Chapter 2: Approaches to Studying Sensation and Perception

Figure 1.1

The Gibsonian Approach

In contrast to the Gestalt approach the Gibsonian approach (named after the American psychologist J.J. Gibson) posits that perception is a passive and direct process. It suggests that our perception of the world reflects the rich sensory information available in our environment. The Gibsonian perspective emphasizes that our perception is shaped by the stimulus hitting our sensory receptors. However, it faces challenges when dealing with visual illusions and situations where our perception does not align with the physical world. Similarly, this approach has difficulties explaining mental imagery (where perceptual experiences occur in the absence of a sensory information).

The Information Processing Approach

The information processing approach views perception as a multi-stage process influenced by various interconnected factors. This model likens perception to a series of stages, each influencing the next. It emphasizes the flow of information through these stages and how they collectively shape our perception of the world. This approach is characterized by the interaction of different stages, demonstrating the dynamic nature of information processing.

The Computational Approach

The computational approach employs mathematical models, such as perceptrons and machine learning, to simulate how humans perceive the world. This perspective forms the foundation of artificial intelligence and is crucial for understanding how computer systems process information. While it is not extensively covered in this class, it plays a vital role in modern technology and the development of AI systems.

The Phenomenological Approach

The phenomenological approach, although less favored among perception researchers today, is rooted in descriptive exploration of human perception. It involves asking individuals to describe their perceptual experiences without imposing experimental constraints. This approach can be valuable for gaining initial insights into perceptual phenomena. For example, it is illustrated by phenomena like the Necker cube (where people describe how the near surface can switch) and the Einstein mask (where people perceive an inverted and stationary mask as following you as you move). The Necker cube is shown here and the Einstein mask was shown in class.

Figure 1.2

Psychophysics

Psychophysics is a method used to quantify the relationship between physical stimuli and our perceptual experiences. Gustav Fechner, credited with establishing this field, introduced the concept of the absolute threshold—the smallest amount of a stimulus required for detection about half of the time.

Weber's Law

Weber's Law, a fundamental principle in psychophysics, states that the just noticeable difference (JND), or the smallest perceptible change in a stimulus, is a constant proportion of the original stimulus. Mathematically, it can be expressed as ΔI/I = k, where ΔI is the change in stimulus intensity, I is the initial stimulus intensity, and k is the constant specific to the dimension being studied. As an example, Imagine you are holding a small bag of rice weighing 1 kilogram (1000 grams). According to Weber's Law, the just noticeable difference (JND) in weight perception should be proportional to the original weight. Let's assume the Weber fraction for weight perception is 0.05, meaning that the JND is 5% of the original weight.

1. Original Weight (I): 1000 grams
2. Change in Weight (ΔI): To calculate the JND, we multiply the original weight by the Weber fraction: ΔI = 1000 grams x 0.05 = 50 grams.

So, according to Weber's Law, you would need a change in weight of at least 50 grams for you to reliably detect a difference. If someone added or removed 50 grams from the bag of rice, you would likely notice the change. However, a change of 10 grams might not be noticeable because it falls below the JND dictated by the Weber fraction.

Stevens's Power Law

Stevens's Power Law builds on the psychophysical tradition, focusing on the perceived magnitude of stimuli. This law asserts that our perception of stimuli is not always linear but follows a power function. It can be expressed as P = k * S^n, where P represents the perceived magnitude, S is the stimulus intensity, k is a constant, and n is the exponent that characterizes the specific sensory dimension. This model helps us understand how our perception responds to changes in stimulus intensity. Notably, it highlights that our perception of different sensory dimensions may exhibit response expansion or response compression.

1. Response Expansion (n > 1): When the exponent (n) is greater than 1, it indicates response expansion. In this case, the perceived sensation increases more rapidly than the increase in stimulus intensity. Small changes in stimulus intensity lead to significant changes in perception.
2. Response Compression (n < 1): When the exponent (n) is less than 1, it indicates response compression. Here, the perceived sensation increases more slowly than the increase in stimulus intensity. It takes a substantial change in stimulus intensity to produce a noticeable change in perception.

Let's apply this concept to two examples:

1. Perception of Electric Shock (Response Expansion): Imagine measuring the perceived pain of an electric shock. Suppose that a small increase in electrical current results in a significantly more painful perception. In this case, the Stevens Power Law might describe this relationship with an exponent (n) greater than 1. This indicates response expansion, meaning that small changes in current result in a disproportionately larger change in perceived pain.
2. Perception of Brightness (Response Compression): Consider the perception of brightness when varying the intensity of a light source. In this case, you might find that increasing the light intensity by a small amount doesn't lead to a very noticeable change in brightness perception. The Stevens Power Law might describe this relationship with an exponent (n) less than 1. This indicates response compression, meaning that you need a relatively large change in light intensity to produce a noticeable change in brightness perception.

Thresholds and Sensitivity

Traditionally, psychophysics has focused on determining sensory thresholds, such as the absolute threshold (the minimum intensity needed to detect a stimulus) and the difference threshold (the smallest change in stimulus intensity that can be detected). However, signal detection theory challenges the concept of thresholds altogether.

Signal detection theory suggests that we don't have fixed thresholds. Instead, our perception is influenced by various factors, including the context of the situation and the instructions given. We then set a criterion for responding, which can vary from being a conservative criterion (requiring strong evidence to indicate that a stimulus was present) to a liberal criterion (requiring little evidence to indicate that a stimulus was present). This criterion can change depending on the situation and the individual.

Signal Detection Theory

Signal detection experiments involve the use of catch trials, where no stimulus is presented, to assess how observers respond.

To understand signal detection theory, let's consider an analogy. Imagine you are on a whale-watching trip, trying to spot whales in the ocean. Your decision to shout "whale" or not depends on various factors, including the situation and the people around you. If you're with family, you might be more liberal in your criterion for identifying a whale. However, with strangers, you may adopt a more conservative criterion to avoid false alarms.

In signal detection experiments, there are two main situations: trials with no signal (just noise; e.g., no whale just waves) and trials with a signal embedded in noise (e.g., a whale in the ocean). Observers must decide whether a stimulus is present or not. This decision is influenced by the amount of evidence suggesting the presence of a signal. Signal detection theory distinguishes between hits (correctly detecting a stimulus), misses (failing to detect a stimulus), false alarms (incorrectly detecting a stimulus when none was present), and correct rejections (correctly identifying the absence of a stimulus).

Sensitivity in signal detection theory is measured using "d prime" (d'). D prime represents the difference between the observer's ability to discriminate between signal and noise. A larger d' value indicates higher sensitivity, while a lower value suggests lower sensitivity. The d prime (d') value quantifies how well an observer can distinguish between signal and noise. The numerical value represents the distance between the mean of the signal distribution and the mean of the noise distribution insignal and noise. A larger d' value indicates higher sensitivity, while a lower value suggests lower sensitivity. The d prime (d') value quantifies how well an observer can distinguish between signal and noise. The numerical value represents the distance between the mean of the signal distribution and the mean of the noise distribution in standard deviation units. A value of zero suggests that there is no separation between the signal and noise distributions (i.e., the observer cannot distinguish between signal and noise). As d' increases the observer can discriminate between signal and noise to some degree. Values between 0.5 and 1.5 indicate fair discrimination and values greater than 2 indicate a strong ability to distinguish between signal and noise (a value of 3 or more is nearly perfect performance).

Additionally, signal detection theory allows for the analysis of an observer's criterion. A liberal criterion results in more hits but also more false alarms, while a conservative criterion leads to fewer hits but fewer false alarms. The choice of criterion can be influenced by factors such as instructions and the consequences of making errors.

Physiological Approaches

Physiological approaches to studying sensation and perception delve into the underlying neural and physiological processes responsible for our perceptual experiences. These approaches focus on understanding how the brain processes sensory information and how physiological factors contribute to perception.

Neural Communication and Physiology

As you will see throughout this course, basic physiology of neural communication play a crucial role in understanding perception. Neurons communicate through electrical signals, involving the flow of ions, particularly sodium and potassium. Neurons have a resting electrical charge, which changes when they are stimulated. Sodium ions rush into the neuron, making the charge more positive, while potassium ions later rush out to restore the resting state.

This process results in an action potential, a brief electrical signal that travels along the neuron. The action potential is an all-or-none response, meaning it occurs at full intensity or not at all. The rate of firing of neurons can change in response to stimulus intensity, rather than the size of the action potential itself (which is unchanged).

Recording Neural Activity

Researchers use various techniques to study neural activity associated:

1. Single Unit Recordings: Microelectrodes are used to measure the firing of individual neurons. This approach provides precise information about when and how specific neurons respond to stimuli. For example, researchers have used single unit recordings to study neurons in the visual cortex responsible for processing specific visual features.
2. Event-Related Potentials (ERPs): ERPs involve recording electrical activity at the scalp's surface while individuals are presented with specific events, such as viewing images or hearing sounds. ERPs offer excellent temporal resolution, revealing the timing of neural responses to stimuli.
3. Positron Emission Tomography (PET): PET imaging relies on a radioactive tracer injected into the bloodstream to measure regional cerebral blood flow. Greater (or reduced) blood flow indicates areas of increased (or decreased) neural activity relative to baseline. PET scans help identify brain regions involved in specific cognitive and perceptual functions.
4. Functional Magentic Resonance Imaging (fMRI): fMRI uses magnetic properties of hemoglobin to measure changes in blood flow, which is indicative of neural activity. While fMRI provides excellent spatial resolution, it can also reveal when certain brain regions become active in response to stimuli.

Modular Organization of the Brain

Physiological approaches have contributed to our understanding of the modular organization of the brain, where specific regions are specialized for particular functions. For example, the fusiform face area (FFA) in the right hemisphere is associated with face processing. These findings support the idea that different brain regions play distinct roles in perception.

In the following chapters, we will delve deeper into the physiological processes and neural mechanisms underlying specific aspects of sensation and perception, exploring how our brain interprets and makes sense of the sensory world around us.

Conclusion

The study of sensation and perception encompasses various approaches that have contributed to our understanding of how we make sense of the world. From the holistic perspective of Gestalt psychology to the computational models of artificial intelligence, these approaches offer unique insights into the complex processes governing our perception. Additionally, psychophysical principles like Weber's Law and Stevens's Power Law provide a mathematical framework for quantifying our perceptual experiences. As we delve further into the realm of sensation and perception, we will explore how these approaches have been used to study specific sensory modalities, such as vision, hearing, touch, taste, and smell.