Research

Though we are interested generally in affect and its interactions with decision-making, the lab's research tends to fall under a few specific areas of interest and is characterized by a variety of techniques used to investigate those areas of interest.

Areas of Interest

Risky Monetary Decision-Making

How do we decide how valuable something is, and then make a choice about it? What roles do different parts of our affective experience play in shaping how we evaluate and weigh the attributes of a choice? How might recent events impact our subsequent evaluations of risk and the choices we make? Risky monetary decision-making is an ideal area in which to explore questions like these and others because of the unique confluence of objective, well-understood features of choice options, our ability to precisely control the timing of choices and events, and the realism and clarity we can achieve in studies using risky monetary choices. Our work here combines quantitative economic models of decision-making with physiological measurements and/or brain imaging to shed light on how affective phenomena interact with specific decision processes. 

Interpersonal Interactions

When our choices affect other people, how we perceive those people, who we are, and societal norms can all shape the decisions we make. We seek to understand how our affective associations with people or social groups can alter how we decide to punish or reward them, trust them, or even just value their preferences in the context of ours. Our work here benefits from social psychological frameworks for understanding social perception and interaction, and research in game theory and behavioral economics that provides principled approaches to considering motivation, utility, and quantifying interactions, while seeking to bring new insights to bear from affective science, cognitive psychology, and neuroscience. 

Emotion Regulation & Self-Control

Emotions don’t just happen to us - we can control or regulate them, thereby changing our own behavior. Knowing that emotions can shape our choices thus provides opportunities to try to change those emotions and so change the choices we make. While we know a lot about emotion regulation in traditional contexts (pictures, fearful stimuli, etc), less is known about it in decision-making - a gap we have worked to bridge. Other work of ours uses computational modeling to think about self-control success & failure as the result of optimal choices based on evaluations of our options and environment, subject to the limits of that environment and ourselves. For example, we have only so much time, and only so many hands/eyes/stomachs. 

Affective Prediction

We often have to take actions that will affect ourselves in the future. We have to put money away now to save for retirement later, exercise & eat right now to live longer & healthier later, and so on. Decades of science have shown that we’re not as good at this as we might think - we apparently find it difficult to anticipate how hungry, happy, angry, or scared we’ll be. What makes this difficult? How do our affective predictions fail, and what might that tell us about the processes underlying those predictions?  

Techniques

We are a technically opportunistic lab: we’ll use whatever technique makes sense for the question at hand! This includes a wide variety of tools and approaches:

• Quantitative behavioral analysis

  • Hierarchical linear regressions (e.g. in R using lmer)

  • Classic maximum likelihood estimation (e.g. in MATLAB or R)

  • Hierarchical non-linear Bayesian Markov-chain Monte Carlo (MCMC) sampling-based methods (e.g. Stan in R)

• Different people in different contexts

  • MTurk or Prolific platforms for online data collection

  • Patients or other special populations

Tools

We’re always trying to up our game in the Sokol-Hessner Lab. For that reason, as a lab we rely heavily on modern digital collaboration tools including Slack (almost all of our lab communication happens on Slack!), GitHub (see our lab page here), Google Calendar, Trello, etc. We have even made a few contributions to StartYourLab! Beyond finding these tools helpful in organizing, sharing, collaborating, and doing our own work, we also believe that learning these tools is smart mentorship and scientific development, as they are often considered standard, required tools in non-academic scientific domains.

• A brief guide to getting starting with online experiments using PsychoPy by Hayley Brooks (September 2022)