Diffusion theory of the antipodal "shadow" mode in continuous-outcome, coherent-motion decisions

Continuous-outcome decisions, in which responses are made on continuous scales, are increasingly used to study perception and memory for stimulus attributes like color, orientation, and motion. This interest has led to the development of models of continuous-outcome decision processes like the circular diffusion model that predict joint distributions of decision outcomes and response times (RTs). We use the circular diffusion model and a new spherical generalization of it to model performance in a continuous-outcome version of the random-dot motion task. The task is a benchmark test of decision models because it yields bimodal distributions of decision outcomes: In addition to a peak or mode in the true direction of motion, there is a secondary, antipodal, mode at 180° to the true direction. Models like the circular diffusion model, in which evidence is accumulated by a single process, are thought to be unable to predict bimodality. We compared diffusion models for the continuous motion task in which evidence is accumulated in either a two-dimensional (2D) or a three-dimensional (3D) representational space. We found that performance was well described by a spherical (3D) diffusion model in which the drift rate encodes perceived motion direction and strength and the points on the bounding sphere representing the decision criterion are projected onto a 2D circle to make a response. A model with an antipodal component of drift rate and drift-rate variability successfully predicted bimodal distributions of decision outcomes and the joint distributions of decision outcomes and RT for individual participants. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Multiphasic value biases in fast-paced decisions

Perceptual decisions are biased toward higher-value options when overall gains can be improved. When stimuli demand immediate reactions, the neurophysiological decision process dynamically evolves through distinct phases of growing anticipation, detection, and discrimination, but how value biases are exerted through these phases remains unknown. Here, by parsing motor preparation dynamics in human electrophysiology, we uncovered a multiphasic pattern of countervailing biases operating in speeded decisions. Anticipatory preparation of higher-value actions began earlier, conferring a 'starting point' advantage at stimulus onset, but the delayed preparation of lower-value actions was steeper, conferring a value-opposed buildup-rate bias. This, in turn, was countered by a transient deflection toward the higher-value action evoked by stimulus detection. A neurally-constrained process model featuring anticipatory urgency, biased detection, and accumulation of growing stimulus-discriminating evidence, successfully captured both behavior and motor preparation dynamics. Thus, an intricate interplay of distinct biasing mechanisms serves to prioritise time-constrained perceptual decisions.

Modeling continuous outcome color decisions with the circular diffusion model: Metric and categorical properties

The circular diffusion model is extended to provide a theory of the speed and accuracy of continuous outcome color decisions and used to characterize eye-movement decisions about the hues of noisy color patches in an isoluminant, equidiscriminability color space. Heavy-tailed distributions of decision outcomes were found with high levels of chromatic noise, similar to those found in visual working memory studies with high memory loads. Decision times were longer for less accurate decisions, in agreement with the slow error property typically found in difficult 2-choice tasks. Decision times were shorter, and responses were more accurate in parts of the space corresponding to nameable color categories, although the number and locations of the categories varied among participants. We show that these findings can be predicted by a theory of across-trial variability in the quality of the evidence entering the decision process, represented mathematically by the drift rate of the diffusion process. The heavy-tailed distributions of decision outcomes and the slow-error pattern can be predicted by either of 2 models of drift rate. One model is based on encoding failures and the other is based on a nonlinear transformation of the stimulus space. Both models predict highly inaccurate stimulus representations on some trials, leading to heavy-tailed distributions and slow errors. The color-category effects were successfully modeled as stimulus biases in a similarity-choice framework, in which the drift rate is the vector sum of the encoded metric and categorical representations of the stimulus. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

The magical number one-on-square-root-two: The double-target detection deficit in brief visual displays

How limited representational capacity is divided when multiple items need to be processed simultaneously is a fundamental question in cognitive psychology. The double-target deficit is the finding that, when monitoring multiple locations or information streams for targets, identification of 2 simultaneous targets is substantially worse than is predicted from the cost of divided attention alone. This finding suggests that targets and nontargets are treated differently by the cognitive system. We investigated the double-target deficit in 4 different visual decision tasks using noisy, backwardly masked targets presented for a range of exposure durations to test the theory that the deficit reflects a capacity limitation of visual short-term memory (VSTM). We quantified the deficit using a sample-size model of VSTM and 2 different models of the decision process: a signal detection MAX model and an optimum likelihood ratio model. We found a double-target deficit in all 4 tasks which increased in magnitude for briefer displays, consistent with the capacity limits of VSTM. We explained the exposure dependency using a competitive interaction model in which nontargets compete for access to VSTM at a slower rate than targets. Our findings support 2-stage models of visual processing in which the most target-like stimuli gain priority access into VSTM before the decision process begins. (PsycINFO Database Record

Multimodal decoding and congruent sensory information enhance reaching performance in subjects with cervical spinal cord injury

Cervical spinal cord injury (SCI) paralyzes muscles of the hand and arm, making it difficult to perform activities of daily living. Restoring the ability to reach can dramatically improve quality of life for people with cervical SCI. Any reaching system requires a user interface to decode parameters of an intended reach, such as trajectory and target. A challenge in developing such decoders is that often few physiological signals related to the intended reach remain under voluntary control, especially in patients with high cervical injuries. Furthermore, the decoding problem changes when the user is controlling the motion of their limb, as opposed to an external device. The purpose of this study was to investigate the benefits of combining disparate signal sources to control reach in people with a range of impairments, and to consider the effect of two feedback approaches. Subjects with cervical SCI performed robot-assisted reaching, controlling trajectories with either shoulder electromyograms (EMGs) or EMGs combined with gaze. We then evaluated how reaching performance was influenced by task-related sensory feedback, testing the EMG-only decoder in two conditions. The first involved moving the arm with the robot, providing congruent sensory feedback through their remaining sense of proprioception. In the second, the subjects moved the robot without the arm attached, as in applications that control external devices. We found that the multimodal-decoding algorithm worked well for all subjects, enabling them to perform straight, accurate reaches. The inclusion of gaze information, used to estimate target location, was especially important for the most impaired subjects. In the absence of gaze information, congruent sensory feedback improved performance. These results highlight the importance of proprioceptive feedback, and suggest that multi-modal decoders are likely to be most beneficial for highly impaired subjects and in tasks where such feedback is unavailable.

Dealing with target uncertainty in a reaching control interface

Prosthetic devices need to be controlled by their users, typically using physiological signals. People tend to look at objects before reaching for them and we have shown that combining eye movements with other continuous physiological signal sources enhances control. This approach suffers when subjects also look at non-targets, a problem we addressed with a probabilistic mixture over targets where subject gaze information is used to identify target candidates. However, this approach would be ineffective if a user wanted to move towards targets that have not been foveated. Here we evaluated how the accuracy of prior target information influenced decoding accuracy, as the availability of neural control signals was varied. We also considered a mixture model where we assumed that the target may be foveated or, alternatively, that the target may not be foveated. We tested the accuracy of the models at decoding natural reaching data, and also in a closed-loop robot-assisted reaching task. The mixture model worked well in the face of high target uncertainty. Furthermore, errors due to inaccurate target information were reduced by including a generic model that relied on neural signals only.

Real-time fusion of gaze and EMG for a reaching neuroprosthesis

For rehabilitative devices to restore functional movement to paralyzed individuals, user intent must be determined from signals that remain under voluntary control. Tracking eye movements is a natural way to learn about an intended reach target and, when combined with just a small set of electromyograms (EMGs) in a probabilistic mixture model, can reliably generate accurate trajectories even when the target information is uncertain. To experimentally assess the effectiveness of our algorithm in closed-loop control, we developed a robotic system to simulate a reaching neuroprosthetic. Incorporating target information by tracking subjects' gaze greatly improved performance when the set of EMGs was most limited. In addition we found that online performance was better than predicted by the offline accuracy of the training data. By enhancing the trajectory model with target information the decoder relied less on neural control signals, reducing the burden on the user.

Real-time evaluation of a noninvasive neuroprosthetic interface for control of reach

Injuries of the cervical spinal cord can interrupt the neural pathways controlling the muscles of the arm, resulting in complete or partial paralysis. For individuals unable to reach due to high-level injuries, neuroprostheses can restore some of the lost function. Natural, multidimensional control of neuroprosthetic devices for reaching remains a challenge. Electromyograms (EMGs) from muscles that remain under voluntary control can be used to communicate intended reach trajectories, but when the number of available muscles is limited control can be difficult and unintuitive. We combined shoulder EMGs with target estimates obtained from gaze. Natural gaze data were integrated with EMG during closed-loop robotic control of the arm, using a probabilistic mixture model. We tested the approach with two different sets of EMGs, as might be available to subjects with C4- and C5-level spinal cord injuries. Incorporating gaze greatly improved control of reaching, particularly when there were few EMG signals. We found that subjects naturally adapted their eye-movement precision as we varied the set of available EMGs, attaining accurate performance in both tested conditions. The system performs a near-optimal combination of both physiological signals, making control more intuitive and allowing a natural trajectory that reduces the burden on the user.

Decoding with limited neural data: a mixture of time-warped trajectory models for directional reaches

Neuroprosthetic devices promise to allow paralyzed patients to perform the necessary functions of everyday life. However, to allow patients to use such tools it is necessary to decode their intent from neural signals such as electromyograms (EMGs). Because these signals are noisy, state of the art decoders integrate information over time. One systematic way of doing this is by taking into account the natural evolution of the state of the body--by using a so-called trajectory model. Here we use two insights about movements to enhance our trajectory model: (1) at any given time, there is a small set of likely movement targets, potentially identified by gaze; (2) reaches are produced at varying speeds. We decoded natural reaching movements using EMGs of muscles that might be available from an individual with spinal cord injury. Target estimates found from tracking eye movements were incorporated into the trajectory model, while a mixture model accounted for the inherent uncertainty in these estimates. Warping the trajectory model in time using a continuous estimate of the reach speed enabled accurate decoding of faster reaches. We found that the choice of richer trajectory models, such as those incorporating target or speed, improves decoding particularly when there is a small number of EMGs available.

Comparison of electromyography and force as interfaces for prosthetic control

The ease with which persons with upper-limb amputations can control their powered prostheses is largely determined by the efficacy of the user command interface. One needs to understand the abilities of the human operator regarding the different available options. Electromyography (EMG) is widely used to control powered upper-limb prostheses. It is an indirect estimator of muscle force and may be expected to limit the control capabilities of the prosthesis user. This study compared EMG control with force control, an interface that is used in everyday interactions with the environment. We used both methods to perform a position-tracking task. Direct-position control of the wrist provided an upper bound for human-operator capabilities. The results demonstrated that an EMG control interface is as effective as force control for the position-tracking task. We also examined the effects of gain and tracking frequency on EMG control to explore the limits of this control interface. We found that information transmission rates for myoelectric control were best at higher tracking frequencies than at the frequencies previously reported for position control. The results may be useful for the design of prostheses and prosthetic controllers.

Dealing with noisy gaze information for a target-dependent neural decoder

We tend to look at targets prior to moving our hand towards them. This means that our eye movements contain information about the movements we are planning to make. This information has been shown to be useful in the context of decoding of movement intent from neural signals. However, this is complicated by the fact that occasionally, subjects may want to move towards targets that have not been foveated, or may be distracted and temporarily look away from the intended target. We have previously accounted for this uncertainty using a probabilistic mixture over targets, where the gaze information is used to identify target candidates. Here we evaluate how the accuracy of prior target information influences decoding accuracy. We also consider a mixture model where we assume that the target may be foveated or, alternatively, that the target may not be foveated. We found that errors due to inaccurate target information were reduced by including a generic model representing movements to all targets into the mixture.

Continuous movement decoding using a target-dependent model with EMG inputs

Trajectory-based models that incorporate target position information have been shown to accurately decode reaching movements from bio-control signals, such as muscle (EMG) and cortical activity (neural spikes). One major hurdle in implementing such models for neuroprosthetic control is that they are inherently designed to decode single reaches from a position of origin to a specific target. Gaze direction can be used to identify appropriate targets, however information regarding movement intent is needed to determine when a reach is meant to begin and when it has been completed. We used linear discriminant analysis to classify limb states into movement classes based on recorded EMG from a sparse set of shoulder muscles. We then used the detected state transitions to update target information in a mixture of Kalman filters that incorporated target position explicitly in the state, and used EMG activity to decode arm movements. Updating the target position initiated movement along new trajectories, allowing a sequence of appropriately timed single reaches to be decoded in series and enabling highly accurate continuous control.