The evolution of lossy compression

Marzen, Sarah; DeDeo, Simon

doi:10.1098/rsif.2017.0166

Cited by 47 publications

(61 citation statements)

References 77 publications

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“…EC (indeed, any ranking or scoring system) thus amounts to a lossy compression of the original data [79], summarizing the behavioral patterns relevant to the establishment of a dominance hierarchy. Conversely, for any given dominance hierarchy there are many possible behavioral patterns.…”

Section: Null Models For Aggressionmentioning

confidence: 99%

Social feedback and the emergence of rank in animal society

Hobson

DeDeo

2015

Preprint

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Dominance hierarchies are group-level properties that emerge from the aggression of individuals. Although individuals can gain critical benefits from their position in a hierarchy, we do not understand how real-world hierarchies form. Nor do we understand what signals and decision-rules individuals use to construct and maintain hierarchies in the absence of simple cues such as size or spatial location. A study of conflict in two groups of captive monk parakeets (Myiopsitta monachus) found that a transition to large-scale order in aggression occurred in newly-formed groups after one week, with individuals thereafter preferring to direct aggression more frequently against those nearby in rank. We consider two cognitive mechanisms underlying the emergence of this order: inference based on overall levels of aggression, or on subsets of the aggression network. Both mechanisms were predictive of individual decisions to aggress, but observed patterns were better explained by rank inference through subsets of the aggression network. Based on these results, we present a new theory, of a feedback loop between knowledge of rank and consequent behavior. This loop explains the transition to strategic aggression and the formation and persistence of dominance hierarchies in groups capable of both social memory and inference. Author SummaryAn individual's success depends critically on socially-constructed properties such as rank. A detailed study of two independent captive parakeet groups reveals how these properties come into being. We show that individuals can use localized patterns in the aggression network to learn the relative ranks of individuals, and that these signals of rank strongly correlate with individual decisions to aggress. Over time, feedback between knowledge and behavior leads to the emergence of strategic aggression: individuals focus their aggression on those nearby in rank.

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Section: Null Models For Aggressionmentioning

confidence: 99%

Social feedback and the emergence of rank in animal society

Hobson

DeDeo

2015

Preprint

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“…Our results suggest that given a particular inherent sensor stochasticity, there is an optimal finite sensor size at which functionality (prediction) is maximized. The size of sensors in biological systems, then, might not always be governed by resource constraints [25–27] but instead governed by degradation of functionality due to unavoidable noise.…”

Section: Resultsmentioning

confidence: 99%

Infinitely large, randomly wired sensors cannot predict their input unless they are close to deterministic

Marzen

2018

PLoS ONE

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Building predictive sensors is of paramount importance in science. Can we make a randomly wired sensor “good enough” at predicting its input simply by making it larger? We show that infinitely large, randomly wired sensors are nonspecific for their input, and therefore nonpredictive of future input, unless they are close to deterministic. Nearly deterministic, randomly wired sensors can capture ∼ 10% of the predictive information of their inputs for “typical” environments.

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“…Note that Ref. [11] considered the effects of a nonzero d min = inf{x : ρ(x) > 0}. Here, we assume that d min = 0.…”

Section: Weak Universality Of the Rate-distortion Functionmentioning

confidence: 99%

“…Here, we use rate-distortion theory to model environments using the framework of Ref. [11]. Distortions d(x,x) and the probability distribution of inputs p(x) are drawn from a probability distribution that represents the range of possible environments an organism might find itself born into.…”

Section: Relating Sensory Costs To the Rate-distortion Functionmentioning

confidence: 99%

“…To show this, we use a model of the environment general enough to apply to a range of biological situations, but rich enough to capture the basic problem of perception and encoding, in which both the probability of * smarzen@berkeley.edu observing a particular environmental symbol and the cost of misperceiving those symbols are randomly drawn [11].…”

Section: Introductionmentioning

confidence: 99%

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Weak universality in sensory tradeoffs

Marzen¹,

DeDeo

2016

Phys. Rev. E

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For many organisms, the number of sensory neurons is largely determined during development, before strong environmental cues are present. This is despite the fact that environments can fluctuate drastically both from generation to generation and within an organism's lifetime. How can organisms get by by hard coding the number of sensory neurons? We approach this question using rate-distortion theory. A combination of simulation and theory suggests that when environments are large, the rate-distortion function-a proxy for material costs, timing delays, and energy requirements-depends only on coarse-grained environmental statistics that are expected to change on evolutionary, rather than ontogenetic, time scales.

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The evolution of lossy compression

Cited by 47 publications

References 77 publications

Social feedback and the emergence of rank in animal society

Social feedback and the emergence of rank in animal society

Infinitely large, randomly wired sensors cannot predict their input unless they are close to deterministic

Weak universality in sensory tradeoffs

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