Editing Cognitive science (section)

==Research methods==
Many different methodologies are used to study cognitive science. As the field is highly interdisciplinary, research often cuts across multiple areas of study, drawing on research methods from [[psychology]], [[neuroscience]], [[computer science]] and [[systems theory]].

===Behavioral experiments===
In order to have a description of what constitutes intelligent behavior, one must study behavior itself. This type of research is closely tied to that in [[cognitive psychology]] and [[psychophysics]]. By measuring behavioral responses to different stimuli, one can understand something about how those stimuli are processed. Lewandowski & Strohmetz (2009) reviewed a collection of innovative uses of behavioral measurement in psychology including behavioral traces, behavioral observations, and behavioral choice.<ref>{{Cite journal| title = Actions can speak as loud as words: Measuring behavior in psychological science | first1 = Gary | last1 = Lewandowski | first2 = David | last2 = Strohmetz | journal = Social and Personality Psychology Compass | volume = 3 | issue = 6 | year = 2009| pages = 992–1002 | doi = 10.1111/j.1751-9004.2009.00229.x }}</ref> Behavioral traces are pieces of evidence that indicate behavior occurred, but the actor is not present (e.g., litter in a parking lot or readings on an electric meter). Behavioral observations involve the direct witnessing of the actor engaging in the behavior (e.g., watching how close a person sits next to another person). Behavioral choices are when a person selects between two or more options (e.g., voting behavior, choice of a punishment for another participant).
* ''Reaction time.'' The time between the presentation of a stimulus and an appropriate response can indicate differences between two cognitive processes, and can indicate some things about their nature. For example, if in a search task the reaction times vary proportionally with the number of elements, then it is evident that this cognitive process of searching involves serial instead of parallel processing.
* ''Psychophysical responses.'' Psychophysical experiments are an old psychological technique, which has been adopted by cognitive psychology. They typically involve making judgments of some physical property, e.g. the loudness of a sound. Correlation of subjective scales between individuals can show cognitive or sensory biases as compared to actual physical measurements. Some examples include:
** sameness judgments for colors, tones, textures, etc.
** threshold differences for colors, tones, textures, etc.
* ''[[Eye tracking]].'' This methodology is used to study a variety of cognitive processes, most notably visual perception and language processing. The fixation point of the eyes is linked to an individual's focus of attention. Thus, by monitoring eye movements, we can study what information is being processed at a given time. Eye tracking allows us to study cognitive processes on extremely short time scales. Eye movements reflect online decision making during a task, and they provide us with some insight into the ways in which those decisions may be processed.<ref>{{cite journal |last1=König |first1=Peter |last2=Wilming |first2=Niklas |last3=Kietzmann |first3=Tim C. |last4=Ossandón |first4=Jose P. |last5=Onat |first5=Selim |last6=Ehinger |first6=Benedikt V. |last7=Gameiro |first7=Ricardo R. |last8=Kaspar |first8=Kai |title=Eye movements as a window to cognitive processes |journal=Journal of Eye Movement Research |date=1 December 2016 |volume=9 |issue=5 |doi=10.16910/jemr.9.5.3 |doi-access=free }}</ref>

===Brain imaging===
{{Main|Neuroimaging}}

[[Image:Hypothalamus.jpg|thumb|150px|Image of the human head with the brain. The arrow indicates the position of the [[hypothalamus]].]]

Brain imaging involves analyzing activity within the brain while performing various tasks. This allows us to link behavior and brain function to help understand how information is processed. Different types of imaging techniques vary in their temporal (time-based) and spatial (location-based) resolution. Brain imaging is often used in [[cognitive neuroscience]].
* ''[[Single-photon emission computed tomography]]'' and ''[[positron emission tomography]]''. SPECT and PET use radioactive isotopes, which are injected into the subject's bloodstream and taken up by the brain. By observing which areas of the brain take up the radioactive isotope, we can see which areas of the brain are more active than other areas. PET has similar spatial resolution to fMRI, but it has extremely poor temporal resolution.
* ''[[Electroencephalography]]''. EEG measures the electrical fields generated by large populations of neurons in the cortex by placing a series of electrodes on the scalp of the subject. This technique has an extremely high temporal resolution, but a relatively poor spatial resolution.
* ''[[Functional magnetic resonance imaging]]''. fMRI measures the relative amount of oxygenated blood flowing to different parts of the brain. More oxygenated blood in a particular region is assumed to correlate with an increase in neural activity in that part of the brain. This allows us to localize particular functions within different brain regions. fMRI has moderate spatial and temporal resolution.
* ''[[Optical imaging]]''. This technique uses infrared transmitters and receivers to measure the amount of light reflectance by blood near different areas of the brain. Since oxygenated and deoxygenated blood reflects light by different amounts, we can study which areas are more active (i.e., those that have more oxygenated blood). Optical imaging has moderate temporal resolution, but poor spatial resolution. It also has the advantage that it is extremely safe and can be used to study infants' brains.
* ''[[Magnetoencephalography]].'' MEG measures magnetic fields resulting from cortical activity. It is similar to [[EEG]], except that it has improved spatial resolution since the magnetic fields it measures are not as blurred or attenuated by the scalp, meninges and so forth as the electrical activity measured in EEG is. MEG uses SQUID sensors to detect tiny magnetic fields.

===Computational modeling===
{{See also|Computational cognition|Cognitive model}}
[[Image:Multi-Layer Neural Network-Vector.svg|thumb|200px|An [[artificial neural network]] with two layers]]

[[computer model|Computational models]] require a mathematically and logically formal representation of a problem. Computer models are used in the simulation and experimental verification of different specific and general [[property|properties]] of [[intelligence]]. Computational modeling can help us understand the functional organization of a particular cognitive phenomenon.
Approaches to cognitive modeling can be categorized as: (1) symbolic, on abstract mental functions of an intelligent mind by means of symbols; (2) subsymbolic, on the neural and associative properties of the human brain; and (3) across the symbolic–subsymbolic border, including hybrid.
* ''Symbolic modeling'' evolved from the computer science paradigms using the technologies of [[knowledge-based systems]], as well as a philosophical perspective (e.g. "Good Old-Fashioned Artificial Intelligence" ([[GOFAI]])). They were developed by the first cognitive researchers and later used in [[information engineering]] for [[expert system]]s. Since the early 1990s it was generalized in [[systemics]] for the investigation of functional human-like intelligence models, such as [[personoid]]s, and, in parallel, developed as the [[Soar (cognitive architecture)|SOAR]] environment. Recently, especially in the context of cognitive decision-making, symbolic cognitive modeling has been extended to the [[socio-cognitive]] approach, including social and organizational cognition, interrelated with a sub-symbolic non-conscious layer.
* ''Subsymbolic modeling'' includes ''[[Connectionism|connectionist/neural network models]].'' Connectionism relies on the idea that the mind/brain is composed of simple nodes and its problem-solving capacity derives from the connections between them. [[Neural nets]] are textbook implementations of this approach. Some critics of this approach feel that while these models approach biological reality as a representation of how the system works, these models lack explanatory powers because, even in systems endowed with simple connection rules, the emerging high complexity makes them less interpretable at the connection-level than they apparently are at the macroscopic level.
* Other approaches gaining in popularity include (1) [[Cognitive model#Dynamical systems|dynamical systems]] theory, (2) mapping symbolic models onto connectionist models (Neural-symbolic integration or [[hybrid intelligent systems]]), and (3) and [[Bayesian cognitive science|Bayesian models]], which are often drawn from [[machine learning]].

All the above approaches tend either to be generalized to the form of integrated computational models of a synthetic/abstract intelligence (i.e. [[cognitive architecture]]) in order to be applied to the explanation and improvement of individual and social/organizational [[decision-making]] and [[Psychology of reasoning|reasoning]]<ref>{{Cite book|last=Lieto|first=Antonio|title=Cognitive Design for Artificial Minds|year=2021|location=London, UK | publisher=Routledge, Taylor & Francis | isbn=9781138207929}}</ref><ref>Sun, Ron (ed.), Grounding Social Sciences in Cognitive Sciences. MIT Press, Cambridge, Massachusetts. 2012.</ref> or to focus on single simulative programs (or microtheories/"middle-range" theories) modelling specific cognitive faculties (e.g. vision, language, categorization etc.).

===Neurobiological methods===
Research methods borrowed directly from [[neuroscience]] and [[neuropsychology]] can also help us to understand aspects of intelligence. These methods allow us to understand how intelligent behavior is implemented in a physical system.
* [[Single-unit recording]]
* [[Transcranial direct current stimulation|Direct brain stimulation]]
* [[Animal models]]
* [[Postmortem studies]]