I am interested in the interplay between attention and value-based decision-making. My specific research interests can be described in two threads: 1) how reward-associated stimuli can modulate attention, and 2) how attention can influence and modulate value-based decision-making,. I pursue these research questions using a number of methodological tools, using both neural (EEG/ERP, oscillatory, fMRI) and non-neural measures (i.e. behavioral and eye-tracking data). A substantive part of my current focus is also upon expanding my skills into computational modeling as well. By combining these different conceptual ideas and methodological tools I aim to completely map out the many possible interactions between attention and value-based decision-making.
How reward-associated stimuli modulate attention
Humans, along with many other organisms, can learn that certain stimuli are more likely to lead to positive outcomes. Learning these stimulus-outcome associations can be quite useful to optimize future choices as well. Recent work has shown that reward-associated features of stimuli can lead to a subsequent biasing of attention, a phenomenon known as value-driven attentional capture (VDAC). This biasing of attention is involuntary, meaning that in some circumstances it can be helpful and in others it can be a hindrance. The unique qualities of this phenomena has radically reshaped our theories of attention (Awh et al., 2012), but there are many remaining questions concerning the nuances of this phenomenon, particularly in its underlying neural systems. My work to-date has generally focused on understanding how these reward-associated stimuli modulate attention at a neural level.
Bachman, M. D*., Wang, L.*, Gamble, M. L., & Woldorff, M. G. (2020). Physical salience and value-driven salience operate through different neural mechanisms to enhance attentional selection. Journal of Neuroscience, 40(28), 5455-5464.
Bachman, M. D., Hunter, M. N., Huettel, S. A., & Woldorff, M. G. (2021). Disruptions of sustained spatial attention can be resistant to the distractor’s prior reward associations. Frontiers in Human Neuroscience, 15, 431.
Attention during choice
When contemplating one's choices, a decision-maker cannot take in information about every alternative at the same time. Instead, their attention will move back and forth between each option until a choice is made. Neuroeconomists have become increasingly interested in understanding how these patterns of attention (typically measured via eye gaze) predict and shape eventual choices. I am currently working on several projects in this area, including: 1) how disruptions in visual attention influence decision-making, 2) how attention as measured by motor movements (i.e. button presses or mouse movements) differs from attention as measure by eye gaze, 3) using EEG to obtain neural measures of choice processes.
Sullivan, N.J., Breslav, A., Doré, S.S., Bachman, M.D.,& Huettel, S.A. (submitted). Would you like fries with that? The golden halon of defaults in the dietary choice decision process.
The neural processing of reward outcomes
There are many nuances to receiving a reward - is it good or bad? large or small? was it expected or unexpected? did you feel in control of your choices? All of these factors can play an important role in shaping our future choices and behaviors. It has been a long-held goal to understand how our brains respond to different aspects of reward outcomes. I've worked on a number of projects in this area, using EEG to understand the fast and complex aspects underlying the processing of reward outcomes. Current projects include investigating how different aspects of outcome expectancy influences the processing of the actual outcomes, and understanding how reward-learning impacts different parts of the neural cascade of outcome processing.
Bachman, M. D., Watts, A. T. M., Collins, P., & Bernat, E. M. (2021). Sequential gains and losses during gambling feedback: Differential effects in time-frequency delta and theta measures. Psychophysiology, 00, e13907. https://doi. org/10.1111/psyp.13907
The oscillatory dynamics underlying cognition
A large proportion of neuroelectric activity is naturally organized into patterns of oscillatory activity. Researchers can use time-frequency decompositions to understand how these oscillations, separated into different frequency bands (e.g. alpha, gamma, beta), relate to a wide host of cognitive processes. However, our ability to measure said oscillatory activity is heavily constrained by the specific time-frequency decomposition that is chosen and applied to the data. Most common decompositions are ill-equipped to measure lower frequency bands like delta and theta, thus undermining our ability to understand how their activity relates to many aspects of cognition. I overcame this problem while working with Dr. Edward Bernat by using an advanced time-frequency decomposition method that excels at measuring such low-frequency activity. We used this tool to understand how delta and theta relate to many different cognitive processes, including target detection, cognitive control, and emotion regulation.
Harper, J., Malone, S. M., Bachman, M. D., & Bernat, E. M. (2016). Stimulus sequence context differentially modulates inhibition‐related theta and delta band activity in a go/no‐go task. Psychophysiology, 53(5), 712-722.