Dataset Information

Integrated information increases with fitness in the evolution of animats.

ABSTRACT: One of the hallmarks of biological organisms is their ability to integrate disparate information sources to optimize their behavior in complex environments. How this capability can be quantified and related to the functional complexity of an organism remains a challenging problem, in particular since organismal functional complexity is not well-defined. We present here several candidate measures that quantify information and integration, and study their dependence on fitness as an artificial agent ("animat") evolves over thousands of generations to solve a navigation task in a simple, simulated environment. We compare the ability of these measures to predict high fitness with more conventional information-theoretic processing measures. As the animat adapts by increasing its "fit" to the world, information integration and processing increase commensurately along the evolutionary line of descent. We suggest that the correlation of fitness with information integration and with processing measures implies that high fitness requires both information processing as well as integration, but that information integration may be a better measure when the task requires memory. A correlation of measures of information integration (but also information processing) and fitness strongly suggests that these measures reflect the functional complexity of the animat, and that such measures can be used to quantify functional complexity even in the absence of fitness data.

SUBMITTER: Edlund JA

PROVIDER: S-EPMC3197648 | biostudies-literature | 2011 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Integrated information increases with fitness in the evolution of animats.

Edlund Jeffrey A JA Chaumont Nicolas N Hintze Arend A Koch Christof C Tononi Giulio G Adami Christoph C

PLoS computational biology 20111020 10

One of the hallmarks of biological organisms is their ability to integrate disparate information sources to optimize their behavior in complex environments. How this capability can be quantified and related to the functional complexity of an organism remains a challenging problem, in particular since organismal functional complexity is not well-defined. We present here several candidate measures that quantify information and integration, and study their dependence on fitness as an artificial age ...[more]

PMID: 22028639

Similar Datasets

Project description:Information integration theory has been developed to quantify consciousness. Since conscious thought requires the integration of information, the degree of this integration can be used as a neural correlate (Φ) with the intent to measure degree of consciousness. Previous research has shown that the ability to integrate information can be improved by Darwinian evolution. The value Φ can change over many generations, and complex tasks require systems with at least a minimum Φ . This work was done using simple animats that were able to remember previous sensory inputs, but were incapable of fundamental change during their lifetime: actions were predetermined or instinctual. Here, we are interested in changes to Φ due to lifetime learning (also known as neuroplasticity). During lifetime learning, the system adapts to perform a task and necessitates a functional change, which in turn could change Φ . One can find arguments to expect one of three possible outcomes: Φ might remain constant, increase, or decrease due to learning. To resolve this, we need to observe systems that learn, but also improve their ability to learn over the many generations that Darwinian evolution requires. Quantifying Φ over the course of evolution, and over the course of their lifetimes, allows us to investigate how the ability to integrate information changes. To measure Φ , the internal states of the system must be experimentally observable. However, these states are notoriously difficult to observe in a natural system. Therefore, we use a computational model that not only evolves virtual agents (animats), but evolves animats to learn during their lifetime. We use this approach to show that a system that improves its performance due to feedback learning increases its ability to integrate information. In addition, we show that a system's ability to increase Φ correlates with its ability to increase in performance. This suggests that systems that are very plastic regarding Φ learn better than those that are not.

Project description:Virus populations may be challenged to evolve in spatially heterogeneous environments, such as mixtures of host cells that pose differing selection pressures. Spatial heterogeneity may select for evolved polymorphisms, where multiple virus subpopulations coexist by specializing on a narrow subset of the available hosts. Alternatively, spatial heterogeneity may select for evolved generalism, where a single genotype dominates the virus population by occupying a relatively broader host niche. In addition, the extent of spatial heterogeneity should influence the degree of divergence among virus populations encountering identical environmental challenges. Spatial heterogeneity creates environmental complexity that should increase the probability of differing adaptive phenotypic solutions, thus producing greater divergence among replicate virus populations, relative to counterparts evolving in strictly homogeneous host environments. Here, we tested these ideas using experimental evolution of RNA virus populations grown in laboratory tissue culture. We allowed vesicular stomatitis virus (VSV) lineages to evolve in replicated environments containing BHK-21 (baby hamster kidney) cells, HeLa (human epithelial) cells, or spatially heterogeneous host cell mixtures. Results showed that generalist phenotypes dominated in evolved virus populations across all treatments. Also, we observed greater variance in host-use performance (fitness) among VSV lineages evolved under spatial heterogeneity, relative to lineages evolved in homogeneous environments. Despite measurable differences in fitness, consensus Sanger sequencing revealed no fixed genetic differences separating the evolved lineages from their common ancestor. In contrast, deep sequencing of evolved VSV populations confirmed that the degree of divergence among replicate lineages was correlated with a larger number of minority variants. This correlation between divergence and the number of minority variants was significant only when we considered variants with a frequency of at least 10 per cent in the population. The number of lower-frequency minority variants per population did not significantly correlate with divergence.

Project description:According to the integrated information theory, the quantity of consciousness is the amount of integrated information generated by a complex of elements, and the quality of experience is specified by the informational relationships it generates. This paper outlines a framework for characterizing the informational relationships generated by such systems. Qualia space (Q) is a space having an axis for each possible state (activity pattern) of a complex. Within Q, each submechanism specifies a point corresponding to a repertoire of system states. Arrows between repertoires in Q define informational relationships. Together, these arrows specify a quale -- a shape that completely and univocally characterizes the quality of a conscious experience. Phi -- the height of this shape -- is the quantity of consciousness associated with the experience. Entanglement measures how irreducible informational relationships are to their component relationships, specifying concepts and modes. Several corollaries follow from these premises. The quale is determined by both the mechanism and state of the system. Thus, two different systems having identical activity patterns may generate different qualia. Conversely, the same quale may be generated by two systems that differ in both activity and connectivity. Both active and inactive elements specify a quale, but elements that are inactivated do not. Also, the activation of an element affects experience by changing the shape of the quale. The subdivision of experience into modalities and submodalities corresponds to subshapes in Q. In principle, different aspects of experience may be classified as different shapes in Q, and the similarity between experiences reduces to similarities between shapes. Finally, specific qualities, such as the "redness" of red, while generated by a local mechanism, cannot be reduced to it, but require considering the entire quale. Ultimately, the present framework may offer a principled way for translating qualitative properties of experience into mathematics.

Dataset Information

Integrated information increases with fitness in the evolution of animats.

Publications

Integrated information increases with fitness in the evolution of animats.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets