Cogs 101B Midterm 2

What is perceptual learning?

Perceptual learning is the process by which we learn to distinguish between perceptually similar stimuli through experience. It can occur via mere exposure (passively encountering stimuli) or discrimination training (actively differentiating with feedback). This mechanism explains how we learn to identify subtle differences in voices, musical instruments, or faces.

How does mere exposure contribute to perceptual learning?

In mere exposure learning, stimuli are presented without explicit feedback. Initially, learners may confuse similar items, but with repeated exposure, they learn to accurately differentiate a target from similar distractors. For example, in the Gibson & Gibson (1955) study, participants improved their ability to identify a target among similar images over time.

What distinguishes discrimination training from mere exposure in perceptual learning?

Discrimination training involves providing feedback to enhance accuracy. This method not only leverages exposure but combines it with operant conditioning. However, it also highlights learning specificity—as demonstrated by Fiorentini & Berardi (1981), where even a small change in stimulus orientation (a 1° rotation) can cause performance to drop back to chance levels.

What is the face inversion effect and why is it significant?

The face inversion effect is the phenomenon where recognizing upside-down faces is significantly harder compared to upright faces. This effect underscores our extensive experience with correctly oriented faces, suggesting that face processing is finely tuned to the normal orientation encountered in daily life. It reflects how our perceptual systems become specialized through experience.The face inversion effect is the phenomenon where recognizing upside-down faces is significantly harder compared to upright faces. This effect underscores our extensive experience with correctly oriented faces, suggesting that face processing is finely tuned to the normal orientation encountered in daily life. It reflects how our perceptual systems become specialized through experience.

What role does latent inhibition play in perceptual learning?

Latent inhibition occurs when prior learning interferes with the acquisition of new distinctions. For instance, our familiarity with upright faces makes recognizing inverted faces more challenging, as previously learned features obscure the new orientation. This principle is crucial when studying how experience shapes learning and later recognition tasks.

How does perceptual priming illustrate latent learning?

Perceptual priming enhances the speed and ease of processing a stimulus that has been encountered before—even if the person cannot consciously recall the prior exposure. This type of latent learning indicates that our internal processing systems are altered by experience, even when this change does not immediately translate to observable behavior.

In what way do perceptual priming and semantic priming compare?

Both perceptual and semantic priming rely on prior exposure: perceptual priming speeds up the visual recognition of stimuli, while semantic priming facilitates faster processing of associated concepts or words. These priming effects operate largely beneath conscious awareness and contribute to the formation of implicit associations, which can later influence behavior and decision-making.

What is statistical learning, and how did the infant studies demonstrate it?

Statistical learning is the ability to detect patterns or regularities in the environment purely through exposure. The study by Saffran, Aslin & Newport (1996) demonstrated that 8-month-old infants could segment continuous auditory streams into distinct “words” based solely on the transitional probabilities between syllables. This shows that even with minimal exposure, humans can extract complex statistical information from sensory inputs.

How does our experience with faces influence recognition and processing?

Face recognition is highly influenced by experience. We typically process faces holistically, which is most effective for familiar faces (often from our own race or social group). When holistic processing becomes less viable, we may revert to feature-based strategies and even default to implicit stereotypes. This dual strategy illustrates how experience shapes both rapid recognition and deeper cognitive biases.

How does perceptual learning help address computational complexity in the real world?

Perceptual learning allows our brains to build detailed internal representations of critical stimulus features. By categorizing and storing these features through repeated exposure, our sensory systems can later use this refined information for quick, efficient recognition. This organization—through both bottom-up feature encoding and top-down processing such as priming—helps the brain manage the vast amount of sensory information in a complex environment.

What is meant by "learning specificity" in the context of perceptual learning?

Learning specificity refers to the observation that training effects are often highly specific to the conditions under which learning occurred. For example, training on visual images rotated by a fixed angle may not generalize to even a slight change in orientation. This specificity underscores the detailed, context-dependent nature of perceptual learning.

How can prior perceptual experiences lead to implicit stereotypical associations?

When holistic processing fails (for example, with difficult-to-identify or atypically oriented stimuli), the brain may fall back on processing individual features. This shift can inadvertently lead to reliance on implicit associations and stereotypes, which, over time, become deeply ingrained. This mechanism explains why we might unconsciously hold certain biases even when consciously disagreeing with them.

What is the segmentation problem in speech perception?

The segmentation problem refers to the challenge of identifying boundaries between words, phonemes, and syllables within the continuous acoustic signal of speech. Because there are no physical breaks in the speech waveform, our brains must rely on cues—such as transitional probabilities, context, and prior language knowledge—to segment and make sense of the incoming stream.

How does top-down processing influence segmentation of speech?

Top-down processing uses our internal knowledge––including grammar, meaning, and familiar word structure––to shape how we parse continuous speech. This prior knowledge helps fill in gaps and guide us toward identifying likely word boundaries, even before the acoustic signal provides clear separations.

What role do innate feature detectors play in perceptual learning?

Several species possess basic, evolutionarily hardwired detectors for critical features. For instance, frogs have “bug” detectors, and mammals (including humans) have simple and complex edge detectors in their visual systems (as shown by Hubel & Wiesel). Additionally, babies can discriminate phonetic sounds for all languages up to about eight to ten months old. These innate abilities set the stage for more advanced, experience-driven learning.

What is a phoneme, and why is it crucial for speech processing?

A phoneme is the smallest unit of speech that can change meaning (for example, distinguishing "bad" from "pad"). The number and nature of phonemes vary by language—English has roughly 47 phonemes, while other languages may have more or fewer. Recognizing these fundamental units is essential for differentiating words and developing accurate speech perception.

What is categorical perception, and how is it demonstrated in speech?

Categorical perception occurs when a continuum of stimulus variations is perceived not as gradual changes but as discrete categories. In speech, experiments altering voice onset time (VOT) between sounds like /ba/ and /pa/ reveal that listeners experience a sudden perceptual shift at a certain threshold rather than detecting incremental changes. This process enables us to achieve perceptual constancy for phonemes, despite minor acoustic variations.

How does statistical learning facilitate speech segmentation and feature perception?

Statistical learning is the mechanism by which our brains pick up on regularities in the sensory input. For example, studies have shown that even infants can segment “words” by tracking the transitional probabilities between syllables, extracting structure and forming expectations based solely on mere exposure. This process helps create the building blocks for meaningful language processing.

How do mere exposure and discrimination training work together in learning speech features?

Mere exposure lets us passively become familiar with the range of sounds around us, gradually tuning our sensory systems. Discrimination training then builds on this foundation by providing feedback and reinforcing differences between similar sounds. Together, these processes refine our ability to form clear categories, such as phonemes, through both bottom-up and top-down learning mechanisms.

What are exogenous and endogenous orienting of attention, and how do they differ?

Exogenous attention is the automatic, bottom-up response to external stimuli—like a sudden, loud noise or a flashing light that captures our focus without conscious effort. In contrast, endogenous attention is internally driven, guided by our goals and expectations—such as deliberately searching for a friend in a busy crowd. Both forms dynamically regulate what we process from our environment.

How does the attention system help manage the computational complexity of our environment?

Our brains are constantly bombarded by vast amounts of sensory information, but we can only process a limited subset at a time. The attention system acts as a filter, selecting the most relevant information based on both external cues and internal goals. This selective processing reduces computational overload, allowing us to focus on what is most important at that moment.

What are the key functions of attention in perceptual processing?

The attention system supports several interrelated functions:

• Focusing/Selecting: Limiting the number of stimuli processed to prevent overload.

• Perceptual Enhancement: Improving the clarity of selected stimuli.

• Binding: Integrating different features into a coherent perception.

• Sustaining Behavior: Maintaining attention over time for consistent performance.

• Action Selection: Guiding decision-making as part of the central executive system.

How are innate abilities and experience combined in the development of speech categories?

Infants begin with species-specific, innate perceptual capabilities (such as sensitivity to basic acoustic cues) that enable initial sound discrimination. Through mere exposure during sensitive periods and further refined by discrimination training, these basic perceptions gradually form into stable speech categories. Statistical learning then helps organize these elements, linking sounds together to reveal the structure of language.

How is attention intertwined with both bottom-up sensory processing and top-down cognitive control?

Attention is not a single, isolated process but a dynamic interplay between bottom-up mechanisms (like the automatic capture of stimuli) and top-down influences (such as internal goals and contextual knowledge). This balance ensures that while incoming sensory signals are efficiently filtered and enhanced, our existing knowledge and intentions also shape what we ultimately perceive and act upon.

What did Gray and Wilburn’s (1960) dichotic listening study reveal about channel selection?

Gray and Wilburn showed that—even when instructed to shadow one audio channel—participants sometimes reported hearing content from the unattended ear. For instance, when the attended channel said “Dear 7 Jane” and the unattended channel said “9 Aunt 6,” listeners reported “Dear Aunt Jane.” This indicates that the brain can use meaning to group information from both channels, challenging early filter models that relied solely on physical characteristics.

How does Treisman’s Attenuation Model modify earlier filter theories of attention?

Anne Treisman (1964) proposed replacing the strict filter with an attenuator. In this model, all incoming information is processed, but the unattended messages are weakened. The system then uses physical properties, language cues, and semantics to decide which words reach a higher activation level—allowing even unattended inputs (especially those with very low thresholds, like one’s own name) to sometimes break through the attenuation.

What role does the "dictionary unit" play in Treisman’s Attenuation Model of Attention?

In this model, every message—whether strong or attenuated—passes to a dictionary unit containing stored words. Each word in memory has a threshold for activation; common or salient words (such as your name) have lower thresholds. Thus, even a weak unattended signal might activate a word if its threshold is minimal, explaining how meaningful content can be detected despite reduced signal strength.

What is perceptual enhancement, and how did Posner’s experiments demonstrate its effect?

Perceptual enhancement occurs when focused attention increases the quality of the signal in a selected channel—improving both the speed and accuracy of stimulus identification. In Posner’s spatial cueing tasks, participants who received a valid cue to a target location were quicker to detect the target than when the cue was invalid. This shows that directing attention enhances processing by boosting the signal relative to extraneous noise.

How does the same-object advantage inform our understanding of object-based attention?

In experiments like those by Egly et al. (1994), participants responded faster to targets on an object that had been pre-cued—even when that target was not at the exact cued location. This same-object advantage implies that attention spreads throughout an object rather than being limited solely by spatial proximity, allowing for more cohesive processing of related features.

In what ways can attention alter the subjective experience of stimuli?

Attention doesn’t just affect response speed and accuracy—it also transforms our perceptual experience. For example, studies have found that attended objects may seem to move faster, appear more richly colored, or even seem larger. These phenomena suggest that attention “amplifies” our perceptual experience, a phenomenon reminiscent of William James’ idea of “taking possession by the mind in clear and vivid form.”

What do early versus late selection theories propose about where the bottleneck in attention occurs?

• Early Selection Theories: Argue that an attentional bottleneck occurs early, filtering sensory input based on low-level physical properties before detailed processing.

• Late Selection Theories: Propose that much more information is initially processed, but limitations arise later—at the level of short-term memory or semantic processing—so that only a subset of meanings can ultimately be handled. These perspectives represent different viewpoints on how our cognitive system manages the overflow of sensory data.

How does Load Theory explain the allocation of attentional resources?

Load Theory, as proposed by Lavie (2010), posits that our processing capacity is limited. Tasks with low perceptual load leave extra capacity to process peripheral information, while high-load tasks consume more of that capacity, reducing interference from irrelevant stimuli. This theory also suggests that different modalities may have separate resource pools, aligning with multiple resource theories of attention.

What is meant by channel selection in attention, and why is it essential?

Channel selection refers to the process by which we focus on one coherent stream of sensory information (or channel) while filtering others. This selection can be driven by both exogenous (external, salient cues) and endogenous (internal, goal-driven) factors. It is crucial for managing the computational complexity of the environment by ensuring that only the most relevant sensory information receives full processing.

How does attention contribute to the binding of features into a coherent percept?

Binding is the process of integrating separate features—such as color, shape, and motion—into a unified, coherent percept of an object. Attention plays a vital role in this by enhancing the processing of features that belong together within a given spatial or object context. This selective enhancement helps counteract the fragmentary nature of raw sensory inputs and allows us to perceive stable, integrated objects.

What is the binding process in perception?

Binding is the process of combining separate features (such as shape, color, motion, and spatial location) into a coherent percept of an object. This involves first focusing on and enhancing the relevant region, then integrating the individual features to form a unified representation—a solution to the binding problem.

What is the "binding problem" in sensory processing?

The binding problem asks: How do the brain regions that process different features (color, shape, motion, etc.) work together so that we experience unified objects rather than disjointed attributes? Attention is thought to serve as the "glue" that brings these separately processed features together.

What does Feature Integration Theory (FIT) propose?

Proposed by Treisman & Gelade (1980), FIT argues that the initial, preattentive stage processes individual features automatically. In a subsequent focused attention stage, these free-floating features are integrated or "bound" into coherent object representations. Thus, when a full conjunction of features is required for identification, additional attentional resources are needed.

How does the conjunction search task provide evidence for feature binding?

In a conjunction search task, the target is defined by a specific combination of features (e.g., a red “O”). Because no single feature uniquely identifies the target, participants must serially search and integrate features at each location. This process generally takes longer as the number of items increases, highlighting that binding requires attention and deliberate processing.

How does perceptual enhancement relate to feature binding?

Perceptual enhancement refers to the improvement in processing accuracy and speed when attention is focused on a particular channel or location. By boosting the signal of the selected features—and, at the same time, increasing external noise—attention helps ensure that the relevant features are strong enough to be bound together effectively into a coherent percept.

What is the "spotlight of attention" and how does it influence binding?

The spotlight of attention is the idea that we can selectively focus processing resources on a particular spatial area. This focused attention maximizes processing of items within the "beam" (either overtly, by directing our gaze, or covertly, by simply shifting our mental focus) ensuring that the features inside the spotlight are well integrated while ignoring extraneous information.

How do overt and covert attention contribute to binding?

Overt attention involves directly looking at (or fixating on) an object, which naturally enhances its features due to high-resolution foveal processing. Covert attention, in contrast, allows us to process an object or region outside the direct line of sight. Both mechanisms boost processing in the attended area and facilitate the binding of features, even if our gaze remains fixed elsewhere.

What evidence shows that attention is necessary for binding features into coherent objects?

Experiments have demonstrated that when attention is distracted or divided, feature binding errors (such as miscombining the correct color with the wrong shape) increase. This supports the idea that attention is essential not only for enhancing signal strength but also for correctly integrating multiple object features.

How does sustained attention—or the lack thereof—affect the process of binding and memory?

Sustained attention is crucial for maintaining coherent feature binding over time and for transferring bound representations into memory. For example, studies like Ceci & Tishman (1984) show that children with ADHD, who struggle with sustained focus, recall task-irrelevant distractors rather than the target items. This suggests that problems with attention can lead to faulty binding and reduced learning of intended material.

What role do load and distractibility play in the binding process?

Load Theory posits that each task consumes a certain amount of processing capacity. Under low-load conditions, there’s spare capacity that may allow distractors to interfere with binding. In high-load tasks, attention is fully occupied with the target features, leading to better filtering of irrelevant stimuli and more effective feature integration. Individual differences in distractibility (even among those without ADHD) can modulate how efficiently features are bound under varying task demands.

How has our understanding of binding evolved with newer models?

Earlier models, such as FIT, emphasized sequential stages—first automatic feature detection, then attentive binding. More recent models use capacity frameworks that view attention as a limited resource allocated dynamically across tasks. These models account for both spatial and object-based attention and reflect the complex interplay between bottom-up and top-down processes in binding features into coherent perceptual experiences.

Why might an object defined by a single salient feature be recognized faster than one requiring binding?

When an object can be identified by a unique feature (for example, a uniquely colored item), it "pops out" of the background due to preattentive processing. No additional processing is required to integrate multiple features, making the identification rapid. In contrast, objects defined by the conjunction of multiple features necessitate the slower, serial process of binding, resulting in longer response times.

What is illusory conjunction?

Illusory conjunction combining two features of different objects into one object 

What factors influence our ability to sustain attention in a complex environment?

Sustaining attention depends on both the internal signal/noise ratio and motivational priorities. When the signal is weak relative to internal noise, many stimuli may seem equally interesting, making focus difficult. Clinical interventions like Ritalin (methylphenidate) work by boosting dopamine levels, increasing the signal gain so that a selected task stands out more clearly from distraction.

Why do new tasks typically demand higher perceptual load compared to practiced tasks?

New tasks involve processing all unfamiliar features and require more cognitive resources to learn their structure. Over time, as one practices and breaks complex tasks into smaller, manageable steps, the perceptual load decreases. This gradual reduction frees capacity for multitasking and reduces processing time, leading to a more automatic performance.

What is automaticity, and how does practice influence it?

Automaticity is the state where a behavior is performed quickly and with minimal conscious effort due to extensive practice. With consistent mapping—where task demands remain unchanged—practice leads to faster, more efficient processing as the task “pops out” of routine. However, if the mapping varies, the task remains controlled and requires more effortful, serial processing.

How did Schneider and Shiffrin’s experiments differentiate between automatic and controlled processing?

In their target detection experiments, consistent mapping (where the same item is always a target) led to decreased reaction times and a “pop-out” effect, indicating automatic processing. In contrast, varied mapping, where targets can switch with distractors, forced participants into a controlled, serial search mode, as evidenced by slower responses and lower accuracy.

What does Logan’s instance theory of automaticity propose?

Logan’s instance theory posits that with consistent mapping, each encounter with a task builds memory traces or “instances.” When the task is repeated, the brain can retrieve the stored instance instead of recalculating the response. This retrieval process is a race against active computation—the faster mechanism wins—resulting in lower perceptual load and near-automatic performance.

How does the concept of capacity sharing and “working in bursts” relate to sustaining attention on complex tasks?

Pashler’s review suggested that even when tasks remain complex and demand constant cognitive load, people often work in bursts. They dynamically switch between tasks and share capacity across them. This means that rather than a continuous distribution, attention is applied in rapid sequences where selective periods of high focus allow for effective performance despite overall task difficulty.

What distinguishes controlled from automatic behaviors in the context of attentional processing?

Controlled behaviors require deliberate cognitive effort and are slower, often because they involve problem solving or task switching. Automatic behaviors, by contrast, result from extensive practice under consistent conditions—they are executed quickly with minimal awareness, sometimes even at the risk of interference with tasks that require careful control, as seen in classical interference effects.

How does the Stroop effect illustrate the interference between automatic and controlled processing?

In the Stroop task, reading words is highly automatic and fast, whereas naming the ink color requires controlled processing. Because the automatic process (reading the word) is so rapid, it interferes with the slower, deliberate task of color naming. This demonstrates how automatized behavior—while efficient—can sometimes impede the execution of tasks that require cognitive control.

What is meant by “cognitive control” and how is it theorized to function in our attentional system?

Cognitive control, often conceptualized as the central executive, is the part of our attentional system responsible for voluntarily prioritizing, selecting, and switching between tasks. It inhibits irrelevant responses and manages the allocation of limited processing resources so that only one action or thought stream dominates at a time, thus preventing overload.

How do practice and task consistency contribute to the reduction of perceptual load?

Practice builds proficiency by gradually reducing the cognitive effort needed for a task. When a task is performed repeatedly under consistent conditions, it requires fewer deliberate resources to execute. This “reduction in perceptual load” makes the task feel more automatic, freeing up attentional capacity for handling other concurrent tasks.

What role do stimulants play in the management of attentional deficits?

Stimulants like Ritalin increase dopamine levels, which enhances the signal-to-noise ratio in neural processing. This improved “signal gain” makes task-relevant information more salient, thereby helping individuals—especially those with attention deficits—to sustain focus and filter out distractors effectively.

How does the dual-process framework explain the interplay between automaticity and controlled processing?

The dual-process framework suggests that behavior is guided by two systems: one that is fast, efficient, and automatic (built through practice and consistent mapping) and another that is slower, more deliberate, and controlled (used when situations deviate from the routine). These systems often “race” one another, with the dominant (usually automatic) process taking precedence, unless controlled intervention is required to override habitual responses.

What is the role of the central executive in attention?

The central executive is the metaphorical “you” that directs attention and selects actions. It is responsible for voluntarily shifting focus to a desired task, inhibiting irrelevant information (as in the Stroop effect), and planning successive actions. Because it can select only one action at a time, it creates a bottleneck in decision making.

How does cognitive control relate to multitasking, and what happens when tasks use similar resources?

Cognitive control, a function of the central executive, involves the intentional selection of tasks and the inhibition of non-relevant ones. When two tasks use similar cognitive resources (e.g., language processing for phone conversations and writing emails), simultaneous processing is typically unachievable. Instead, the brain switches between tasks, and each switch incurs a “switch cost” or time delay, highlighting the limitations of multitasking.

What does the Psychological Refractory Period (PRP) task reveal about task-switching?

In the PRP task, two tasks are presented in close temporal succession. When the second task appears soon after the first, its response time is significantly delayed compared to when it is presented alone. This delay occurs because processing, selection, and inhibition for the first task occupy the central executive, demonstrating a central bottleneck that limits simultaneous decision-making.

Are heavy multimedia users better at multitasking?

Research shows that heavy multimedia users are more susceptible to distractions and perform worse on task-switching tests. They tend to have more lapses of attention and overestimate their multitasking skills—an effect consistent with the Dunning-Kruger phenomenon. Their tendency to split focus often leads to reduced studying efficiency (e.g., lower GPAs among frequent social media users).

How can the central executive bottleneck be beneficial despite its limits?

Although this bottleneck restricts simultaneous decision-making, it allows for simultaneous sensory processing across different modalities and prevents error-prone simultaneous motor commands. This separation aids in prioritizing actions and enables dynamic planning—helping you to revise or halt actions rapidly when needed.

What evidence from simulated driving studies underscores the impact of task-switching on performance?

In simulated driving tasks, dual-task conditions—such as responding to red brake lights while using a cell phone—result in increased braking reaction times and a higher number of missed signals. For instance, studies (Strayer & Johnston, 2001; Levy, Pashler, & Boer, 2006) demonstrated that when braking is coupled with another task, the reaction time delay can average 174 ms. At highway speeds (65 mph), this delay represents about 16 feet of additional stopping distance, markedly heightening crash risk.

How do real-world findings, such as those from the 100-car Naturalistic Driving Study, relate to central executive limitations?

The 100-car study documented that in 80% of crashes and 67% of near-crashes, drivers were inattentive in the 3 seconds preceding an event. This inattention reflects the central bottleneck's limitation: insufficient time to shift focus back to a critical task (like braking) when multitasking or distracted, which has dire consequences in dynamic environments like driving.

What are the effects of mobile devices on attention during everyday tasks?

Personalized digital devices serve as supernormal stimuli that capture and hold attention. They trigger recurring task loops, making it challenging to disengage. The National Electronic Injury Surveillance System (NEISS) estimated over 29,000 distracted walking injuries from 2011–2019, highlighting that the pull of mobile devices can disrupt our capacity to shift attention even during everyday activities like walking.

How does the central executive manage task switching despite the limitations of simultaneous processing?

The central executive manages task switching by rapidly alternating focus between tasks, a process governed by capacity sharing. Although this dynamic switching introduces switch costs (measurable delays), it allows overall performance in high-load, multi-task environments. Ultimately, the executive’s control system helps prioritize tasks—albeit at the expense of introducing delays that can lead to interference, as seen in phenomena like the Stroop effect.

How does practice affect multitasking and cognitive load, and what happens when consistent mapping is not possible?

When tasks are consistently mapped (i.e., targets and distractors remain static), practice automates responses, lowering processing load and freeing cognitive resources. However, when mapping varies (requiring flexible decision-making), tasks remain high load and necessitate capacity sharing and serial switching—making controlled, voluntary processing essential but less efficient. This interplay determines whether behavior appears automatic or remains under conscious control.

What is inattentional blindness and what did Mack & Rock (1998) demonstrate about it?

Inattentional blindness is the failure to notice an object—even one directly in your central vision—when your attention is fully engaged in a demanding task. Mack & Rock (1998) found that 89% of central objects went undetected when participants performed a high-load spatial task. However, when participants were warned (i.e., provided with top-down information), their failure rate dropped to 10%. This shows that attention is a prerequisite for conscious perception.

How does the "basketball and gorilla" experiment illustrate the limits of attention?

The "basketball and gorilla" experiment (Simons & Chabris, 1999) demonstrates that when people are focused on a particular task, such as tracking a basketball, they can completely miss unexpected events—like a person in a gorilla suit walking through the scene. This striking example underscores the phenomenon of inattentional blindness and the limits of our visual awareness when attention is narrowly focused.

What is change blindness, and why does it occur even when observers are actively looking for changes?

Change blindness is the inability to notice changes between visual scenes, even when you’re actively searching for them. This occurs because—due to computational complexity—we cannot fully encode every element of our environment. When a change happens, if the item wasn’t sufficiently encoded in the first place, the observer may never notice the difference. Foveating (directly looking at) the changing item nearly eliminates change blindness.

Describe the attentional blink phenomenon and its underlying reasons

The attentional blink is a brief period (typically when the second target appears 180–450 ms after the first) during which the ability to process a new target is significantly impaired. In rapid serial visual presentation (RSVP) tasks—for example, when participants must report an “S” (Task 1) followed by a “B” (Task 2)—processing the initial target creates a bottleneck. This delay, partly due to switch costs in the central executive, limits the capacity to register subsequent targets.

How do consistent and varied mapping influence automaticity and attentional load in task performance?

With consistent mapping, where the target remains the same across trials, practice leads to automation—reducing the perceptual load and producing faster, near-effortless responses. In varied mapping, where targets and distractors exchange roles, practice improves capacity sharing but the tasks remain more demanding, necessitating controlled processing and serial switching. This distinction helps explain why some skills become automatic while others continue to require active attention.

How does repeated exposure (perceptual/statistical learning) facilitate training of attention?

Repeated exposure enables faster and more accurate categorization of stimuli by learning consistent statistical regularities between them. When one stimulus reliably predicts another, the computational load needed to process them together decreases. This reduction in complexity helps speed up reaction times and improve identification accuracy, effectively training the sensory system to allocate attention more efficiently.

What evidence supports the claim that action video game training can improve attentional capacity?

Research by Green & Bavelier (2003) found that experienced video game players (VGPs) exhibited a reduced attentional blink and improved detection of peripheral targets compared to non-game players. Importantly, training novices on an action game for 10 days resulted in similar improvements that generalized beyond the gaming environment. This suggests that the rapid, reward-driven context of action games enhances both low-level sensory processing and high-level cognitive functions.

What mechanisms in action video games may enhance both low-level and high-level attentional processes?

Action video games provide strong reward signals that increase motivation, thereby priming the brain for learning. They demand rapid selection of task-relevant information while suppressing distractors. The resulting activation of dopaminergic reward systems facilitates neural plasticity that not only improves automatization of sensory processing (low-level) but also enhances cognitive flexibility, goal maintenance, and inhibition (high-level).

How does the Psychological Refractory Period (PRP) illustrate the central executive bottleneck?

The PRP task involves two tasks presented in close succession. When the second task (e.g., making a manual choice or response) occurs shortly after the first, reaction times slow down because the central executive is still occupied with processing the first task. This delay—reflecting the “switch cost”—demonstrates a central bottleneck in attentional control, which remains even with extensive practice.

What do simulated driving studies tell us about the importance of controlled attention?

Simulated driving tasks have shown that engaging in dual tasks (e.g., driving and using a cell phone) significantly delays responses, such as braking. For example, delays of 174 ms—equating to an extra 16 feet of stopping distance at highway speeds—result in higher risks of collisions. These findings emphasize the limitations of multitasking under high-load conditions and the critical role of focused, controlled attention in safety-critical tasks like driving.

What crucial insights about memory were revealed by the case of Henry Molaison (H.M.)?

Henry Molaison’s case revealed that short-term (immediate) memory and long-term memory are functionally separable. After surgery removing significant portions of his medial temporal lobes—including the hippocampus—H.M. developed anterograde amnesia. Although he could process recent information temporarily, he was unable to form new long-term memories. This landmark case underscored the anatomical basis of memory formation and the distinct roles of short-term and long-term memory systems.

In summary, how does training affect attentional performance despite inherent processing bottlenecks?

Although central bottlenecks like the Psychological Refractory Period cannot be entirely eliminated, training reduces the attentional load on tasks with consistent mapping (leading to automaticity) and improves capacity sharing in tasks with varied mapping. This results in faster reaction times, more accurate stimulus processing, and enhanced transfer of trained skills to real-world situations—including improved peripheral awareness and safer performance during demanding tasks like driving.