Damian A. Stanley

The fusiform face area is selective for faces not animals

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.

Cluster-based analysis of FMRI data

We propose a method for the statistical analysis of fMRI data that tests cluster units rather than voxel units for activation. The advantages of this analysis over previous ones are both conceptual and statistical. Recognizing that the fundamental units of interest are the spatially contiguous clusters of voxels that are activated together, we set out to approximate these cluster units from the data by a clustering algorithm especially tailored for fMRI data. Testing the cluster units has a two-fold statistical advantage over testing each voxel separately: the signal to noise ratio within the unit tested is higher, and the number of hypotheses tests compared is smaller. We suggest controlling FDR on clusters, i.e., the proportion of clusters rejected erroneously out of all clusters rejected and explain the meaning of controlling this error rate. We introduce the powerful adaptive procedure to control the FDR on clusters. We apply our cluster-based analysis (CBA) to both an event-related and a block design fMRI vision experiment and demonstrate its increased power over voxel-by-voxel analysis in these examples as well as in simulations.

Implicit race attitudes predict trustworthiness judgments and economic trust decisions

Trust lies at the heart of every social interaction. Each day we face decisions in which we must accurately assess another individuals 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 individuals explicit evaluations of trustworthiness and, more crucially, his or her economic decisions to trust is predicted by that persons bias in implicit race attitude. Importantly, this relationship is robust and is independent of the individuals 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 individuals implicit race bias. The core dimension of social trust can be shaped, to some degree, by attitudes that reside outside conscious awareness and intention.

The Neural Basis of Implicit Attitudes

Evidence that human preferences, beliefs, and behavior are influenced by sources that are outside the reach of conscious awareness, control, intention, and self-reflection is incontrovertible. Recent advances in neuroscience have enabled researchers to investigate the neural basis of these implicit attitudes, particularly attitudes involving social groups. From this research, a model with three identified neural components related to the automatic activation and regulation of implicit attitudes is beginning to emerge. The amygdala is implicated in the automatic evaluation of socially relevant stimuli, while the anterior cingulate and dorsolateral prefrontal cortices are involved in the detection and regulation, respectively, of implicit attitudes. Further support for this model comes from the inclusion of these regions in current models concerning the cognitive regulation of emotion and the detection of conflict. The identification of a putative neural substrate for implicit attitudes has had a direct impact on psychological research into their nature and operational characteristics. We discuss how this emerging neural model has influenced current research on implicit attitudes and describe the importance of such models for directing future research.

Toward a Neural Basis for Social Behavior

Nearly 25 years ago, the shared interests of psychologists and biologists in understanding the neural basis of social behavior led to the inception of social neuroscience. In the past decade, this field has exploded, in large part due to the infusion of studies that use fMRI. At the same time, tensions have arisen about how to prioritize a diverse range of questions and about the authority of neurobiological data in answering them. The field is now poised to tackle some of the most interesting and important questions about human and animal behavior but at the same time faces uncertainty about how to achieve focus in its research and cohesion among the scientists who tackle it. The next 25 years offer the opportunity to alleviate some of these growing pains, as well as the challenge of answering large questions that encompass the nature and bounds of diverse social interactions (in humans, including interactions through the internet); how to characterize, and treat, social dysfunction in psychiatric illness; and how to compare social cognition in humans with that in other animals.

Cuing effects of faces are dependent on handedness and visual field

Faces are unlike other visual objects we encounter, in that they alert us to potentially relevant social information. Both face processing and spatial attention are dominant in the right hemisphere of the human brain, with a stronger lateralization in right- than in left-handers. Here, we demonstrate behavioral evidence for an effect of handedness on performance in tasks using faces to direct attention. Nonpredictive, peripheral cues (faces or dots) directed exogenous attention to contrast-varying stimuli (Gabor patches)—a tilted target, a vertical distractor, or both; observers made orientation discriminations on the target stimuli. Whereas cuing with dots increased contrast sensitivity in both groups, cuing with faces increased contrast sensitivity in right- but not in left-handers, for whom opposite hemifield effects resulted in no net increase. Our results reveal that attention modulation by face cues critically depends on handedness and visual hemifield. These previously unreported interactions suggest that such lateralized systems may be functionally connected.

Implicit Social Biases in People With Autism

Implicit social biases are ubiquitous and are known to influence social behavior. A core diagnostic criterion of autism spectrum disorders (ASD) is abnormal social behavior. We investigated the extent to which individuals with ASD might show a specific attenuation of implicit social biases, using Implicit Association Tests (IATs) involving social (gender, race) and nonsocial (nature, shoes) categories. High-functioning adults with ASD showed intact but reduced IAT effects relative to healthy control participants. We observed no selective attenuation of implicit social (vs. nonsocial) biases in our ASD population. To extend these results, we supplemented our healthy control data with data collected from a large online sample from the general population and explored correlations between autistic traits and IAT effects. We observed no systematic relationship between autistic traits and implicit social biases in our online and control samples. Taken together, these results suggest that implicit social biases, as measured by the IAT, are largely intact in ASD.

Reply to Krueger: Good point, wrong paper

In his letter, Krueger (1) raises an important issue regarding questions of prediction of behavior from tests of underlying mental states. We think it is important to make the distinction here between using the implicit association test (IAT) for diagnostic purposes (e.g., to classify individuals as ‘racist’) and using the IAT for research purposes (i.e., to investigate a potential contributing factor to trust behavior in our study) (2). We emphatically agree with Krueger (1) that, in its current instantiation, the IAT should not be used for diagnostic purposes. Indeed, the concern of Krueger (1) is exactly the one that we would raise ourselves if …