The Rey Auditory Verbal Learning Test (RAVLT) is an efficient neuropsychological instrument for testing episodic declarative memory. Performance on this test can be influenced by demographic and cultural variables. The objective of the present study was to analyze the influence of demographics variables, such as age, gender, and education, on RAVLT performance in young and elderly adults. A Portuguese version of the RAVLT was administered to 302 healthy participants. Subjects were from both genders, ranging in age from 17 to 85 years (mean 50.6 ± 15.9 years) and in education from 1 to 20 years (mean 11.3 ± 3.7 years). Participants were grouped by age: 17-34 years old, 34-49 years old, 50-64 years old, and 65-85 years old. Pearson's Correlation analysis showed a significant association between RAVLT performance and both age and education (p < .001), but not between RAVLT performance and gender. Two-way analysis of variance revealed significant effects of age on all RAVLT measurements and significant effects of years of education on all measurements, with the exception of recognition. The present data indicate that participant age and education significantly influence performance on the RAVLT.

The Trail Making Test (TMT) has been a useful tool for neuropsychological assessment. The present study analyzed the influence of age and education level on TMT performance in healthy adult Portuguese speakers. The criteria for exclusion were a history of neurological disease, psychiatric illness, substance abuse, learning disabilities, or any other difficulty that may interfere with testing. The sample (n = 318) was divided into four age groups: (i) very young (n = 92; 18-34 years old), (ii) young (n = 66; 35-49 years old), (iii) middle-age (n = 117; 50-64 years old), and (iv) elderly (n = 43; 65-81 years old). The sample was also divided into three education levels: (i) low (2-8 years), (ii) middle (9-11 years), and (iii) high (> 12 years). Correlations among the demographic variables and scores on Trails A and B showed that age and education level were the two variables that most affected TMT scores (p < 0.001, analysis of covariance). Post hoc analyses of age did not reveal significant differences between the middle-age and elderly groups in TMT-A performance or between middle and high education level in TMT-A or TMT-B performance. These results are consistent with previous studies and demonstrate that age and education level affect the performance of Brazilian subjects on the TMT.

OBJETIVO: Avaliar o desempenho de sujeitos adultos na Geração Aleatória de Números (RNG), em função da velocidade da resposta (VR). MÉTODO: Participaram do experimento 69 universitários, de ambos os sexos. A média de idade dos participantes foi 25,05±6,71 anos. Os participantes foram divididos aleatoriamente em 3 grupos que diferiram quanto à VR para gerar cada número; 1, 2 e 4 segundos. As respostas foram avaliadas através do índice RNG de Evans. RESULTADOS: A análise dos resultados evidenciou diferença estatisticamente significante entre os 3 grupos [F (3, 68) = 7,120; p < 0,05], com diferenças entre as VR de 1 e 2 segundos (p = 0,004) e de 1 e 4 segundos (p = 0,006). Não foram observadas diferenças entre as VR de 2 e 4 segundos (p = 0,985). CONCLUSÃO: Os resultados mostraram que a velocidade da resposta na produção dos números aleatórios influencia o desempenho da tarefa de Geração Aleatória de Números.

OBJECTIVE: To evaluate the performance of adult young subjects in a Random Number Generation (RNG) task by controlling the response speed (RS). METHOD: Sixty-nine university students of both sexes took part in the experiment (25.05 ± 6.71 year-old). Participants were alloted into 3 groups which differed in RS rates to generate numbers: 1, 2 and 4 seconds to generate each number. A digital metronomer was used to control RS. Participants were asked to generate 100 numbers. The responses were mensured through Evans's RNG Index. RESULTS: There were statistically significant differences among the groups [F (3, 68) = 7.120; p < .05]. Differences were localized between 1 and 2 seconds (p = 0.004) and between 1 and 4 seconds (p = 0.006). No differences were observed between 2 and 4 seconds (p = 0.985). CONCLUSION: The present results suggest that the response speed in production of random numbers influences the performance of the Random Numbers Generation task.