I understand that the field is very broad, but I'm trying to arrive at a better idea of how different disciplines use the word "data". For example, for computers it is:

the quantities, characters, or symbols on which operations are performed by a computer, which may be stored and transmitted in the form of electrical signals and recorded on magnetic, optical, or mechanical recording media.

However, the same cannot be always so for data in the cognitive sciences. Can you give some examples of data in your research?


In The Personality Puzzle (2012), David Funder describes a taxonomy of four different kinds of data that I've always found useful. The Personality Puzzle is a textbook in personality psychology, so its take on psychology is somewhat more as a social than biological science (though biology has its place too).

Briefly, here are the four kinds of data as described therein:

  • Self-reports: information provided by oneself about oneself, especially subjective info to which no one would otherwise have access; e.g., attitudes, beliefs, emotions, opinions, preferences, etc. This is often the domain of the Likert scale, though that's only one of very many assessment methods.
  • Informant reports: info provided about one person by others that is intended to describe the first person (not project info of interest about those reporting on that person). This info may also be subjective or socially constructed, but may be best operationalized as descriptions or judgments polled from people in a manner that welcomes this subjectivity. For example, a person's reputation or other characteristics may be assessed through the perspectives of others who may (or may not) know that person. Methods are otherwise similar to self-report in general.
  • Behavioral data: strictly objective and often observed in controlled environments or otherwise collected with a repeatable, classically empirical sampling method. This includes counts, measurements, or categorizations of behaviors like lever presses, choices or performance in a game or test, reaction time, stimulus discrimination accuracy, etc. These also include observations about autonomically regulated biological processes such as results from f/MRI, PET, CAT, EEGs, EKGs, heart rate or blood pressure readings, GSR, pupil dilation, etc., even enlargement of erectile genital organs (those studies are always interesting).
  • Life outcomes: also strictly objective, but generally less available to experimental manipulation, naturalistic, and often historical (i.e., already recorded or otherwise resolved to the state of interest). Examples include info about birth, graduation, employment, marriage, childbirth, divorce, death, etc., such as age at the time, location, partner, etc.

Really, there's two kinds of data in cognitive science, information (the data used by the cognitive systems we're interested in), and experimental data (and similar, such as correlational data), which we collect and analyse to try and better understand cognition.

It sounds like your question is about information, so I'll focus on that, but please correct me if that's not what you meant.

The Computational Approach

Most cognitive scientists, from the 1960s onwards, explicitly or implicitly accept what's referred to as the "computational theory of mind", which says the purpose of a brain, or any other cognitive system, is to processes information, in much the same way as a computer does. From this point of view your "data" are neural representations of just what you talk about in computer science, with patterns of neural activation representing logical symbolic states, which are manipulated.

Alternative Approaches


An extremely influential counterpart to this "brain as computer" approach is the the Connectionist, or "parallel distributed processing" approach. Here, the brain still represents information in patterns of neural activation, but rather than using "symbols" as you would in a digital computer, sub-symbollic features of the world around us are represented by various overlapping patterns across nodes of neurons, in a way first proposed by McCulloch and Pits (1943), but developed hugely since then, and "data" constitutes various combinations of these different feature node.

Dynamical, Embodied, Situated (and beyond) approaches

As a final note, a completely different approach to cognitive science, the "dynamical approach", argues that the brain is best seen as a complex, non-linear dynamical system, with a state (pattern of firing neurons) that is constantly evolving through exposure to the environment, and complex recurrent feedback loops (Spivey and Dale, 2004 give a good introduction to this).

When looked at this way, the brain doesn't deal with discrete "data" (which, remember, is the plural of "datum"), but rather represents the world as continuous patterns of neural activation, connections between neurons and patterns. Extending this notion, it has been argued, and shown experimentally, that information defined this way goes beyond what's in the brain: we can store and manipulate data using our bodies ("embodied cognition"), and even our immediate environment (situated cognition, see Hutchins, 1995, for an excellent discussion of this in the context of flying a plane). Finally, although I can't remember where, I have seen studies that claim that the same can be said for our clothes, giving rise to "enclothed cognition".


Data could be defined like: Information stored and obtained by a method. The information could be a way to measure the status of a system in a moment or across the time. Why I used the terms "stored" and "methods", because the information could be under the influence of the technology used to save it and the measure method also affect to the reality. This stuff made data a representation of the reality.

In Cognitive Science data could be any representation of the reality (Mainly behaviors and brain-derived information) that help us to understand the animal cognition.


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