Month: April 2022

Evolution of metamemory based on self-reference to own memory in artificial neural network with neuromodulation

Yusuke Yamato, Reiji Suzuki & Takaya Arita
Scientific Reports volume 12, Article number: 6233 (2022)

The ability of humans to self-monitor and control their memory processes is called metamemory and has been widely studied as a component of metacognition in cognitive psychology. Metamemory in non-human animals has also been investigated in recent years, although it had been regarded as a truly unique characteristic of human memory. We attempt to evolve artificial neural networks with neuromodulation, which have a metamemory function. Our constructive approach is expected to contribute, by introducing a novel dimension of evolutionary plausibility, to the discussion of animal experiments to detect metamemory. In this study, we demonstrate the evolution of neural networks that have a metamemory function based on the self-reference of memory, including the analysis of the evolved mechanism of metamemory. In addition, we discuss the similarity between the structure of the evolved neural network and the metamemory model defined by Nelson and Narens.

Read the full article at: www.nature.com

The probabilistic pool punishment proportional to the difference of payoff outperforms previous pool and peer punishment

Tetsushi Ohdaira 
Scientific Reports volume 12, Article number: 6604 (2022

The public goods game is a multiplayer version of the prisoner’s dilemma game. In the public goods game, punishment on defectors is necessary to encourage cooperation. There are two types of punishment: peer punishment and pool punishment. Comparing pool punishment with peer punishment, pool punishment is disadvantageous in comparison with peer punishment because pool punishment incurs fixed costs especially if second-order free riders (those who invest in public goods but do not punish defectors) are not punished. In order to eliminate such a flaw of pool punishment, this study proposes the probabilistic pool punishment proportional to the difference of payoff. In the proposed pool punishment, each punisher pays the cost to the punishment pool with the probability proportional to the difference of payoff between his/her payoff and the average payoff of his/her opponents. Comparing the proposed pool punishment with previous pool and peer punishment, in pool punishment of previous studies, cooperators who do not punish defectors become dominant instead of pool punishers with fixed costs. However, in the proposed pool punishment, more punishers and less cooperators coexist, and such state is more robust against the invasion of defectors due to mutation than those of previous pool and peer punishment. The average payoff is also comparable to peer punishment of previous studies.

Read the full article at: www.nature.com

Biocosmology: Biology from a cosmological perspective

Marina Cortês, Stuart A. Kauffman, Andrew R. Liddle, Lee Smolin
The Universe contains everything that exists, including life. And all that exists, including life, obeys universal physical laws. Do those laws then give adequate foundations for a complete explanation of biological phenomena? We discuss whether and how cosmology and physics must be modified to be able to address certain questions which arise at their intersection with biology. We show that a universe that contains life, in the form it has on Earth, is in a certain sense radically non-ergodic, in that the vast majority of possible organisms will never be realized. We argue from this that complete explanations in cosmology require a mixture of reductionist and functional explanations.

Read the full article at: arxiv.org

Multilayer Networks: Analysis and Visualization: Introduction to muxViz with R, by Manlio De Domenico

Provides practical recipes to use muxViz for specific purposes, bypassing theoretical obstacles
Includes dozens of examples whose R code is provided and directly linked from inside the text
Comes with, and builds on, a significant extension of the muxViz platform that can be used without needing a GUI

More at: link.springer.com