Decoding the Brain’s Algorithm for Categorization from Its Neural Implementation

TL;DR: The results suggest that a bilateral attentional control network comprising the intraparietal sulcus, precuneus, and dorsolateral prefrontal cortex is involved in selecting what dimensions are relevant to the task at hand, effectively updating the task representation through trial and error.

TL;DR: It is argued that verifying, rejecting, and expanding these explanations for suboptimal behavior – rather than assessing optimality per se – should be among the major goals of the science of perceptual decision making.

TL;DR: Current methods in human neuroscience are highlighted, highlighting the ways that they have been used to study the neural bases of the human mind and the prospects for real-world applications and new scientific challenges for human neuroscience.

TL;DR: The findings suggest that the brain reorganizes when concepts change and provide support for a neurocomputational theory of concept formation and propose that when task goals change, object representations in HPC can be organized in new ways, resulting in updated concepts that highlight the features most critical to the new goal.

TL;DR: Marr's posthumously published Vision (1982) influenced a generation of brain and cognitive scientists, inspiring many to enter the field of visual perception as discussed by the authors, where the process of vision constructs a set of representations, starting from a description of the input image and culminating with three-dimensional objects in the surrounding environment, a central theme and one that has had farreaching influence in both neuroscience and cognitive science, is the notion of different levels of analysis.

TL;DR: A new method is proposed which attempts to keep the sensitivity benefits of cluster-based thresholding (and indeed the general concept of "clusters" of signal), while avoiding (or at least minimising) these problems, and is referred to as "threshold-free cluster enhancement" (TFCE).

TL;DR: The functional architecture of the object vision pathway in the human brain was investigated using functional magnetic resonance imaging to measure patterns of response in ventral temporal cortex while subjects viewed faces, cats, five categories of man-made objects, and nonsense pictures, and a distinct pattern of response was found for each stimulus category.

TL;DR: A new experimental and data-analytical framework called representational similarity analysis (RSA) is proposed, in which multi-channel measures of neural activity are quantitatively related to each other and to computational theory and behavior by comparing RDMs.