The field of perceptual learning has identified changes in perceptual templates as a powerful mechanism mediating the learning of statistical regularities in our environment. By measuring thresholdvs.-contrast curves using an orientation identification task under varying levels of external noise, the perceptual template model (PTM) allows one to disentangle various sources of signal-to-noise changes that can alter performance. We use the PTM approach to elucidate the mechanism that underlies the wide range of improvements noted after action video game play. We show that action video game players make use of improved perceptual templates compared with nonvideo game players, and we confirm a causal role for action video game play in inducing such improvements through a 50-h training study. Then, by adapting a recent neural model to this task, we demonstrate how such improved perceptual templates can arise from reweighting the connectivity between visual areas. Finally, we establish that action gamers do not enter the perceptual task with improved perceptual templates. Instead, although performance in action gamers is initially indistinguishable from that of nongamers, action gamers more rapidly learn the proper template as they experience the task. Taken together, our results establish for the first time to our knowledge the development of enhanced perceptual templates following action game play. Because such an improvement can facilitate the inference of the proper generative model for the task at hand, unlike perceptual learning that is quite specific, it thus elucidates a general learning mechanism that can account for the various behavioral benefits noted after action game play.action video games | perceptual templates | external noise method | learning | probabilistic inference P laying action video games substantially improves performance in a range of attentional, perceptual, and cognitive tasks. In the case of attention, playing action video games has been shown to result in a variety of enhancements, such as a faster visual search rate, a reduction in the size of the attentional blink, better change detection, and an increase in the number of items that can be simultaneously tracked (1-3). These changes in attentional control are also accompanied by enhanced performance in visual tasks such as crowding acuity (4), backward masking (5), and contrast sensitivity (6), as well as by improved performance in high-level cognitive tasks such as mental rotation (7) and multitasking (8, 9). Such benefits even seem to carry over to real-world domains, because pilots and laparoscopic surgeons have been shown to outperform their peers after fast-paced, action-packed video game training (10-12). Together, these results suggest that action game play, unlike perceptual learning, which is usually specific to the learned task (13), may act to increase signal-to-noise ratio and facilitate improved distractor exclusion during perceptual processing (14), which is notable because such changes hold the potential to affect, for ...

Extensive training on simple tasks like fine orientation discrimination results in large improvements in performance, a form of learning known as perceptual learning. Previous neural models have argued that perceptual learning is the result of sharpening and amplification of tuning curves in early visual areas. However, these models are at odds with the conclusions of psychophysical experiments manipulating external noise, which argue for improved decision making, presumably in later visual areas. Here, we explore the possibility that perceptual learning for fine orientation discrimination is due to improved probabilistic inference in early visual areas. We show that this mechanism captures both the changes in response properties observed in early visual areas and the changes in performance observed in psychophysical experiments. We also suggest that sharpening and amplification of tuning curves may play only a minor role in improving performance, in comparison to the role played by the reshaping of inter-neuronal correlations.

Thousands of functional magnetic resonance imaging (fMRI) studies have provided important insight into the human brain. However, only a handful of these studies tested infants while they were awake, because of the significant and unique methodological challenges involved. We report our efforts to address these challenges, with the goal of creating methods for awake infant fMRI that can reveal the inner workings of the developing, preverbal mind. We use these methods to collect and analyze two fMRI datasets obtained from infants during cognitive tasks, released publicly with this paper. In these datasets, we explore and evaluate data quantity and quality, task-evoked activity, and preprocessing decisions. We disseminate these methods by sharing two software packages that integrate infant-friendly cognitive tasks and eye-gaze monitoring with fMRI acquisition and analysis. These resources make fMRI a feasible and accessible technique for cognitive neuroscience in awake and behaving human infants.

A simple expression for a lower bound of Fisher information is derived for a network of recurrently connected spiking neurons that have been driven to a noise-perturbed steady state. We call this lower bound linear Fisher information, as it corresponds to the Fisher information that can be recovered by a locally optimal linear estimator. Unlike recent similar calculations, the approach used here includes the effects of nonlinear gain functions and correlated input noise and yields a surprisingly simple and intuitive expression that offers substantial insight into the sources of information degradation across successive layers of a neural network. Here, this expression is used to (1) compute the optimal (i.e., informationmaximizing) firing rate of a neuron, (2) demonstrate why sharpening tuning curves by either thresholding or the action of recurrent connectivity is generally a bad idea, (3) show how a single cortical expansion is sufficient to instantiate a redundant population code that can propagate across multiple cortical layers with minimal information loss, and (4) show that optimal recurrent connectivity strongly depends on the covariance structure of the inputs to the network.