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.

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.

Neurocomputational mechanisms of prior-informed perceptual decision-making in humans.

To interact successfully with diverse sensory environments, we must adapt our decision processes to account for time constraints and prior probabilities. The full set of decision-process parameters that undergo such flexible adaptation has proven to be difficult to establish using simplified models that are based on behaviour alone. Here, we utilize well-characterized human neurophysiological signatures of decision formation to construct and constrain a build-to-threshold decision model with multiple build-up (evidence accumulation and urgency) and delay components (pre- and post-decisional).

A diffusion model analysis of target detection in near-threshold visual search.

When searching for a target briefly presented among distractors how do people combine information across display locations to make a decision and how does the quality of the evidence entering the decision process vary with the type of items in the display? Research on accuracy in near-threshold visual search has had difficulty in distinguishing between models that make similar predictions about accuracy but make different assumptions about the underlying psychological processes. We used the diffusion model to analyse response times and accuracy data from four near-threshold search tasks which showed striking asymmetries between response-time distributions on target-present and target-absent trials. We found that performance was better explained by a model in which evidence was accumulated in parallel about each stimulus separately than one in which the evidence was pooled into a single decision process.

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.

Speeded multielement decision-making as diffusion in a hypersphere: Theory and application to double-target detection.

We generalize the circular 2D diffusion model of Smith (Psychological Review, 123, 425-451: 2016) to provide a new model of speeded decision-making in multielement visual displays. We model decision-making in tasks with multielement displays as evidence accumulation by a vector-valued diffusion process in a hypersphere, whose radius represents the decision criterion for the task. We show that the methods used to derive response time and accuracy predictions for the 2D model can be applied, with only minor changes, to predict performance in higher-dimensional spaces as well.

The power law of visual working memory characterizes attention engagement.

The quality or precision of stimulus representations in visual working memory can be characterized by a power law, which states that precision decreases as a power of the number of items in memory, with an exponent whose magnitude typically varies in the range 0.5 to 0.75.

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.

The attention-weighted sample-size model of visual short-term memory: Attention capture predicts resource allocation and memory load.

We investigated the capacity of visual short-term memory (VSTM) in a phase discrimination task that required judgments about the configural relations between pairs of black and white features. Sewell et al. (2014) previously showed that VSTM capacity in an orientation discrimination task was well described by a sample-size model, which views VSTM as a resource comprised of a finite number of noisy stimulus samples.

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.

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.

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.

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.

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.

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.

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.

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.