Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Conformity01:20

Conformity

48.2K
Conformity is the change in a person’s behavior to go along with the group, even if that person does not agree with the group.
48.2K
Conformations of Butane02:20

Conformations of Butane

18.1K
Unlike ethane and propane that have only two major conformations, butane has more than two conformers. The staggered form of butane in which the bulky methyl groups on the two carbons are placed on opposite sides, that is, at a dihedral angle of 180°, is the lowest energy, most stable form — called the anti conformer. This conformation is stabilized due to the absence of steric repulsion between the largely spaced out methyl groups. The other two staggered conformations are...
18.1K
Conformations of Cycloalkanes02:29

Conformations of Cycloalkanes

14.5K
Adolf von Baeyer attempted to explain the instabilities of small and large cycloalkane rings using the concept of angle strain — the strain caused by the deviation of bond angles from the ideal 109.5° tetrahedral value for sp3  hybridized carbons. However, while cyclopropane and cyclobutane are strained, as expected from their highly compressed bond angles, cyclopentane is more strained than predicted, and cyclohexane is virtually strain-free. Hence, Baeyer’s theory that...
14.5K
Conformations of Cyclohexane02:11

Conformations of Cyclohexane

15.7K
Cyclohexane does not exist in a planar form due to the high angle and torsional strain it would experience in the planar structure. Instead, it adopts non-planar chair and boat conformations.
The chair form is the most stable and derives its name from its resemblance to the “easy chair.” In the chair conformation, two carbon atoms are arranged out-of-plane — one above and one below, minimizing the torsional strain. In the chair form, the bond angle is very close to the ideal...
15.7K
Conformations of Ethane and Propane02:18

Conformations of Ethane and Propane

17.2K
In an organic molecule, free rotation about the carbon-carbon single bond results in energetically different conformers of the molecule. Due to this rotation, called the internal rotation, ethane has two major conformations — staggered and eclipsed.
Staggered conformation is a low energy and more stable conformation with the C-H bonds on the front carbon placed at 60°dihedral angles relative to the C-H bonds on the back carbon, leading to a reduced torsional strain. In staggered...
17.2K
Chair Conformation of Cyclohexane02:02

Chair Conformation of Cyclohexane

18.7K
The chair conformation is the most stable form of cyclohexane due to the absence of angle and torsional strain. The absence of angle strain is a result of cyclohexane’s bond angle being very close to the ideal tetrahedral bond angle of 109.5° in its chair conformer. Similarly, the torsional strain is also absent owing to the perfectly staggered arrangement of bonds.
The hydrogen atoms linked to carbons are arranged in two different axial and equatorial orientations to achieve this...
18.7K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Machine Learning for Diagnosis and Differentiation of Central Disorders of Hypersomnolence: A Systematic Review.

European journal of neurology·2026
Same author

Experiment-based calibration: Inference and decision-making.

Behavior research methods·2026
Same author

Psychophysiological Outcome Responses in Human Pavlovian Fear Conditioning: A Prediction Error Analysis.

Psychophysiology·2026
Same author

Effects of doxycycline on intrusive experimental trauma memory: a pre-registered, randomized double-blind placebo-controlled trial.

Translational psychiatry·2026
Same author

Eye-Tracking-BIDS: the Brain Imaging Data Structure extended to gaze position and pupil data.

bioRxiv : the preprint server for biology·2026
Same author

Directed cortico-limbic dialogue in the human brain.

Nature communications·2026

Related Experiment Video

Updated: Feb 5, 2026

A Novel Pavlovian Fear Conditioning Paradigm to Study Freezing and Flight Behavior
09:26

A Novel Pavlovian Fear Conditioning Paradigm to Study Freezing and Flight Behavior

Published on: January 5, 2021

7.4K

Human Pavlovian fear conditioning conforms to probabilistic learning.

Athina Tzovara1,2,3,4, Christoph W Korn1,2,5, Dominik R Bach1,2,3

  • 1Clinical Psychiatry Research, Department of Psychiatry, Psychotherapy, and Psychosomatics, University of Zurich, Zurich, Switzerland.

Plos Computational Biology
|September 1, 2018
PubMed
Summary

Humans learn to predict threats using probabilistic inference, with autonomic nervous system (ANS) responses reflecting expected outcomes and uncertainty. Skin conductance and pupil size responses provide distinct insights into this aversive learning process.

More Related Videos

Fear Incubation Using an Extended Fear-Conditioning Protocol for Rats
13:38

Fear Incubation Using an Extended Fear-Conditioning Protocol for Rats

Published on: August 22, 2020

8.8K
Trace Fear Conditioning in Mice
07:02

Trace Fear Conditioning in Mice

Published on: March 20, 2014

34.0K

Related Experiment Videos

Last Updated: Feb 5, 2026

A Novel Pavlovian Fear Conditioning Paradigm to Study Freezing and Flight Behavior
09:26

A Novel Pavlovian Fear Conditioning Paradigm to Study Freezing and Flight Behavior

Published on: January 5, 2021

7.4K
Fear Incubation Using an Extended Fear-Conditioning Protocol for Rats
13:38

Fear Incubation Using an Extended Fear-Conditioning Protocol for Rats

Published on: August 22, 2020

8.8K
Trace Fear Conditioning in Mice
07:02

Trace Fear Conditioning in Mice

Published on: March 20, 2014

34.0K

Area of Science:

  • Neuroscience
  • Computational Psychiatry
  • Psychophysiology

Background:

  • Pavlovian threat conditioning is crucial for survival, involving learning associations between neutral cues and aversive events.
  • Existing computational models of this aversive learning process offer fragmented and sometimes conflicting predictions regarding learning dynamics.
  • Understanding the neural mechanisms of threat prediction requires integrating computational learning theory with precise measures of physiological responses.

Purpose of the Study:

  • To investigate human autonomic nervous system (ANS) responses during Pavlovian threat conditioning.
  • To compare the explanatory power of different computational models, including probabilistic and non-probabilistic approaches, in explaining trial-by-trial ANS activity.
  • To determine how specific physiological measures, such as skin conductance and pupil size, relate to computational learning parameters.

Main Methods:

  • Employed a statistical framework for psychophysiological modeling to obtain precise, single-subject estimates of ANS responses.
  • Tested various computational models, including previously proposed non-probabilistic models, a simple probabilistic model, and non-learning models.
  • Utilized different observation functions to link learning models with ANS activity, analyzing both skin conductance responses (SCR) and pupil size responses (PSR) across three experiments.

Main Results:

  • A probabilistic learning model provided the best account of human ANS responses during threat conditioning.
  • Skin conductance responses (SCR) reflected a combination of expected outcome and uncertainty.
  • Pupil size responses (PSR) specifically tracked the expected outcome, indicating distinct information encoded by different ANS measures.

Conclusions:

  • Human Pavlovian threat prediction relies on probabilistic inference, incorporating the estimation of uncertainty.
  • The findings integrate computational learning theory with psychophysiological data, offering a more unified understanding of aversive learning.
  • This research can inform theories on the neural implementation of threat prediction and aversive learning mechanisms.