1.2 History of Cognitive Psychology

Philosophical Roots of Psychology

While most of this chapter will focus on modern psychology as a formalized field of study, it is worth acknowledging that humans have been asking (and attempting to answer) questions about human nature, behavior, and the mind for millennia.

Greek philosopher Aristotle (384-322 BCE) studied and reasoned about human memory and dreams, among other topics related to the inner workings of the mind. Empiricists like Aristotle emphasized the importance of empirical evidence (information gathered through observation and experimentation) to support their ideas.

(Andrade & Walker, 2020)

School of psychology	Description	Important contributors
Structuralism	Uses the method of introspection to identify the basic elements or “structures” of psychological experience	Wilhelm Wundt, Edward B. Titchener
Functionalism	Attempts to understand why animals and humans have developed the particular psychological aspects that they currently possess	William James
Psychodynamic	Focuses on the role of our unconscious thoughts, feelings, and memories and our early childhood experiences in determining behavior	Sigmund Freud, Carl Jung, Alfred Adler, Erik Erickson
Behaviorism	Based on the premise that it is not possible to objectively study the mind, and therefore that psychologists should limit their attention to the study of behavior itself	John B. Watson, B. F. Skinner
Cognitive	The study of mental processes, including perception, thinking, memory, and judgments	Hermann Ebbinghaus, Sir Frederic Bartlett, Jean Piaget
Social-cultural	The study of how the social situations and the cultures in which people find themselves influence thinking and behavior	Fritz Heider, Leon Festinger, Stanley Schachter

 

Hermann von Helmholtz

Hermann von Helmholtz (1821-1894), pictured right, was inspired by research in psychophysics – the scientific study of the relation between physical stimuli, sensation, and perception). He measured the speed of the neural impulse and explored the physiology of hearing and vision. His work indicated that our senses can deceive us and are not a mirror of the external world. Such work showed that even though the human senses were fallible, the mind could be measured using the methods of science. In all, it suggested that a science of psychology was feasible.

 

An important implication of Helmholtz’s work was that there is a psychological reality and a physical reality and that the two are not identical. For example, he discussed the idea of Unconscious Inference, in which we make automatic assumptions/inferences about the world based on experience with the environment. One application for this with occluded objects: when we see an image like that to the left, we have a tendency to assume there is a square blocking our view of a complete circle, rather than a square adjacent to a circle with a piece missing (or Pac-Man trying to eat a square!).

Wilhelm Wundt and Structuralism

Wilhelm Wundt (1832-1920), a student of Helmholtz, was a German physician, physiologist, and philosopher often considered one of the founding figures of modern psychology. He promoted the idea that psychology could be an experimental field and by providing classes, textbooks, and a laboratory for training students. In 1875, he joined the faculty at the University of Leipzig and quickly began to make plans for the creation of a program of experimental psychology. In 1879, he established a research lab to study the elements and organization of consciousness – an approach to psychology that was brought to the U.S. as Structuralism by his student, Edward Titchener. Some highlights:

• Students in Wundt’s lab were trained to give systematic detailed self-reports of their reactions to various stimuli, a procedure known as introspection. Due to the reliance on self-report from participants, this did not contribute as strongly to experimental psychology as was hoped.
• Wundt and F.C. Donders (1868) also utilized Mental Chronometry (what we now discuss as reaction time or processing speed) to time cognitive processes. Donders, working with Wundt, developed an approach in 1868 known as the subtractive method. The goal was to time the completion of tasks that offered graduated differences in difficulty and number of processes employed. E.g.:
• Simple reaction time (RT) task: participant pushes a button quickly after light appears. (This represents time it takes to react to a stimulus.)
• Choice reaction time task: participant pushes one button if the light is on the right, another if light is on the left. (This represents the time it takes to make a simple choice + the time it takes to react to a stimulus.)
• Then, Choice RT – Simple RT = time it takes to make a simple choice.

While we now know the cognitive processes involved are not as directly “additive” in the way Donders assumed above (e.g., Vidal et al., 2011), the work of Wundt and his students demonstrated that the mind could be measured (if not directly, at least inferred from behavior) and the nature of consciousness could be revealed through scientific means. This went a long

way in shaping psychology into an empirical science. It was an exciting proposition, and one that found great interest in America.

Gestalt Psychology

The Gestalt movement began in Germany with the work of Max Wertheimer (1880–1943). Opposed to the reductionist approach of Wundt’s laboratory psychology, Wertheimer and his colleagues Kurt Koffka (1886–1941), Wolfgang Kohler (1887–1967), and Kurt Lewin (1890– 1947) believed that studying the whole of any experience was richer than studying individual aspects of that experience. The saying “the whole is greater than the sum of its parts” is a Gestalt perspective. Consider that a melody is an additional element beyond the collection of notes that comprise it. The Gestalt psychologists proposed that the mind often processes information simultaneously rather than sequentially. For instance, when you look at a photograph, you see a whole image, not just a collection of pixels of color. Using Gestalt principles, Wertheimer and his colleagues also explored the nature of learning and thinking. E.g., in the image to the left, we experience the Gestalt principle of “Closure” (to be discussed later) in which we tend to see a cohesive pattern/image in an incomplete stimulus.

Behaviorism

Behaviorism emerged early in the 20th century and became a major force in American psychology. Championed by psychologists such as John B. Watson (1878–1958) and B. F. Skinner (1904–1990), behaviorism rejected any reference to mind and viewed overt and observable behavior as the proper subject matter of psychology. Through the scientific study of behavior, it was hoped that laws of learning could be derived that would promote the prediction and control of behavior. Russian physiologist Ivan Pavlov (1849–1936) influenced early behaviorism in America. His work on conditioned learning, popularly referred to as classical conditioning, provided support for the notion that learning and behavior were controlled by events in the environment and could be explained with no reference to mind or consciousness (Fancher, 1987).

Below, find a video of Skinner utilizing operant conditioning (in which a preceding behavior is shaped by its consequences) to train a pigeon to turn counterclockwise. In this case, Skinner “shapes” the behavior of turning counterclockwise by waiting for a little more of that behavior to appear each time before providing a reinforcer (presenting food).

Contributions to Cognitive Psychology’s “Birth”

While it strengthened the goal of making psychology an empirical science, Behaviorism’s emphasis on objectivity and focus on external behavior pulled psychologists’ attention away from the mind for a prolonged period of time. By the 1950s, new disciplinary perspectives in linguistics, neuroscience, and computer science were emerging, and these areas revived interest in the mind as a focus of scientific inquiry. This perspective has come to be known as the cognitive revolution (Miller, 2003).

The work of psychologist Edward Tolman (1886-1959) proved influential during this time. While he utilized behaviorist methodology, he was less “radical” in his approach than Skinner. Tolman highlighted an important limitation of behaviorism in that not all learning is demonstrated through external observable behaviors. In one study (Tolman, Ritchie, & Kalish, 1946), Tolman placed hungry rats in a maze with no reward for finding their way through it. He also studied a comparison group that was rewarded with food at the end of the maze. As the unreinforced rats explored the maze, they developed a cognitive map: a mental picture of the layout of the maze. After 10 sessions in the maze without reinforcement, food was placed in a goal box at the end of the maze. As soon as the rats became aware of the food, they were able to find their way through the maze quickly, just as quickly as the comparison group, which had been rewarded with food all along. This is known as latent learning: learning that occurs but is not observable in behavior until there is a reason to demonstrate it (Spielman et al., 2020).

Noam Chomsky was another very influential figure in the early days of this movement. Chomsky (1928–), an American linguist, was dissatisfied with the influence that behaviorism had had on psychology. He believed that psychology’s focus on behavior was short-sighted and that the field had to reincorporate mental functioning into its purview if it were to offer any meaningful contributions to understanding behavior (Miller, 2003).

In the context of language acquisition, Skinner (as a behaviorist) placed much of the emphasis on imitation and reinforcement of language behaviors. Chomsky argued that we come into the world ready to distinguish different grammatical classes, like nouns, verbs, and adjectives, and sensitive to the order in which words are spoken. Then, using this innate sensitivity, we quickly learn from listening to our parents about how to organize our own language. Psychologists disagree about the extent to which language learning is innate, but at a minimum, the discussion highlighted the limitations of behaviorism in focusing solely on observable, measurable behavior.

From the Mid-1950’s on, psychologists became increasingly influenced by computational analysis, and dominant models of memory and information processing began to mirror advances in computer technology. Though somewhat outdated now, Broadbent’s Filter Model (1958) (below) suggested a flow of sensory information processing and attention selection dictated by channel capacity and layers of filters.

Advances in neuroscience and brain imaging technology have also influenced the field, such that Cognitive Neuroscience is a common approach associated with Cognitive Psychology. This can involve utilizing equipment such as Positron Emission Tomography (PET) scans, Electroencephalograms (EEGs), and Functional Magnetic Resonance Imaging (fMRIs).

Deep Learning with Connectionist, Parallel Distributed Processing, Artificial Neural Network Models

Contemporary models of information processing build on the work of philosophers, psychologists, and neuroscientists. These network models are based on the concept of Connectionism. Connectionism is an approach in cognitive science that models mental or behavioral phenomena as the emergent processes of interconnected networks that consist of simple units. Network models of memory storage emphasize the role of connections between stored memories in the brain. The basis of these theories is that neural networks connect and interact to store memories by modifying the strength of the connections between neural units. In network theory, each connection is characterized by a weight value that indicates the strength of that particular connection. The stronger the connection, the easier a memory is to retrieve.

Early influences of Connectionism include Associationism, in which philosophers proposed “laws” of association to explain how different ideas can be linked together so that if one arises, then the association between them causes the other to arise as well. William James (founder of Functionalism, listed in the earlier table of psychological schools), criticized philosophical associationism’s emphasis on associations between mental contents. He proposed a mechanistic, biological theory of associationism instead, claiming that associations were made between brain states:

We ought to talk of the association of objects, not of the association of ideas. And so far as association stands for a cause, it is between processes in the brain—it is these which, by being associated in certain ways, determine what successive objects shall be thought. (James, 1890a, p. 554, original italics)

James (1890a) went on to propose a “law of habit,” stating that “When two elementary brain- processes have been active together or in immediate succession, one of them, on reoccurring, tends to propagate its excitement into the other” (p. 566). He illustrated the action of this law with a figure (James, 1890a, p. 570, Figure 40), a version of which is presented below.

The image above illustrates two ideas, A and B, each represented as a pattern of activity in its own set of neurons. A is represented by activity in neurons a, b, c, d, and e; B is represented by activity in neurons l, m, n, o, and p. The assumption is that A represents an experience that occurred immediately before B. When B occurs, activating its neurons, residual activity in the neurons representing A permits the two patterns to be associated by the law of habit. That is, the “tracts” connecting the neurons (the “modifiable connections” in Figure 4-1) have their strengths modified.

The ability of A’s later activity to reproduce B is due to these modified connections between the two sets of neurons.

James’ (1890a) biological account of association reveals three properties that are common to modern connectionist networks. First, his system is parallel: more than one neuron can be operating at the same time. Second, his system is convergent: the activity of one of the output neurons depends upon receiving or summing the signals sent by multiple input neurons. Third, his system is distributed: the association between A and B is the set of states of the many “tracts” illustrated in the above image; there is not just a single associative link.

Connectionism more formally was discussed in the 1940s by Donald Hebb, who said the famous phrase, “Cells that fire together wire together.” This concept was very much inspired by the mechanisms proposed by William James. This is the key to understanding network models: neural units that are activated together strengthen the connections between themselves. Notably, connectionism also emphasizes the idea of distributed representations; meaning is not contained within a single symbolic unit (e.g., a neuron) but is formed by an interaction of a set of units (e.g., a neural network).

Another important feature of contemporary connectionist models (inspired by James) is parallel processing. Where many early information-processing models (See Broadbent’s model above) involve serial processing (performing one operation at a time), parallel processing allows hundreds of operations to be completed simultaneously – in parallel. This would occur at both conscious levels and automatic subconscious levels of cognition.

Applied to human neural networks and information-processing, this all suggests that memories are stored by modifying the strength of connections between neural units across Parallel Distributed Processing (PDP) networks. Neurons that fire together frequently (which occurs when a particular behavior or mental process is engaged many times) have stronger connections between them. If these neurons stop interacting, the memory’s strength weakens. Modern neuroscience has discovered a phenomenon called long-term potentiation that is often cited as a biologically plausible instantiation of Hebb’s theory (Gerstner & Kistler, 2002; Martinez & Derrick, 1996; van Hemmen & Senn, 2002).

Though network models of human learning and recall that rely on the Hebb rule have been somewhat successful, they produce errors when they are overtrained, are easily confused by correlated training patterns, and do not learn from their errors (Dawson, 2004). An additional notion required by modern connectionist models is that of nonlinear processing. John Stuart Mill, building on the associationism of his father (Mill & Mill, 1869; Mill, 1848), proposed a mental chemistry “in which it is proper to say that the simple ideas generate, rather than . . . compose, the complex ones” (Mill, 1848, p. 533). Mill’s mental chemistry is an early example of emergence, where the properties of a whole (i.e., a complex idea) are more than the sum of the properties of the parts (i.e., a set of associated simple ideas).

Mathematically, emergence results from nonlinearity (Luce, 1999). If a system is linear, then its whole behavior is exactly equal to the sum of the behaviors of its parts. This characterizes the model proposed by James above – output unit activity is exactly equal to net input. In order to increase the power of this type of model—to facilitate emergence—a nonlinear relationship between input and output must be introduced.

Neurons demonstrate one powerful type of nonlinear processing. The inputs to a neuron are weak electrical signals, called graded potentials, which stimulate and travel through the dendrites of the receiving neuron. If enough of these weak graded potentials arrive at the neuron’s soma at roughly the same time, then their cumulative effect disrupts the neuron’s resting electrical state. This results in a massive depolarization of the membrane of the neuron’s axon, called an action potential, which is a signal of constant intensity that travels along the axon to eventually stimulate some other neuron.

A crucial property of the action potential is that it is an all-or-none phenomenon, representing a nonlinear transformation of the summed graded potentials. The neuron converts continuously varying inputs into a response that is either on (action potential generated) or off (action potential not generated). This has been called the all-or-none law (Levitan & Kaczmarek, 1991, p. 43): “The all-or-none law guarantees that once an action potential is generated it is always full size, minimizing the possibility that information will be lost along the way.”

Connectionist Models and Technological Advances

Such Parallel Distributed Processing and Artificial Neural Network Models (stemming back to the late 1800s with William James) set the foundation for many present-day technological advancements. For example, humans are presently witnessing a seemingly meteoric rise in the influence of AI programs like ChatGPT on our lives. Many such programs are trained using artificial neural networks like Large Language Models (LLMs) that gather vast amounts of statistical data on information from across the internet, then use this to modify language predictions for text generation. So, despite the abrupt way in which these programs have entered our cultural sphere of knowledge and expectations, the theoretical basis for modifying the strengths of neural network connections based on experience has been in place for well over one hundred years.