Precision of terminology is really important. Many brain and analysis topics involve tricky terms that either have unclear meaning or many different meanings. Writing in a clear manner (i.e. a manner where you define explicitly or implicitly your terminology) will help you avoid confusion from your readers, and thus will help you create better scientific outputs. Furthermore, I would say that getting your writing and terminology clearer is part of how we advance scientific knowledge.
Another benefit of defining your terms is that it forces you to see if you actually understand something. (Imagine explaining the term to a first-year graduate student.)
It is annoying when you listen to someone who ASSUMES you already know all of their terminology. So, when you explain/discuss with someone, make sure you are on the same page and are using the same terminology.
- Refers to methodology of acquiring images of brains
- Taking pictures of neuron-related stuff
- Can be invasive or non-invasive
- It requires 2-D spatial measure (i.e. image), and NOT just time: EEG with single channel, Skin-conductance
- Measuring local field potentials (fluctuations in the electromagnetic field). For example, magnetoencephalography (MEG), electroencephalography (EEG), or electrocorticography (ECoG).
- Some unit (voxel or neuron or brain area) responds more or less strongly to certain conditions than others.
- Alternatively, a unit is selective if it responds "very" strongly to one thing and not so much to other things). This can be viewed as a very specific type of selectivity, one where there is one magical thing.
- Some unit (voxel or neuron or brain area) that responds more or less strongly to a certain condition than others. (gray matter vs. white matter)
- Like, focusing on the differences in responses to different conditions (across condition)
- The ability of a measurement method to measure something of interest with high reliability
- SNR is often equated with sensitivity
- Like, focusing on responses to the same condition
- you have good sensitivity, you can consider whether you have specificity
- The level of consistency of a measure after repeatedly measuring the same phenomenon.
- A predetermined amount of volume of space.
- Something (event/thing) that evokes reaction to the sensory system.
- Resistance(?) of a number when there is a change in the parameters that is compatible to the initial parameter.
- A number when multiplied with a model (a design matrix column) matches the scale of variance to data.
- A weight in a linear regression model
- fMRI-specific definition: weight in a regression model corresponding to some experimental condition of interest, often expressed in units of percent signal change
- The second letter of Greek alphabet
- [Behavior-oriented] The proportion of attempts one gets "correct" out of all the attempts. e.g., number of trials one participant gets "correct" out of number of all trials. This assumes there is even a notion of 'correctness'.
- [More general] How close you are to the ground truth
- Distinct or isolated region/section/part of sensory input that a neuron is to. Receptive fields are not restrictive to visual, can also be auditory, somatosensory, motor, etc. Historically, the term receptive field comes from Hartline's observation in 1938 in the frog: where an isolated optic nerve fiber is excited by light falling on a small circular area of the retina.
- Distinct part of the visual field that a neuron or population of neurons in a voxel is responsive to. Usually expressed in units of degrees of visual angle.
- Stimulus → Response. Predicting the response given the presented stimulus.
- Response → Stimulus. Predicting the stimulus that was presented given the response.