scholarly journals A cognitive fingerprint in human random number generation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Marc-André Schulz ◽  
Sebastian Baier ◽  
Benjamin Timmermann ◽  
Danilo Bzdok ◽  
Karsten Witt
Keyword(s):  
Cognitive Process ◽  
Random Number ◽  
Edit Distance ◽  
Individual Preference ◽  
Random Number Generation ◽  
Random Generation ◽  
Number Generation ◽  
Number Sequences ◽  
Healthy Participants

AbstractIs the cognitive process of random number generation implemented via person-specific strategies corresponding to highly individual random generation behaviour? We examined random number sequences of 115 healthy participants and developed a method to quantify the similarity between two number sequences on the basis of Damerau and Levenshtein’s edit distance. “Same-author” and “different author” sequence pairs could be distinguished (96.5% AUC) based on 300 pseudo-random digits alone. We show that this phenomenon is driven by individual preference and inhibition of patterns and stays constant over a period of 1 week, forming a cognitive fingerprint.

Auditory Distraction Compromises Random Generation

2013 ◽  
Vol 60 (4) ◽  
pp. 279-292 ◽  
Author(s):  
John E. Marsh ◽  
Patrik Sörqvist ◽  
Niklas Halin ◽  
Anatole Nöstl ◽  
Dylan M. Jones
Keyword(s):  
Random Number ◽  
Random Number Generation ◽  
Random Order ◽  
Auditory Distraction ◽  
Random Generation ◽  
Alphabetical Order ◽  
Number Generation ◽  
Random Letter ◽  
Canonical Order ◽  
Mere Presence

Auditory distraction of random generation – a quintessentially executive control task – was explored in three experiments. Random number generation was impaired by the mere presence of irrelevant auditory sequences that comprise digits, but not letters, and then only if the digits were heard in a canonical order (1, 2, 3 … or 3, 2, 1 …), not in random order (Experiments 1 and 2). Random letter generation was impaired by irrelevant letters heard in alphabetical order (a, b, c …) and reversed alphabetical order (i, h, g …), but not by numbers in canonical order or letters in random order (Experiment 3). Attempting to ignore canonical sequences – with items that are members of the same category as the to-be-generated items – reduced the randomness of the generated sequence, by decreasing the tendency to change the direction of the produced sequence for random number generation, and by increasing resampling of responses for random letter generation. Like other selective attention tasks, the cost of distraction to random generation appears to stem from preventing habitual responses assuming the control of action.

scholarly journals INCREMENTAL EVOLUTION OF CELLULAR AUTOMATA FOR RANDOM NUMBER GENERATION

2003 ◽  
Vol 14 (07) ◽  
pp. 881-896 ◽  
Author(s):  
SHENG-UEI GUAN ◽  
SHU ZHANG
Keyword(s):  
Cellular Automata ◽  
Random Number ◽  
Structural Complexity ◽  
Random Number Generation ◽  
Number Generation ◽  
Multi Objective Genetic Algorithm ◽  
Incremental Evolution ◽  
Number Sequences ◽  
Output Efficiency ◽  
Pseudorandom Number Generation

Cellular automata (CA) have been used in pseudorandom number generation for over a decade. Recent studies show that controllable CA (CCA) can generate better random sequences than conventional one-dimensional (1D) CA and compete with two-dimensional (2D) CA. Yet the structural complexity of CCA is higher than that of 1D programmable cellular automata (PCA). It would be good if CCA can attain a good randomness quality with the least structural complexity. In this paper, we evolve PCA/CCA to their lowest complexity level using genetic algorithms (GAs). Meanwhile, the randomness quality and output efficiency of PCA/CCA are also evolved. The evolution process involves two algorithms — a multi-objective genetic algorithm (MOGA) and an algorithm for incremental evolution. A set of PCA/CCA are evolved and compared in randomness, complexity, and efficiency. The results show that without any spacing, the CCA could generate good random number sequences that could pass DIEHARD. To obtain the same randomness quality, the structural complexity of the CCA is not higher than that of 1D CA. Furthermore, the methodology developed could be used to evolve other CA or serve as a yardstick to compare different types of CA.

scholarly journals A Cognitive Fingerprint in Human Random Number Generation

2020 ◽  
Author(s):  
Marc-Andre Schulz ◽  
Sebastian Baier ◽  
Benjamin Böhme ◽  
Danilo Bzdok ◽  
Karsten Witt
Keyword(s):  
Random Number ◽  
Individual Preference ◽  
Random Number Generation ◽  
General Question ◽  
Pseudorandom Sequences ◽  
Number Generation ◽  
Brain Structure And Function ◽  
And Function ◽  
Recurrent Patterns ◽  
Human Specific

Most work in the neurosciences collapses data from multiple subjects to obtain robust statistical results. This research agenda ignores that even in healthy subjects brain structure and function are known to be highly variable. Recently, Finn and colleagues showed that the brain's functional organisation is unique to each individual and can yield human-specific connectome fingerprints. This raises the question whether unique functional brain architecture may reflect a unique implementation of cognitive processes and problem solving - i.e. "Can we identify single individuals based on how they think?". The present study addresses the general question of interindividual differences in the specific context of human random number generation. We analyzed the deployment of recurrent patterns in the pseudorandom sequences to develop an identification scheme based on subject-specific volatility patterns. We demonstrate that individuals can be reliably identified based on how they how they generate randomness patterns alone. We moreover show that this phenomenon is driven by individual preference and inhibition of patterns, together forming a cognitive fingerprint.

scholarly journals RandseqR: An R Package for Describing Performance on the Random Number Generation Task

2021 ◽  
Vol 12 ◽  
Author(s):  
Wouter Oomens ◽  
Joseph H. R. Maes ◽  
Fred Hasselman ◽  
Jos I. M. Egger
Keyword(s):  
Random Number ◽  
Random Number Generation ◽  
R Package ◽  
Assessment Procedure ◽  
Generation Task ◽  
Number Generation ◽  
Executive Behavior ◽  
Number Sequences ◽  
Quantification Analysis ◽  
Current Article

The Random Number Generation (RNG) task has a long history in neuropsychology as an assessment procedure for executive functioning. In recent years, understanding of human (executive) behavior has gradually changed from reflecting a static to a dynamic process and this shift in thinking about behavior gives a new angle to interpret test results. However, this shift also asks for different methods to process random number sequences. The RNG task is suited for applying non-linear methods needed to uncover the underlying dynamics of random number generation. In the current article we present RandseqR: an R-package that combines the calculation of classic randomization measures and Recurrence Quantification Analysis. RandseqR is an easy to use, flexible and fast way to process random number sequences and readies the RNG task for current scientific and clinical use.

Random Number Generation Based on the Rossler Attractor

2014 ◽  
Vol 1 ◽  
pp. 272-275 ◽  
Author(s):  
Vincent Canals ◽  
Antoni Morro ◽  
Josep L. Rosselló
Keyword(s):  
Random Number ◽  
Random Number Generation ◽  
Number Generation

On-chip tunable SOI interferometer for quantum random number generation based on phase diffusion in lasers

2021 ◽  
Vol 485 ◽  
pp. 126736
Author(s):  
Muhammad Imran ◽  
Vito Sorianello ◽  
Francesco Fresi ◽  
Bushra Jalil ◽  
Marco Romagnoli ◽  
...  
Keyword(s):  
Random Number ◽  
Random Number Generation ◽  
Phase Diffusion ◽  
Number Generation ◽  
On Chip

scholarly journals True Random Number Generator Based on Fibonacci-Galois Ring Oscillators for FPGA

2021 ◽  
Vol 11 (8) ◽  
pp. 3330
Author(s):  
Pietro Nannipieri ◽  
Stefano Di Matteo ◽  
Luca Baldanzi ◽  
Luca Crocetti ◽  
Jacopo Belli ◽  
...  
Keyword(s):  
Field Programmable Gate Array ◽  
Random Number ◽  
Random Number Generator ◽  
Random Number Generation ◽  
Galois Ring ◽  
Number Generator ◽  
True Random Number Generator ◽  
Number Generation ◽  
Field Programmable ◽  
Gate Array

Random numbers are widely employed in cryptography and security applications. If the generation process is weak, the whole chain of security can be compromised: these weaknesses could be exploited by an attacker to retrieve the information, breaking even the most robust implementation of a cipher. Due to their intrinsic close relationship with analogue parameters of the circuit, True Random Number Generators are usually tailored on specific silicon technology and are not easily scalable on programmable hardware, without affecting their entropy. On the other hand, programmable hardware and programmable System on Chip are gaining large adoption rate, also in security critical application, where high quality random number generation is mandatory. The work presented herein describes the design and the validation of a digital True Random Number Generator for cryptographically secure applications on Field Programmable Gate Array. After a preliminary study of literature and standards specifying requirements for random number generation, the design flow is illustrated, from specifications definition to the synthesis phase. Several solutions have been studied to assess their performances on a Field Programmable Gate Array device, with the aim to select the highest performance architecture. The proposed designs have been tested and validated, employing official test suites released by NIST standardization body, assessing the independence from the place and route and the randomness degree of the generated output. An architecture derived from the Fibonacci-Galois Ring Oscillator has been selected and synthesized on Intel Stratix IV, supporting throughput up to 400 Mbps. The achieved entropy in the best configuration is greater than 0.995.

scholarly journals On speeding up stochastic simulations by parallelization of random number generation

2015 ◽  
Vol 137 ◽  
pp. 828-836 ◽  
Author(s):  
Che-Chi Shu ◽  
Vu Tran ◽  
Jeremy Binagia ◽  
Doraiswami Ramkrishna
Keyword(s):  
Random Number ◽  
Random Number Generation ◽  
Stochastic Simulations ◽  
Number Generation
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