Time may change me, but I can trace time: Examining aspects of time course in our data Stewart Shankman, Ph.D. Professor University of Illinois at Chicago (dedicated to David Bowie: 1947-2016)

Thesis statement of this Flash talk Examining the time course of trials within your tasks can elucidate important processes Slope/habituation over time Initial reactivity (intercept) Variability over time

What’s the current problem? In many experimental studies, we average response/performance across lots of trials of a particular condition, and compare conditions. Affect: Positive vs. negative valence stimuli Working memory (N-Back): 0-back vs. 2 or 3-back This is great, BUT there could be other psychological mechanisms that are masked by averaging your trials

Slope (habituation?) of Threat Potentiated Startle Panic Disorder moderates habituation to unpredictable (but not predictable) threat Startle is a basic reflex that’s mediated by limbic regions and reflects defensive motivation….. We’ve examined how startle gets modulated during two kinds of threatening contexts – unpredictable threat and predictable threat. Rather than just averaging together response, can look at how this response changes over the course of the experiment Gorka et al., 2015 – Int J of Psychophys

Initial reactivity (Intercept) of threat modulated startle Pred threat intercept Unpred threat Startle Magnitude Another aspect of time course that one examine is intercept (i.e., estimated point that the person had the very first time they were presented with the threatening condition) We examined individual differences in intercept and found that those depression and/or anxiety disorders exhibited heightened estimated intercept values suggesting that initial reactivity may be a transdiagnostic abnormality for both depression and anxiety. Gorka et al., 2015 – Int J of Psychophys

Variability over time (e.g., of Attention Bias from Dot Probe) Yields ‘average’ attentional bias to threat Attention bias variability: Price et al (2015) propose ‘binning’ every 20 trials and calculating standard deviation across bins of attentional bias “Variability in attentional bias” perhaps reflects attentional control Demonstrates strong test-retest reliability Price et al., 2015, Psych Assessment; also see my student’s, Huiting Liu, poster on Sunday!

If studying time course, must consider… Sometimes a task cannot be decomposed into “trials” (e.g., Trails A and B) Order of conditions Block design (trials are blocked within condition) or event-related design (conditions mixed)? If block design, cannot look at time course of individual trials, but can still look at time course of block

If studying time course, must consider… Reliability of a single trial (handful trials?) Signal-noise ratio for single trial (e.g., ERP, fMRI)? How many trials make an internally consistent ‘signal’? Why isn’t this signal-noise issue applied to time course studies of self-report affect (cf., EMA studies)? What’s the internal consistency of a single mood rating? If block design, cannot look at time course of individual trials, but can still look at time course of block

Recap of thesis: Examining the time course of trials within your tasks can elucidate important processes Slope of response Initial intercept of response (either time 0 or endpoint) Variability over time Sensitization, time to peak response, etc. Of course there are other aspects of time course that one could examine….. Final pic of Bowie Thank you! Funding sources: NIMH (R21 MH080689; R01 MH106175)