Dissertations and Theses

Date of Award


Document Type



Biomedical Engineering

First Advisor

Simon P. Kelly

Second Advisor

Lucas Parra

Third Advisor

Eric Fertuck


Neuroscience, value, perception, EEG, cognitive modeling, personality disorders


In dynamic environments, split-second sensorimotor decisions must be prioritized according to potential payoffs to maximize overall rewards. The impact of relative value on deliberative perceptual judgments has been examined extensively, but relatively little is known about value-biasing mechanisms in the common situation where physical evidence is strong but the time to act is severely limited. This research examines the behavioral and electrophysiological indices of how value biases split-second perceptual decisions and the possible mechanisms underlying the process. In prominent decision models, a noisy but statistically stationary representation of sensory evidence is integrated over time to an action-triggering bound, and value-biases are effected by starting the integrator closer to the more valuable bound. Here we show significant departures from this account for humans making rapid sensory-instructed action choices.

We show that on a time-constrained color discrimination task, behavior is best explained by a simple model in which accumulator “drift rate” (effectively, the mean of the evidence being accumulated) is itself biased by value and is non-stationary, increasing over the short decision time frame. Because the value bias initially dominates, the model uniquely predicts a dynamic ‘‘turn-around’’ effect on low-value cues, where the accumulator first launches toward the incorrect action but is then re-routed to the correct one. This was clearly exhibited in electrophysiological signals reflecting motor preparation and evidence accumulation. Furthermore, we constructed an extended model that implements this dynamic effect through plausible sensory neural response modulations and demonstrates the correspondence between decision signal dynamics simulated from a behavioral fit of that model and the empirical decision signals.

To follow up on the finding that drift rate biases dominate over starting point biases, we examined the generality of this effect across different forms of value association. We found that drift rate biases dominate not only when value has a long-term association with the sensory alternatives as in the first experiment, but also when value has a long-term association with motor alternative. To follow up on the proposed sensory neural response modulation model, we further examined whether the model can capture dynamic manipulations of the sensory stimulus onset, and confirmed that it can. This model shows that value and sensory information can exert simultaneous and dynamically countervailing influences on the trajectory of the accumulation-to-bound process, driving rapid, sensory-guided actions.

We thus conclude that 1) value-based prioritization is clearly not only exerted through shifting starting points, but also through strong modulations of the rate of evidence accumulation ("drift rate"), 2) in order to accurately quantify these biases in very fast decisions, it is necessary for models to allow for dynamic changes in drift rate.



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