The colloquialism “a picture is worth a thousand words” has reverberated through the decades, yet there is very little basic cognitive research assessing the merit of drawing as a mnemonic strategy. In our recent research, we explored whether drawing to-be-learned information enhanced memory and found it to be a reliable, replicable means of boosting performance. Specifically, we have shown this technique can be applied to enhance learning of individual words and pictures as well as textbook definitions. In delineating the mechanism of action, we have shown that gains are greater from drawing than other known mnemonic techniques, such as semantic elaboration, visualization, writing, and even tracing to-be-remembered information. We propose that drawing improves memory by promoting the integration of elaborative, pictorial, and motor codes, facilitating creation of a context-rich representation. Importantly, the simplicity of this strategy means it can be used by people with cognitive impairments to enhance memory, with preliminary findings suggesting measurable gains in performance in both normally aging individuals and patients with dementia.
Self-organization can be broadly defined as the ability of a system to display ordered spatio-temporal patterns solely as the result of the interactions among the system components. Processes of this kind characterize both living and artificial systems, making self-organization a concept that is at the basis of several disciplines, from physics to biology to engineering. Placed at the frontiers between disciplines, Artificial Life (ALife) has heavily borrowed concepts and tools from the study of self-organization, providing mechanistic interpretations of life-like phenomena as well as useful constructivist approaches to artificial system design. Despite its broad usage within ALife, the concept of self-organization has been often excessively stretched or misinterpreted, calling for a clarification that could help with tracing the borders between what can and cannot be considered self-organization. In this review, we discuss the fundamental aspects of self-organization and list the main usages within three primary ALife domains, namely "soft" (mathematical/computational modeling), "hard" (physical robots), and "wet" (chemical/biological systems) ALife. Finally, we discuss the usefulness of self-organization within ALife studies, point to perspectives for future research, and list open questions.
New Data & Society report recommends editorial “better practices” for reporting on online bigots and manipulators; interviews journalists on accidental amplification of extreme agendas
This report draws on in-depth interviews by scholar Whitney Phillips to showcase how news media was hijacked from 2016 to 2018 to amplify the messages of hate groups.
Offering extremely candid comments from mainstream journalists, the report provides a snapshot of an industry caught between the pressure to deliver page views, the impulse to cover manipulators and “trolls,” and the disgust (expressed in interviewees’ own words) of accidentally propagating extremist ideology.
After reviewing common methods of “information laundering” of radical and racist messages through the press, Phillips uses journalists’ own words to propose a set of editorial “better practices” intended to reduce manipulation and harm.
As social and digital media are leveraged to reconfigure the information landscape, Phillips argues that this new domain requires journalists to take what they know about abuses of power and media manipulation in traditional information ecosystems; and apply and adapt that knowledge to networked actors, such as white nationalist networks online.
This work is the first practitioner-focused report from Data & Society’s Media Manipulation Initiative, which examines how groups use the participatory culture of the internet to turn the strengths of a free society into vulnerabilities.
Original source of Erdős–Rényi model.
In the mathematical field of graph theory, the Erdős–Rényi model is either of two closely related models for generating random graphs. They are named after mathematicians Paul Erdős and Alfréd Rényi, who first introduced one of the models in 1959, while Edgar Gilbert introduced the other model contemporaneously and independently of Erdős and Rényi. In the model of Erdős and Rényi, all graphs on a fixed vertex set with a fixed number of edges are equally likely; in the model introduced by Gilbert, each edge has a fixed probability of being present or absent, independently of the other edges. These models can be used in the probabilistic method to prove the existence of graphs satisfying various properties, or to provide a rigorous definition of what it means for a property to hold for almost all graphs.
Networks of coupled dynamical systems have been used to model biological oscillators1–4, Josephson junction arrays5,6, excitable media7, neural networks8–10, spatial games11, genetic control networks12 and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned through this middle ground: regular networks ‘rewired’ to introduce increasing amounts of disorder. We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. We call them ‘small-world’ networks, by analogy with the small-world phenomenon13,14 (popularly known as six degrees of separation15). The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks. Models of dynamical systems with small-world coupling display enhanced signal-propagation speed, computational power, and synchronizability. In particular, infectious diseases spread more easily in small-world networks than in regular lattices.
“Professionals and amateurs in a variety of fields have passionately argued for either one or two spaces following this punctuation mark,” they wrote in a paper published last week in the journal Attention, Perception, & Psychophysics.
They cite dozens of theories and previous research, arguing for one space or two. A 2005 study that found two spaces reduced lateral interference in the eye and helped reading. A 2015 study that found the opposite. A 1998 experiment that suggested it didn't matter.
“However,” they wrote, “to date, there has been no direct empirical evidence in support of these claims, nor in favor of the one-space convention.”
I love that the permalink for this article has a trailing 2, which indicates to me that it took the editors a second attempt to add the additional space into the headline for their CMS. And if nothing else, this article is interesting for its layout and typesetting.
I’ll circle back to read the full journal article shortly.1
Let’s be honest, reading a paper:
1. Read abstract
2. Look at pictures
3. Scan conclusions
4. Read 2-3 paragraphs of lit review
5. Scan references in case you’ve missed something juicy
6. Ear-mark to read ‘properly’ later
7. Take on all train journeys for next year. Don’t read.
— Jenny Andrew (@DrAndrewV2) April 28, 2018
During decades the study of networks has been divided between the efforts of social scientists and natural scientists, two groups of scholars who often do not see eye to eye. In this review I present an effort to mutually translate the work conducted by scholars from both of these academic fronts hoping to continue to unify what has become a diverging body of literature. I argue that social and natural scientists fail to see eye to eye because they have diverging academic goals. Social scientists focus on explaining how context specific social and economic mechanisms drive the structure of networks and on how networks shape social and economic outcomes. By contrast, natural scientists focus primarily on modeling network characteristics that are independent of context, since their focus is to identify universal characteristics of systems instead of context specific mechanisms. In the following pages I discuss the differences between both of these literatures by summarizing the parallel theories advanced to explain link formation and the applications used by scholars in each field to justify their approach to network science. I conclude by providing an outlook on how these literatures can be further unified.