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One expression of executive control involves proactive preparation for future events, and this contrasts with stimulus driven reactive control exerted in response to events. Here we describe findings from a response inhibition task, delivered using a smartphone-based platform, that allowed us to index proactive and reactive inhibitory self-control in a large community sample (n = 12,496). Change in stop-signal reaction time (SSRT) when participants are provided with advance information about an upcoming trial, compared to when they are not, provides a measure of proactive control while SSRT in the absence of advance information provides a measure of reactive control. Both forms of control rely on overlapping frontostriatal pathways known to deteriorate in healthy aging, an age-related decline that occurs at an accelerated rate in men compared to women. Here we ask whether these patterns of age-related decline are reflected in similar changes in proactive and reactive inhibitory control across the lifespan. As predicted, we observed a decline in reactive control with natural aging, with a greater rate of decline in men compared to women (~10 ms versus ~8 ms per decade of adult life). Surprisingly, the benefit of preparation, i.e. proactive control, did not change over the lifespan and women showed superior proactive control at all ages compared to men. Our results suggest that reactive and proactive inhibitory control partially rely on distinct neural substrates that are differentially sensitive to age-related change. [ABSTRACT FROM AUTHOR]

By 2015, there will be an estimated two billion smartphone users worldwide. This technology presents exciting opportunities for cognitive science as a medium for rapid, large-scale experimentation and data collection. At present, cost and logistics limit most study populations to small samples, restricting the experimental questions that can be addressed. In this study we investigated whether the mass collection of experimental data using smartphone technology is valid, given the variability of data collection outside of a laboratory setting. We presented four classic experimental paradigms as short games, available as a free app and over the first month 20,800 users submitted data. We found that the large sample size vastly outweighed the noise inherent in collecting data outside a controlled laboratory setting, and show that for all four games canonical results were reproduced. For the first time, we provide experimental validation for the use of smartphones for data collection in cognitive science, which can lead to the collection of richer data sets and a significant cost reduction as well as provide an opportunity for efficient phenotypic screening of large populations. [ABSTRACT FROM AUTHOR]

This paper shows that the various computations underlying spatial cognition can be implemented using statistical inference in a single probabilistic model. Inference is implemented using a common set of ‘lower-level’ computations involving forward and backward inference over time. For example, to estimate where you are in a known environment, forward inference is used to optimally combine location estimates from path integration with those from sensory input. To decide which way to turn to reach a goal, forward inference is used to compute the likelihood of reaching that goal under each option. To work out which environment you are in, forward inference is used to compute the likelihood of sensory observations under the different hypotheses. For reaching sensory goals that require a chaining together of decisions, forward inference can be used to compute a state trajectory that will lead to that goal, and backward inference to refine the route and estimate control signals that produce the required trajectory. We propose that these computations are reflected in recent findings of pattern replay in the mammalian brain. Specifically, that theta sequences reflect decision making, theta flickering reflects model selection, and remote replay reflects route and motor planning. We also propose a mapping of the above computational processes onto lateral and medial entorhinal cortex and hippocampus. [ABSTRACT FROM AUTHOR]