Avaliação do tempo de reação em crianças portadoras do transtorno do déficit de atenção/hiperatividade (TDAH)

Attention deficit, impulsivity and hyperactivity are the cardinal features of attention deficit hyperactivity disorder (ADHD) but executive function (EF) disorders, as problems with inhibitory control, working memory and reaction time, besides others EFs, may underlie many of the disturbs associated with the disorder. OBJECTIVE: To examine the reaction time in a computerized test in children with ADHD and normal controls. METHOD: Twenty-three boys (aged 9 to 12) with ADHD diagnosis according to Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, 2000 (DSM-IV) criteria clinical, without comorbidities, Intelligence Quotient (IQ) >89, never treated with stimulant and fifteen normal controls, age matched were investigated during performance on a voluntary attention psychophysical test. RESULTS: Children with ADHD showed reaction time higher than normal controls. CONCLUSION: A slower reaction time occurred in our patients with ADHD. This findings may be related to problems with the attentional system, that could not maintain an adequate capacity of perceptual input processes and/or in motor output processes, to respond consistently during continuous or repetitive activity.Déficit de atenção, impulsividade e hiperatividade são os pontos cardinais do transtorno do déficit de atenção/hiperatividade (TDAH), mas as desordens da função executiva (FE) tais como os problemas no controle inibitório, memória operacional e tempo de reação, dentre outras funções executivas (FEs) podem estar subjacentes a muitos distúrbios associados a esta desordem. OBJETIVO: Avaliar o tempo de reação em meninos portadores do TDAH. MÉTODO: Participaram 23 pacientes do sexo masculino, de idade entre 9 a 12 anos de idade, com diagnóstico de TDAH sem co-morbidades, estabelecido segundo os critérios do Diagnostic and Statistical Manual of Mental Disorders Fourth Edition (DSM-IV), com Quoeficiente Intelectual (QI) >89, que não tivessem sido medicados para o TDAH. Grupo controle, seguindo os mesmos critérios em relação ao sexo, idade, QI. O teste utilizado foi o teste psicofísico da atenção voluntária (TPAV). RESULTADOS: Os pacientes do TDAH apresentaram maior tempo de reação na execução do teste em relação aos controles. CONCLUSÃO: O tempo de reação apresentou-se mais lento em nossos pacientes portadores de TDAH. Estes achados podem estar relacionados aos problemas do sistema atencional; este grupo não pôde manter uma adequada capacidade de percepção de dados processados e/ou, em responder regularmente durante atividades contínuas ou repetitivas

A Simple Artificial Life Model Explains Irrational Behavior in Human Decision-Making

Although praised for their rationality, humans often make poor decisions, even in simple situations. In the repeated binary choice experiment, an individual has to choose repeatedly between the same two alternatives, where a reward is assigned to one of them with fixed probability. The optimal strategy is to perseverate with choosing the alternative with the best expected return. Whereas many species perseverate, humans tend to match the frequencies of their choices to the frequencies of the alternatives, a sub-optimal strategy known as probability matching. Our goal was to find the primary cognitive constraints under which a set of simple evolutionary rules can lead to such contrasting behaviors. We simulated the evolution of artificial populations, wherein the fitness of each animat (artificial animal) depended on its ability to predict the next element of a sequence made up of a repeating binary string of varying size. When the string was short relative to the animats’ neural capacity, they could learn it and correctly predict the next element of the sequence. When it was long, they could not learn it, turning to the next best option: to perseverate. Animats from the last generation then performed the task of predicting the next element of a non-periodical binary sequence. We found that, whereas animats with smaller neural capacity kept perseverating with the best alternative as before, animats with larger neural capacity, which had previously been able to learn the pattern of repeating strings, adopted probability matching, being outperformed by the perseverating animats. Our results demonstrate how the ability to make predictions in an environment endowed with regular patterns may lead to probability matching under less structured conditions. They point to probability matching as a likely by-product of adaptive cognitive strategies that were crucial in human evolution, but may lead to sub-optimal performances in other environments

Accuracy for Different Strategies and Frequencies of Majority Digit in the Repeated Binary Choice Experiment.

<p>Predicted accuracy in the repeated binary choice experiment depending on the frequency of the majority digit and the employed strategy: <i>PM</i> (probability matching without pattern decoding), <i>Max</i> (perseveration) and <i>Pattern</i> (pattern decoding). For all digit frequencies except the frequency 1.0, the difference in accuracy between pattern decoding and perseveration (arrow A) is larger than the difference in accuracy between perseveration and probability matching without pattern decoding (arrow B).</p

Example outcomes resulting from repetitive input patterns of length 3 and 729.

<p>Example of outcomes resulting from repetitive input patterns of length 3 and 729 under two task conditions: <i>Pattern matching</i> (when the animat evolved in an environment where it had to predict the next element of a sequence composed of a repetitive string of length 3 or 729), and <i>Random sequence</i> (when the animat, after evolving under a repetitive string of length 3 or 729, had to predict the next element of a completely random sequence). For sequences composed by short strings (3 digit long), the animat predicts all the elements correctly (pattern matching), but does probability matching when faced with the prediction of the next element in a shuffled random sequence. When the input sequence is composed of a very long repetitive string (729-digit long), the animat is not able to learn it, adopting a perseveration strategy, making many (expected) mistakes; but when the same animat has to predict the next element of a randomly shuffled sequence, it perseverates as well, achieving a better performance in comparison with the animats that had been able to learn a short-patterned sequence (3-digit long).</p

Neural Network Architectures Used in the Simulations.

<p>Two different neural network architectures were used in the simulations. Networks had one input node, one or two layers of four hidden nodes, and one output node.</p

Exploration and recency as the main proximate causes of probability matching: a reinforcement learning analysis

Abstract Research has not yet reached a consensus on why humans match probabilities instead of maximise in a probability learning task. The most influential explanation is that they search for patterns in the random sequence of outcomes. Other explanations, such as expectation matching, are plausible, but do not consider how reinforcement learning shapes people’s choices. We aimed to quantify how human performance in a probability learning task is affected by pattern search and reinforcement learning. We collected behavioural data from 84 young adult participants who performed a probability learning task wherein the majority outcome was rewarded with 0.7 probability, and analysed the data using a reinforcement learning model that searches for patterns. Model simulations indicated that pattern search, exploration, recency (discounting early experiences), and forgetting may impair performance. Our analysis estimated that 85% (95% HDI [76, 94]) of participants searched for patterns and believed that each trial outcome depended on one or two previous ones. The estimated impact of pattern search on performance was, however, only 6%, while those of exploration and recency were 19% and 13% respectively. This suggests that probability matching is caused by uncertainty about how outcomes are generated, which leads to pattern search, exploration, and recency