Cognitive and behavioral aspects of executive functions in children born very preterm

This study investigated whether children aged between 8 and 12 years born very preterm (VPT) and/or at very low birth weight (VLBW) performed lower than same-aged term-born controls in cognitive and behavioral aspects of three executive functions: inhibition, working memory, and shifting. Special attention was given to sex differences. Fifty-two VPT/VLBW children (26 girls, 50%) born in the cohort of 1998-2003 and 36 same-aged term-born children (18 girls, 50%) were recruited. As cognitive measures, children completed tasks of inhibition (Color-Word Interference Test, D-KEFS; Delis, Kaplan, & Kramer, 2001 ), working memory (digit span backwards, HAWIK-IV; Petermann & Petermann, 2008 ), and shifting (Trail Making Test, number-letter-switching, D-KEFS; Delis et al., 2001 ). As behavioral measures, mothers completed the Behavior Rating Inventory of Executive Function (BRIEF; Gioia, Isquith, Guy, & Kenworthy, 2000 ). Scales of interest were inhibit, working memory, and shift. Analyses of the cognitive aspects of executive functions revealed that VPT/VLBW children performed significantly lower than controls in the shifting task but not in the working memory and inhibition tasks. Analyses of behavioral aspects of executive functions revealed that VPT/VLBW children displayed more problems than the controls in working memory in everyday life but not in inhibition and shifting. No sex differences could be detected either in cognitive or behavioral aspects of executive functions. To conclude, cognitive and behavioral measures of executive functions were not congruent in VPT/VLBW children. In clinical practice, the combination of cognitive and behavioral instruments is required to disclose children's executive difficulties.

Do Cognitive and behavioral assessments of executive functions measure the same concept in children born very preterm?

Aims: This study investigated whether children aged between 8 - 12 years born very preterm (VPT) and/or at very low birth weight (VLBW) performed lower than same-aged term-born controls in cognitive and behavioral aspects of three executive functions: inhibition, working memory, and shifting. Special attention was given to sex differences. Methods: Fifty-two VPT/VLBW children (26 girls) born in the cohort of 1998–2003 at the Children’s University Hospital in Bern, Switzerland, and 36 same-aged term-born controls (18 girls) were recruited. As cognitive measures, children completed tasks of inhibition (Colour-Word Interference Test, D-KEFS), working memory (digit span backwards, WISC-IV) and shifting (Trail Making Test, number-letter switching, D-KEFS). As behavioral measures, mothers completed the Behavior Rating Inventory of Executive Function (BRIEF), assessing executive functions in everyday life.

Elucidating underlying processes of psychometric intelligence: A comparison of behavioral response latency and P3 latency in a modified Hick reaction time paradigm

The mental speed approach explains individual differences in intelligence by faster information processing in individuals with higher compared to lower intelligence - especially in elementary cognitive tasks (ECTs). One of the most examined ECTs is the Hick paradigm. The present study aimed to contrast reaction time (RT) and P3 latency in a Hick task as predictors of intelligence. Although both, RT and P3 latency, are commonly used as indicators of mental speed, it is also known that they measure different aspects of information processing. Participants were 113 female students. RT and P3 latency were measured while participants completed the Hick task with four levels of complexity. Intelligence was assessed with Cattell's Culture Fair Test. A RT factor and a P3 factor were extracted by employing a PCA across complexity levels. There was no significant correlation between the factors. Commonality analysis was used to determine the proportions of unique and shared variance in intelligence explained by the RT and P3 latency factors. RT and P3 latency explained 5.5% and 5% of unique variance in intelligence. However, the two speed factors did not explain a significant portion of shared variance. This result suggests that RT and P3 latency in the Hick paradigm are measuring different aspects of information processing that explain different parts of variance in intelligence.

Spatio-temporal credit assignment in population learning

Learning by reinforcement is important in shaping animal behavior, and in particular in behavioral decision making. Such decision making is likely to involve the integration of many synaptic events in space and time. However, using a single reinforcement signal to modulate synaptic plasticity, as suggested in classical reinforcement learning algorithms, a twofold problem arises. Different synapses will have contributed differently to the behavioral decision, and even for one and the same synapse, releases at different times may have had different effects. Here we present a plasticity rule which solves this spatio-temporal credit assignment problem in a population of spiking neurons. The learning rule is spike-time dependent and maximizes the expected reward by following its stochastic gradient. Synaptic plasticity is modulated not only by the reward, but also by a population feedback signal. While this additional signal solves the spatial component of the problem, the temporal one is solved by means of synaptic eligibility traces. In contrast to temporal difference (TD) based approaches to reinforcement learning, our rule is explicit with regard to the assumed biophysical mechanisms. Neurotransmitter concentrations determine plasticity and learning occurs fully online. Further, it works even if the task to be learned is non-Markovian, i.e. when reinforcement is not determined by the current state of the system but may also depend on past events. The performance of the model is assessed by studying three non-Markovian tasks. In the first task, the reward is delayed beyond the last action with non-related stimuli and actions appearing in between. The second task involves an action sequence which is itself extended in time and reward is only delivered at the last action, as it is the case in any type of board-game. The third task is the inspection game that has been studied in neuroeconomics, where an inspector tries to prevent a worker from shirking. Applying our algorithm to this game yields a learning behavior which is consistent with behavioral data from humans and monkeys, revealing themselves properties of a mixed Nash equilibrium. The examples show that our neuronal implementation of reward based learning copes with delayed and stochastic reward delivery, and also with the learning of mixed strategies in two-opponent games.

Spatio-temporal credit assignment in population learning

Learning by reinforcement is important in shaping animal behavior. But behavioral decision making is likely to involve the integration of many synaptic events in space and time. So in using a single reinforcement signal to modulate synaptic plasticity a twofold problem arises. Different synapses will have contributed differently to the behavioral decision and, even for one and the same synapse, releases at different times may have had different effects. Here we present a plasticity rule which solves this spatio-temporal credit assignment problem in a population of spiking neurons. The learning rule is spike time dependent and maximizes the expected reward by following its stochastic gradient. Synaptic plasticity is modulated not only by the reward but by a population feedback signal as well. While this additional signal solves the spatial component of the problem, the temporal one is solved by means of synaptic eligibility traces. In contrast to temporal difference based approaches to reinforcement learning, our rule is explicit with regard to the assumed biophysical mechanisms. Neurotransmitter concentrations determine plasticity and learning occurs fully online. Further, it works even if the task to be learned is non-Markovian, i.e. when reinforcement is not determined by the current state of the system but may also depend on past events. The performance of the model is assessed by studying three non-Markovian tasks. In the first task the reward is delayed beyond the last action with non-related stimuli and actions appearing in between. The second one involves an action sequence which is itself extended in time and reward is only delivered at the last action, as is the case in any type of board-game. The third is the inspection game that has been studied in neuroeconomics. It only has a mixed Nash equilibrium and exemplifies that the model also copes with stochastic reward delivery and the learning of mixed strategies.

Spatio-temporal credit assignment in population learning

We present a model for plasticity induction in reinforcement learning which is based on a cascade of synaptic memory traces. In the cascade of these so called eligibility traces presynaptic input is first corre lated with postsynaptic events, next with the behavioral decisions and finally with the external reinforcement. A population of leaky integrate and fire neurons endowed with this plasticity scheme is studied by simulation on different tasks. For operant co nditioning with delayed reinforcement, learning succeeds even when the delay is so large that the delivered reward reflects the appropriateness, not of the immediately preceeding response, but of a decision made earlier on in the stimulus - decision sequence . So the proposed model does not rely on the temporal contiguity between decision and pertinent reward and thus provides a viable means of addressing the temporal credit assignment problem. In the same task, learning speeds up with increasing population si ze, showing that the plasticity cascade simultaneously addresses the spatial problem of assigning credit to the different population neurons. Simulations on other task such as sequential decision making serve to highlight the robustness of the proposed sch eme and, further, contrast its performance to that of temporal difference based approaches to reinforcement learning.