http://www.ece.uvic.ca/~bctill/papers/facerec/HaxHofGob2000.pdf

The distributed human neural system for face perception.

Machine learning is a means to derive artificial intelligence by discovering patterns in existing data. Here, we show that applying machine learning to ordinary human language results in human-like semantic biases. We replicated a spectrum of known biases, as measured by the Implicit Association Test, using a widely used, purely statistical machine-learning model trained on a standard corpus of text from the World Wide Web. Our results indicate that text corpora contain recoverable and accurate imprints of our historic biases, whether morally neutral as toward insects or flowers, problematic as toward race or gender, or even simply veridical, reflecting the status quo distribution of gender with respect to careers or first names. Our methods hold promise for identifying and addressing sources of bias in culture, including technology.

/pdf/semantics-derived-automatically-from-language-corpora-4lzxrze2vb.pdf

Semantics derived automatically from language corpora contain human-like biases

https://medweb4.unige.ch/labnic/papers/PV_Neuron2001.pdf

Effects of attention and emotion on face processing in the human brain: An event-related fMRI study

Humans can recognize the gist of a novel image in a single glance, independent of its complexity. How is this remarkable feat accomplished? On the basis of behavioral and computational evidence, this paper describes a formal approach to the representation and the mechanism of scene gist understanding, based on scene-centered, rather than object-centered primitives. We show that the structure of a scene image can be estimated by the mean of global image features, providing a statistical summary of the spatial layout properties (Spatial Envelope representation) of the scene. Global features are based on configurations of spatial scales and are estimated without invoking segmentation or grouping operations. The scene-centered approach is not an alternative to local image analysis but would serve as a feed-forward and parallel pathway of visual processing, able to quickly constrain local feature analysis and enhance object recognition in cluttered natural scenes.

/pdf/building-the-gist-of-a-scene-the-role-of-global-image-4yx9upzmu9.pdf

Building the gist of a scene: the role of global image features in recognition.

Connectivity analyses and computational modeling of human brain function from fMRI data frequently require the specification of regions of interests (ROIs). Several analyses have relied on atlases derived from anatomical or cyto-architectonic boundaries to specify these ROIs, yet the suitability of atlases for resting state functional connectivity (FC) studies has yet to be established. This article introduces a data-driven method for generating an ROI atlas by parcellating whole brain resting-state fMRI data into spatially coherent regions of homogeneous FC. Several clustering statis- tics are used to compare methodological trade-offs as well as determine an adequate number of clus- ters. Additionally, we evaluate the suitability of the parcellation atlas against four ROI atlases (Talairach and Tournoux, Harvard-Oxford, Eickoff-Zilles, and Automatic Anatomical Labeling) and a random parcellation approach. The evaluated anatomical atlases exhibit poor ROI homogeneity and do not accurately reproduce FC patterns present at the voxel scale. In general, the proposed func- tional and random parcellations perform equivalently for most of the metrics evaluated. ROI size and hence the number of ROIs in a parcellation had the greatest impact on their suitability for FC analysis. With 200 or fewer ROIs, the resulting parcellations consist of ROIs with anatomic homol- ogy, and thus offer increased interpretability. Parcellation results containing higher numbers of ROIs (600 or 1,000) most accurately represent FC patterns present at the voxel scale and are preferable when interpretability can be sacrificed for accuracy. The resulting atlases and clustering software have been made publicly available at: http://www.nitrc.org/projects/cluster_roi/. Hum Brain Mapp

A whole brain fMRI atlas generated via spatially constrained spectral clustering

The parahippocampal place area: recognition, navigation, or encoding?

To test whether the human fusiform face area (FFA) responds not only to faces but to anything human or animate, we used fMRI to measure the response of the FFA to six new stimulus categories. The strongest responses were to stimuli containing faces: human faces (2.0% signal increase from fixation baseline) and human heads (1.7%), with weaker but still strong responses to whole humans (1.5%) and animal heads (1.3%). Responses to whole animals (1.0%) and human bodies without heads (1.0%) were significantly stronger than responses to inanimate objects (0.7%), but responses to animal bodies without heads (0.8%) were not. These results demonstrate that the FFA is selective for faces, not for animals.

The fusiform face area is selective for faces not animals.

fMRI activation in response to illusory contours and salient regions in the human lateral occipital complex.

Trust lies at the heart of every social interaction. Each day we face decisions in which we must accurately assess another individual's trustworthiness or risk suffering very real consequences. In a global marketplace of increasing heterogeneity with respect to nationality, race, and multiple other social categories, it is of great value to understand how implicitly held attitudes about group membership may support or undermine social trust and thereby implicitly shape the decisions we make. Recent behavioral and neuroimaging work suggests that a common mechanism may underlie the expression of implicit race bias and evaluations of trustworthiness, although no direct evidence of a connection exists. In two behavioral studies, we investigated the relationship between implicit race attitude (as measured by the Implicit Association Test) and social trust. We demonstrate that race disparity in both an individual's explicit evaluations of trustworthiness and, more crucially, his or her economic decisions to trust is predicted by that person's bias in implicit race attitude. Importantly, this relationship is robust and is independent of the individual's bias in explicit race attitude. These data demonstrate that the extent to which an individual invests in and trusts others with different racial backgrounds is related to the magnitude of that individual's implicit race bias. The core dimension of social trust can be shaped, to some degree, by attitudes that reside outside conscious awareness and intention.

/pdf/implicit-race-attitudes-predict-trustworthiness-judgments-jgboezla3i.pdf

Implicit race attitudes predict trustworthiness judgments and economic trust decisions

http://www.math.tau.ac.il/~ruheller/Papers/CBAforFMRI.pdf

Damian A. Stanley

Papers

The parahippocampal place area: recognition, navigation, or encoding?

The fusiform face area is selective for faces not animals.

fMRI activation in response to illusory contours and salient regions in the human lateral occipital complex.

Implicit race attitudes predict trustworthiness judgments and economic trust decisions

Cluster-based analysis of FMRI data