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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 and Strohmetz (2009) review a collection of innovative uses of behavioral measurement in psychology including behavioral traces, behavioral observations, and behavioral choice.[10] 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. Brain imaging__Main article: Neuroimaging 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__ A neural network with two layers.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 properties of intelligence. Computational modeling can help us to understand the functional organization of a particular cognitive phenomenon. There are two basic approaches to cognitive modeling. The first is focused on abstract mental functions of an intelligent mind and operates using symbols, and the second, which follows the neural and associative properties of the human brain, is called subsymbolic. Symbolic modeling evolved from the computer science paradigms using the technologies of Knowledge-based systems, as well as a philosophical perspective, see for example "Good Old-Fashioned Artificial Intelligence" (GOFAI). They are developed by the first cognitive researchers and later used in information engineering for expert systems . Since the early 1990s it was generalized in systemics for the investigation of functional human-like intelligence models, such as personoids, and, in parallel, developed as the SOAR environment. Recently, especially in the context of cognitive decision making, symbolic cognitive modeling is extended to socio-cognitive approach including social and organization cognition interrelated with a sub-symbolic not conscious layer. Subsymbolic modeling includes Connectionist/neural network models. Connectionism relies on the idea that the mind/brain is composed of simple nodes and that the power of the system comes primarily from the existence and manner of connections between the simple nodes. 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, they lack explanatory powers because complicated systems of connections with even simple rules are extremely complex and often less interpretable than the system they model. Other approaches gaining in popularity include the use of dynamical systems theory and also techniques putting symbolic models and connectionist models into correspondence (Neural-symbolic integration). Bayesian models, often drawn from machine learning, are also gaining popularity. All the above approaches tend to be generalized to the form of integrated computational models of a synthetic/abstract intelligence, in order to be applied to the explanation and improvement of individual and social/organizational decision-making and reasoning. 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 Direct brain stimulation Animal models Postmortem studies |
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