signal-noise   50

Why are children in the same family so different from one another? - PubMed - NCBI
- Plomin et al

The article has three goals: (1) To describe quantitative genetic methods and research that lead to the conclusion that nonshared environment is responsible for most environmental variation relevant to psychological development, (2) to discuss specific nonshared environmental influences that have been studied to date, and (3) to consider relationships between nonshared environmental influences and behavioral differences between children in the same family. The reason for presenting this article in BBS is to draw attention to the far-reaching implications of finding that psychologically relevant environmental influences make children in a family different from, not similar to, one another.
study  essay  article  survey  spearhead  psychology  social-psych  biodet  behavioral-gen  🌞  methodology  environmental-effects  signal-noise  systematic-ad-hoc  composition-decomposition  pdf  piracy  volo-avolo  developmental  iq  cog-psych  variance-components  GxE  nonlinearity  twin-study  personality  sib-study 
15 days ago by nhaliday
Does Learning to Read Improve Intelligence? A Longitudinal Multivariate Analysis in Identical Twins From Age 7 to 16
Stuart Richie, Bates, Plomin

SEM: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4354297/figure/fig03/

The variance explained by each path in the diagrams included here can be calculated by squaring its path weight. To take one example, reading differences at age 12 in the model shown in Figure​Figure33 explain 7% of intelligence differences at age 16 (.262). However, since our measures are of differences, they are likely to include substantial amounts of noise: Measurement error may produce spurious differences. To remove this error variance, we can take an estimate of the reliability of the measures (generally high, since our measures are normed, standardized tests), which indicates the variance expected purely by the reliability of the measure, and subtract it from the observed variance between twins in our sample. Correcting for reliability in this way, the effect size estimates are somewhat larger; to take the above example, the reliability-corrected effect size of age 12 reading differences on age 16 intelligence differences is around 13% of the “signal” variance. It should be noted that the age 12 reading differences themselves are influenced by many previous paths from both reading and intelligence, as illustrated in Figure​Figure33.

...

The present study provided compelling evidence that improvements in reading ability, themselves caused purely by the nonshared environment, may result in improvements in both verbal and nonverbal cognitive ability, and may thus be a factor increasing cognitive diversity within families (Plomin, 2011). These associations are present at least as early as age 7, and are not—to the extent we were able to test this possibility—driven by differences in reading exposure. Since reading is a potentially remediable ability, these findings have implications for reading instruction: Early remediation of reading problems might not only aid in the growth of literacy, but may also improve more general cognitive abilities that are of critical importance across the life span.

Does Reading Cause Later Intelligence? Accounting for Stability in Models of Change: http://sci-hub.cc/10.1111/cdev.12669
Results from a state–trait model suggest that reported effects of reading ability on later intelligence may be artifacts of previously uncontrolled factors, both environmental in origin and stable during this developmental period, influencing both constructs throughout development.
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16 days ago by nhaliday
New Theory Cracks Open the Black Box of Deep Learning | Quanta Magazine
A new idea called the “information bottleneck” is helping to explain the puzzling success of today’s artificial-intelligence algorithms — and might also explain how human brains learn.

sounds like he's just talking about autoencoders?
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24 days ago by nhaliday
Intersection of diverse neuronal genomes and neuropsychiatric disease: The Brain Somatic Mosaicism Network
Towards explaining non-shared-environment effects on intelligence, psychiatric disorders, and other cognitive traits - developmental noise such as post-conception mutations in individual cells or groups of cells
pdf  study  psychology  cog-psych  neuro  neuro-nitgrit  brain-scan  biodet  genetics  genomics  GWAS  🌞  psychiatry  behavioral-gen  mutation  environmental-effects  roots  org:nat  gwern  random  autism  proposal  signal-noise  developmental  composition-decomposition 
may 2017 by nhaliday
Optimal Dither and Noise Shaping in Image Processing
Cameron Nicklaus Christou

“Dithered quantization and noise shaping is well known in the audio community. The image processing community seems to be aware of this same theory only in bits and pieces, and frequently under conflicting terminology. This thesis attempts to show that dithered quantization of images is an extension of dithered quantization of audio signals to higher dimensions.

Dithered quantization, or “threshold modulation”, is investigated as a means of suppressing undesirable visual artifacts during the digital quantization, or requantization, of an image. Special attention is given to the statistical moments of the resulting error signal. Afterwards, noise shaping, or “error diffusion” methods are considered to try to improve on the dithered quantization technique.

We also take time to develop the minimum-phase property for two-dimensional systems. This leads to a natural extension of Jensen’s Inequality and the Hilbert transform relationship between the log-magnitude and phase of a two-dimensional system. We then describe how these developments are relevant to image processing.”

Quote, re: this paper: “The following paper has the best explanation of dither that I've run across. Although it's about image dither, the first half covers the theory and practice of dither in audio before extending its use into images …”

/via https://people.xiph.org/~xiphmont/demo/neil-young.html
audio  signal-processing  mathematics  dithering  signal-noise  image-processing  to-read  whitepaper 
may 2017 by elliottcable
Epidemiology, epigenetics and the ‘Gloomy Prospect’: embracing randomness in population health research and practice | International Journal of Epidemiology | Oxford Academic
Despite successes in identifying causes, it is often claimed that there are missing additional causes for even reasonably well-understood conditions such as lung cancer and coronary heart disease. Several lines of evidence suggest that largely chance events, from the biographical down to the sub-cellular, contribute an important stochastic element to disease risk that is not epidemiologically tractable at the individual level. Epigenetic influences provide a fashionable contemporary explanation for such seemingly random processes. Chance events—such as a particular lifelong smoker living unharmed to 100 years—are averaged out at the group level. As a consequence population-level differences (for example, secular trends or differences between administrative areas) can be entirely explicable by causal factors that appear to account for only a small proportion of individual-level risk. In public health terms, a modifiable cause of the large majority of cases of a disease may have been identified, with a wild goose chase continuing in an attempt to discipline the random nature of the world with respect to which particular individuals will succumb.

choice quote:
"With the perception (in my view exaggerated) that genome-wide association studies (GWASs) have failed to deliver on initial expectations,5 the next phase of enhanced risk prediction will certainly shift to ‘epigenetics’6,7—the currently fashionable response to any question to which you do not know the answer."
study  bio  medicine  genetics  genomics  sib-study  twin-study  cancer  cardio  essay  variance-components  signal-noise  random  causation  roots  gwern  explanation  methodology  🌞  biodet  QTL  correlation  epigenetics  GWAS  epidemiology  big-picture  public-health  composition-decomposition 
march 2017 by nhaliday
Arthur Schopenhauer: Chapter XXIV, On Reading and Books
§290
Ignorance degrades a man only when it is found in company with wealth. A poor man is subdued by his poverty and distress; with him his work takes the place of knowledge and occupies his thoughts. On the other hand, the wealthy who are ignorant live merely for their pleasures and are like animals, as can be seen every day. Moreover, there is the reproach that wealth and leisure have not been used for that which bestows on them the greatest possible value.

§291
When we read, someone else thinks for us; we repeat merely his mental process. It is like the pupil who, when learning to write, goes over with his pen the strokes made in pencil by the teacher. Accordingly, when we read, the work of thinking is for the most part taken away from us. Hence the noticeable relief when from preoccupation with our thoughts we pass to reading. But while we are reading our mind is really only the playground of other people’s ideas; and when these finally depart, what remains? The result is that, whoever reads very much and almost the entire day but at intervals amuses himself with thoughtless pastime, gradually loses the ability to think for himself; just as a man who always rides ultimately forgets how to walk. But such is the case with very many scholars; they have read themselves stupid. [...]
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march 2017 by nhaliday
Measurement error and the replication crisis | Science
In a low-noise setting, the theoretical results of Hausman and others correctly show that measurement error will attenuate coefficient estimates. But we can demonstrate with a simple exercise that the opposite occurs in the presence of high noise and selection on statistical significance.
study  org:nat  science  meta:science  stats  signal-noise  gelman  methodology  hypothesis-testing  replication  social-science  error  metabuch  unit  nibble  bounded-cognition  measurement  🔬  info-dynamics 
february 2017 by nhaliday

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