We developed a continuous-outcome version of the classic dot motion decision task in which participants had to identify the direction of motion from a complete 360 degree range, and report their response using either a joystick or eye movement. We isolated a novel sensory-level EEG decision signal representing the strength of the dot motion in visual areas of the brain, and a signal associated with the accumulation of sensory evidence, both of which were stronger for more accurate decisions. We found that individuals had strong biases towards particular directions of motion, but these varied by individual and were idiosyncratic. These directional biases were associated with the evidence-accumulation EEG signal but not the sensory-level signal, suggesting that they occur as part of the cognitive decision process and are not just the result of movement biases. The strength of sensory encoding, on the other hand, probably reflects trial to trial variations in arousal but does not cause the biases. These results were presented at the 2021 Society for Neuroscience and Neuromatch 4.0 conferences (see video on the project webpage), and will soon be submitted to a journal. Another interesting finding from these experiments was that some participants, on a small but appreciable number of trials, perceived the motion in the exact opposite direction to the true direction of motion. We developed a new decision model that could explain this unexpected behaviour (currently under review in a journal), but the cause of this phenomenon is not yet understood and will be the subject of future research.
Finally, we used EEG motor preparation signals to guide the construction, and constrain key parameters of, a multilevel model of biased decision making. Perceptual decisions are biased toward higher-value options when overall gains can be improved. When decisions are made under time pressure, it is possible to observe the neurophysiological decision process in human EEG dynamically evolving through distinct phases of growing anticipation, detection and discrimination. By parsing motor preparation signals we uncovered a multiphasic pattern of biases evolving over the course of the decision. Before the stimulus appeared, people began preparing for higher-value actions earlier conferring a “starting point” advantage, but then quickly countered with increased preparation of lower-value actions. We used these anticipatory motor preparation signals to constrain motor-level parameters of a decision model which was then able to explain both behaviour and motor preparation dynamics. This work showed that the interplay of distinct biasing mechanisms in time-constrained perceptual decisions is much more complex than can be captured by standard models based on behaviour alone. This paper has been published as a preprint and a science communication video describing the results is available on the project webpage.
