Behavioral Research – Lit Search 2a&b

Directions for Lit Search 2

Currently, my research lab is studying the role of adverse childhood experiences in economic decision-making during young adulthood. The background behind this area of research relates to Life History Strategy, which broadly suggests that animals adopt different life strategies depending on the predictability & opportunities earlier in life (it’s actually much more complex than this..). This theoretical framework is typically linked to evolutionary biology but also plays a role in learned psychological behaviors.

For my purposes, I’m interested in how unpredictability due to childhood adversity could prompt diverging life strategies. For example, growing up in an unpredictable environment may lead one to discount the future more and seek out more risk. I am also interested in how early life experiences may relate to and interact with neurobiology. I am specifically interested in whether dopaminergic functioning modifies the relationship between childhood adversity and discounting. For this lit search, I’m focusing on a few more recent articles related to measuring dopamine using spontaneous eye blink rate, intertemporal choice, and temporal discounting.

Here is an example for how I would summarize articles for lit search 2:

Korpanay et al. (2017) suggest that impulsive behavior is associated with disadvantageous decision-making that often leads to worse life outcomes. Understanding factors that contribute to impulsive behavior will help us identify ways to possibly reduce impulsivity in the future (this is the big picture/significance).

Impulsivity is associated with dopaminergic transmission. For example, drugs that increase levels of dopamine in the reward system of the brain lead to increased impulsivity. Dopamine levels are challenging to measure without expensive equipment (e.g., PET). However, research suggests that spontaneous eye blink rate positively correlates with dopaminergic functioning.

Korpany et al. (2017) used multiple measures of reward processing and decision making to examine the relationship between impulsivity and neurobiology. The authors hypothesized that impulsivity would negatively correlate with gray matter in the prefrontal cortex. The authors also predicted a (nonspecified) relationship between impulsivity and spontaneous eyeblink rate and resting state functional connectivity. Before reading further, I would personally hypothesize that spontaneous eyeblink rate (indexing higher levels of dopaminergic functioning/availability) positively correlates with impulsivity.

Impulsivity (the outcome variable, dependent variable) was operationally defined as self-ratings of impulsivity on the Barratt Impulsiveness Scale and by performance on an experimental task called the Go/No-Go Task.  Each of these measures has several more specific facets (e.g., non planning impulsivity, attentional impulsivity…). Predictors (i.e., quasi-IVs) were neurobiological measures (i.e., spontaneous eyeblink rate, prefrontal cortex volumes, resting state functional connectivity). The authors used a correlational design as none of the variables in this study were manipulated, and 127 healthy adults participated in this research study.

I’m most interested in learning a bit more about spontaneous eyeblink measures. So, I read this part more closely than the methods details about neuroimaging. The authors specifically measured spontaneous eyeblink at 7 pm because this measure is affected by time of day. The participants spontaneous eyeblinks were measured over the course of about 10 minutes: the first two minutes captured eyes closed, then they were instructed to keep their eyes open while looking at a fixation on a screen for six minutes, then they closed their eyes for another two minutes. They did not provide specific instructions related to blinking behavior.

Korponay et al. (2017) started by computing bivariate pearson’s correlations between behavioral scores, for which they observed a number of weak, trending relationships. Gray matter volume in the orbitofrontal cortex (the most ventral part of the prefrontal cortex) negatively correlated with scores on the Barratt Impulsiveness Scale (BIS). Most interesting to me, spontaneous eyeblink rate positively correlated with accuracy on the go trials in the Go/No-Go tasks and was negatively correlated with motor impulsivity on the BIS. I found this surprising. The authors discuss this further in their discussion. Specifically, they discuss some research that shows that higher dopamine can actually improve motor inhibition in some health participants.  However, most research suggests that higher spontaneous eyeblink relates to lower inhibitory ability (which contradicts their findings). Overall, the authors broadly conclude that neurobiological measures predict individual differences in impulsivity. However, there was so much heterogeneity in their results, that it is challenging to pull out a central conclusion.

Korponay, C., Dentico, D., Kral, T., Ly, M., Kuis, A., Goldman, .R, Lutz, A., & Davidson, R. J. (2017). Neurobiological correlates of impulsivity in healthy adults: Lower prefrontal gray matter volume and spontaneous eye-blink rate but greater resting-state functional connectivity in basal ganglia-thalamo-cortical circuitryNeuroImage, 157, 288-296.

I’m always intrigued by factors that influence human time preference, discounting, risk-taking, self control, impatience, or impulsivity. So when I found the next article I’m reviewing for my lit search 2 in a search for most recent articles on intertemporal choice, I was intrigued.

Pain (acute/chronic physical pain and social pain [although, social pain was not discussed in this article]) impacts a large proportion of the population, and, as Koppel et al. (2017) suggest, this makes pain a condition that could influence decision-making. Specifically, the authors propose that pain might favor “fast,” system 1 decision-making (more present-oriented, riskier) relative to “slow,” system 2 decision-making patterns (more deliberate, effortful). Pain may impair slow decision-making because pain impacts physiological markers of good/bad decisions that help individuals learn optimal strategies. Pain also demands attention and could take up cognitive load, leaving fewer resources for reflective, slow thinking. The authors did not explicate their hypotheses, but based on their summary of the background literature, we can predict that pain will lead to steeper discounting and more risk taking because it impairs more reflective, system 2 (slow) decision-making.

Koppel et al. (2017) used a within participants research design with pain as the independent variable (no pain, pain). Order was counterbalanced, so half of the (N=109) participants completed the tasks under pain first and the other half completed the tasks under no pain first. Pain was induced (at each individual’s subjective threshold) using heat stimulation to the participants forearm. Importantly, the max threshold was set for safety reasons.

Participants completed three decision tasks twice (once in pain, once with no pain). The three decision tasks were administered in the same order for each participant. These tasks were each 60 seconds long and participants had a 30 second break between tasks for a break from pain (during the pain condition). The first decision task was a risky gains task. This involved five choices between a safe option and a risky option. The second task was a risky loss task. This involved five choices between a safe loss and a risky loss. The final task was an intertemporal choice task, in which participants choose between a sooner, smaller reward and a later, larger reward. These choices were incentive compatible, meaning participants were paid based on a randomly selected choice.

Koppel et al. (2017) first conducted paired samples t-tests (since it was a w/in participants design) comparing choices in the pain condition versus the no-pain condition. They found that participants made more risky choices (in the risky gains task) under pain than no pain. The authors also found that participants discounted the future more when in pain than no pain. 

The authors interpret their results in line with past work connecting state affect to risk taking and discounting, e.g. (as cited by Koppel et al., 2017),

  • sadness relates to more risk seeking due to goals to seek reward (see Raghunathan & Pham, 1999)
  • anxiety relates to risk aversion due to goals to reduce uncertainty
  • anticipation of pain relates to risk aversion (key word: anticipation)

The authors pose future questions for research. For example, does pain impact the valuation process? How does social pain influence decision-making?

Koppel, L., Andersson, D., Morrison, I., Posadzy, K., Västfjäll, D., & Tinghög, G. (2017). The effect of acute pain on risky and intertemporal choice. Experimental Economics, 20, 878-893. Doi: 10.1007/s10683-017-9515-6

one more coming in separate post

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