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Cognitive psychology in some form has been a field of study for thousands of years. Early philosophers addressed questions about cognition that are still viable today. For example, Aristotle suggested an early metaphor for the mind to explain how memory processes work. He proposed that our memory could be envisioned as a wax tablet with memories formed in the tablet like molds in hot wax. The durability of the memory depended on different factors in the same way that the durability of molds in wax can vary; if the wax tablet is heated, the form can become distorted or disappear. As you will read later in this chapter and in Chapters 5 , 6 , and 7 , memory researchers still seek models of memory in current research.

As scientific methods were developed in other fields (e.g., physics, biology, chemistry), researchers began to apply these methods to the study of the mind. Wilhelm Wundt (one of the first psychologists) studied conscious experience through introspective methods that involved systematic self-reports of a person’s thoughts. In this way, some early psychologists studied how people perceived sounds, colors, and other sensory experiences. Others (e.g., Fechner, Helmholtz) studied perception using psychophysical methods with a goal of developing laws of perception. Another early psychologist, Ebbinghaus, studied the processes of memory by testing his own memory extensively to determine the savings in relearning that can be gained from previous exposures to information. He measured the decline in his memory performance over time and thus mapped out the forgetting curve that researchers still find in current studies of memory performance over time (see Chapter 6 for further discussion of the forgetting curve).

In the early to mid-twentieth century, the study of cognition fell out of favor in psychology with the rise in popularity of the behaviorist perspective. Behaviorists argued that introspective methods, such as the methods used by Wundt, were biased by the perspective of the subject. How did the researcher know that the mental processes of the mind were consciously accessible and could be verbally reported in an accurate way? Instead, behaviorists focused on behaviors they could directly observe, with the thought processes behind the behaviors of less interest. However, by the mid-twentieth century, with the development of information-processing approaches to studying the mind and behavior, cognitive psychology as a field took hold and has been a driving force in psychology ever since. An important influence in this change was an attack on the behaviorist approach to language learning by the linguist Noam Chomsky. A prominent behaviorist, B. F. Skinner, had proposed that language learning occurs through conditioning processes (Skinner, 1957). In other words, language development occurred through the imitation of speakers around a child and the feedback (reinforced or punished) the child’s speech elicited. Chomsky (1959) presented a strong counter to this proposal, the centerpiece of which pointed out that children produce language that has never been produced around them or reinforced (e.g., original sentences never heard before, incorrect grammar). Instead, Chomsky suggested that children have the mental capacity to learn the rules of the language(s) spoken around them without explicit feedback on the language they produce. In other words, language abilities result from cognitive processes inherent in humans. From Chomsky’s argument, psychologists began to realize that the study of cognitive processes is an important part of understanding behavior—that understanding the processes behind the overt behaviors would advance our understanding of the mind and behavior in important ways. Still, behaviorism did influence the way we study cognitive processes today. Its focus on the experimental examination of behavior shaped the way researchers approach the study of mental processes. Experimentation is still the focus, but cognitive psychologists examine the behaviors resulting from the mental processes being studied.

Despite his important contribution, the field of cognitive psychology now differs somewhat from the approach Neisser discussed in his book. For one thing, the topics in this text are broader in scope than those from Neisser when he first described cognitive psychology. For example, in each chapter of this text you will find discussion of work in neuroscience, a field that examines the biological underpinnings of cognitive processes. Cognitive neuroscience has become one of the most influential areas of cognitive psychology. It is a topic introduced in Chapter 2 and comes up throughout the book, as research in neuroscience informs theory about many different cognitive processes. Thus, this area of cognitive psychology brings together different topics under the umbrella of a biological approach to the study of cognition. In another example, researchers today take a more holistic approach to memory than was taken in Neisser’s book. He discussed memory by modality of information (e.g., “visual memory,” “active verbal memory”) instead of as one connected topic. In fact, the study of memory has become a large part of the study of cognition in the decades that followed the publication of Neisser’s book. A glance at the table of contents of this text shows three chapters ( Chapters 5 – 7 ) devoted to memory, and this topic is touched on in additional chapters as it connects with other topics (e.g., concepts, imagery). Finally, cognitive psychology is not an isolated field. It has important connections to other fields such as social psychology, philosophy, biology, and the law. You will see some of these connections illustrated in the text as we discuss the mental processes that make up the field.

Consider once again the shopping story with which we started the chapter (see Photo 1.5 ). This story includes many behaviors that we (as cognitive psychologists) may wish to understand. Let’s focus on one of them: deciding whether to take the “buy 2, get 50% off” deal. Our behavior of interest here is how one makes this and other similar decisions. In the context of research, the behavior of interest is typically referred to as the dependent (or response) variable . Having identified what we want to explain, the next step is to identify what and how different variables might affect this dependent variable. The variable you have control over and can control and manipulate is known as the independent (or explanatory) variable . The set of variables and how they are related to each other is what constitutes our theory. For this example there may be many relevant variables, but for our purposes here let’s keep it simple and just pick two: the nature of the deal being offered (“buy 2, get 50% off”) and the starting price of the product. Suppose our theory says that people make their decisions based on how they frame their potential gains and losses (e.g., Sinha & Smith, 2000; Thaler, 1985). In other words, the shopper’s decision may depend on whether he or she is thinking about the deal as either a gain or a reduced loss. How the deal is presented may have an impact on how shoppers view the deal. Consider three ways of presenting what is essentially the same deal: “50% off,” “buy one, get one free,” and “buy 2, get 50% off” (so if you buy two boxes with an initial price of $1 each, you’ll pay only $1 total with all three deals; see Figure 1.1 ). The first case frames the deal in terms of price savings (a reduced loss), the second in terms of getting extra product (a gain), and the third is a mixture of the two. With a starting price of $1, consumers may view the potential of gaining an extra product as most important. However, if the starting price of the product is larger (e.g., $5), then consumers may change their decision-making processes in favor of reduced losses. These last statements amount to predictions or hypotheses made by the theory. The next step is to design research studies to test the predictions derived from the theory.

Research Methodologies

While following chapters describe research and theories across a broad spectrum of behaviors, the methods used can generally be classified into three approaches: case studies, correlational studies, and experimental studies.

Our intuition tells us that we experience the world as it happens. Thinking feels very fast, and until the mid 1850s, it was generally assumed that thought moved at speeds similar to the speed of light. That, combined with the internal nature of thought (as something that goes on inside the head), led most to assume that thought was unmeasurable. That changed when German physiologist Herman von Helmholtz (1850/1853) began attaching electrical wires to the leg muscles of frogs. His studies established that the speed of neural transmission is approximately one meter/second (substantially slower than the 299,792,458 m/s that light travels). Suddenly, the potential to measure mental processes did not seem so out of reach. Following Helmholtz’s discovery, researchers began using measures like accuracy (e.g., percentage of correct responses) and response time (e.g., how fast subjects make a response to a stimulus) as indicators of mental processes (sometimes referred to as mental chronometry).

1. Enter the letter for the approach to the study of cognition next to its corresponding definition below:
   1. Representationalism
   2. Embodied cognition
   3. Biologically motivated models
      • ___ describe cognitive processes in a similar fashion to the physiological functioning of the brain
      • ___ describe cognitive processes as operating on knowledge concepts represented in our minds
      • ___ describe cognitive processes as the interplay between the body and the environment
2. Which core principle of the scientific method involves the identification of the underlying causes of behavior?
   1. empiricism
   2. determinism
   3. parsimony
   4. testability

3. Which core principle of the scientific method involves the assumption that simpler explanations of behavior are preferred?

   1. empiricism
   2. determinism
   3. parsimony
   4. testability

   Use the following study description to answer questions 4 through 7:

   A researcher is interested in examining the relationship between one’s actual memory abilities and one’s perception of how good his or her memory abilities are. Subjects in this study are given a study list of words and asked to remember these words after a short delay. They are also given a questionnaire and asked how good the subject thinks his or her memory is, where a high score means the subject thinks he or she has high memory abilities. The researcher finds a small but positive relationship between the memory test scores and the questionnaire scores.

4. What type of research design is used in this study?
   1. experiment
   2. case study
   3. correlational study
5. Explain how you know which research design is being used.
6. Which of the following are dependent (response) variables in this study? (Choose all that apply)
   1. the delay between the study list and the memory test
   2. the score on the questionnaire
   3. the score on the memory test
   4. the number of words in the study list
7. The results indicated a positive relationship between the variables that were measured. Explain what this means.
8. In what way does an experiment differ from other research designs?
9. The measure used by researchers that indicates the speed with which someone completes a task is known as
   1. accuracy.
   2. reaction time.
   3. self-report.
   4. an independent variable.
10. What are two “metaphors of the mind” that have influenced the development of theories of cognition?
11. What are two developments that led to a rapid expansion of the field of cognitive psychology after the mid-twentieth century?
12. Describe Donders’s experiments and explain how they propose to measure cognitive processes.

• 1.1. List four cognitive processes studied by cognitive psychologists.

  Processes involved in understanding and using information are studied by cognitive psychologists. These generally include attention, memory, perception, language, concept formation, imagery, and judgment and decision making. These processes include the neurobiological processes involved.

• 1.2. What three events influenced the development of the field of cognitive psychology?

  The three main influences on the development of cognitive psychology as a unified field are (1) Chomsky’s arguments against a behaviorist description of language development, (2) the development of computer technology models of information processing, and (3) the publication of Ulric Neisser’s book tying together different topics of study under the field of cognitive psychology.

• 1.3. From the description of the types of processes studied in cognitive psychology, what processes do you think were involved in generating your responses to the two previous questions?

  Many answers are possible here, but some involve memory of the information, perception of the writing on the page, attention to the individual words in the sentence, and interpretation of the language in the writing.

• 1.4. How are the representationalist and biological approaches connected?

  The representationalist approach proposes that information is represented symbolically in the mind. The biological approach suggests a physiological means of representing information in the brain.

• 1.5. What does embodied cognition mean?

  The embodied cognition approach assumes that cognition serves the purpose of allowing us to interact bodily in the world and developed around the structure of the human body for that purpose.

• 1.6. In what ways are the biological features of the brain important in the study of cognition?

  The activity of neurons provides a physical structure and mechanism for cognitive processes to take place in the mind. Features of neuron processing are considered when current models of cognition are developed.

• 1.7. Given what you know so far about cognitive psychology, which of the approaches described in this section do you think you would follow as a researcher in psychology? Why?

  Answers will vary.

• 1.8. What core principles is the scientific method founded on?

  The core principles are determinism, empiricism, parsimony, and testability. Determinism is the assumption that events in the world have identifiable causes. Empiricism is the assumption that those causes can be understood through observation of the world. Parsimony is the assumption that simpler causes are more likely to be true. Testability is the assumption that theories about causes can be tested through observation of the world.

• 1.9. What are the main differences between case study, correlational, and experimental designs?

  In a case study, researchers are interested in learning about different aspects of the behavior of an individual or group. In a correlational study, researchers are interested in learning about relationships between measured variables. In an experiment, researchers are interested in learning about a cause and effect relationship between variables to test hypotheses about the causes of behaviors.

• 1.10. What are the main advantages and disadvantages of the different approaches?

  Case studies allow for thorough study of a unique individual or group, but they do not allow for tests of causal relationships, and the results may not generalize beyond the individual or group being studied. Correlational studies allow study of behaviors of large groups of individuals but do not allow researchers to fully test the cause of those behaviors. Experiments allow for tests of causal relationships but due to control of extraneous factors may not generalize to real-life behaviors.

• 1.11. Consider one of the other behaviors described in the shopping example. Identify potential variables that may impact that behavior and design a research study to examine how those variables are related.

  Answers will vary.

• 1.12. What are two commonly used dependent measures in cognitive psychology?

  Two commonly used dependent measures in cognitive psychology are accuracy and reaction time.

• 1.13. Briefly explain the logic used in Donders’s subtractive method.

  Donders compared reaction times in two situations: one in which subjects are asked to press a single button when a light comes on and one in which subjects must choose between two buttons to press depending on which side (right or left) the light appears on. In the first situation, the reaction time includes the motor processes involved in pressing a button at a specific time. In the second situation, the reaction time involves everything from the first situation plus the decision-making process needed to decide which button to press. By subtracting the first reaction time from the second, what is left is the time it takes to decide which button to press—in other words, the time it takes to “think.”

• 1.14. Think back to the shopping story that started the chapter. Suppose that you were interested in studying how the shopper understood the bagger’s question “Paper or plastic?” How might you design a study to investigate this issue?

  Answers will vary.

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Figure 2.1 Phineas Gage’s Brain

Source: Van Horn, J. D., Irimia, A., Torgerson, C. M., Chambers, M. C., Kikinis, R., & Toga, A. W. (2012). Mapping connectivity damage in the case of Phineas Gage. PLoS One, 7(5): e37454. doi:10.1371/journal.pone.0037454

The Neuron

The brain is composed of billions of microscopic neuron cells forming the basic structure seen in Figure 2.3 . Neuron activity is both chemical and electrical. Chemicals called neurotransmitters are first brought into the cell by the dendrites at the top end of the neuron. These neurotransmitters provide signals to the cell that are either excitatory (i.e., more likely to fire) or inhibitory (i.e., less likely to fire). The cell body of the neuron takes in these chemical signals from the dendrites and determines if there is enough of an excitatory signal to allow the neuron to fire. If so, an action potential occurs that creates an electrical signal that travels down the neuron’s axon . This electrical signal is detected in some of the brain recording techniques used by researchers. Once the electrical signal reaches the end of the axon, the terminal buttons release neurotransmitters into the synapse to be collected by other neurons nearby. Then the process begins again.

The process of the action potential is what creates the electrical signal in the neuron when it fires. This activity occurs within the axon of the cell. Before the neuron fires, the inside of the axon contains a resting state negative charge due to the division of ions in the fluid inside and outside the cell (see Figure 2.4 ). The action potential redistributes these ions through channels in the axon’s membrane that control the flow of potassium (K ⁺ ), sodium (Na ⁺ ), and chlorine (Cl ^– ) ions in and out of the cell. When the excitatory signal comes down the axon from the cell body, the axon opens specific channels in the axon membrane to allow sodium to flow into the axon, producing a positive charge inside the cell. The channels open quickly in sequence from the top of the axon (at the axon hillock) near the cell body down to the end near the terminals that contain the neurotransmitter (see Figure 2.5 ). This positive charge can be detected and recorded by electrodes that are placed either inside the cell or on top of the scalp, as we see shortly in the discussion of brain recording techniques. Once the action potential is complete, other channels open in the axon membrane to allow potassium (K ⁺ ) to flow out of the cell and the sodium channels close. This redistributes the ions back to the resting negative state inside the axon. The excitatory message then reaches the terminals and a neurotransmitter is released into the synapse (see Figure 2.6 ).

The synapse is the small gap between neurons in the brain. Each neuron is connected to other neurons in an organized network that allows the pattern of firing in the network to translate into specific thoughts or behaviors. This is how information is processed and stored in the brain: through the pattern of firing across multiple neurons within the network (i.e., specific neurons being active or not active or firing at different rates) and types of connections (excitatory or inhibitory) across the neurons connected in each network.

The Brain

The brain is composed of the networks described in the previous section , which are organized according to their cognitive functions. This is known as localization of function. Many of the clinical cases reviewed in the previous section provided the initial information we have about localization and lateralization (i.e., the two hemispheres of the brain contribute to different types of tasks) of brain function through the deficits present in different areas of brain damage. Looking at the kind of task deficits these patients exhibited helped researchers to identify brain areas (i.e., the damaged areas) that were important for completing those tasks. These early studies suggested that different areas of the brain specialized in different functions. Figure 2.7 shows the four lobes of the brain and functions that are localized in those brain areas. Recent research in cognitive neuroscience has used the knowledge gained in early case studies to focus on different areas of the brain as researchers examine the functioning in different cognitive tasks. The newer brain recording techniques described in the next sections have allowed researchers to go beyond the basic knowledge of localization and lateralization of function to map out more specific brain areas and to piece together full neural systems (i.e., a collection of brain areas organized in pathways) that are involved in different tasks. We explore some of the most recent research in cognitive neuroscience throughout the subsequent chapters that cover different cognitive processes in further detail.

A technique available to record the electrical signals from neurons is single-cell recording . In this technique a tiny recording needle is inserted into a neuron in an area of the brain the researcher is interested in (see Figure 2.8 ). However, this technique requires surgical insertion of the needle and bonding to the head to keep the needle steady (see Figure 2.9 ). Thus, this technique is typically used only in research with laboratory animals. Such recordings have contributed important information about cognition. For example, using single-cell recordings from monkeys, Rizzolatti, Fadiga, Gallese, and Fogassi (1996) discovered a new type of neuron they called a mirror neuron. This neuron fired both when the monkeys picked up an object and when the monkeys were watching the researchers or other monkeys perform that action. In other words, these neurons were active when motor actions were performed and when the monkeys were just watching a motor action they knew how to perform. Since this discovery, researchers have suggested that mirror neurons may play a role in many sorts of social cognitions, including understanding others’ actions, imitation of others’ actions, and facilitation of language through gestures (Rizzolatti & Craighero, 2004). Other work using single-cell recordings has shown that neuronal cell responses can be extremely specific. For example, Quiroga, Reddy, Kreiman, Koch, and Fried (2005) found neurons in the hippocampus (known to be involved in memory functioning) that were selectively responsive to photos of celebrities such as Jennifer Aniston and Halle Berry (in recordings from epilepsy patients undergoing treatment). These results are consistent with the idea that neurons serve as feature detectors (see Chapter 3 for more discussion of feature detection); in this case, the features are specific faces. These neurons have been called “grandmother cells” (Gross, 2002), because they suggest that we might even have a neuron (or set of neurons) that selectively responds to the face of our grandmother (assuming we have met her before).

Another technique that records the electrical signals from neurons is electroencephalography or EEG . When recording an EEG, a set of electrodes is placed on the head (see Photo 2.1 ) to record the electrical signals from groups of neurons in different areas of the brain. Because the electrodes are recording from outside the skull, it is the activity of the neurons closest to the skull (primarily neurons in the outer cortex) that is being recorded. The activity is recorded over time to detect changes (positive or negative) in the electrical signals (see Figure 2.10 ). Researchers can use EEG recordings to examine an event-related potential (ERP), which is a change in activity related to a specific event like the presentation of a stimulus. In that way, they can determine if there is an effect of that stimulus presentation on neuron activity and in what general area of the brain the effect occurs. Electrical activity patterns can be overlaid onto a map of the brain to show the general location on the cortex of the different levels of electrical activity.

An even newer technique that also relies on the electrical activity in the brain involves transcranial magnetic stimulation (TMS) . With TMS, researchers use a magnetic field to excite or inhibit neuron activity to investigate functioning in specific areas or processing systems of the brain. Like EEG and MEG, this technique is noninvasive, as it involves tracing a magnetic coil over the area of the brain the researcher wishes to study (see Figure 2.11 ). The electrical activity (an increase or decrease) can then be recorded using one of the brain imaging techniques discussed in the next section (e.g., magnetic resonance imaging). Studies (e.g., Sach et al., 2007) using TMS have shown that some cognitive tasks (e.g., making spatial judgments for visual stimuli) use a broader range of brain areas (e.g., frontal lobe) than what was previously thought using other brain recording techniques.

A similar technique is transcranial direct current stimulation (tDCS) . Like TMS, neuron activity can either be excited or inhibited using this technique. However, where TMS uses a magnetic field to create the electrical current, tDCS delivers a small electric current to the brain through electrodes attached to the scalp. Thus, it is also a noninvasive technique. tDCS is cheaper and easier to use than TMS but produces a weaker effect on neuron activity than TMS. Both of these techniques are becoming more popular for use in cognitive neuroscience research.

Recording Activity in the Living Brain

Throughout this text, we discuss studies that have used the brain recording techniques described in this chapter to illustrate the connection between brain function and the cognitive processes discussed. Here we highlight two of these studies to illustrate the use of these techniques in cognitive neuroscience. In later chapters, we discuss some of the most recent cognitive neuroscience studies in perception, attention, memory, and language.

In an example of this type of study, Barron, Riby, Greer, and Smallwood (2011) used ERP recordings to examine the factors that contribute to mind wandering (i.e., thinking about things other than the current task you are working on). Do you ever start thinking about something going on in your life (e.g., an argument with your boyfriend, girlfriend, or spouse or an assignment that is due at the end of the week) while you are reading this text? If so, then you have experienced the type of mind wandering that Barron et al. studied. These researchers recorded EEGs during a task where subjects were asked to respond to a rare target event (a red circle appearing) that occurred in a series of presented stimuli (green and blue squares). However, the nontarget stimuli were presented in different proportions. Green squares were presented often and blue squares were presented as infrequently as the red circles. This type of stimulus presentation was used to see if the blue squares would capture the subjects’ attention even if they were not asked to respond to them (see Figure 2.13 ).

Past studies of this task have shown an increase in neuron activity in the parietal cortex about 300–500 milliseconds (ms) after the red circle is presented, which is believed to be related to maintenance of the stimulus in memory. Further, a similar increase in activity is shown in the frontal lobe if the blue square (that requires no response) is presented. The activity in the frontal lobe that occurs with this distracting rare event is believed to be due to attention being paid to this stimulus because it occurs infrequently in the trials (see Figure 2.14 ).

In the Barron et al. (2011) study, subjects also completed a survey at the end of the trials to gauge the amount of mind wandering that occurred during the task. Subjects were separated into groups: high, medium, and low mind wandering. The study was designed to investigate different theories about how mind wandering occurs for those who reported off-task thoughts during the task (i.e., the high mind wandering group). For example, mind wandering might happen because something distracts the person from his or her main task. If so, subjects in the high group should show greater brain activity in the frontal cortex area when the distracting blue squares are presented. Alternatively, mind wandering might be due to subjects completely disengaging from the task and focusing attention on other thoughts. If so, subjects in the high group should show lower brain activity in both the frontal and parietal cortexes when the target and nontarget rare events (red circles and blue squares) are shown, because they are not attending well to any of the stimuli in the task. The results of the Barron et al. (2011) study showed that subjects with high levels of mind wandering had lower levels of brain activity in response to both the red circles and blue squares, supporting the idea that subjects were not attending to the task while their minds were wandering (see Figure 2.15 ). The researchers concluded from these data that suppression of the external events (i.e., not paying attention to the rare events, regardless of whether a response is required) contributes to mind wandering. This study shows how EEG/ERP studies can be used to test theories about cognitive processes.

Brain imaging techniques are also frequently used in cognitive neuroscience studies. An example of this type of study was done by Segaert, Menenti, Weber, Petersson, and Hagoort (2012) to investigate the link in processing between language production and comprehension. The similarities and differences between language comprehension and production have been a topic of interest in the past few decades within language research as researchers in this area develop and test theories of how these processes occur (see Chapter 9 for more discussion of language comprehension and production processes). Segaert et al. (2012) used fMRI recordings to test the idea that the same brain areas are active during syntactic processing (i.e., understanding how words fit together grammatically in sentences) in both language comprehension and production. Subjects were asked to complete a task of either comprehending a sentence or producing a sentence when a verb and a picture were presented. The color of the verb (green or gray) indicated whether a comprehension trial or a production trial was used. fMRI scans of the subjects’ brains were taken during the task. The researchers examined the change in brain activity when the same syntactic structure of sentences was repeated in the trials to see if adaptation to the structure (i.e., lowered brain activity) would be seen. They then compared the adaptation effects across the comprehension and production trials to see if adaptation was similar across speaking and listening trials. Results showed adaptation to the repeated syntactic structure of the sentences in both comprehension and production trials. In addition, the same level of adaptation was found in both speaking and listening trials. The researchers concluded that the same brain activity contributes to syntactic processing in both comprehension and production of language.

The newest brain recording technologies have allowed cognitive neuroscientists to gain important knowledge about the connection between brain function and cognitive processes. As an example from language research suggests (Pulvermüller, 2010b), four key questions can be answered from cognitive neuroscience research: (1) where the brain activity occurs during specific cognitive tasks, (2) when the brain activity occurs during a task (e.g., at stimulus presentation or after a delay when processing has begun), (3) how the brain activity occurs (e.g., in specific networks of brain areas), and (4) why brain activity occurs (i.e., testing hypotheses about how the processing occurs in particular cognitive tasks). Thus, there is a great advantage in using brain recording techniques in cognitive neuroscience research to learn about these specific aspects of cognitive functioning. However, some disadvantages exist as well. One disadvantage is that not all cognitive tasks are easily adapted to the brain recording techniques. The neuroscientific study of insight (i.e., that “aha” moment when you suddenly realize how to solve a problem; see Chapter 11 ), for example, has been difficult to conduct because it is hard to predict when insight will occur for a specific problem. Luo and Knoblich (2007) describe the difficulties in using fMRI and EEG techniques to study the process of insight and some of their methods to adapt insight studies to brain recording techniques. Another disadvantage is the limited availability of brain scan technology. Because MRI machines are expensive and also serve as a medical tool, it can be difficult for researchers to obtain time available for use of these devices. EEG machines and technology are relatively cheaper and more readily available for research, but their use can be time-consuming for subject participation. Thus, although these recording techniques represent significant advances in our ability to connect cognitive function with brain activity, they are not without drawbacks.

1. Explain how this study used recordings of brain activity to test a theoretical description of a cognitive process.
2. What was the primary manipulated variable in this experiment? (Hint: Review the Research Methodologies section in Chapter 1 for help in answering this question.)
3. Do you think the researchers would have achieved similar results if they had used EEG instead of fMRI in this study? Why or why not?
4. Explain why it was important for the researchers to show that subjects were slower in performing the nonfocal than the focal prospective memory task.

• How is the examination of brain activity involved in the study of cognition?

  A number of brain activity recording techniques are used by cognitive neuroscientists to better understand how brain activity is tied to cognition. All rely in some way on neuron activity, with some (single-cell recordings, EEG) measuring the electrical signals from neurons and others (PET, fMRI) recording images of neuron activity in larger areas of the brain.

• How do case studies of individuals with cognitive deficits inform us about the connection between cognition and brain function?

  Individuals who have suffered a brain lesion can help us connect cognitive deficits to specific areas of the brain. By examining the area(s) of the lesion and which cognitive deficits the individuals have, researchers can make hypotheses about the primary function of different areas of the brain. Much of the early knowledge of localization of function in the brain came from such clinical case studies.

• What can be learned about cognition through measurements of neuron activity in the brain?

  Like clinical case studies, researchers can connect specific brain areas with cognitive abilities. However, measurements of brain activity also allow researchers to provide better tests of hypotheses about brain function because experiments can be conducted with brain activity as the dependent measures.

• Can all behavior be explained in terms of brain activity?

  Some studies suggest that it can, at least for simple behaviors. However, the answer to this question is not yet known.

• Axon 27
• Dendrites 27
• Electroencephalography (EEG) 33
• Functional magnetic resonance imaging (fMRI) 36
• Magnetic resonance imaging (MRI) 35
• Magnetoencephalography (MEG) 34
• Neuron 27
• Positron emission tomography (PET) 36
• Single-cell recording 31
• Synapse 27
• Transcranial direct current stimulation (tDCS) 34
• Transcranial magnetic stimulation (TMS) 34

• 2.1. Explain why controlled experiments cannot always be conducted to determine how different types of brain damage cause cognitive deficits.

  In order to conduct an experiment of this type, one would need control over the brain damage that occurs. This would be unethical in humans. However, animal models can provide some information about how brain damage affects behavior; thus, experiments are possible to conduct with animal subjects.

• 2.2. Describe some of the limitations of using the clinical case study method in cognitive neuroscience.

  Because the brain damage is not controlled in these cases, it can be difficult to connect deficits with a specific brain region. It is also difficult to provide good tests of hypotheses about how brain function affects cognitive abilities.

• 2.3. What type of neuron activity is recorded in single-cell, EEG, and MEG recordings?

  Electrical activity from a single neuron or multiple neurons is recorded with these techniques.

• 2.4. What type of brain activity is detected in PET and fMRI scans? Why is an fMRI scan preferred to a PET scan in most cases?

  Blood flow to active regions of the brain is recorded in these techniques. fMRI scans are typically preferred because they are less invasive than PET scans. The subject or patient does not need to ingest anything to conduct an fMRI scan.

• 2.5. In general, what has been learned about the organization of brain activity using cognitive neuroscience techniques?

  Research has uncovered the localization of function in the brain, and how different areas are specialized for different tasks.

• 2.6. Does research connecting brain activity with cognitive task performance gain causal information or merely correlational information? Explain your answer.

  This knowledge is primarily correlational due to measurement of the relationship between two measured variables (brain activity and cognitive performance on a task). However, some causal information can be gained by manipulating the cognitive task the subject performs to examine how this affects the brain activity being measured.

• 2.7. How has the use of brain recording techniques allowed researchers to test causal relationships between brain activity and cognitive functions?

  Brain recording techniques allow for the measurement of brain activity during the manipulation of cognitive tasks.

• 2.8. Suppose that you were interested in learning about the brain areas involved in memory processing. You are specifically interested in testing whether the retrieval of accurate and false memories relies on the same underlying processes in brain function. Describe a study using one of the brain recording techniques described in this chapter that would test this question.

  Answers will vary, but the key is to measure brain activity during the retrieval of accurate and false memories to compare these situations. Chapter 7 provides some discussion of how false memories can be created experimentally for such studies.

• 2.9. Suppose research determined that specific brain activity is present when someone is lying and not present when the person is telling the truth. Do you think this knowledge could be used to develop a foolproof lie detector? Why or why not?

  Answers will vary.

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Figure 3.1 Diagram of the Visual Sensory System Showing the Four Parts of the System

Sources: photo of dog: Janie Airey/Digital Vision/Thinkstock; photo of eye: Christopher Robbins/Photodisc/Thinkstock; photo of brain: Hemera Technologies/PhotoObjects.net/Thinkstock.

Psychologists first used a computational approach to study our perceptions. In fact, some of the first psychologists studied perception through this approach in the field of psychophysics, where the goal was to discover fundamental knowledge of perception that showed us the scope and limits of our perceptual abilities. This approach to perception considers how we use features of objects and scenes to interpret and understand them. The features or cues in the environment help us turn the distal stimulus into the proximal stimulus in our minds. One process that aids in creating a proximal stimulus is bottom-up processing. Using bottom-up processing , perception is conducted starting with the most basic units or features of a stimulus and adding the parts together to understand and identify a coherent whole object. For example, consider how bottom-up processing might allow us to identify the words on this page. Figure 3.3 illustrates how bottom-up processing works to perceive the word safe in a feature detection model, where information is passed up through a hierarchical system that identifies more complex forms of written language at each level. The lines and curves of the individual letters are combined to identify each letter, and then the letters are combined to identify words. This bottom-up process can work for verbal language as well: phonemes of the language can be detected and activate words that contain those sounds (see Chapter 9 for more discussion of bottom-up processing in language).

Bottom-up processing as described in feature detection models received early physiological support. Using the single-cell recording technique described in Chapter 2 , researchers Hubel and Wiesel (1959) identified neurons in the visual cortex that are selectively activated by features in the environment. They recorded the activity from neurons in the striate cortex (an area in the occipital lobe) of cats as different shapes of light were presented to their retinas. Recordings from the neurons showed that some cells were active when horizontal bars were presented, others when vertical bars were presented, and still others when diagonal bars of one orientation were presented (see Figure 3.4 for activity of a neuron specialized for vertical bars). These results suggest that visual feature detection is done at the neuron level and is consistent with how the visual cortex functions. Others (e.g., Bullock, 1961) suggested that feature detection specialization in the brain also exists for other sensory systems, such as the auditory system.

Another example of bottom-up processing from the computational approach is a theory about object recognition based on features of the objects called geons . Geons are the basic three-dimensional pieces of objects, such as cylinders, cones, and blocks (see Figure 3.5 for examples of geons and some objects that can be created from them). Biederman (1987) proposed that we identify objects by first identifying the geons that make up the object. We then match the geons we perceive against representations of objects stored in memory to identify the whole object. He showed that we can easily identify objects from different angles and objects that are occluded based on the geons. This is similar to the feature detection model described in Figure 3.3 for perceiving words; however, in Biederman’s object recognition model, the features are three-dimensional geons instead of the two-dimensional lines and shapes seen in Figure 3.3 .

One process the computational approach to perception has focused on is the use of basic cues in the environment as a means of interpreting the stimuli with which we are presented. For example, cues in visual stimuli help us estimate objects’ size and distance. See Photos 3.1 and 3.2 here for illustrations of the use of these cues. In Photo 3.1 , we can use the linear perspective of the tracks to help determine the distance between the electrical poles on the right. The tracks seem to converge (i.e., get closer together) higher up in the photo. This implies that the tracks go off into the distance in a three-dimensional environment. We can also use the size of the image of an object on our retina to help us determine the object’s distance. In Photo 3.2 , we perceive that the woman is closer to us than the buildings, partly because the image of the woman imposed on our retina is larger than the image of the buildings. However, we also need to use some knowledge about the objects to make these judgments. Knowing that the woman is not as tall as the buildings can help us judge the objects’ distance as well. In Photo 3.2 , the size is similar for the images of the woman and the tallest building. Thus, we use additional knowledge we have about the objects to determine that the building must be farther away because at the same distance, the building should have a larger retinal image size.

Consider the Ponzo illusion. In Photo 3.3 , two cats are placed on the tracks shown in Photo 3.1 . Which cat looks larger? Most people perceive the cat near the top of the photo as larger. This is because in the photo it looks like it is farther away at a point where the tracks are closer together, but, in fact, the images of the two cats are exactly the same size (measure them to see!). Because the images of the two cats in the photo are the same size, they create retinal images that are also the same size. Thus, retinal image is not the only cue we use to determine the size and distance of objects. This type of illusion is interesting to perceptual researchers because it shows how we use the linear perspective cues in the scene to misinterpret the size of the objects on the tracks. However, in many cases, the linear perspective gives us an accurate depiction of the objects’ distance, as it does in Photo 3.1 when we judge the distance of the two signs and the trees in the scene. The illusion shows that we use the linear perspective cues present in the environment to perceive the distance of objects.

Neuropsychologists have recently studied the relationship between brain function and organization and the perception of illusions. For example, Schwarzkopf, Song, and Rees (2011) examined the relationship between the strength of the Ponzo illusion seen in Photo 3.3 and the size of the primary visual cortex area in the occipital lobe known as V1. They found a positive correlation such that the subjects with larger V1 areas also perceived stronger illusions (i.e., the subjects reported a larger size difference between the two objects in the image when they were actually the same size).

Even though we use other cues to help determine size and distance of stimuli, retinal image size is an important cue for our interpretation of objects’ size and distance. Try this yourself: Hold two objects of the same length (e.g., two pencils) in front of you with one object held right in front of your face and the other object held out at arm’s length. You can easily see that you perceive the object held at arm’s length as farther away. Figure 3.6 shows how two pencils held at different distances create different-sized images on the retina. The closer pencil has a larger image size, helping us perceive it as closer to us in the environment. This is how retinal image size serves as a cue in judging objects’ distance and size.

Examining object perception using cues such as linear perspective and retinal image size led to the theory of unconscious inference proposed by one of the first perceptual psychologists, Hermann von Helmholtz. The theory of unconscious inference suggests that we make unconscious inferences about the world when we perceive it. In other words, we use our top-down processing unconsciously to perceive and interpret the environment. Consider the objects in Photo 3.4 . How would you describe these objects? Most people would say something like “A cat is lying in a pot.” However, the entire cat is not actually visible in this picture. Thus, it is possible that only a portion of a cat is there and the rest of the cat is missing. But since that is an unlikely scenario, we interpret the scene as a whole cat in a pot with some of the cat hidden from view. This illustrates the likelihood principle that is part of the theory of unconscious inference. We perceive the object that is most likely in the scene when we view it, even if there are other possible interpretations of the scene.

In summary, the computational approach to perception focuses on cues in the environment as a means of perceiving and interpreting stimuli. Both bottom-up and top-down processing contribute to object and scene interpretations. Cues such as linear perspective and retinal image size help us determine the size and distance of objects in the environment. However, those cues can be incorrectly interpreted and create errors in our perceptions in certain situations. But the errors are simply a by-product of a perceptual system that works by means of processing these cues in consistent ways. We will encounter another example of how our normal cognitive processes can inadvertently create errors in Chapter 7 , when we consider memory errors.

Gestalt Approaches

Take a look at the scene in Figure 3.7 . What do you see there? Most people perceive a triangle with the points overlaid on top of circles. However, consider what is actually in the figure: Are there any triangles or circles in the figure? No, so why do we see these shapes? The Gestalt psychology approach to perception suggests that interpretation of a scene involves applying principles of how the world is organized. In other words, top-down processing is a key component of Gestalt approaches to perception. According to the Gestalt approach, perception occurs through applying a set of organizational principles that follow physical processes of the natural world. In applying these organizational principles, our perception of a scene is “more than the sum of its parts.” Table 3.1 summarizes some of the first organizational principles proposed by Gestaltists (see Wagemans et al., 2012, for a more complete listing of principles), and each of these is described with illustrative examples.

Perception/Action Approaches

Where computational and Gestalt approaches focus more on the “what” of perception, perception/action approaches focus more on the “what for” aspect of perception. What are the possible affordances of this environment (i.e., possible behaviors in a given environment)? Can I pass through that space? Can I use this stick to hammer in that nail? If I jump over this gap, will I make it without falling? According to these approaches, perception and action are intricately linked. One must consider them together to understand each one. Because the perception/action approach examines perception according to how it aids in performing behaviors, it is consistent with the embodied cognition approach described in Chapter 1 .

This approach has its roots in ecological psychology, first suggested by James Gibson (1979) as an alternative to representationalist approaches to perception. The computational approach describes perception to some degree as relying on representations of the world, with a proximal stimulus created in our minds to represent the distal stimulus in the environment. Thus, the focus is on how we interpret stimuli in the environment and the processes responsible for those interpretations. With the ecological approach, Gibson suggested that information about the world is available in the detectable patterns in the environment such that we directly perceive without first transforming a distal stimulus into a proximal stimulus. From this approach, the focus in studies of perception should be on how we perform goal-directed behaviors (Fajen, Riley, & Turvey, 2009). For example, how are we able to avoid bumping into objects when we move around in the environment?

For the past few decades, researchers following the ecological view have focused on this question in perceptual research: How do we perform goal-directed perceptual behaviors? Optic flow was one of the first concepts to be studied in this research. If you drive a car (or ride in one), consider what you experience as you move through the environment. Objects in the environment that are closer to you seem to pass by faster than objects that are farther away, even though you are moving and they are not (see Photo 3.10 ). This is an example of optic flow. It is the movement pattern generated by objects at different distances as you move past them. Photo 3.10 shows the optic flow that might be experienced from a moving train. The people on the train view closer objects as moving past the window very quickly, but objects in the distance (e.g., the trees) as moving more slowly. Optic flow is an important part of our perception of the environment. According to the perception/action approach, we perceive objects’ distance based on the optic flow, not from first representing the object in our minds based on its retinal image size.

The perception/action approach is broader than the ecological view of perception. In some perception/action approaches, actions are an important part of the process of perception, but perceiving an object may still involve representations of that object in the mind. Thus, perception/action approaches often blend elements of the ecological view and the representationalist view. For example, the perception of a chair may result from knowing that a chair can be used to sit or stand on because that is what you are currently looking for in your environment, but you can still identify the object as a chair if someone asks you what the object is.

Research with a perception/action approach has considered how perception and action are tied together. Consider the following scenario: You are shown the room setup that appears in Photo 3.11a . Given this room configuration, would you prefer to (1) walk to the left of the table, pick up the bucket with your right hand, and place the bucket on the near stool, or (2) walk to the right side of the table, pick up the bucket with your left hand, and place the bucket on the far stool? How about the room setup in Photo 3.11b or in Photo 3.11c ? Would you choose the same path or change your path? These were scenarios faced by subjects in a study by David Rosenbaum (2012). In this task, the reaction time to choose a path was recorded for different scenarios to determine if people simulated the paths in their minds one by one (i.e., sequential processing) before choosing the shorter path or if they considered all the paths at once (i.e., parallel processing) and chose the shorter path more quickly. Their reaction time data showed that the time it took to choose a path was a function of the difference in length of the two paths, supporting the suggestion that both paths are considered at once (i.e., parallel processing of path possibilities). Reaction times did not increase with the overall lengths of the paths, which is contrary to what is predicted if subjects simulate each path one at a time before choosing the shortest path. In other words, if reaction times increased based on the total length of the two paths, this would mean the decision takes as long as it takes to first mentally travel the length of one path and then mentally travel the length of the second path. Rosenbaum also showed that the paths chosen in this study were consistent with data collected in a previous study (Rosenbaum, Brach, & Semenov, 2011), where subjects chose a path in the actual environment and then performed the requested action (i.e., walk along the side of the table, lift the bucket off the table, and place the bucket on the stool). See Figure 3.10 for a graph of these results. The consistency in path choice across the two studies indicates that the plan to perform the action is the same as when the action is actually performed.

In another example of perception/action research, Witt, Linkenauger, and Proffitt (2012) examined the effect of a perceptual illusion on putting performance in golf. These researchers asked subjects to perform golf putts to a hole with projected surrounding circles. This was done in the context of a perceptual illusion: Larger circles around the hole make the hole appear smaller than if the hole is surrounded by smaller circles (see Figure 3.11 ). This is known as the Ebbinghaus illusion. When subjects saw the hole surrounded by larger circles, as in Figure 3.11a , their putting performance was worse than when they saw the hole surrounded by smaller circles, as in Figure 3.11b . These results are shown in the graph in Figure 3.12 . Witt et al.’s (2012) study showed the important connection between sports performance and perception. Another study by two of these researchers (Witt & Proffitt, 2005) also showed that softball players with higher batting averages judged the size of the ball as larger when they were shown images of balls and asked to choose the correct size of the softball, further illustrating the link between perception and action.

Research in this area has also shown that judgments about the environment can be influenced by our current body perspective, even when no action was planned. Malek and Wagman (2008) asked subjects to judge whether they could stand upright on an inclined surface either while wearing a weighted backpack on their back or on their front (see photo in Figure 3.13 ). Wearing the backpack on their back pulled the subjects’ center of mass backward, whereas wearing the backpack on their front pulled the subjects’ center of mass forward. If one stands on an inclined surface, having your center of mass pulled backward makes it more difficult to stand on the surface, but having your center of mass pulled forward makes it easier to stand on the surface. Malek and Wagman (2008) asked if this difference in backpack position would affect perceptual judgments of affordances (i.e., possibilities for standing behavior) even though the subjects did not have to actually stand on the surface. They found results consistent with a perception/action perspective: When wearing the backpack on their front, subjects judged they could stand on higher-angled surfaces more often than when they wore the backpack on their backs. These results suggest that perception is influenced by possible actions, even when those actions do not actually need to be performed.

Additional applications of the perception/action approach are shown in studies where subjects are asked to judge possibilities for use of objects with only tactile information. Wagman and Hajnal (2014) examined subjects’ judgments of whether they could stand on a ramp with an exploration of the ramp’s angle by use of hand-held stick (the subjects in the study were blindfolded). Figure 3.13 shows how the subjects in this study performed this task. The researchers found that even without seeing the ramp, subjects were accurate in identifying ramps that afforded standing on in various situations (dominant and non-dominant hand tool exploration, sitting and standing, foot-controlled tool exploration). This study shows that we use more than just our visual sense to judge affordances for actions.

Is there brain activity evidence for a connection between perception and action? The answer is controversial. There is evidence that different brain areas are responsible for recognition of an object and the location of an object (Milner & Goodale, 2008). Since the location of an object is more important for actions related to that object, if these two functions are separate and independent, this might suggest that perception and action are also separate. The “what” brain pathway responsible for recognition of an object is located in the lower occipital lobe and leads to the temporal lobe, where language functions are controlled. This is known as the ventral pathway (or ventral stream) because it is on the underside of the cortex. The “where” brain pathway responsible for locating an object is in the upper occipital lobe and leads to the parietal lobe where the motor cortex resides. This is known as the dorsal pathway (or dorsal stream) because it is on the top of the cortex (think of a dorsal fin on a shark to help you remember where this is located). See Figure 3.14 for the location of these pathways in the brain. There is evidence that the ventral and dorsal pathways process the “what” and “where” information for visual and auditory stimuli (e.g., Rauschecker & Tian, 2000; Ungerleider & Haxby, 1994). Figure 3.15 shows active brain areas for tasks of locating sounds (red areas) and identifying sounds (green areas), showing the dorsal (where) areas at the top of the cortex in red and the ventral (what) areas at the bottom in green from a study by Maeder et al. (2001).

The controversy here comes from the mixed evidence in studies attempting to dissociate ventral and dorsal pathway functions. For example, Ganel, Tanzer, and Goodale (2008) reported that although subjects showed the Ponzo illusion (see Photo 3.3 ) in their size judgments of objects, their reaching behaviors were not affected by the illusion. However, as described earlier, Witt et al. (2012) showed that the Ebbinghaus size illusion (see Figure 3.11 ) affected subjects’ golf performance. Thus, studies have produced data both in support of a dissociation between perception and action (i.e., showing that a variable affects one behavior but does not affect other behaviors) and in contradiction to such a dissociation. One possibility is that some actions have stronger links with perception than others. For example, many of the studies showing dissociations between the ventral and dorsal pathway functions involved reaching and/or grasping behaviors, behaviors that require real-time location information for objects. In addition, McIntosh and Lashley (2008) showed that expected object size affected reaching behaviors, indicating a link between perception and action, but Borchers, Christensen, Ziegler, and Himelbach (2010) showed that this effect only occurred when the objects being reached for were familiar to the subjects after long-term use (e.g., objects they used in everyday life). Thus, different types of action behaviors may vary in the strength of their connection to visual perception.

A neuropsychological finding that provides stronger support for the link between perception and action is the discovery of mirror neurons (first described in Chapter 2 ). Mirror neurons were discovered in a study by Rizzolatti, Fadiga, Gallese, and Fogassi (1996). These researchers were recording activity from neurons in an area of the brain known as F5 in the premotor cortex that contains neurons involved in sensation and movement in the hands. Neuron activity was recorded using the single-cell recording technique (see Chapter 2 ) on monkey subjects. These subjects were trained to reach into a box and grasp an object. Neurons in the F5 area were active during this grasping task. However, the researchers also showed that these neurons were active when hand movements related to grasping were performed by the researchers while the monkey watched (e.g., grasping an object, placing an object on a surface). These neurons were not active when the researchers performed other movements not related to grasping the object (e.g., picking up the object with a tool). Rizzolatti et al. (1996) called these neurons mirror neurons because they were active both for tasks the monkeys knew how to do and when they saw those actions performed by others. In other words, mirror neurons seem to be specialized for the connection between perception and action of known movements.

More recent studies have shown mirror neuron function in humans. For example, Calvo-Merino, Glaser, Grèzes, Passingham, and Haggard (2005) conducted fMRI scans (see Chapter 2 ) of the premotor cortex areas where motor neurons reside. Subjects were experts in classical ballet, experts in capoeira (a Brazilian martial art), or nonexpert control subjects. During the fMRI scans, subjects viewed similar movements in classical ballet and capoeira. However, brain activity in the premotor cortex was only greater when subjects viewed movements in their area of expertise (e.g., ballet experts viewing ballet movements). This study showed that mirror neurons are active in humans when they view movements that they know how to perform, suggesting a link in brain activity between perception and action.

1. Which of the three approaches to the study of perception do you think this study most adheres to?
2. What was the primary manipulated variable in this experiment? (Hint: Review the Research Methodologies section in Chapter 1 for help in answering this question.)
3. From this study, is there evidence of bottom-up and/or top-down processing in scene categorization? Explain your answer.
4. Of the results described, which are most informative about the research question in this study? Explain your answer.

1. Which of the three approaches to perception would describe perception of an object in terms of the geons that make up the object?
   1. Gestalt
   2. computational
   3. perception/action
2. Which of the three approaches to perception would describe perception of a doorway in terms of whether it can be walked through?
   1. Gestalt
   2. computational
   3. perception/action
3. Which of the three approaches to perception would describe perception of a tree as more than the addition of its branches, leaves, roots, and flowers?

   1. Gestalt
   2. computational
   3. perception/action

4. Which of the following parts of a sensory system is responsible for transforming stimulus energy into neural signals?
   1. sense organ
   2. brain areas
   3. receptor cells
   4. nerve conduit
5. In which lobe of the brain is visual information first processed?
   1. parietal
   2. frontal
   3. temporal
   4. occipital
6. Two objects appear in a scene: an elephant and a mouse. The mouse is much closer than the elephant. Explain how you might know that the mouse is closer from cues in the scene.
7. Regarding question 6, what aspects of the scene would be of interest to a perception/action researcher?
8. According to the perception/action approach, explain how the perception of the gap in my backyard fence would differ between the rabbit in my backyard and me.
9. Look around the room you are in and describe your perception in terms of the Gestalt principles of proximity, similarity, and closure.
10. Explain the difference in processing of visual stimuli that occurs in the ventral and dorsal brain pathways.
11. In what way does the discovery of mirror neurons support the connection between perception and action?
12. How might mirror neurons be useful in social perception?
13. The _____________ visual pathway extends into motor cortex, whereas the ____________ visual pathway extends into the temporal lobe where language is processed.
14. The information in the environment about movement where farther objects appear to be passing by more slowly than closer objects is called _______________.
15. Perception of the taste of food begins in the __________.

• 3.1. Describe the four parts of a sensory system.

  The four parts of a sensory system are (1) sense organ (eyes, ears, nose, tongue, skin), (2) receptor cells in each sense organ that receive stimulus energy and convert it to neural signals, (3) nerve conduit that carries the neural signal from the sense organ to the brain, and (4) brain area(s) that processes the neural signals received from the sense organ.

• 3.2. What is the role of receptor cells in perception?

  The receptor cells serve the important role of converting stimulus energy (e.g., light, sound waves) to neural signals that can be received and processed by the brain.

• 3.3. What are the advantages to having a perceptual system that has automatic input of all environmental stimuli but only consciously processes a small portion of those stimuli?

  Answers will vary, but a primary advantage is that we can focus our attention on (or attention can be captured by) any stimuli in the environment because all are being received. Thus, we have the ability to consciously process any stimulus in our environment.

• 3.4. Can you think of a situation where your perception of your environment did not match the reality of the environment? Why do you think that error occurred?

  Answers will vary based on personal experiences. The illusions described in the chapter provide some examples of these errors.

• 3.5. Explain what it means to interpret scenes based on cues present in those scenes.

  This describes the computational approach to the study of perception. Cues in the stimuli such as basic features, linear perspective, and retinal size help us interpret the size and distance of objects in the environment and also help us identify those objects.

• 3.6. In what way do illusions illustrate the normal processes of perception?

  Because we use cues to interpret stimuli, those cues can sometimes lead to an inaccurate interpretation when they conflict with or are not an accurate representation of the environment.

• 3.7. You see a light approaching on the road at night. According to the likelihood principle, which of the following are you most likely to perceive: (a) a deer crossing the road wearing a headlight, (b) a UFO, or (c) an approaching car? Explain your answer.

  In this situation, the most likely object causing this stimulus is (c) an approaching car. The likelihood principle states that we interpret stimuli based on the most likely event.

• 3.8. In the scene in Photo 3.4 , describe some cues you can use to determine that the front of the pot is closer to you than the cat.

  The retinal image size of the pot is larger than the retinal image of the cat. The cat is also higher in the photo; thus, linear perspective may help us determine that it is farther away.

• 3.9. People report a “moon illusion” such that the full moon appears larger when it is lower in the sky and close to the horizon than when it is high in the sky and above us. Using what you learned about the use of cues in this section, why do you think the moon illusion occurs?

  One possible explanation of this illusion is that we misinterpret the size of the moon based on the comparison of retinal images of objects near the horizon (e.g., buildings and trees that can be seen along with the moon when it is low in the sky). When the moon is high in the sky, there are typically no other objects to compare it with. However, the explanation of the moon illusion is still debated within research in perception so there is no one right answer to this question.

• 3.10. How does the Gestalt approach to perception differ from the computational approach to perception?

  The Gestalt approach to perception focuses almost entirely on top-down processing in the form of organizational principles of the world that we use to interpret stimuli in the environment. Adding cues or features together, as in the computational approach, is seen as providing an incomplete perception of objects and scenes.

• 3.11. How is top-down processing involved in the Gestalt approach to perception?

  Top-down processing is involved in the use of knowledge about how the world is organized. We use this knowledge to mentally organize scenes (e.g., by proximity, similarity).

• 3.12. Look around your environment and describe some examples of good continuation in the objects around you.

  Answers will vary.

• 3.13. Consider the moon illusion described in Stop and Think 3.9. Would the Gestalt approach to perception explain this illusion differently than the computational approach? Why or why not?

  The Gestalt approach would provide a different explanation of this illusion because it would not consider cues such as retinal image size to explain the illusion.

• 3.14. How do perception/action approaches to cognition differ from computational approaches?

  Perception/action approaches consider perception as a means to achieve behavioral action goals.

• 3.15. What is an affordance?

  An affordance is a possibility for behaviors in a given environment.

• 3.16. I am looking at the lilac tree in bloom outside my window. I immediately imagine going out and smelling the flowers. Explain how my perception of the lilac flowers fits a perception/action approach.

  Answers will vary but should include some description of an action goal (e.g., smelling the flowers).

• 3.17. Would a perception/action researcher be interested in explaining the moon illusion described in Stop and Think 3.9? Why or why not?

  A perception/action researcher would only be interested in this illusion in terms of any behaviors it might influence.

• 3.18. Reconsider the scenario presented at the beginning of the chapter where you are walking across your crowded campus. How would each of the three approaches describe perception in this situation?

  Answers will vary.

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One idea of how attention operates as a cognitive process is as a filter of information. In other words, attention works to filter out the irrelevant stimuli in the environment such that the only aspect(s) of the environment left in our consciousness is what we choose to pay attention to. According to Broadbent (1958), a researcher who used this description of attention in his model, our attention is limited by the amount of information we can focus on at a particular time. This occurs due to our attention process paring down the vast amount of information in the environment to just a small amount we can focus on. Thus, there is a “bottleneck” in our processing that filters out everything except the information we are attending to (see Figure 4.1 ). The filter acts as an early processor of the information to only let in what is relevant to one’s current task or focus.

Some support for the filter model of attention comes from research using what is known as a shadowing task . In this task, subjects are asked to repeat a message played over headphones to one ear. During this task, a competing message is played to the other ear such that subjects must focus their attention on the target message they have been asked to repeat. Research (e.g., Cherry, 1953) has shown that subjects can complete this task quite well. When subjects are asked what they heard in the competing message, they often cannot accurately report the content of that message, supporting the idea that it was filtered out during the shadowing task.

However, this does not mean that the competing information is not being processed at all. If a salient stimulus is played in the nonattended ear (e.g., the subject’s name), some subjects are able to switch their attention to the other message during the task, as occurred in our party scenario at the beginning of the chapter. This is known as the cocktail party effect , and research has shown that about a third of subjects will detect their name in the nonattended message (Moray, 1959; Wood & Cowan, 1995). The cocktail party effect suggests that more salient (i.e., important) information can get through the filter to capture attention.

Conway, Cowan, and Bunting (2001) investigated the factors that contribute to the cocktail party effect in subjects. What causes some people to detect their name in the unattended message? Subjects were asked to repeat a message played over headphones in their right ear, while ignoring the competing message played in their left ear (see Figure 4.2 ). For all subjects, their first name was inserted into the message played in their left ear. A posttest questionnaire examined whether subjects detected their name in the nonshadowed message. Subjects also completed a task where they had to verify the accuracy (responding with yes or no) of mathematical equations while also remembering words presented with the equations. This type of task tests a subject’s ability to keep track of several pieces of information at once and is known as a working memory task (see Chapter 5 for more discussion of working memory). The score on this task indicates the capacity of one’s working memory abilities. Thus, the researchers hypothesized that the score on this task would be related to the subjects’ ability to filter out the competing message in their left ear during the shadowing task. Subjects were grouped according to their score on the working memory task into high- and low-score groups. Results of the study showed that more of the low-score subjects (65%) noticed their name in the competing message than the high-score subjects (20%). These results support the researchers’ hypothesis that individual differences in filtering abilities influence the cocktail effect.

Other research suggests that the salience of the message in the competing ear is not the only factor in switching attention. Treisman (1960, 1961, 1964) showed that subjects report information from the competing message in a shadowing task when the information is meaningfully related to the information in the attended-to message. For example, if a sentence is separated such that alternating words are presented to different ears, the subject will sometimes report the full sentence. In other words, if “BLACK/runs/MEOWS/funny” is presented to the right (attended) ear and “march/CAT/clock/LOUDLY” is presented to the competing left ear, subjects will sometimes report the capitalized words as part of a meaningful sentence: BLACK CAT MEOWS LOUDLY. This suggests that information in the competing channel is not being completely filtered out. Thus, either the filter occurs early in the perceiving process (as suggested by Broadbent’s filter model), but is only a partial filter, or attention acts as a full filter but occurs later in the perceiving process such that most or all of the information is being processed to some degree before the relevant information for one’s attention has been selected (Fernandez-Duque & Johnson, 1999).

Treisman (1960) suggested a modified filter model of the first type: An early process partial filter allows some information to pass through but only after it has been attenuated (i.e., decreased in importance according to the relevance of the information). This is as if some of the information (e.g., information in the competing message in a shadowing task) is being passed through the filter but at a lower volume than the most relevant information (e.g., information in the attended-to message). This is known as the attenuation theory of attention. Figure 4.3 illustrates how this might work for the CAT example described earlier. The attenuator filters the incoming information such that it allows the attended message to come through at full strength, but the meaning-related parts of the competing message come through at lower strength because they are in the less relevant message for the shadowing task (the ear not being attended to). Treisman also proposed a second stage of processing in the form of a dictionary unit where information is stored with a threshold value. The lower the threshold, the more likely the information is attended to. Thus, information with a low threshold, such as important information like one’s name or meaning-related information to the attended-to information, can reach one’s consciousness, even if it comes through the attenuator with a low strength (see Figure 4.3 ). Treisman (Treisman & Gelade, 1980) later revised her ideas about how attentional processes work (see the Attention as a Feature Binder section that follows), but her attenuation theory shows how models of cognitive processes go through revision when new results suggest that the original model is not quite right.

The attenuation theory relies on the separation between what is operating at the level of consciousness and what is operating below consciousness or without our awareness. When someone performs the shadowing task described here, the information in the attended ear is in the person’s consciousness—they are intentionally attending to the information in the attended ear. However, according to the attenuation model, the information in the unattended ear is attended to at a level below the level of consciousness until relevant information is detected and attention is switched to this information and it enters consciousness. In this way, the listener is controlling their attention to the attended information, but an automatic process filters information from the unattended ear and allows some of that information (the most important information) to get through to the conscious level. This difference between conscious controlled processing and automatic processing will be important in additional descriptions of attention discussed in this chapter.

Attention as a Feature Binder

As described in the Attention as an Information Filter section earlier in this chapter, Anne Treisman refined her ideas about attention into what she called the feature-integration theory of attention (Treisman & Gelade, 1980; Treisman, Sykes, & Gelade, 1977). In this model of attention, separate stages of processing contribute to focused attention. The first stage is an automatic identification and processing of the features within a scene in the environment. These features could be the colors, shapes, or brightness present in the scene. Because this processing occurs automatically, we are typically not aware of the identification of these features, and this stage occurs before attention processes kick in. In other words, it happens outside of our conscious awareness. The second stage in the model involves conscious, focused attention to combine the features of the scene and allows us to understand and think about what we are focused on in the scene. In this stage, attention is viewed as the glue that binds the features of the objects together. Thus, this second stage operates at the level of consciousness. Figure 4.7 illustrates the stages of this model.

Treisman and Gelade (1980) presented evidence from several experiments to support the feature-integration model. In these experiments, subjects were asked to identify a target based on color, shape, or both color and shape (called the conjunction condition, because it involved a conjunction between both color and shape). For example, in Experiment 1 of their study, subjects had to identify whether any blue letter, an S , or a green T was present in the display (see Figure 4.8 for examples of these conditions). Reaction time to detect the target was measured. When the target differed by only one feature from the distractors (blue letter or an S ), subjects very quickly detected the target in all displays, regardless of how many items were in the display. However, when they had to detect a conjunction target (a green T ), subjects were slower as the number of distractors increased. In other words, the blue letter and the S seemed to pop out of the display easily because there was only one feature difference with the distractors, but subjects had to search for the conjunction target based on both features and it took longer to search with more items to search through. The search was harder when two features differed between the target and distractors, illustrating the importance of the features in attending to the target.

Consider the six displays in Figure 4.8 . How easy is it to find the blue T in the top two displays compared with the green T in the bottom two displays? Many people report that the blue T in the first displays and the brown S in the middle displays seem to pop out of the rest of the distractors and are easily detected. This illustrates a concept known as attention capture. It shows how our attention can be easily attracted to something that is different from the rest of a scene (in Figure 4.8 by one important feature). This was seen in the chapter-opening party scenario when you noticed Brandon’s new nose ring. It seemed to pop out at you and capture your attention because it was something you had not expected to see. The attention capture phenomenon illustrated by this example and in Figure 4.8 provides support for the feature-integration model of attention. The more features that must be integrated in using attention to search for an object in a scene, the more difficult and slow the search is.

The feature-integration model is also consistent with current knowledge of brain function in processing features of scenes. As described in Chapter 3 , different sensory systems are designed to receive and process different types of sensory input from the environment. Due to localization of function in the brain (see Chapter 2 ), information from different modalities (e.g., visual, auditory, tactile) is processed in different brain areas. Further, as described in Chapter 3 , recent cognitive neuroscience studies (e.g., Fiebelkorn, Foxe, Schwartz, & Molholm, 2010; Zaretskaya, Anstis, & Bartels, 2013) have shown evidence of feature binding in the occipital and parietal cortex areas of the brain. For example, single-cell recording studies have provided evidence for feature integration based on features presented to the visual fields in each eye (Baars, 2007). Feature areas of the visual cortex respond to the features presented to both eyes’ visual fields, but activation only occurs for the consciously attended features in the temporal cortex where conscious identification takes place.

Further evidence for feature integration was presented by Zaretskaya et al. (2013). These researchers conducted fMRI scans during a task in which subjects identified whether the display illustrated movement of local features of the display or global features of the display. Local feature movement was created by moving each of four dots on a screen within its quadrant of the screen. Global feature movement was created by moving each of the four dots across a larger area of the screen. Figure 4.9 shows the displays used in the task. Subjects fixated on the red dot in the center of the screen for each trial. They were then asked to identify whether they saw local or global movement in each display by pressing one of two buttons. The researchers examined the brain activity that accompanied each type of display (see Figure 4.10 ) and found that activity in the right parietal cortex was present in the global condition that was not present in the local condition. These results indicate that distinctive brain activity is present when features of a display are bound together to view a global percept. Thus, this model of attention is consistent with the results of current studies examining the connection between attentional processes and brain function.

Imagine that you are walking across the quad at your school and someone stops to ask you where the student union building is. While you are giving the person directions, some students walk between you and this person carrying a large art project that blocks your view of the person. You stop and wait for them to walk by and then continue giving directions. If the person you were talking to had been replaced by someone else when your view was blocked, would you notice? This is a similar situation to the one described in the opening party scenario, where you did not notice that a different person had joined your conversation while your attention was diverted by hearing your name across the room. Most people think they would notice, but Simons’s research has shown that many do not. Simons and Levin (1998) created the situation just described in their study. After the unsuspecting subject finished giving directions, the researcher informed the subject that they were conducting a study and asked the subject if he or she noticed anything unusual when the object passed between them. See Photo 4.3 for an illustration of the situation in this study. In Experiment 1, only 7 of the 15 subjects noticed the change, and in Experiment 2, only 4 of the 12 subjects noticed the change.

Try this for yourself. Take the test for selective attention on Daniel Simons’s website ( www.simonslab.com/videos.html ). Did you notice the change? This phenomenon has been called inattentional or change blindness because people fail to notice a change in the scene through lack of attention. One possibility is that the subjects have their attention focused on other aspects of the scene, keeping them from noticing the change. An example of this lack of attention is seen in the scenario from Simons and Chabris’s (1999) study. In their study, subjects were shown a video of people passing a basketball (like the video on Simons’s website). Some people wore white shirts and others wore black shirts. Subjects were asked to count the number of passes made by one of the teams (black shirts or white shirts). However, while the passing task was going on, either a person wearing a gorilla suit or a person carrying an open umbrella walked through the scene. At the end of the video, subjects were asked to write down their count for the number of passes. Then they were asked if they noticed anything unusual in the video. If they failed to report the gorilla or umbrella, they were explicitly asked if they saw these in the video. Overall, more than half of the subjects did not notice the gorilla or umbrella. This number was actually lower (56%) for people who did not notice the gorilla, even though this event seems more unusual and more likely to capture attention. These studies show that not all salient events will capture our attention in a scene.

Incompatibilities Tax Attention: The Simon Effect

Have you ever used the roller ball on a computer mouse (or track pad) to make the text on the screen move down? Or played a game where you moved the joystick down to go faster and up to go slower? With practice, you likely were able to do these tasks, but it was probably harder to do the first time you played because the action and the response were not consistent. These examples illustrate the decrement to attention that occurs when a task and response are incompatible.

This effect was first shown by Richard Simon (1969; Simon & Rudell, 1967; Simon & Wolf, 1963). The task was fairly simple: Subjects were asked to press a key on the left side when they saw or heard one target and a key on the right side when they saw or heard a different target. For example, they might press the left key when they heard the word left over headphones and the right key when they heard the word right over headphones (Simon & Rudell, 1967). Results showed, however, that subjects’ reaction time to complete this simple task was affected by the location of presentation. Subjects were much slower when the word right was presented in the left ear and when the word left was presented in the right ear. In another example of the task, Nicoletti and Umiltá (1989) asked subjects to press the right key when a square was presented and a left key when a circle was presented. The objects appeared in one of six boxes on the screen, three to the left of the center fixation and three to the right of the center fixation (see Figure 4.11 ). Subjects were faster when the object appeared on the side of the screen that was consistent with the key press, with larger distances from center showing slower reaction times. These results are shown in Figure 4.12 . The objects appearing on the right of the screen were overall more quickly responded to with the right key, and the objects appearing on the left side of the screen were more quickly responded to with the left key. This effect weakened as objects appeared farther from the center of the screen. These results illustrate the Simon effect.

A well-known task that measures one’s ability to inhibit automatic processes and focus attention on a conflicting task is the Stroop task . In Stroop’s (1935) original study, subjects were asked to name the color of blocks or words on a page under different conditions. Try this for yourself: Time how long it takes you to name the color of print of the words in Column A of Figure 4.13 . Then compare this time with how long it takes you to name the color of the print of the words in Column B. Which took longer? For most people Column B takes much longer. What do you notice about the difference between the words in the columns? In Column A, it is generally easier to name the color because the color is consistent with the word itself, facilitating the naming of the color. In Column B, the print color and the words are inconsistent, interfering with your ability to name the color. This interference occurs even though you are not asked to read the words because reading is an automatic process once you know how to read. It is a task you have had a lot of practice with. You cannot help but read the words as you are attempting to name the print color and the processing of what that word is can either aid in your color naming task (as in Column A) or interfere with your color naming task (as in Column B).

Stroop (1935) also included a control condition that did not involve reading as a comparison to the condition where reading interferes with the color naming task. In this condition, subjects simply named the color of blocks presented to them. This is an easy task, requiring little attention. However, compared with this task, naming the color of the words in Column B of Figure 4.13 is quite difficult and requires more attention. Stroop also found that with practice, subjects got better at the task: They could name the color of ink faster in the interference condition after completing the task several times. The Stroop task shows that some cognitive processes require very little attention and are considered automatic processes. Reading in a native language is one of those processes. Tasks that we have less practice with (such as color naming of words) require more effort and attention because they are controlled processes that are not performed automatically. If I presented color words to you in Spanish (e.g., rojo, azul, verde) and you do not know the Spanish language (or are not very good at reading it), you would be able to perform the color naming task almost as easily as the subjects who simply named the color of the blocks, because reading Spanish is not an automatic process for you. We consider automatic processes and their effect on attention further in the next section of the chapter.