From which perspective does cognitive science study the mind, intelligence, and behavior?

Cognitive science is defined as the field of study that interfaces multiple disciplines such as neuroscience, computer science, psychology, artificial intelligence (AI), philosophy, linguistics, and anthropology to understand the cognitive functioning of the human mind and the underlying mental processes. This article explains cognitive science in detail, its essential methods, and the top five applications in the real world.

What Is Cognitive Science?

Cognitive science refers to the field of study that interfaces multiple disciplines such as neuroscience, computer science, psychology, artificial intelligence (AI), philosophy, linguistics, and anthropology to understand the cognitive functioning of the human mind and the underlying mental processes. It focuses on comprehending the nature of the human mind and how it uses mental representations to realize, process, transform, and manipulate knowledge.

Cognitive researchers aim to develop a deeper understanding of human intelligence and behavior by investigating the functions of nervous systems that involve critical mental faculties such as perception, memory, emotional experience, learning, reasoning, problem-solving, decision-making, and language. The history of cognitive science dates back to the 1950s, coinciding with the emergence of artificial intelligence.

Fundamentally, cognitive science relies on developing representative structures of the mind and analyzing computational procedures that run on those structures to understand better how the thinking process unfolds within the human brain. Here, mental representations are similar to computer data structures, while the computational procedures are analogous to computational algorithms that operate on the said data structures.

Cognitive science encompasses several approaches to reveal the nature of mental representations and computational procedures. The most commonly used methods include:

1. Rule-based models

The rule-based approach has typically been around since the 1970s. According to this model, thinking constitutes the application of inference rules of the kind ‘IF…THEN…’ to symbols to represent the structure of language sentences.

For example, consider the rule ‘IF you run too fast in a full-marathon, THEN you will suffer from injuries’. Applying this rule to the symbol ‘you ran too fast in a full marathon’ gives the output symbol ‘you will suffer from injuries’. On using another rule, ‘IF you suffer from injuries, THEN your body should rest for 10 hours to recover’, to the latter symbol, it yields the symbol ‘your body should rest for 10 hours to recover’.

Rule-based models have a critical role in modeling the complex facets of human thinking such as language use or problem-solving. Such rule-based approaches have been extensively used in the medical field to develop expert systems for practical purposes.

2. Connectionist models

The connectionist approach emerged in the 1980s and referred to parallel-distributed processing models. These use artificial neural networks (ANN) to model neural structures in the brain to simulate human thinking. In contrast to rule-based approaches, connectionist models run parallel computational processes rather than in a serial fashion.

Typically, rule-based systems use inference rules to simulate thinking phenomena. However, connectionism is more about simultaneously satisfying several criteria and data-processing constraints. Thus, the connectionist model is used in modern facial recognition applications. It is also used to comprehend psychological aspects that include language learning, which is key to deciphering human thinking.

3. Theoretical neuroscience

The development of theoretical neuroscience came to the fore with the ultimate integration of the rule-based model and the connectionism approach. This happened in the 1990s and 2000s as it was when cognitive science got linked to neuroscience with the development of brain-related technologies and instruments such as magnetic resonance imaging (MRI) and functional MRI (fMRI) that could observe brain activity in real-time in experimental setups.

Moreover, with the emergence of theoretical neuroscience, computational models were developed that could visualize the firing neurons (brain activity) within the brain. This helped decode the decision-making process, an intricate process (high-level reasoning) happening in the prefrontal cortex region of the brain. Moreover, these models also helped better understand human emotions associated with the brain’s amygdala region.

Specifically, the field combined inferential rules and neural networks to build models that could simulate far more mental functions than individual rule-based, or connectionist models could achieve.

4. Bayesian models

Cognitive science relies heavily on Bayesian models to understand psychological traits such as vision, motor control, social cognition, learning, and others. The foundations of these models are derived from Bayes’ theorem, which associates probability factors with human understanding. Such models are also used in modern robotic applications.

5. Deep learning models

Cognitive science has been using artificial intelligence to decode the human mind since the 1950s. Moreover, with recent advancements in AI, deep learning approaches are used in applications such as gaming, object recognition, language translation, and other allied areas.

On the fundamental level, deep learning is developed on the foundation of connectionist models and theoretical neuroscience. However, it uses far more complex and advanced algorithms, along with neural networks having a multitude of neural layers. Taking a step further, deep learning is now being explored through reinforcement learning; wherein learning is combined with the rewards from previous examples to fine-tune the model.

• DeepMind’s AlphaGo is an example of such a reinforcement learning model that defeated a human Go player in 2016.

Today, deep learning is penetrating neuroscience and is bringing about significant development in the areas of cognitive psychology.

Apart from the approaches mentioned above, mental representations and computations that reveal the details of how the mind works (how neurons fire while performing specific tasks) have been explored through cognitive theories of logical rules, concepts, images, propositions, and analogies.

See More: Top 10 Machine Learning Algorithms in 2022

Key Cognitive Science Methods

Cognitive science aligns diverse outlooks and fields to realize the scientific study of the mind and intelligence. The primary methods involved in cognitive science include psychological experiments performed on human participants, brain imaging techniques, and computer modeling approaches.

Let’s understand each method in greater detail:

1. Psychological/behavioral experiments

To quantify and comprehend intelligent behavior, it is inevitable to study subjects’ behavioral responses to various stimuli as it reveals the way any stimuli are processed within the human brain. One can achieve this by performing psychological or behavioral experiments that involve participants. Such experiments show the behavioral traits and behavioral choices of users.

These experiments help in tracking the following variables for the involved participants:

• Participant’s reaction time: This variable records the period between stimulus presentation and a user’s response. The time gap fundamentally indicates the individual’s cognition ability, how they think, and how the person processes the stimuli, which also talks about the individual’s innate nature.
• Psychophysical response: Psychophysical experiments refer to the field of cognitive psychology, wherein, user responses to certain events are recorded to better understand the subject’s cognitive biases and sensory responses. For example, experiments can be performed to determine how a user may react to loud music or traffic horns. It can also identify how users judge different color textures or respond to sounds beyond a certain threshold.
• Eye tracking: Cognitive processes such as visual perception and language processing are identified through this variable. When a user is focused on something, his eyes show minimal movement over a while. One example could be tracking the subject’s eyes while performing an online task. The observation can reveal the speed of decision-making and help develop insight into how the concerned person processed the decision.

2. Brain imaging

Brain imaging allows one to observe the subject’s brain activity in real time while performing a task. It associates user behavior with cognitive brain function to draw insights into how the brain processes information. Brain imaging techniques are central to cognitive neuroscience.

Some of the instruments used for imaging purposes:

• SPET (Single-photon emission computed tomography) and PET (Positron emission tomography): SPECT and PET inject radioactive isotopes into the user’s blood that are carried further to the brain. By monitoring the interaction of radioactive isotopes in the brain, one can reveal the active brain areas and compare them to the inactive ones.
• EEG (Electroencephalography): EEG refers to placing electrodes over the subject’s scalp and recording neural firings by detecting electrical fields that neurons within the cortex generate as thoughts and are processed within the brain.
• fMRI (Functional magnetic resonance imaging): fMRI records the quantity of oxygenated blood flowing into the brain, wherein, the amount of oxygenated blood is directly proportional to the neural activity in the brain. This metric is used to identify the brain regions that perform a specific function.
• Optical imaging: This process uses infrared transmitters and receivers to determine the amount of light reflected by different brain regions. As oxygenated and deoxygenated blood reflect light differently, one can localize active and inactive brain regions based on the light reflection variable.
• MEG (Magnetoencephalography): The MEG process records magnetic fields around the cortex, similar to EEG electrodes. It helps reveal the active brain regions while the subject is exposed to a particular task.

3. Computational modeling

Computational modeling requires a systematic representation of a problem that is logical to interpret and address. This is where the mental representation discussed earlier comes into play. Here, computational models similar to mental operations are tested for evaluating the performed behavioral or psychological experiments. Models, once built, are used in the future to assess the brain activity of individuals.

Fundamental approaches are used for modeling purposes:

• Symbolic modeling: In this approach, mental functions are mapped onto symbols. Symbolic cognitive modeling is used to investigate and simulate cognitive decision-making, social cognition, and organizational cognition.
• Sub-symbolic modeling: This approach is used in connectionist models that tend to rely on neural networks. The connectionist model dwells on the fact that the mind or brain is a composition of multiple nodes and its problem-solving features are an outcome of the connections that connect such nodes. ANN is best suited for such implementation.
• Hybrid modeling: Hybrid modeling combines two or more approaches, such as using symbolic models to map onto connectionist models that help develop hybrid intelligent systems. Moreover, Bayesian models derived from machine learning are also a part of hybrid modeling.

All the above-discussed approaches are either used as standalone computational models or hybrid ones to model cognitive faculties such as language, vision, perception, etc. Such models play a crucial role in gaining a better understanding of human cognition and, in turn, enable the development of intelligent systems that have decision-making and reasoning abilities at par with that of humans.

Other neurobiological methods to help understand the human mind include direct brain stimulation, single-unit recording, and autopsy studies. In direct brain stimulation, a current is delivered to a subject through electrodes for cognitive enhancement. The single-unit recording method employs a microelectrode system to determine the subject’s electro-physiological responses of a single neuron while the subject performs a task.

Post mortem studies refer to the brain study performed on an individual’s brain after their demise in cases where the subject suffered from some kind of phenomenological condition such as speaking problems, paralysis, or Alzheimer’s disease.

See More: What Is HCI (Human-Computer Interaction)? Meaning, Importance, Examples, and Goals

Top 5 Applications of Cognitive Science

Cognitive science develops empirical theories of the mind that find application in several domains such as education, operations research, human-factors engineering, human resource management, medicine, etc. Let’s dig deeper into the role played by cognitive science in driving each of these fields.

1. Education

Cognitive science contributes significantly to the academic arena by making learning more engaging. Here, critical factors such as visual perception or response to auditory tones that affect the learner are studied to improve the process of learning. This is achieved by answering fundamental questions such as ‘how long can a learner stay fully focused on a task?’, ‘how does the brain decode the delivered information?’, and ‘how can one avoid distractions to improve focus?’, which help the teacher develop an understanding of the cognitive psychology of a group of students in a classroom. This helps in designing the academic curriculum in a better way.

Moreover, with cognitive science, the personalization of learning programs is also possible, which can help the teaching staff design performance-based learning plans for students with similar cognitive traits. The teaching faculty gains insights into the mental capacity of learners through visual and auditory behavioral patterns. It allows them to formulate personalized academic programs for individuals based on perceptive cues.

2. Operations research

Operations research is a field of study that applies scientific methods for the seamless management and administration of military, government, industrial, and commercial processes by making decisions responsible for controlling the system’s operations. This implies that it focuses on how managerial decisions are made or should be made, how to process the data necessary for effective decision-making, and how to implement decisions. All these questions are answered through cognitive science, as decision-making is a mental phenomenon and is better understood by unraveling the mysteries of the human mind.

Thus, cognitive science in operations research tends to improve the operations of existing organizational and administrative systems. Here, cognitive science integrates several other knowledge streams, such as communications theory, decision theory, behavioral sciences, and general systems theory, to boost the outcome of operations research.

3. Human-factors engineering

Human-factors engineering is an applied science discipline that focuses on designing the working and living space based on the user’s requirements. This applied field emerged during World War II when new military equipment was so complex that it was difficult for anyone to operate it safely. This brought the focus onto the ‘human-factor’ as it became essential to develop equipment that everyone could easily manage.

With cognitive science coming to the fore, development along the lines of human-factors engineering gained steam. The focus today is to design products, equipment, facilities, and environments based on the suitability for people and, at the same time, ensure that the efficiency of the work performed by people is enhanced along the way.

In short, cognitive science works hand-in-hand with the human-factors field by applying information that defines human cognitive abilities, psychological capabilities, and behavior in designing systems that are well-suited for people of all categories.

4. Human resource management

Human resource departments see a paradigm shift in their operations as HR professionals increasingly use tools and applications that reveal human-like intelligence. Cognitive systems that use data-driven models are being explored for recruitment, which not just help in talent search and tracking but also define the working styles in a company. Moreover, HR staff are also using cognitive tools to improve workforce decision-making. 

Business managers also benefit from cognitive systems as team member insights are directly shared with them on easily accessible apps and tools, reducing the workload on HR professionals. It allows HR members to take up other tasks such as building business relationships with new vendors/companies or providing strategic advice to top-level management, and so on.

5. Medicine

Cognitive science-based psychological experiments and computational models have played a key role in developing apt medical reasoning approaches that have helped diagnose and treat illnesses, diseases, and ailments. Moreover, with rising awareness about mental health, comprehending the cognitive and neural processes that unfold in a healthy mind has become crucial. That’s where cognitive science plays a key role.

Cognitive science also provides us with ways to deal with mental issues such as depression or even handle diseases such as Alzheimer’s and schizophrenia.

Key developments

Besides the applications discussed above, cognitive science also impacts several other industries. Let’s look at the current trends where cognitive science is contributing immensely.

• Artificial general intelligence: AGI intends to develop machines that showcase human-like reasoning capabilities. Today, deep neural and multi-modal neural networks are used as tools with reinforcement learning to create full-fledged AGI systems.
  With developments in cognitive science and artificial intelligence, experts in the domain expect the first AGI system to emerge within the next three decades.

• Brain-machine interfaces: Brain-machine interfaces provide a direct connection between neural structures and machines. Today, these interfaces enable users to control their prosthetic hands and feet simply by giving commands through the mind (thoughts).
  In 2019, the U.S. Defense Advanced Research Projects Agency (DARPA) offered funds to a team of researchers from Carnegie Mellon University to develop a neural interface that can be worn as a wearable device and can record and simulate brain activity in real-time. Also, companies such as Neuralink are trying to develop brain interfaces that will allow us to record and replicate brain functions.

• Brain imaging: Brain imaging techniques offer a pathway to understanding how the brain processes information. Moreover, it also helps investigate how a disease can disrupt the data processing capacity of the brain.
  The Human Brain Project, funded by the EU, began in 2013 to understand brain networks, consciousness, and ANN by developing a complete nervous system model. Cognitive science is a big part of this project. Similarly, a team of scientists working on the OpenWorm project has successfully uploaded the mind of a roundworm (Caenorhabditis elegans) into a robot named Lego through software. The software makes the robot think, act, and behave like a worm.

Looking at the current developments, one can say that we are inching closer to understanding our minds and brains better. Such efforts are making us better at designing machines that can ‘think and act’ for themselves.

See More: What Is Super Artificial Intelligence (AI)? Definition, Threats, and Trends

Takeaway

Cognitive science emerged with the human desire and curiosity to better understand how we think and act and how the brain processes vast volumes of data. With growing sophistication in technologies such as AI, brain imaging, and powerful neural interfaces, decoding the mind has become much easier.

Moreover, cognitive experiments and techniques have allowed us to explore the mental faculties that play a role in diseases such as Parkinson’s, Alzheimer’s, and schizophrenia. Advances in cognitive science have also enabled us to develop computer algorithms that can mimic the human brain. If our technological prowess continues to grow at the current pace, the day is not far when we will be able to decode the meaning of every single neural firing within the brain.

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