Rapid and reversible manipulations of neural activity in behaving animals are transforming our understanding of brain function. An important assumption underlying much of this work is that evoked behavioural changes reflect the function of the manipulated circuits. We show that this assumption is problematic because it disregards indirect effects on the independent functions of downstream circuits. Transient inactivations of motor cortex in rats and nucleus interface (Nif) in songbirds severely degraded task-specific movement patterns and courtship songs, respectively, which are learned skills that recover spontaneously after permanent lesions of the same areas. We resolve this discrepancy in songbirds, showing that Nif silencing acutely affects the function of HVC, a downstream song control nucleus. Paralleling song recovery, the off-target effects resolved within days of Nif lesions, a recovery consistent with homeostatic regulation of neural activity in HVC. These results have implications for interpreting transient circuit manipulations and for understanding recovery after brain lesions.

High-level visual categories (e.g., faces, bodies, scenes, and objects) have separable neural representations across the visual cortex. Here, we show that this division of neural resources affects the ability to simultaneously process multiple items. In a behavioral task, we found that performance was superior when items were drawn from different categories (e.g., two faces/two scenes) compared to when items were drawn from one category (e.g., four faces). The magnitude of this mixed-category benefit depended on which stimulus categories were paired together (e.g., faces and scenes showed a greater behavioral benefit than objects and scenes). Using functional neuroimaging (i.e., functional MRI), we showed that the size of the mixed-category benefit was predicted by the amount of separation between neural response patterns, particularly within occipitotemporal cortex. These results suggest that the ability to process multiple items at once is limited by the extent to which those items are represented by separate neural populations.working memory | capacity limitations | representational similarity | competition | visual cognition A n influential idea in neuroscience is that there is an intrinsic relationship between cognitive capacity and neural organization. For example, seminal cognitive models claim there are distinct resources devoted to perceiving and remembering auditory and visual information (1, 2). This cognitive distinction is reflected in the separate cortical regions devoted to processing sensory information from each modality (3). Similarly, within the domain of vision, when items are placed near each other, they interfere more than when they are spaced farther apart (4, 5). These behavioral effects have been linked to receptive fields and the retinotopic organization of early visual areas, in which items that are farther apart activate more separable neural populations (6-8). Thus, there are multiple cognitive domains in which it has been proposed that capacity limitations in behavior are intrinsically driven by competition for representation at the neural level (4, 7-10).However, in the realm of high-level vision, evidence linking neural organization to behavioral capacities is sparse, although neural findings suggest there may be opportunities for such a link. For example, results from functional MRI (fMRI) and single-unit recording have found distinct clusters of neurons that selectively respond to categories such as faces, bodies, scenes, and objects (11, 12). These categories also elicit distinctive activation patterns across the ventral stream as measured with fMRI (13,14). Together, these results raise the interesting possibility that there are partially separate cognitive resources available for processing different object categories.In contrast, many prominent theories of visual cognition do not consider the possibility that different categories are processed by different representational mechanisms. For example, most models of attention and working memory assume or imply that these process...

Human cognition has a limited capacity that is often attributed to the brain having finite cognitive resources, but the nature of these resources is usually not specified. Here, we show evidence that perceptual interference between items can be predicted by known receptive field properties of the visual cortex, suggesting that competition within representational maps is an important source of the capacity limitations of visual processing. Across the visual hierarchy, receptive fields get larger and represent more complex, high-level features. Thus, when presented simultaneously, high-level items (e.g., faces) will often land within the same receptive fields, while low-level items (e.g., color patches) will often not. Using a perceptual task, we found long-range interference between high-level items, but only short-range interference for low-level items, with both types of interference being weaker across hemifields. Finally, we show that long-range interference between items appears to occur primarily during perceptual encoding and not during working memory maintenance. These results are naturally explained by the distribution of receptive fields and establish a link between perceptual capacity limits and the underlying neural architecture.

Making optimal decisions in the face of noise requires balancing short-term speed and accuracy. But a theory of optimality should account for the fact that short-term speed can influence long-term accuracy through learning. Here, we demonstrate that long-term learning is an important dynamical dimension of the speed-accuracy trade-off. We study learning trajectories in rats and formally characterize these dynamics in a theory expressed as both a recurrent neural network and an analytical extension of the drift-diffusion model that learns over time. The model reveals that choosing suboptimal response times to learn faster sacrifices immediate reward, but can lead to greater total reward. We empirically verify predictions of the theory, including a relationship between stimulus exposure and learning speed, and a modulation of reaction time by future learning prospects. We find that rats’ strategies approximately maximize total reward over the full learning epoch, suggesting cognitive control over the learning process.