Regulation of arousal via online neurofeedback improves human performance in a demanding sensory-motor task.

Our state of arousal can significantly affect our ability to make optimal decisions, judgments, and actions in real-world dynamic environments. The Yerkes-Dodson law, which posits an inverse-U relationship between arousal and task performance, suggests that there is a state of arousal that is optimal for behavioral performance in a given task. Here we show that we can use online neurofeedback to shift an individual's arousal from the right side of the Yerkes-Dodson curve to the left toward a state of improved performance. Specifically, we use a brain-computer interface (BCI) that uses information in the EEG to generate a neurofeedback signal that dynamically adjusts an individual's arousal state when they are engaged in a boundary-avoidance task (BAT). The BAT is a demanding sensory-motor task paradigm that we implement as an aerial navigation task in virtual reality and which creates cognitive conditions that escalate arousal and quickly results in task failure (e.g., missing or crashing into the boundary). We demonstrate that task performance, measured as time and distance over which the subject can navigate before failure, is significantly increased when veridical neurofeedback is provided. Simultaneous measurements of pupil dilation and heart-rate variability show that the neurofeedback indeed reduces arousal. Our work demonstrates a BCI system that uses online neurofeedback to shift arousal state and increase task performance in accordance with the Yerkes-Dodson law.

Neural mechanisms underlying catastrophic failure in human-machine interaction during aerial navigation.

OBJECTIVE: We investigated the neural correlates of workload buildup in a fine visuomotor task called the boundary avoidance task (BAT). The BAT has been known to induce naturally occurring failures of human-machine coupling in high performance aircraft that can potentially lead to a crash-these failures are termed pilot induced oscillations (PIOs). APPROACH: We recorded EEG and pupillometry data from human subjects engaged in a flight BAT simulated within a virtual 3D environment. MAIN RESULTS: We find that workload buildup in a BAT can be successfully decoded from oscillatory features in the electroencephalogram (EEG). Information in delta, theta, alpha, beta, and gamma spectral bands of the EEG all contribute to successful decoding, however gamma band activity with a lateralized somatosensory topography has the highest contribution, while theta band activity with a fronto-central topography has the most robust contribution in terms of real-world usability. We show that the output of the spectral decoder can be used to predict PIO susceptibility. We also find that workload buildup in the task induces pupil dilation, the magnitude of which is significantly correlated with the magnitude of the decoded EEG signals. These results suggest that PIOs may result from the dysregulation of cortical networks such as the locus coeruleus (LC)-anterior cingulate cortex (ACC) circuit. SIGNIFICANCE: Our findings may generalize to similar control failures in other cases of tight man-machine coupling where gains and latencies in the control system must be inferred and compensated for by the human operators. A closed-loop intervention using neurophysiological decoding of workload buildup that targets the LC-ACC circuit may positively impact operator performance in such situations.

Visual attention mitigates information loss in small- and large-scale neural codes.

The visual system transforms complex inputs into robust and parsimonious neural codes that efficiently guide behavior. Because neural communication is stochastic, the amount of encoded visual information necessarily decreases with each synapse. This constraint requires that sensory signals are processed in a manner that protects information about relevant stimuli from degradation. Such selective processing--or selective attention--is implemented via several mechanisms, including neural gain and changes in tuning properties. However, examining each of these effects in isolation obscures their joint impact on the fidelity of stimulus feature representations by large-scale population codes. Instead, large-scale activity patterns can be used to reconstruct representations of relevant and irrelevant stimuli, thereby providing a holistic understanding about how neuron-level modulations collectively impact stimulus encoding.

Attention improves transfer of motion information between V1 and MT.

Selective attention modulates activity within individual visual areas; however, the role of attention in mediating the transfer of information between areas is not well understood. Here, we used fMRI to assess attention-related changes in coupled BOLD activation in two key areas of human visual cortex that are involved in motion processing: V1 and MT. To examine attention-related changes in cross-area coupling, multivoxel patterns in each visual area were decomposed to estimate the trial-by-trial response amplitude in a set of direction-selective "channels." In both V1 and MT, BOLD responses increase in direction-selective channels tuned to the attended direction of motion and decrease in channels tuned away from the attended direction. Furthermore, the modulation of cross-area correlations between similarly tuned populations is inversely related to the modulation of their mean responses, an observation that can be explained via a feedforward motion computation in MT and a modulation of local noise correlations in V1. More importantly, these modulations accompany an increase in the cross-area mutual information between direction-selective response patterns in V1 and MT, suggesting that attention improves the transfer of sensory information between cortical areas that cooperate to support perception. Finally, our model suggests that divisive normalization of neural activity in V1 before its integration by MT is critical to cross-area information coupling, both in terms of cross-area correlation as well as cross-area mutual information.

Computational advances towards linking BOLD and behavior.

Traditionally, fMRI studies have focused on analyzing the mean response amplitude within a cortical area. However, the mean response is blind to many important patterns of cortical modulation, which severely limits the formulation and evaluation of linking hypotheses between neural activity, BOLD responses, and behavior. More recently, multivariate pattern classification analysis (MVPA) has been applied to fMRI data to evaluate the information content of spatially distributed activation patterns. This approach has been remarkably successful at detecting the presence of specific information in targeted brain regions, and provides an extremely flexible means of extracting that information without a precise generative model for the underlying neural activity. However, this flexibility comes at a cost: since MVPA relies on pooling information across voxels that are selective for many different stimulus attributes, it is difficult to infer how specific sub-sets of tuned neurons are modulated by an experimental manipulation. In contrast, recently developed encoding models can produce more precise estimates of feature-selective tuning functions, and can support the creation of explicit linking hypotheses between neural activity and behavior. Although these encoding models depend on strong - and often untested - assumptions about the response properties of underlying neural generators, they also provide a unique opportunity to evaluate population-level computational theories of perception and cognition that have previously been difficult to assess using either single-unit recording or conventional neuroimaging techniques.

Spatial attention improves the quality of population codes in human visual cortex.

Selective attention enables sensory input from behaviorally relevant stimuli to be processed in greater detail, so that these stimuli can more accurately influence thoughts, actions, and future goals. Attention has been shown to modulate the spiking activity of single feature-selective neurons that encode basic stimulus properties (color, orientation, etc.). However, the combined output from many such neurons is required to form stable representations of relevant objects and little empirical work has formally investigated the relationship between attentional modulations on population responses and improvements in encoding precision. Here, we used functional MRI and voxel-based feature tuning functions to show that spatial attention induces a multiplicative scaling in orientation-selective population response profiles in early visual cortex. In turn, this multiplicative scaling correlates with an improvement in encoding precision, as evidenced by a concurrent increase in the mutual information between population responses and the orientation of attended stimuli. These data therefore demonstrate how multiplicative scaling of neural responses provides at least one mechanism by which spatial attention may improve the encoding precision of population codes. Increased encoding precision in early visual areas may then enhance the speed and accuracy of perceptual decisions computed by higher-order neural mechanisms.

Population response profiles in early visual cortex are biased in favor of more valuable stimuli.

Voluntary and stimulus-driven shifts of attention can modulate the representation of behaviorally relevant stimuli in early areas of visual cortex. In turn, attended items are processed faster and more accurately, facilitating the selection of appropriate behavioral responses. Information processing is also strongly influenced by past experience and recent studies indicate that the learned value of a stimulus can influence relatively late stages of decision making such as the process of selecting a motor response. However, the learned value of a stimulus can also influence the magnitude of cortical responses in early sensory areas such as V1 and S1. These early effects of stimulus value are presumed to improve the quality of sensory representations; however, the nature of these modulations is not clear. They could reflect nonspecific changes in response amplitude associated with changes in general arousal or they could reflect a bias in population responses so that high-value features are represented more robustly. To examine this issue, subjects performed a two-alternative forced choice paradigm with a variable-interval payoff schedule to dynamically manipulate the relative value of two stimuli defined by their orientation (one was rotated clockwise from vertical, the other counterclockwise). Activation levels in visual cortex were monitored using functional MRI and feature-selective voxel tuning functions while subjects performed the behavioral task. The results suggest that value not only modulates the relative amplitude of responses in early areas of human visual cortex, but also sharpens the response profile across the populations of feature-selective neurons that encode the critical stimulus feature (orientation). Moreover, changes in space- or feature-based attention cannot easily explain the results because representations of both the selected and the unselected stimuli underwent a similar feature-selective modulation. This sharpening in the population response profile could theoretically improve the probability of correctly discriminating high-value stimuli from low-value alternatives.

Estimating the influence of attention on population codes in human visual cortex using voxel-based tuning functions.

In order to form stable perceptual representations, populations of sensory neurons must pool their output to overcome physiological noise; selective attention is then required to ensure that behaviorally relevant stimuli dominate these 'population codes' to gain access to awareness. However, the role that attention plays in shaping population response profiles has received little direct investigation, in part because most traditional neurophysiological methods cannot simultaneously assess changes in activity across large populations of sensory neurons. Based on single-unit recording studies, current theories hold that attending to a relevant feature sharpens the population response profile and improves the signal-to-noise ratio of the resulting perceptual representation. Here, we test this hypothesis using fMRI and an analysis approach that is able to estimate the influence of feature-based attentional modulations on population response profiles. We first derive orientation tuning functions for single voxels in human primary visual cortex, and then use these tuning functions to sort voxels according to their orientation preference. We then show that selective attention systematically biases population response profiles so that behaviorally relevant stimuli are represented in the visual system at the expense of behaviorally irrelevant stimuli. Collectively, the present results (1) provide a new approach for precisely characterizing feature-selective responses in human sensory cortices and (2) reveal how behavioral goals can shape population response profiles to support the formation of coherent perceptual representations.