Psychologists often use peripheral physiological measures to infer a psychological variable. It is desirable to make this inverse inference in the most precise way, ideally standardized across research laboratories. In recent years, psychophysiological modeling has emerged as a method that rests on statistical techniques to invert mathematically formulated forward models (psychophysiological models, PsPMs). These PsPMs are based on psychophysiological knowledge and optimized with respect to the precision of the inference. Building on established experimental manipulations, known to create different values of a psychological variable, they can be benchmarked in terms of their sensitivity (e.g., effect size) to recover these values-we have termed this predictive validity. In this review, we introduce the problem of inverse inference and psychophysiological modeling as a solution. We present background and application for all peripheral measures for which PsPMs have been developed: skin conductance, heart period, respiratory measures, pupil size, and startle eyeblink. Many of these PsPMs are task invariant, implemented in open-source software, and can be used off the shelf for a wide range of experiments. Psychophysiological modeling thus appears as a potentially powerful method to infer psychological variables.

Pavlovian fear conditioning is widely used as a laboratory model of associative learning in human and nonhuman species. In this model, an organism is trained to predict an aversive unconditioned stimulus from initially neutral events (conditioned stimuli, CS). In humans, fear memory is typically measured via conditioned autonomic responses or fear‐potentiated startle. For the latter, various analysis approaches have been developed, but a systematic comparison of competing methodologies is lacking. Here, we investigate the suitability of a model‐based approach to startle eyeblink analysis for assessment of fear memory, and compare this to extant analysis strategies. First, we build a psychophysiological model (PsPM) on a generic startle response. Then, we optimize and validate this PsPM on three independent fear‐conditioning data sets. We demonstrate that our model can robustly distinguish aversive (CS+) from nonaversive stimuli (CS‐, i.e., has high predictive validity). Importantly, our model‐based approach captures fear‐potentiated startle during fear retention as well as fear acquisition. Our results establish a PsPM‐based approach to assessment of fear‐potentiated startle, and qualify previous peak‐scoring methods. Our proposed model represents a generic startle response and can potentially be used beyond fear conditioning, for example, to quantify affective startle modulation or prepulse inhibition of the acoustic startle response.

Respiratory physiology is influenced by cognitive processes. It has been suggested that some cognitive states may be inferred from respiration amplitude responses (RAR) after external events. Here, we investigate whether RAR allow assessment of fear memory in cued fear conditioning, an experimental model of aversive learning. To this end, we built on a previously developed psychophysiological model (PsPM) of RAR, which regards interpolated RAR time series as the output of a linear time invariant system. We first establish that average RAR after CS+ and CS− are different. We then develop the response function of fear‐conditioned RAR, to be used in our PsPM. This PsPM is inverted to yield estimates of cognitive input into the respiratory system. We analyze five validation experiments involving fear acquisition and retention, delay and trace conditioning, short and medium CS‐US intervals, and data acquired with bellows and MRI‐compatible pressure chest belts. In all experiments, CS+ and CS− are distinguished by their estimated cognitive inputs, and the sensitivity of this distinction is higher for model‐based estimates than for peak scoring of RAR. Comparing these data with skin conductance responses (SCR) and heart period responses (HPR), we find that, on average, RAR performs similar to SCR in distinguishing CS+ and CS−, but is less sensitive than HPR. Overall, our work provides a novel and robust tool to investigate fear memory in humans that may allow wide and straightforward application to diverse experimental contexts.

HighlightsWe develop a novel method for analysing event-related respiratory responses.This method is based on a Psychophysiological Model (PsPM) of interpolated time series.We analyse respiration period (RP), amplitude (RA) and flow rate (RFR).RA and RFR estimates distinguish different event types, and all three measures distinguish events from non-events.The new method could be useful for fMRI experiments using respiration belts.