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ItemTask-independent metrics of computational hardness predict human cognitive performance(NATURE PORTFOLIO, 2022-07-28)The survival of human organisms depends on our ability to solve complex tasks in the face of limited cognitive resources. However, little is known about the factors that drive the complexity of those tasks. Here, building on insights from computational complexity theory, we quantify the computational hardness of cognitive tasks using a set of task-independent metrics related to the computational resource requirements of individual instances of a task. We then examine the relation between those metrics and human behavior and find that they predict both time spent on a task as well as accuracy in three canonical cognitive tasks. Our findings demonstrate that performance in cognitive tasks can be predicted based on generic metrics of their inherent computational hardness.
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ItemIndividual Differences in Intertemporal Choice(Frontiers Media, 2021-04-08)Intertemporal choice involves deciding between smaller, sooner and larger, later rewards. People tend to prefer smaller rewards that are available earlier to larger rewards available later, a phenomenon referred to as temporal or delay discounting. Despite its ubiquity in human and non-human animals, temporal discounting is subject to considerable individual differences. Here, we provide a critical narrative review of this literature and make suggestions for future work. We conclude that temporal discounting is associated with key socio-economic and health-related variables. Regarding personality, large-scale studies have found steeper temporal discounting to be associated with higher levels of self-reported impulsivity and extraversion; however, effect sizes are small. Temporal discounting correlates negatively with future-oriented cognitive styles and inhibitory control, again with small effect sizes. There are consistent associations between steeper temporal discounting and lower intelligence, with effect sizes exceeding those of personality or cognitive variables, although socio-demographic moderator variables may play a role. Neuroimaging evidence of brain structural and functional correlates is not yet consistent, neither with regards to areas nor directions of effects. Finally, following early candidate gene studies, recent GWAS approaches have revealed the molecular genetic architecture of temporal discounting to be more complex than initially thought. Overall, the study of individual differences in temporal discounting is a maturing field that has produced some replicable findings. Effect sizes are small-to-medium, necessitating future hypothesis-driven work that prioritizes large samples with adequate power calculations. More research is also needed regarding the neural origins of individual differences in temporal discounting as well as the mediating neural mechanisms of associations of temporal discounting with personality and cognitive variables.
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ItemSeparating Probability and Reversal Learning in a Novel Probabilistic Reversal Learning Task for Mice(Frontiers Media SA, 2020-01-09)The exploration/exploitation tradeoff – pursuing a known reward vs. sampling from lesser known options in the hope of finding a better payoff – is a fundamental aspect of learning and decision making. In humans, this has been studied using multi-armed bandit tasks. The same processes have also been studied using simplified probabilistic reversal learning (PRL) tasks with binary choices. Our investigations suggest that protocols previously used to explore PRL in mice may prove beyond their cognitive capacities, with animals performing at a no-better-than-chance level. We sought a novel probabilistic learning task to improve behavioral responding in mice, whilst allowing the investigation of the exploration/exploitation tradeoff in decision making. To achieve this, we developed a two-lever operant chamber task with levers corresponding to different probabilities (high/low) of receiving a saccharin reward, reversing the reward contingencies associated with levers once animals reached a threshold of 80% responding at the high rewarding lever. We found that, unlike in existing PRL tasks, mice are able to learn and behave near optimally with 80% high/20% low reward probabilities. Altering the reward contingencies towards equality showed that some mice displayed preference for the high rewarding lever with probabilities as close as 60% high/40% low. Additionally, we show that animal choice behavior can be effectively modelled using reinforcement learning (RL) models incorporating learning rates for positive and negative prediction error, a perseveration parameter, and a noise parameter. This new decision task, coupled with RL analyses, advances access to investigate the neuroscience of the exploration/exploitation tradeoff in decision making.
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ItemEffective brain connectivity at rest is associated with choice-induced preference formation.(Wiley, 2020-04-03)Preferences can change as a consequence of making a hard decision whereby the value of chosen options increases and the value of rejected options decreases. Such choice-induced preference changes have been associated with brain areas detecting choice conflict (anterior cingulate cortex, ACC), updating stimulus value (dorsolateral prefrontal cortex, dlPFC) and supporting memory of stimulus value (hippocampus and ventromedial prefrontal cortex, vmPFC). Here we investigated whether resting-state neuronal activity within these regions is associated with the magnitude of individuals' preference updates. We fitted a dynamic causal model (DCM) to resting-state neuronal activity in the spectral domain (spDCM) and estimated the causal connectivity among core regions involved in preference formation following hard choices. The extent of individuals' choice-induced preference changes were found to be associated with a diminished resting-state excitation between the left dlPFC and the vmPFC, whereas preference consistency was related to a higher resting-state excitation from the ACC to the left hippocampus and vmPFC. Our results point to a model of preference formation during which the dynamic network configurations between left dlPFC, ACC, vmPFC and left hippocampus at rest are linked to preference change or stability.