A cognitive fingerprint in human random number generation

Schulz, Marc-André; Baier, Sebastian; Timmermann, Benjamin; Bzdok, Danilo; Witt, Karsten

doi:10.1038/s41598-021-98315-y

Cited by 8 publications

(14 citation statements)

References 18 publications

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“…Therefore, it is an open question whether individual differences in the capacity of working memory might explain the variability in the ability to produce random series reported in previous studies (e.g. Gauvrit, Zenil, Soler-Toscano, Delahaye, & Brugger, 2017;Schulz et al, 2021). Here, we expect that the longer series people are able to maintain active the more complex series they are able to generate.…”

Section: Random Generation Modelmentioning

confidence: 80%

“…That is because they examine longer sequences for regularities and consequently try to avoid them. Even if as Schulz et al (2021) suggests in human-generated series some patterns repeat cyclically people with better working memory capacity can inhibit them longer. Therefore, the gaps between potentially reoccurring patterns are longer which in turn makes the sequence more random.…”

Section: Resultsmentioning

confidence: 98%

“…Therefore, it is not transferable in-between different contexts. This explains both the idiosyncrasy of patterns found in human-generated data (Schulz et al, 2021) and the context dependency of the ability to produce random-like series Biesaga et al (2021). People simply have their individual specific to certain domain definition of what randomness looks like and generate series accordingly.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, they select the next element of the series based on the knowledge about an indefinitely long random series applied locally to the subsequence of past elements. Moreover, Schulz, Baier, Timmermann, Bzdok, and Witt (2021) results show that these strategies people use for the selection of the next element are highly unique and driven by individual preferences and individual inhibitions.…”

Section: Random Generation Modelmentioning

confidence: 97%

See 3 more Smart Citations

The role of the working memory storage component in the random-likes series generation

Biesaga¹,

Nowak²

2022

Preprint

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People do not possess an internal random series generator. Instead, they try to reproduce outputs of a known random process. However, this process is restricted by their working memory. Here, we investigate the model of random-like series generation proposed by Biesaga, Talaga, and Nowak (2021) that accounts for the involvement of storage and processing components of working memory. In two studies, we used a modern, robust measure of randomness to assess human-generated series. In Study 1, in an experimental design with the visibility of the last generated elements as a between-subjects variable, we tested whether decreasing cognitive load on working memory would mitigate the decay in the level of randomness of the generated series. Moreover, we investigated the relationship between randomness judgment and algorithmic complexity of human-generated series. In Study 2, in a correlational design, we examined the relationship between working memory capacity and the ability to produce random-like series. The results of Study 1 show that when people did not have to solemnly rely on their working memory storage component to maintain active past choices they were able to prolongate their high-quality performance. Moreover, people who were able to better distinguish more complex patterns at the same time generated more random series. The results of Study 2 showed that individuals with longer working memory capacity length also were able to produce more complex series. These results indicate that although people do not have an inner randomness generator their limitations cannot be solemnly ascribed to the limits of their cognitive resources

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Section: Random Generation Modelmentioning

confidence: 80%

Section: Resultsmentioning

confidence: 98%

Section: Discussionmentioning

confidence: 99%

Section: Random Generation Modelmentioning

confidence: 97%

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The role of the working memory storage component in the random-likes series generation

Biesaga¹,

Nowak²

2022

Preprint

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show abstract

“…Although the proposed method uses fingerprint recognition technology, there is no need to determine whether or not the fingerprints match if the password does not match. Therefore, it is first checked whether the registered passwords match the numeric keypad provided in a random pattern [23]. If the passwords match, the next check would see if the fingerprints registered in each password match.…”

Section: A Proposed Door Lock Design 1) Structure Of Door Lockmentioning

confidence: 99%

Design on Novel Door Lock Using Minimizing Physical Exposure and Fingerprint Recognition Technology

Jeong

2022

JOIV : Int. J. Inform. Visualization

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Digital door locks are widely used not only in general homes such as houses and apartments, but also in spaces where external intrusion must be prevented based on high security and convenience. Recently, smart door locks with additional technologies such as fingerprint recognition and Bluetooth communication have also been developed, and the door lock market is on the rise. Digital door locks are more convenient to use compared to the existing key-type door locks. However, there are often cases of exploiting security vulnerabilities such as exploiting and invading the user's trace remaining on the door lock. This paper proposes a door lock with a structure that can complement the shape of the current door lock exposed to the outside and minimize the user's fingerprint trace. In addition, a method of reinforcing security is applied using fingerprint recognition through image processing and a random pattern number arrangement. An experiment was conducted to confirm whether the door lock of this type was actually usable, and the recognition of partially damaged fingerprints was also confirmed. It was shown that the door lock structure proposed in this paper can maximize security by combining fingerprint recognition technology and random pattern numbering while minimizing external exposure.

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Counting on random number generation: Uncovering mild executive dysfunction in congenital heart disease

Schlosser

Naef

Ehrler

et al. 2023

Brain and Cognition

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A cognitive fingerprint in human random number generation

Cited by 8 publications

References 18 publications

The role of the working memory storage component in the random-likes series generation

The role of the working memory storage component in the random-likes series generation

Design on Novel Door Lock Using Minimizing Physical Exposure and Fingerprint Recognition Technology

Counting on random number generation: Uncovering mild executive dysfunction in congenital heart disease

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