Update

Letters

Detecting deception from neuroimaging signals – a data-driven perspective

Recent advances in neuroscience and technology have made it possible to record from large assemblies of neurons and to decode their activity to extract information. At the same time, available methods to stimulate the brain and influence ongoing processing are also rapidly expanding. These developments pave the way for advanced neurotechnological applications that directly read from, and write to, the human brain. While such technologies are still primarily used in restricted therapeutic contexts, this may change in the future once their performance has improved and they become more widely applicable. Here, we provide an overview of methods to interface with the brain, speculate about potential applications, and discuss important issues associated with a neurotechnologically assisted future.

Understanding the neural basis of human honesty and deception has enormous potential scientific and practical value. However, past approaches, largely developed out of studies with forensic applications in mind, are increasingly recognized as having serious methodological and conceptual shortcomings. Here we propose to address these challenges by drawing on so-called signaling games widely used in game theory and ethology to study behavioral and evolutionary consequences of information transmission and distortion. In particular, by separating and capturing distinct adaptive problems facing signal senders and receivers, signaling games provide a framework to organize the complex set of cognitive processes associated with honest and deceptive behavior. Furthermore, this framework provides novel insights into feasibility and practical challenges of neuroimaging-based lie detection.

The introduction of functional brain imaging based on BOLD-fMRI, twenty years ago, has revolutionized the field of human brain research. However, right from its inception it became clear that the BOLD signal suffers from a serious limitation— it reflects the averaged activity of large neuronal populations and hence can not, on its own, index the functional properties of individual neurons. The method of fMR-adaptation (also termed repetition suppression) was developed to circumvent this problem and use the BOLD signal to assess functional specializations at the individual neuron level. The approach is based on the tendency of cortical neurons to reduce their activity upon stimulus repetition. By examining the sensitivity of the adaptation effect to stimulus manipulation, insight can be gained about the invariant and selective properties of neuronal networks. It has been argued that the adaptation effect occurs at the level of synaptic inputs— and hence may be mislocalized. However, it is critical to consider the adaptation effect in the context of the cortical network architecture. This cortical anatomical organization, dominated by short range intrinsic connections, ensures that the fMR-adaptation largely reflects the response profile of the neurons located within the imaged voxel proper.