Memory Lecture Notes PDF

The concept of working memory applied to the study of executive functioning The alternative approach to investigating working memory, as discussed by Miyake et al. (2000) and Friedman & Miyake (2012), focuses on executive processes that control and regulate thought and action. These processes are c...

The concept of working memory applied to the study of executive functioning The alternative approach to investigating working memory, as discussed by Miyake et al. (2000) and Friedman & Miyake (2012), focuses on executive processes that control and regulate thought and action. These processes are closely associated with the frontal lobes of the brain, and deficits in these areas can be observed in individuals with brain injuries. Traditionally, neuropsychological tests like the Wisconsin Card Sort have been used to identify these executive processes, but these tests are often considered “impure” measures due to their complexity and the multiple cognitive functions they engage. Miyake et al. (2000) identified three independent executive processes: updating, shifting, and inhibition. Updating involves the constant monitoring and rapid addition or deletion of working memory contents. Shifting refers to the ability to switch flexibly between tasks or mental sets. Inhibition is the deliberate overriding of dominant or prepotent responses. These processes have been shown to have di erent relationships with cognitive development and intelligence, highlighting their distinct roles in cognitive functioning. Friedman et al.'s (2011) twin study conducted a factor analysis to investigate the three components of executive function: updating, shifting, and inhibition. Here are the key findings: 1. Common Executive Function (EF) Factor: The study found that updating, shifting, and inhibition are all related to a common EF factor. This means that these three executive functions share a significant amount of variance, indicating a unified underlying cognitive process. 2. Independent Contributions: Despite their commonalities, updating and shifting also make separate, independent contributions to cognitive processes. This suggests that while they are related, each function also has unique aspects that contribute to overall executive functioning.  3. Genetic Contributions: The study revealed substantial, independent genetic contributions to both the common EF factor and the specific components of updating, shifting, and inhibition. This indicates that genetic factors play a significant role in individual di erences in these executive functions. Overall, the findings highlight the dual nature of executive functions, showing both their shared (unity) and distinct (diversity) aspects. This understanding is crucial for comprehending how these cognitive processes contribute to goal-directed behavior and cognitive flexibility12. Psychometric approaches: Measuring the work done by working memory Just and Carpenter (1992) argue that a valid measure of working memory capacity, especially in real-world tasks, must simultaneously assess the demands of both storage and processing. Their theory, known as the capacity theory of comprehension, posits that working memory is not just a passive storage space but an active workspace where both storage and processing occur concurrently. Here are the key points of their argument: 1. Interdependence of Storage and Processing: In real-world tasks, cognitive processes often require the simultaneous handling of information (storage) and the manipulation or transformation of that information (processing). For example, understanding a complex sentence involves holding the words in memory (storage) while parsing their grammatical structure (processing). 2. Activation-Based Model: Just and Carpenter propose that both storage and processing are mediated by a common resource called activation. The total amount of activation available in working memory varies among individuals, which a ects their ability to perform tasks that require both storage and processing. 3. Individual Di erences: The theory explains that individual di erences in working memory capacity can account for variations in cognitive performance. People with higher working memory capacity have more activation available, allowing them to better manage the demands of both storage and processing simultaneously. 4. Real-World Relevance: To accurately measure working memory capacity in real- world tasks, assessments must reflect the dual demands of storage and processing. Simple span tasks, which only measure storage, do not capture the full complexity of working memory. Instead, complex span tasks, which require both storage and processing, provide a more valid measure of working memory capacity. In summary, Just and Carpenter’s argument emphasizes that a comprehensive assessment of working memory must consider the intertwined nature of storage and processing demands to reflect real-world cognitive tasks accurately12. Simple Span Tasks Simple span tasks primarily measure short-term memory by requiring participants to remember a sequence of items in the correct order. These tasks focus on the storage component of working memory without involving significant processing demands. Examples include:  Digit Span Task: Participants are asked to recall a sequence of numbers in the order they were presented.  Word Span Task: Participants are asked to recall a list of words in the order they were presented. Complex Span Tasks Complex span tasks measure working memory by requiring participants to both store and process information simultaneously. These tasks involve a dual-task paradigm where participants must remember items while performing an additional cognitive task. Examples include:  Reading Span Task: Participants read sentences and must recall the last word of each sentence while comprehending the sentences.  Operation Span Task: Participants solve mathematical equations and must remember a sequence of unrelated words presented between the equations. Application to Verbal and Numerical Skills Both simple and complex span tasks can be adapted to assess verbal and numerical skills:  Verbal Skills: o Simple Span: Word span tasks where participants recall lists of words. o Complex Span: Reading span tasks where participants read sentences and recall specific words.  Numerical Skills: o Simple Span: Digit span tasks where participants recall sequences of numbers.  o Complex Span: Operation span tasks where participants solve equations and recall numbers or words presented between the equations. Key Di erences  Processing Demands: Simple span tasks focus solely on storage, while complex span tasks require both storage and processing.  Task Complexity: Complex span tasks are more demanding as they involve additional cognitive processes beyond mere memorization.  Correlation with Cognitive Abilities: Complex span tasks tend to have higher correlations with higher-order cognitive abilities, such as reading comprehension and problem-solving, compared to simple span tasks 12. By incorporating both types of tasks, researchers can gain a more comprehensive understanding of an individual’s working memory capacity and its role in various cognitive domains. Daneman and Merikle (1996) conducted a study to investigate the relationship between working memory capacity and language comprehension. They focused on two types of span tasks: simple span tasks and complex span tasks. Simple Span Tasks  Definition: Simple span tasks involve recalling a sequence of items (e.g., digits, letters, or words) in the order they were presented.  Example: Digit span task, where participants are asked to recall a list of numbers in the correct order. Complex Span Tasks  Definition: Complex span tasks involve not only recalling items but also performing an additional cognitive task between the presentation of each item.  Example: Reading span task, where participants read sentences and remember the last word of each sentence while also comprehending the sentences. Key Findings 1. Predictive Power: Daneman and Merikle found that complex span tasks were better predictors of language comprehension than simple span tasks. This  suggests that the additional cognitive load in complex span tasks (e.g., processing sentences while remembering words) more closely mirrors the demands of real-world language comprehension. 2. Correlation with Comprehension: The study demonstrated that performance on complex span tasks was significantly correlated with measures of reading comprehension and other language-related abilities. This indicates that working memory capacity, as measured by complex span tasks, plays a crucial role in language comprehension. 3. Implications for Working Memory: The findings support the idea that working memory is not just about storage capacity but also involves the ability to manage and manipulate information while performing other cognitive tasks. Conclusion Daneman and Merikle’s study highlighted the importance of complex span tasks in assessing working memory capacity and its relationship to language comprehension. Their work has had a significant impact on the understanding of working memory and its role in cognitive processes12. Span tasks are correlated with reading (comprehension) tasks. This means the domain-general (across domains) contributions are shown by correlations from the complex maths (.30) and simple maths (.14) with the comprehension task. The domain-specific contributions can be seen by the higher correlation for the verbal tasks with the comprehension tasks (.41 is higher than.30, and.28 is higher than.14). Interestingly the domain-general contribution from the maths complex span task (.30) is about the as stronger and the domain-specific contribution of the simple span verbal task (.28) showing how much more useful/applicable complex span tasks are at predicting performance. Sanchez and Wiley (2006) Sanchez and Wiley (2006) conducted a study to explore how complex span measures of working memory predict performance in real-world tasks, particularly focusing on learning and comprehension from complex science texts. Here’s a summary of their approach and findings: Study Design 1. Participants: The study involved participants who were assessed on their working memory capacity using complex span tasks. 2. Complex Span Tasks: These tasks included the reading span and operation span tasks, which require participants to process and store information simultaneously. For example, in the reading span task, participants read sentences and remember the last word of each sentence. 3. Real-World Tasks: The real-world tasks involved learning and comprehending complex science texts. Participants were asked to read and understand scientific passages, and their comprehension was assessed through various measures, such as recall and application questions. Key Findings 1. Prediction of Comprehension: The study found that performance on complex span tasks was a significant predictor of participants’ ability to comprehend and learn from complex science texts. This suggests that individuals with higher working memory capacity, as measured by complex span tasks, are better at processing and integrating information from challenging texts. 2. Domain-General Contributions: The results indicated that working memory capacity, as assessed by complex span tasks, contributes to general cognitive abilities that are crucial for real-world learning and comprehension. This aligns with the idea that working memory supports a wide range of cognitive functions beyond simple storage.  3. Implications for Education: The findings have important implications for educational practices, suggesting that enhancing working memory capacity could improve students’ ability to learn and understand complex material. Sanchez and Wiley and seductive images Sanchez and Wiley (2006) demonstrated that complex span measures of working memory are valuable predictors of performance in real-world tasks, particularly in the context of learning from complex science texts. Their study highlights the importance of working memory capacity in supporting higher-order cognitive processes involved in real-world learning and comprehension1. Sanchez and Wiley (2006) investigated how di erent types of illustrations (nonillustrated, conceptual, and seductive) a ect learning outcomes, particularly focusing on individuals with varying working memory capacities (WMC). Key Findings 1. Number of Correct Causes in Essays: o Low WMC Participants:  Nonillustrated: Performed moderately well.  Conceptual: Showed improved performance compared to nonillustrated.  Seductive: Performance dropped significantly, indicating distraction. o High WMC Participants:  Nonillustrated: Performed well.  Conceptual: Performed slightly better than with nonillustrated.  Seductive: Performance remained high, indicating they were not distracted by seductive images. 2. Inference Verification Task (IVT) Performance: o Low WMC Participants:   Nonillustrated: Performed moderately well.  Conceptual: Showed improved performance.  Seductive: Performance dropped significantly. o High WMC Participants:  Nonillustrated: Performed well.  Conceptual: Performed slightly better.  Seductive: Performance remained high. Interpretation  Low WMC Participants: These individuals were more susceptible to distraction by seductive images, leading to poorer performance in both essay writing and IVT tasks. Conceptual images helped improve their performance by providing relevant visual aids.  High WMC Participants: These individuals were able to maintain high performance regardless of the type of illustration, indicating that their higher working memory capacity helped them manage potential distractions from seductive images. Conclusion The study highlights the importance of considering working memory capacity when designing educational materials. For individuals with lower WMC, seductive images can be particularly distracting and detrimental to learning, while conceptual images can aid in comprehension and performance. High WMC individuals are less a ected by such distractions and can maintain performance across di erent types of illustrations123. Kane et al. (2007) Mind wandering study Kane et al.'s 2007 study, titled “For Whom the Mind Wanders, and When: An Experience- Sampling Study of Working Memory and Executive Control in Daily Life,” explored the relationship between working memory capacity (WMC) and mind wandering in everyday activities12. Key Points of the Study: 1. Participants and Method: o The study involved 124 undergraduates who were pretested on complex memory-span tasks to assess their WMC.  o Over seven days, participants carried personal digital assistants (PDAs) that signaled them eight times daily to report whether their thoughts had wandered from their current activity and to describe their psychological and physical context12. 2. Findings: o WMC and Mind Wandering: The study found that individuals with higher WMC were better at maintaining on-task thoughts during challenging activities that required concentration and e ort. In contrast, those with lower WMC experienced more mind wandering during these tasks 12. o Cognitive Demand: The relationship between WMC and mind wandering was moderated by the cognitive demand of the activity. Higher-WMC individuals were less likely to mind wander during demanding tasks compared to lower-WMC individuals12. 3. Implications: o The results support theories emphasizing the role of executive attention and control processes in determining individual di erences in cognitive performance. o It suggests that WMC is a crucial factor in managing attention and reducing mind wandering, especially in tasks that require significant cognitive e ort12. Working memory – the real capacity? Cowan (2010) 1. Pure WM Capacity/Executive Processes: o This refers to the inherent ability of working memory to hold and manipulate information. It’s the basic capacity of your working memory system, including executive functions like attention control and cognitive flexibility. 2. E ects of Strategies: o These are techniques that individuals use to enhance their memory performance. Common strategies include:  Rehearsal: Repeating information to keep it active in memory.   Chunking: Grouping individual pieces of information into larger, meaningful units (e.g., remembering a phone number as chunks of digits rather than individual numbers). Cowan (2010) suggests that both these aspects—innate WM capacity and the use of memory strategies—contribute to how well someone performs on tasks that require working memory. Essentially, it’s not just about how much information your working memory can hold, but also how e ectively you use strategies to manage and recall that information. Working memory and fluid intelligence Shipstead et al. (2016) explored the relationship between working memory (WM) capacity and fluid intelligence, proposing that these two cognitive abilities are strongly correlated due to complementary processes that facilitate complex cognition12. Key Points of the Study: 1. Complementary Processes: o Maintenance: This refers to the ability to keep relevant information active and accessible in working memory. High WM capacity allows individuals to maintain and manipulate information accurately, which is crucial for problem-solving and reasoning tasks. o Disengagement: This involves the ability to discard outdated or irrelevant information. E ective disengagement helps individuals avoid cognitive overload and focus on new, pertinent information. 2. Problem-Solving: o High WM capacity enables individuals to represent and maintain a problem accurately, facilitating hypothesis testing and logical reasoning. o As hypotheses are disproven or become untenable, the ability to disengage from these outdated problem-solving attempts becomes important. This allows for the generation and testing of new hypotheses. 3. Top-Down Processing Goals: o Both maintenance and disengagement are organized around top-down processing goals, meaning they are driven by the individual’s intentions and goals. This organization helps in managing attention and cognitive resources e ectively. Implications:   The study suggests that the strong correlation between WM capacity and fluid intelligence is not due to one ability causing the other. Instead, it is because both abilities rely on separate but complementary attention-demanding mental functions.  These functions, maintenance and disengagement, are essential for complex cognitive tasks, explaining why individuals with high WM capacity often exhibit higher fluid intelligence. In summary, Shipstead et al. (2016) highlight that the relationship between WM and fluid intelligence is rooted in the ability to maintain relevant information and disengage from irrelevant information, both of which are crucial for e ective problem-solving and reasoning12. Cowan (2010): Is working memory really separate from long term memory? Nelson Cowan’s (2010) view on working memory (WM) suggests that it is not a separate cognitive system from long-term memory (LTM). Instead, he proposes that WM is an activated subset of LTM. Here’s a detailed explanation: Key Concepts: 1. Embedded-Processes Model: o Cowan’s model posits that WM consists of the currently activated portion of LTM. This means that the information we are actively thinking about or using is part of our LTM that has been temporarily brought into an active state. 2. Focus of Attention: o Within this activated portion of LTM, there is a more limited focus of attention. This focus can hold about 3-4 items or chunks of information at a time. These items are the ones we are consciously aware of and can manipulate. Implications:  Integration of WM and LTM: o According to Cowan, WM is not a distinct system but rather a dynamic state of LTM. This integration means that the boundaries between WM  and LTM are fluid, with information constantly moving between these states based on our cognitive needs and tasks.  Cognitive Processes: o The processes that govern WM, such as attention and executive control, are also involved in managing LTM. This interconnectedness helps explain how we can retrieve and use information from LTM in real-time problem- solving and decision-making. Practical Example:  Remembering Directions: o When you try to remember directions to a new place, the steps you actively recall and use are part of your WM. However, these directions are stored in your LTM and are activated when needed. As you navigate, your focus of attention shifts to the next step, demonstrating the fluid interaction between WM and LTM. In summary, Cowan’s (2010) view emphasizes that WM is an active subset of LTM, with a focus of attention that allows us to manipulate a limited amount of information at any given time. This perspective highlights the continuous interaction and overlap between WM and LTM in our cognitive processes12. Neuroscience, working memory and long term memory: Ranganath et al. (2003) Ranganath et al.'s (2003) study investigated the neural mechanisms underlying working memory (WM) and long-term memory (LTM) using event-related functional magnetic resonance imaging (fMRI). Here are the key points: Study Overview: 1. Objective: o The study aimed to determine whether the same prefrontal cortex (PFC) regions are involved in both WM and LTM tasks, or if distinct regions are responsible for each type of memory. 2. Method: o Participants performed both WM and LTM tasks while their brain activity was monitored using fMRI. o The tasks involved encoding and recognizing information, allowing the researchers to compare brain activation patterns during these processes. Findings: 1. Common Brain Regions: o The study found that the same bilateral ventrolateral prefrontal regions (near Brodmann’s Areas 6, 44, 45, and 47) and dorsolateral prefrontal regions (BA 9/46) were activated during both WM and LTM tasks1. o Additionally, a region in the left anterior middle frontal gyrus (BA 10/46) was engaged during the recognition phases of both WM and LTM tasks 1. 2. Implications: o These results suggest that the same prefrontal regions support reflective processes in both WM and LTM. o The findings challenge the notion that WM and LTM are entirely distinct systems, instead indicating that they share common neural substrates. Conclusion: Ranganath et al.'s (2003) study provides evidence that WM and LTM tasks engage overlapping regions in the prefrontal cortex. This supports the idea that both types of memory rely on similar neural mechanisms, particularly in the prefrontal cortex, which is crucial for maintaining and manipulating information over short and long periods 1.