Random Number Generation in Bilingual Balinese and German Students: Preliminary Findings from an Exploratory Cross-Cultural Study

Strenge, H; Lesmana, Cokorda Bagus Jaya; Suryani, Luh Ketut

doi:10.2466/pms.109.1.61-75

Cited by 3 publications

(2 citation statements)

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“…In normal subjects, random generation varies with personal characteristics or states, such as belief in the paranormal (Brugger, Landis, & Regard, 1990) or cultural background (Strenge, Lesmana, & Suryani, 2009;Vandewiele, D'Hondt, Didillon, Iwawaki, & Mwamwendat, 1986). It affords insight into the cognitive effects of aging (Heuer, Janczyk, & Kunde, 2010), hemispheric neglect (Loetscher & Brugger, 2008), schizophrenia (Chan, Hui, Chiu, Chan, & Lam, 2011), aphasia (Proios, Asaridou, & Brugger, 2008), and Down syndrome (Rinehart, Bradshaw, Moss, Brereton, & Tonge, 2006).…”

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confidence: 98%

Algorithmic complexity for short binary strings applied to psychology: a primer

et al. 2013

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As human randomness production has come to be more closely studied and used to assess executive functions (especially inhibition), many normative measures for assessing the degree to which a sequence is randomlike have been suggested. However, each of these measures focuses on one feature of randomness, leading researchers to have to use multiple measures. Although algorithmic complexity has been suggested as a means for overcoming this inconvenience, it has never been used, because standard Kolmogorov complexity is inapplicable to short strings (e.g., of length l ≤ 50), due to both computational and theoretical limitations. Here, we describe a novel technique (the coding theorem method) based on the calculation of a universal distribution, which yields an objective and universal measure of algorithmic complexity for short strings that approximates Kolmogorov-Chaitin complexity.

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confidence: 98%

Algorithmic complexity for short binary strings applied to psychology: a primer

et al. 2013

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“…The first principle reflects a symbolic number representation by using Ara-bic digits on the imagined objects. It has been applied with healthy normal persons (Baddeley, 1966;Horne, Evans, & Orne, 1982;Jahanshahi, et al, 2006;Peters, Giesbrecht, Jelicic, & Merckelbach, 2007;McMorris, Collard, Corbett, Dicks, & Swain, 2008;Strenge, Lesmana, & Suryani, 2009) and with patients with psychiatric, neurologic, and hearing disorders (Watkins & Brown, 2002;Jahanshahi, Rowe, & Fuller, 2003;Peters, et al, 2007;Strenge & Müller-Deile, 2007). The instruction with analogy of throwing a die refers to a nonsymbolic number representation by considering the arrays of dots (Verguts & Fias, 2004;Nieder, 2009).…”

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Strategic Use of Number Representation is Independent of Test Instruction in Random Number Generation

Strenge

Rogge

2010

Percept Mot Skills

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The effects of different instructions on verbal random number generation were examined in 40 healthy students who attempted to generate random sequences of the digits 1 to 6. Two groups of 20 received different instructions with alternative numerical representations. The Symbolic group (Arabic digits) was instructed to randomize while continuously using the analogy of selecting and replacing numbered balls from a hat, whereas the Nonsymbolic group (arrays of dots) was instructed to imagine repeatedly throwing a die. Participants asked for self-reports on their strategies reported spontaneously occurring visuospatial imagination of a mental number line (42%), or imagining throwing a die (23%). Individual number representation was not affected by the initial instruction. There were no differences in randomization performance by group. Comprehensive understanding of the nature of the randomization task requires considering individual differences in construction of mental models.

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