complete manual updates

doc/PsPM.bib

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 %% This BibTeX bibliography file was created using BibDesk.
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+%% Created for Dominik at 2024-12-14 13:02:37 +0100 
 
 
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+@article{Xia:2023aa,
+	annote = {10.1101/lm.053781.123},
+	author = {Xia, Yanfang and Wehrli, Jelena and Gerster, Samuel and Kroes, Marijn and Houtekamer, Maxime and Bach, Dominik R.},
+	date = {2023/07/01},
+	date-added = {2024-12-14 13:02:21 +0100},
+	date-modified = {2024-12-14 13:02:21 +0100},
+	journal = {Learning \& Memory},
+	journal1 = {Learning \& Memory},
+	month = {07},
+	n2 = {Fear conditioning is a laboratory paradigm commonly used to investigate aversive learning and memory. In context fear conditioning, a configuration of elemental cues (conditioned stimulus {$[$}CTX{$]$}) predicts an aversive event (unconditioned stimulus {$[$}US{$]$}). To quantify context fear acquisition in humans, previous work has used startle eyeblink responses (SEBRs), skin conductance responses (SCRs), and verbal reports, but different quantification methods have rarely been compared. Moreover, preclinical intervention studies mandate recall tests several days after acquisition, and it is unclear how to induce and measure context fear memory retention over such a time interval. First, we used a semi-immersive virtual reality paradigm. In two experiments (N = 23 and N = 28), we found successful declarative learning and memory retention over 7 d but no evidence of other conditioned responses. Next, we used a configural fear conditioning paradigm with five static room images as CTXs in two experiments (N = 29 and N = 24). Besides successful declarative learning and memory retention after 7 d, SCR and pupil dilation in response to CTX onset differentiated CTX+/CTX−during acquisition training, and SEBR and pupil dilation differentiated CTX+/CTX−during the recall test, with medium to large effect sizes for the most sensitive indices (SEBR: Hedge's g = 0.56 and g = 0.69; pupil dilation: Hedge's g = 0.99 and g = 0.88). Our results demonstrate that with a configural learning paradigm, context fear memory retention can be demonstrated over 7 d, and we provide robust and replicable measurement methods to this end.},
+	number = {7},
+	pages = {139--150},
+	title = {Measuring human context fear conditioning and retention after consolidation},
+	url = {http://learnmem.cshlp.org/content/30/7/139.abstract},
+	volume = {30},
+	year = {2023},
+	bdsk-url-1 = {http://learnmem.cshlp.org/content/30/7/139.abstract}}
+
+@article{Wehrli:2022aa,
+	author = {Wehrli, Jelena M. and Xia, Yanfang and Gerster, Samuel and Bach, Dominik R.},
+	date = {2022/12/01},
+	date-added = {2024-12-14 12:58:27 +0100},
+	date-modified = {2024-12-14 12:58:27 +0100},
+	doi = {https://doi.org/10.1111/psyp.14119},
+	isbn = {0048-5772},
+	journal = {Psychophysiology},
+	journal1 = {Psychophysiology},
+	journal2 = {Psychophysiology},
+	journal3 = {Psychophysiology},
+	keywords = {calibration; fear conditioning; fear memory; fear-potentiated startle; human neuroscience; psychophysiological modeling; retrodictive validity; trace fear memory},
+	month = {2024/12/14},
+	n2 = {Abstract Trace fear conditioning is an important research paradigm to model aversive learning in biological or clinical scenarios, where predictors (conditioned stimuli, CS) and aversive outcomes (unconditioned stimuli, US) are separated in time. The optimal measurement of human trace fear conditioning, and in particular of memory retention after consolidation, is currently unclear. We conducted two identical experiments (N1 =?28, N2 =?28) with a 15-s trace interval and a recall test 1 week after acquisition, while recording several psychophysiological observables. In a calibration approach, we explored which learning and memory measures distinguished CS+ and CS? in the first experiment and confirmed the most sensitive measures in the second experiment. We found that in the recall test without reinforcement, only fear-potentiated startle but not skin conductance, pupil size, heart period, or respiration amplitude, differentiated CS+ and CS?. During acquisition without startle probes, skin conductance responses and pupil size responses but not heart period or respiration amplitude differentiated CS+ and CS?. As a side finding, there was no evidence for extinction of fear-potentiated startle over 30 trials without reinforcement. These results may be useful to inform future substantive research using human trace fear conditioning protocols.},
+	number = {12},
+	pages = {e14119},
+	publisher = {John Wiley \& Sons, Ltd},
+	title = {Measuring human trace fear conditioning},
+	url = {https://doi.org/10.1111/psyp.14119},
+	volume = {59},
+	year = {2022},
+	year1 = {2022},
+	bdsk-url-1 = {https://doi.org/10.1111/psyp.14119}}
+
+@article{Gent:2019aa,
+	abstract = {Heart rate data are often collected in human factors studies, including those into vehicle automation. Advances in open hardware platforms and off-the-shelf photoplethysmogram (PPG) sensors allow the non-intrusive collection of heart rate data at very low cost. However, the signal is not trivial to analyse, since the morphology of PPG waveforms differs from electrocardiogram (ECG) waveforms and shows different noise patterns. Few validated open source available algorithms exist that handle PPG data well, as most of these algorithms are specifically designed for ECG data. In this paper we present the validation of a novel algorithm named HeartPy, useful for the analysis of heart rate data collected in noisy settings, such as when driving a car or when in a simulator. We benchmark the performance on two types of datasets and show that the developed algorithm performs well. Further research steps are discussed.},
+	author = {van Gent, Paul and Farah, Haneen and van Nes, Nicole and van Arem, Bart},
+	date = {2019/10/01/},
+	date-added = {2024-12-14 12:53:34 +0100},
+	date-modified = {2024-12-14 12:53:34 +0100},
+	doi = {https://doi.org/10.1016/j.trf.2019.09.015},
+	isbn = {1369-8478},
+	journal = {Transportation Research Part F: Traffic Psychology and Behaviour},
+	keywords = {Human factors; Heart rate analysis; Physiological signals; Signal analysis; Open source},
+	pages = {368--378},
+	title = {HeartPy: A novel heart rate algorithm for the analysis of noisy signals},
+	url = {https://www.sciencedirect.com/science/article/pii/S1369847818306740},
+	volume = {66},
+	year = {2019},
+	bdsk-url-1 = {https://www.sciencedirect.com/science/article/pii/S1369847818306740},
+	bdsk-url-2 = {https://doi.org/10.1016/j.trf.2019.09.015}}
+
+@article{xia_liu:2024,
+	author = {Yanfang Xia and Huaiyu Liu and Oliver K. K{\"a}lin and Samuel Gerster and Dominik R. Bach},
+	date-added = {2024-12-14 11:45:31 +0100},
+	date-modified = {2024-12-14 11:46:54 +0100},
+	journal = {pre-print},
+	title = {Measuring human Pavlovian appetitive conditioning and memory retention},
+	volume = {https://doi.org/10.31234/osf.io/qs7ku},
+	year = {2024}}
+
+@article{Mancinelli:2024aa,
+	abstract = {Fear conditioning, also termed threat conditioning, is a commonly used learning model with clinical relevance. Quantification of threat conditioning in humans often relies on conditioned autonomic responses such as skin conductance responses (SCR), pupil size responses (PSR), heart period responses (HPR), or respiration amplitude responses (RAR), which are usually analyzed separately. Here, we investigate whether inter-individual variability in differential conditioned responses, averaged across acquisition, exhibits a multi-dimensional structure, and the extent to which their linear combination could enhance the precision of inference on whether threat conditioning has occurred. In a mega-analytic approach, we re-analyze nine data sets including 256 individuals, acquired by the group of the last author, using standard routines in the framework of psychophysiological modeling (PsPM). Our analysis revealed systematic differences in effect size between measures across datasets, but no evidence for a multidimensional structure across various combinations of measures. We derive the statistically optimal weights for combining the four measures and subsets thereof, and we provide out-of-sample performance metrics for these weights, accompanied by bias-corrected confidence intervals. We show that to achieve the same statistical power, combining measures allows for a relevant reduction in sample size, which in a common scenario amounts to roughly 24{\%}. To summarize, we demonstrate a one-dimensional structure of threat conditioning measures, systematic differences in effect size between measures, and provide weights for their optimal linear combination in terms of maximal retrodictive validity.},
+	author = {Mancinelli, Federico and Sporrer, Juliana K. and Myrov, Vladislav and Melinscak, Filip and Zimmermann, Josua and Liu, Huaiyu and Bach, Dominik R.},
+	date = {2024/09/01},
+	date-added = {2024-12-14 11:44:25 +0100},
+	date-modified = {2024-12-14 11:44:25 +0100},
+	doi = {10.3758/s13428-024-02341-3},
+	id = {Mancinelli2024},
+	isbn = {1554-3528},
+	journal = {Behavior Research Methods},
+	number = {6},
+	pages = {6119--6129},
+	title = {Dimensionality and optimal combination of autonomic fear-conditioning measures in humans},
+	url = {https://doi.org/10.3758/s13428-024-02341-3},
+	volume = {56},
+	year = {2024},
+	bdsk-url-1 = {https://doi.org/10.3758/s13428-024-02341-3}}
+
 @article{Bach:2020ab,
 	abstract = {Quantification of fear conditioning is paramount to many clinical and translational studies on aversive learning. Various measures of fear conditioning co-exist, including different observables and different methods of pre-processing. Here, we first argue that low measurement error is a rational desideratum for any measurement technique. We then show that measurement error can be approximated in benchmark experiments by how closely intended fear memory relates to measured fear memory, a quantity that we term retrodictive validity. From this perspective, we discuss different approaches commonly used to quantify fear conditioning. One of these is psychophysiological modelling (PsPM). This builds on a measurement model that describes how a psychological variable, such as fear memory, influences a physiological measure. This model is statistically inverted to estimate the most likely value of the psychological variable, given the measured data. We review existing PsPMs for skin conductance, pupil size, heart period, respiration, and startle eye-blink. We illustrate the benefit of PsPMs in terms of retrodictive validity and translate this into sample size required to achieve a desired level of statistical power. This sample size can differ up to a factor of three between different observables, and between the best, and the current standard, data pre-processing methods.},
 	author = {Bach, Dominik R. and Melinscak, Filip},
@@ -49,7 +136,7 @@ @article{Bach:2020aa
 
 @article{Bach:2024aa,
 	abstract = {Psychometrics is historically grounded in the study of individual differences. Consequently, common metrics such as quantitative validity and reliability require between-person variance in a psychological variable to be meaningful. Experimental psychology, in contrast, deals with variance between treatments, and experiments often strive to minimise within-group person variance. In this article, I ask whether and how psychometric evaluation can be performed in experimental psychology. A commonly used strategy is to harness between-person variance in the treatment effect. Using simulated data, I show that this approach can be misleading when between-person variance is low, and in the face of methods variance. I argue that this situation is common in experimental psychology, because low between-person variance is desirable, and because methods variance is no more problematic in experimental settings than any other source of between-person variance. By relating validity and reliability with the corresponding concepts in measurement science outside psychology, I show how experiment-based calibration can serve to compare the psychometric quality of different measurement methods in experimental psychology.},
-	author = {Bach, Dominik R. },
+	author = {Bach, Dominik R.},
 	date = {2024/08/01},
 	date-added = {2024-12-11 19:25:09 +0100},
 	date-modified = {2024-12-11 19:25:09 +0100},
@@ -208,7 +295,8 @@ @inproceedings{Greco:2014aa
 	organization = {IEEE},
 	pages = {2290--2293},
 	title = {Electrodermal activity processing: {A} convex optimization approach},
-	year = {2014}}
+	year = {2014},
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 @article{Berntson:1995,
 	author = {Berntson, Gary G and Cacioppo, John T and Quigley, Karen S},