigenic evolution of human influenza A H3 viruses

The current influenza A antigenic evolution paradigm suggesting that antigenic evolution is highly constrained, with successful new viruses being near optimal at maximizing their antigenic distance from past strains. This begs the question of whether influenza’s antigenic evolution is fundamentally predictable, or if it takes place on a much higher dimensional antigenic space with multiple possible trajectories. We tackle this issue by building a genotype to phenotype map validated on historical hemagglutination inhibition assay data by using machine learning methods. This map uses amino acid physiochemical properties for inference, learning the expected antigenic distance given the differences in polarity and hydrophobicity observed across any two viral sequences, and is thus applicable to newly sampled viruses with previously unseen amino acids. This allows us to accurately blindly predict the antigenic relevance of soon to be vaccine viral strains. We couple the genotype to phenotype map with a molecular evolutionary simulation algorithm to explore the limits of influenza’s antigenic evolution and infer to what extent it is in fact predictable. Although we do uncover some canalization of antigenic trajectories, we find that multiple antigenic lineages are equally viable at any one point in time even though typically only one of those trajectories is actually realized.