A pupil size response model to assess fear learning

Abstract

During fear conditioning, pupil size responses dissociate between conditioned stimuli that are contingently paired (CS+) with an aversive unconditioned stimulus, and those that are unpaired (CS‐). Current approaches to assess fear learning from pupil responses rely on ad hoc specifications. Here, we sought to develop a psychophysiological model (PsPM) in which pupil responses are characterized by response functions within the framework of a linear time‐invariant system. This PsPM can be written as a general linear model, which is inverted to yield amplitude estimates of the eliciting process in the central nervous system. We first characterized fear‐conditioned pupil size responses based on an experiment with auditory CS. PsPM‐based parameter estimates distinguished CS+/CS‐ better than, or on par with, two commonly used methods (peak scoring, area under the curve). We validated this PsPM in four independent experiments with auditory, visual, and somatosensory CS, as well as short (3.5 s) and medium (6 s) CS/US intervals. Overall, the new PsPM provided equal or decisively better differentiation of CS+/CS‐ than the two alternative methods and was never decisively worse. We further compared pupil responses with concurrently measured skin conductance and heart period responses. Finally, we used our previously developed luminance‐related pupil responses to infer the timing of the likely neural input into the pupillary system. Overall, we establish a new PsPM to assess fear conditioning based on pupil responses. The model has a potential to provide higher statistical sensitivity, can be applied to other conditioning paradigms in humans, and may be easily extended to nonhuman mammals.

Modeling startle eyeblink electromyogram to assess fear learning

“…The shared variance between expected response with unit amplitude and actual data, assessed, for example, in a regression model, can then be used to quantify response magnitude. This approach makes use of the entire data time series and theoretically affords more robust fear memory assessment—something we have shown empirically for SCR (Bach et al, ; Staib et al, ), HPR (Castegnetti et al, ), and pupil size (Korn et al, ). Here, we seek to create a model for SEBR in the existing PsPM framework.…”

“…In contrast, humans do not exhibit overt freezing. Instead, human fear memory is often assessed via activity of the autonomic nervous system as measured with skin conductance response (SCR; Bach, Daunizeau, Friston, & Dolan, ; Staib, Castegnetti, & Bach, ), heart period response (HPR; Castegnetti et al, ), or pupil size (Kluge et al, ; Korn, Staib, Tzovara, Castegnetti, & Bach, ; Reinhard, Lachnit, & König, ). Yet, SCR are susceptible to internal emotional, cognitive, and motor processes unrelated to fear memory and typically require long intertrial intervals (ITIs) because of slowness of the peripheral signal (Boucsein, ; Hamm & Vaitl, ).…”

Assessing fear learning via conditioned respiratory amplitude responses

“…Therefore, since the combination of these processes is not accessible to the experimenter, we phenomenologically construct the RF by studying the response of the system to a set of known inputs. This approach led to the successful development of a model for SCR (Bach, Flandin, Friston, & Dolan, ; Bach, Flandin et al, ), HPR (Castegnetti et al, ), pupil (Korn et al, ), and startle responses (Khemka et al, ).…”

“…We built a response function aimed at discriminating between the RAR to CS+ and CS−. To this end, we built the RF from the difference between the grand means of the responses to the two different stimuli obtained from the first data set, as in a previous approach to HPR (Castegnetti et al, ) and pupil responses (Korn et al, ). As a first step, we visually identified a function class that qualitatively resembled the shape of the difference between grand means.…”

“…Second, the PsPM embodies assumptions about what response components are relevant in distinguishing between conditions and what can be treated as noise. Indeed, this approach has been shown to afford more precise distinction of CS+/CS− trials (i.e., predictive validity) in the analysis of SCR (Bach, Flandin, Friston, & Dolan, ; Staib, Castegnetti, & Bach, ), heart period responses (HPR, Castegnetti et al, ), and pupil size responses (Korn et al, ). To ensure that our results are not driven by features of the particular data used for model construction, we validated the method on five further data sets.…”