Fiche de révision : Cognitive and Perception Principles

📋 Course Outline

1. Cognitive Models
2. Perception Principles
3. Processing Types
4. Memory Systems
5. Memory Phenomena
6. Reasoning Types
7. Language Properties
8. Basic Cognitive Skills
9. Statistics Measures
10. Hypothesis Testing
11. Statistical Tests
12. Distribution Characteristics

📖 1. Cognitive Models

🔑 Key Concepts & Definitions

• Executive Function: A set of high-level cognitive processes (e.g., planning, decision-making, problem-solving) that regulate and control other cognitive functions (Luria, 1966).
• Cognitive Model: A theoretical framework that describes mental processes and their interactions, often using computational or schematic representations (Newell & Simon, 1972).
• Working Memory (WM): A limited-capacity system responsible for temporarily holding and manipulating information necessary for complex tasks (Baddeley & Hitch, 1974).
• Central Executive: The component of Baddeley's WM model that directs attention, manages cognitive tasks, and integrates information from subsystems (Baddeley & Hitch, 1974).
• Cognitive Architecture: The underlying structure of cognitive processes, including modules, pathways, and rules that simulate human cognition (Anderson, 1990).
• Production System: A computational model where behavior is governed by condition-action rules ("if-then" statements), used to simulate problem-solving and decision-making (Newell & Simon, 1972).

📝 Essential Points

• Executive function is crucial for goal-directed behavior; deficits (e.g., in frontal lobe damage) impair planning and decision-making (Luria, 1966).
• Cognitive models aim to explain how mental processes are organized and interact, often tested via simulations or computational algorithms (Newell & Simon, 1972).
• Working memory distinguishes from long-term memory by its limited capacity (~7±2 items; Miller, 1956) and active manipulation of info (Baddeley & Hitch, 1974).
• The central executive coordinates subsystems: phonological loop, visuospatial sketchpad, and episodic buffer (Baddeley & Hitch, 1974).
• Production systems are used to model reasoning and problem-solving, exemplified by ACT-R architecture (Anderson, 1990).
• Cognitive architecture models (e.g., ACT-R, SOAR) simulate human cognition for understanding and predicting behavior in tasks like reasoning, language, and perception.

💡 Key Takeaway

Cognitive models provide structured, often computational, representations of mental processes, with the working memory and executive functions being central to understanding complex cognition and decision-making.

📖 2. Perception Principles

🔑 Key Concepts & Definitions

• Gestalt Principles (Wertheimer, 1923): Innate perceptual organization rules that explain how we group visual elements, e.g., proximity, similarity, closure, continuity, and figure-ground segregation.
• Top-Down Processing: Perception driven by prior knowledge, expectations, and context, influencing how sensory information is interpreted (see section 4. Memory Phenomena for context).
• Bottom-Up Processing: Perception based solely on raw sensory input, with data flowing from sensory receptors to higher cognitive processes.
• Perceptual Load Theory (Lavie, 1995): Suggests that perceptual capacity is limited; high perceptual load tasks reduce processing of irrelevant stimuli, whereas low load allows more distractor processing.
• Mirror Neurons (Rizzolatti et al., 1996): Neurons that fire both when an individual performs an action and when they observe the same action performed by others, linking perception and action.
• Cherry’s Dichotic Listening Experiment (1953): Demonstrated selective attention by showing participants could focus on one auditory stream while ignoring another, but often failed to notice meaningful information in unattended channels.

📝 Essential Points

• Gestalt principles explain perceptual grouping; crucial for understanding visual illusions and figure-ground segregation.
• Top-down processing involves expectations and prior knowledge (e.g., reading jumbled words if first and last letters are correct).
• Bottom-up processing is data-driven; essential when encountering novel stimuli.
• Perceptual load influences attentional resources; high load tasks limit distractor processing, reducing interference.
• Mirror neurons provide a neural basis for imitation, empathy, and social cognition.
• Cherry’s experiment revealed that selective attention filters sensory input, but some unattended information (e.g., own name) can break through (e.g., cocktail party effect).
• Theories of perception often integrate both bottom-up and top-down processes; understanding their interaction is key in perception questions.
• Perceptual load theory explains phenomena like inattentional blindness and change blindness.

💡 Key Takeaway

Perception is a complex interplay of innate organizational principles and top-down influences, with attention modulating how sensory information is processed based on perceptual load and prior knowledge.

📖 3. Processing Types

🔑 Key Concepts & Definitions

• Top-down processing: Information processing driven by prior knowledge, expectations, and experiences; influences perception by filling in gaps (see Gestalt principles).
• Bottom-up processing: Data-driven perception starting from sensory input, building up to higher-level understanding without prior expectations.
• Perceptual load theory: Proposed by Lavie (1995), suggests that perceptual capacity is limited; high load tasks reduce distractor processing, while low load tasks allow more distractor influence.
• Mirror neurons: Neurons, identified by Rizzolatti et al. (1996), that activate both during action execution and observation, linking perception and action understanding.
• Cherry’s dichotic listening experiment: Demonstrated selective attention by showing participants could focus on one auditory channel and ignore the other, revealing limits of auditory processing (1953).
• Treisman’s attenuation model: Suggests unattended stimuli are not blocked but weakened; relevant for understanding how some unattended info can still be processed (1964).

📝 Essential Points

• Processing types determine how sensory info is transformed into perception and cognition.
• Top-down involves expectations shaping perception; bottom-up relies solely on sensory data.
• Perceptual load theory explains attentional focus: high load tasks limit processing of irrelevant stimuli, reducing distraction.
• Mirror neurons provide a neural basis for imitation, empathy, and understanding actions (Rizzolatti).
• Cherry’s experiment established the limits of selective attention, foundational for understanding dichotic listening and attentional filtering.
• Treisman’s model accounts for phenomena like cocktail party effect, where some unattended info still influences perception.

💡 Key Takeaway

Processing types—top-down and bottom-up—are fundamental to understanding how perception is shaped by sensory input and prior knowledge, with models like Treisman’s attenuation explaining attention limits.

Note: For exam success, recognize scenarios such as:

• "Participants focus on one conversation but still notice their name in the unattended ear" (Treisman).
• "A high perceptual load task reduces distractor influence" (Lavie).
• "Mirror neuron activity during both action and observation" (Rizzolatti).

📖 4. Memory Systems

🔑 Key Concepts & Definitions

• Working Memory (Baddeley, 2000): A limited-capacity system responsible for temporarily holding and manipulating information necessary for complex cognitive tasks like reasoning, learning, and comprehension. Composed of subsystems: phonological loop, visuospatial sketchpad, central executive, and episodic buffer.

• Episodic Buffer (Baddeley, 2000): A component of working memory that integrates information from different sources into a coherent episode, allowing for conscious awareness and long-term storage.

• Long-Term Memory (LTM): The system responsible for storing information over extended periods, subdivided into explicit (declarative) and implicit (non-declarative) memory.

• Flashbulb Memories (Brown & Kulik, 1977): Highly detailed, vivid memories of significant events, often emotionally charged, believed to be stored with high confidence but susceptible to distortion.

• Source Monitoring Errors (Johnson et al., 1998): Mistakes in recalling the origin of a memory, such as confusing an imagined event with a real one, contributing to false memories.

• Double Dissociation: A neuropsychological pattern where damage to one brain area impairs one function but not another, and vice versa, demonstrating the independence of different memory systems (e.g., hippocampus vs. basal ganglia in memory).

📝 Essential Points

• Memory Systems Distinction:

  • Working memory is active and manipulative, essential for immediate tasks.
  • Long-term memory stores information for future retrieval; subdivided into explicit (facts, events) and implicit (skills, conditioned responses).

• Memory Phenomena:

  • Flashbulb memories are vivid but not immune to errors (e.g., Bahrick et al., 1975: high confidence does not mean accuracy).
  • Autobiographical memory involves personal life events, often reconstructed, susceptible to source monitoring errors.

• Memory Disorders:

  • Anterograde amnesia: inability to form new memories (e.g., H.M. case).
  • Retrograde amnesia: loss of past memories, often temporally graded.

• Memory Models & Evidence:

  • Budson & Price (2005): Memory is like a network with nodes; damage affects specific pathways, explaining double dissociation.
  • Von Restorff effect: distinctive items are more likely to be remembered (distinctiveness enhances encoding).

• Memory Retrieval & Errors:

  • Source monitoring errors can lead to false memories, especially under suggestive conditions.
  • Illusory truth effect: repeated statements are more likely to be believed, affecting memory confidence.

💡 Key Takeaway

Memory involves multiple systems with distinct functions; understanding their differences and interactions is crucial for explaining phenomena like vivid memories, false memories, and effects of brain damage on cognition.

📖 5. Memory Phenomena

🔑 Key Concepts & Definitions

• Flashbulb Memory: A vivid, detailed memory of an emotionally significant event, often perceived as highly accurate, but subject to distortion over time (Brown & Kulik, 1977).
• Illusory Truth Effect: The tendency to believe false information is true after repeated exposure, due to familiarity (Hasher, Goldstein & Toppino, 1977).
• Source Monitoring Errors: Mistakes in recalling the origin of a memory, leading to misattributions (Johnson, Hashtroudi & Lindsay, 1993).
• Von Restorff Effect: Enhanced memory for distinctive or unusual items within a list, due to their salience (Von Restorff, 1933).
• Autobiographical Memory: Memory for events and facts related to one's personal life, often involving both episodic and semantic components (Conway & Pleydell-Pearce, 2000).
• Memory Dissociations (Double Dissociation): Demonstrating that two memory processes or systems operate independently, often through neuropsychological evidence (see section 4).

📝 Essential Points

• Flashbulb memories are emotionally charged but prone to inaccuracies; they are not immune to forgetting or distortion (Brown & Kulik, 1977).
• The illusory truth effect demonstrates how familiarity influences belief, relevant in misinformation scenarios (Hasher et al., 1977).
• Source monitoring errors contribute to false memories, especially in eyewitness testimony (Johnson et al., 1993).
• The Von Restorff effect indicates that distinctive items are more memorable, useful in designing effective learning or advertising strategies.
• Autobiographical memory involves complex interactions between episodic and semantic memory, often influenced by personal relevance and emotional state (Conway & Pleydell-Pearce, 2000).
• Memory dissociations (double dissociations) are critical in understanding that different memory types (e.g., semantic vs episodic) can be independently impaired or preserved (Milner, 1962).

💡 Key Takeaway

Memory phenomena reveal that memory is reconstructive, influenced by emotion, salience, and familiarity, and is susceptible to distortions and errors, especially regarding source attribution and emotional significance.

📖 6. Reasoning Types

🔑 Key Concepts & Definitions

• Deductive reasoning: Logical process where conclusions are derived from general premises; if premises are true, conclusion must be true (e.g., syllogisms). Wason (1966) demonstrated how people often fail to test their hypotheses properly in this reasoning type.
• Inductive reasoning: Derives general principles from specific observations; conclusions are probable, not certain. Example: noticing that the sun rises every morning and predicting it will do so tomorrow.
• Wason card selection task: A classic experiment illustrating confirmation bias in inductive reasoning; participants often choose cards that confirm rather than disprove hypotheses.
• Linear syllogisms: Logical arguments with two premises and a conclusion, evaluated for validity; e.g., "All A are B, all B are C, therefore all A are C."
• Propositional reasoning: Reasoning about propositions connected by logical operators (and, or, if-then); often tested with logic puzzles or conditional statements.
• Reasoning heuristics: Mental shortcuts used to solve problems quickly, which can lead to biases; e.g., availability heuristic affecting inductive reasoning.

📝 Essential Points

• Deductive reasoning guarantees conclusion truth if premises are true; common in formal logic and syllogisms.
• Inductive reasoning involves probabilistic conclusions; more prone to biases like confirmation bias (see Wason).
• Wason (1966): Demonstrated that people tend to seek confirming evidence rather than disconfirming, illustrating a common reasoning bias.
• Syllogisms can be valid or invalid; validity depends on logical form, not truth of premises.
• Logical fallacies often occur in reasoning tasks, especially with heuristics and biases.
• Reasoning errors: Confirmation bias, belief bias, and illusory correlations are common pitfalls in reasoning tasks.

💡 Key Takeaway

Reasoning types—deductive and inductive—differ in certainty and evidence; understanding their processes and biases (e.g., confirmation bias in inductive reasoning) is essential for analyzing reasoning-based exam questions.

📖 7. Language Properties

🔑 Key Concepts & Definitions

• Productivity: The capacity of language to generate an infinite number of novel sentences using a finite set of rules and elements (Chomsky, 1957).
• Arbitrariness: The lack of inherent connection between a word’s form and its meaning, allowing for flexible symbol assignment (Saussure, 1916).
• Displacement: The ability of language to refer to objects, events, or concepts not immediately present in space or time (Chomsky, 1957).
• Lexicon: The mental repository of words, including their meanings, pronunciations, and grammatical properties (Fromkin & Rodman, 1998).
• Morpheme: The smallest meaningful unit of language, which can be a root, prefix, or suffix (Bloomfield, 1933).
• Syntactic Priming: The phenomenon where exposure to a specific syntactic structure increases the likelihood of using the same structure subsequently (Bock & Clark, 1996).

📝 Essential Points

• Language’s productivity distinguishes human communication from animal signaling (Chomsky, 1957).
• Arbitrariness underpins the symbolic nature of language, enabling diverse languages with different sounds for the same concepts (Saussure, 1916).
• Displacement allows language to communicate about past, future, or hypothetical scenarios, crucial for complex thought (Chomsky, 1957).
• The lexicon is dynamic, constantly updated with new words and meanings; it is central to language comprehension and production.
• Morphemes combine to form words; understanding morphemes aids in decoding unfamiliar words (Bloomfield, 1933).
• Syntactic priming facilitates language learning and conversation flow; it is used in language therapy and AI language models.

💡 Key Takeaway

Language’s unique properties—productivity, arbitrariness, and displacement—enable complex, flexible, and abstract communication, setting humans apart from other species.

📖 8. Basic Cognitive Skills

🔑 Key Concepts & Definitions

• Executive Function: A set of high-level cognitive processes including planning, decision-making, problem-solving, and inhibitory control, essential for goal-directed behavior (Miyake et al., 2000).
• Gestalt Principles: Rules describing how humans naturally organize visual elements into groups or unified wholes (e.g., proximity, similarity, closure) (Koffka, 1935).
• Mirror Neurons: Neurons that fire both when an individual performs an action and when they observe the same action performed by others, implicated in imitation and empathy (Rizzolatti et al., 1996).
• Propositional Theory of Imagery: Suggests mental images are represented as abstract propositions rather than sensory-like images, explaining how imagery and language share common structures (Kosslyn, 1980).
• Change Blindness: A failure to notice significant changes in a visual scene, often demonstrated via the flicker paradigm, highlighting limits of visual awareness (Simons & Levin, 1997).
• Source Monitoring Errors: Mistakes in recalling the origin of a memory, such as confusing an imagined event with a real one, contributing to false memories (Johnson et al., 1993).

📝 Essential Points

• Executive functions are crucial for complex tasks; deficits (e.g., in frontal lobe damage) impair planning and inhibitory control.
• Gestalt principles underpin perceptual organization; understanding these helps explain phenomena like figure-ground segregation and perceptual grouping.
• Mirror neurons support imitation, empathy, and language development; their discovery links perception and action.
• Propositional imagery accounts for how mental images are stored as abstract data, contrasting with sensory-based models.
• Change blindness reveals that attention is selective; even large scene changes can go unnoticed if attention is elsewhere (Simons & Chabris, 1999).
• Source monitoring errors are common in eyewitness testimony; understanding them helps reduce false accusations or memories.
• Cognitive skills like attention, working memory, and reasoning are foundational for higher cognition and are assessed via tasks like the Posner cueing paradigm or syllogisms.

💡 Key Takeaway

Basic cognitive skills such as attention, perception, and executive functions form the foundation for complex mental processes; understanding their mechanisms and limitations is essential for analyzing human cognition and behavior.

📖 9. Statistics Measures

🔑 Key Concepts & Definitions

• Mean (Average): The sum of all data points divided by the number of points; sensitive to outliers, used to represent central tendency (M).
• Median: The middle value when data are ordered; resistant to outliers, useful for skewed distributions.
• Mode: The most frequently occurring value; can be bimodal or multimodal, indicates commonality in data.
• Skewness: Asymmetry in the distribution; positive skew has a tail on the right, negative skew on the left (Pearson, 1895).
• Z-score: Standardized score indicating how many standard deviations a data point is from the mean; formula: $z = \frac{X - \mu}{\sigma}$.
• Standard deviation (SD): Measure of variability; average distance of data points from the mean, used in calculating z-scores and t-tests.

📝 Essential Points

• Distribution shape affects measure choice: skewed data favor median over mean (Pearson, 1895).
• Z-scores allow comparison across different distributions; critical in hypothesis testing and identifying outliers.
• Standard deviation influences the size of the critical region in hypothesis tests; larger SD means more variability, affecting test sensitivity.
• Semi-interquartile range: Half the difference between the third and first quartiles; robust measure of variability for skewed data.
• Skewed distributions can lead to misleading mean values; median provides a better central tendency measure in such cases.
• Hypothesis testing involves comparing a test statistic (like t or z) to critical values based on alpha levels (α), controlling Type I error.
• Levene’s test assesses homogeneity of variance, a key assumption in t-tests and ANOVA.

💡 Key Takeaway

Understanding how measures of central tendency and variability describe data distributions is essential for selecting appropriate statistical tests and accurately interpreting results in cognitive psychology research.

📖 10. Hypothesis Testing

🔑 Key Concepts & Definitions

• Null Hypothesis (H₀): The default assumption that there is no effect or difference; it is tested against the alternative hypothesis (AUTHOR (1950): foundational concept in inferential statistics).
• Alternative Hypothesis (H₁ or Ha): The hypothesis that indicates a significant effect or difference exists; it opposes H₀.
• Significance Level (α): The threshold probability (commonly 0.05) used to determine whether to reject H₀; represents the risk of Type I error (AUTHOR (1950): standard in hypothesis testing).
• Type I Error: Incorrectly rejecting H₀ when it is true (false positive).
• Type II Error: Failing to reject H₀ when H₁ is true (false negative).
• Critical Region: The set of values of the test statistic that leads to rejection of H₀; determined by α and degrees of freedom.
• p-value: The probability of obtaining the observed data (or more extreme) assuming H₀ is true; if p ≤ α, reject H₀ (AUTHOR (1950): central to hypothesis testing).

📝 Essential Points

• Hypothesis testing involves comparing a test statistic (e.g., t, z) to a critical value derived from α.
• The p-value approach assesses significance directly; if p ≤ α, H₀ is rejected.
• t-tests (one-sample, independent, dependent) are used depending on the experimental design (AUTHOR (1950): standard statistical procedures).
• Degrees of freedom influence the critical value; for example, df = n - 1 for one-sample t-test.
• Power of a test: the probability of correctly rejecting H₀ when H₁ is true; affected by sample size, effect size, and α.
• Effect of sample size: larger samples increase test sensitivity, reducing Type II errors.
• Common errors: Confusing p-value with effect size; misinterpreting significance as practical importance.
• Decision rule: If test statistic falls into the critical region or p-value ≤ α, reject H₀; otherwise, fail to reject.
• Effect of multiple testing: increases risk of Type I errors; corrections like Bonferroni are used.

💡 Key Takeaway

Hypothesis testing evaluates whether observed data provide enough evidence to reject the null hypothesis, balancing the risks of Type I and Type II errors, with significance levels guiding decision-making.

📖 11. Statistical Tests

🔑 Key Concepts & Definitions

• Null hypothesis (H₀): The default assumption that there is no effect or difference between groups or variables (e.g., no difference in memory recall between two conditions). (Fisher, 1935)
• Alternative hypothesis (H₁): The hypothesis that there is an effect or difference, contradicting H₀.
• Critical region: The set of values in the tail(s) of the distribution where H₀ is rejected if the test statistic falls within this region.
• p-value: The probability of obtaining the observed data, or more extreme, if H₀ is true. A small p-value (e.g., < 0.05) leads to rejecting H₀. (Fisher, 1925)
• Type I error: Incorrectly rejecting H₀ when it is true (false positive). Controlled by alpha level.
• Type II error: Failing to reject H₀ when H₁ is true (false negative).
• t-statistic: A ratio used to determine if sample means are significantly different, considering sample size and variance. (Student, 1908)
• Degrees of freedom (df): The number of independent values in a calculation that are free to vary, affecting the shape of the t or F distribution.
• Standard error (SE): The estimated standard deviation of the sampling distribution of a statistic, often the mean.
• Effect size: A measure of the magnitude of a phenomenon (e.g., Cohen’s d), important for understanding practical significance beyond p-values.

📝 Essential Points

• Choosing the test depends on the data type and design:
  • One-sample t-test: compares sample mean to a known value (e.g., population mean).
  • Independent samples t-test: compares means between two independent groups (e.g., experimental vs control).
  • Dependent/paired t-test: compares means within the same group across two conditions (e.g., pre- and post-test).
• Assumptions: normality, homogeneity of variance (Levene’s test), independence.
• Effect of sample size: Larger samples increase power, making it easier to detect true effects.
• Interpreting results:
  • If p < alpha (commonly 0.05), reject H₀.
  • If p ≥ alpha, fail to reject H₀.
• Common traps:
  • Confusing correlation with causation.
  • Misinterpreting non-significant results as evidence of no effect.
• Effect size importance: Even with significant p-values, small effect sizes may lack practical relevance.

💡 Key Takeaway

Statistical tests determine whether observed data support the presence of an effect, with careful consideration of assumptions, effect size, and error types; understanding when and how to use each test is crucial for valid conclusions in cognitive psychology research.

📖 12. Distribution Characteristics

🔑 Key Concepts & Definitions

• Skewness: Measure of asymmetry in a distribution.

  • Positive skew: Tail extends to the right; mean > median.
  • Negative skew: Tail extends to the left; mean < median.
  • Source: Hogg & Craig (1978): "Skewness quantifies the degree of asymmetry."

• Kurtosis: Degree of peakedness or flatness in a distribution.

  • Leptokurtic: Sharp peak, heavy tails.
  • Platykurtic: Flat peak, light tails.
  • Source: Fisher (1925): "Kurtosis describes the extremity of deviations."

• Semi-Interquartile Range (SIQR): Measure of variability, half the difference between the third and first quartiles.

  • Formula: SIQR = (Q3 - Q1) / 2
  • Use: Robust to outliers, complements IQR.

• Z-score: Standardized score indicating how many standard deviations a value is from the mean.

  • Formula: Z = (X - μ) / σ
  • Interpretation: Z > 0 (above mean), Z < 0 (below mean).

• Distribution Shape: Describes how data points are spread, characterized by skewness and kurtosis.

  • Normal distribution: Symmetric, bell-shaped, skewness = 0, kurtosis = 3.

📝 Essential Points

• Skewness affects the mean and median relationship; in positively skewed distributions, the mean exceeds the median, and vice versa.
• Kurtosis indicates the likelihood of extreme values; high kurtosis (leptokurtic) suggests more outliers.
• The semi-interquartile range (SIQR) is less affected by outliers than standard deviation, useful for skewed data.
• Z-scores allow comparison across different distributions; critical for identifying outliers or standardizing scores.
• Distribution shape impacts statistical analysis; many tests assume normality, so skewness and kurtosis are diagnostic tools.
• Recognizing skewness and kurtosis helps interpret data patterns and choose appropriate statistical tests.

💡 Key Takeaway

Distribution characteristics like skewness and kurtosis reveal data asymmetry and peakedness, guiding interpretation and analysis choices in cognitive and statistical assessments. Understanding these features is essential for accurate data description and hypothesis testing.

📊 Synthesis Tables

⚠️ Common Pitfalls & Confusions

1. Confusing working memory with short-term memory—working memory involves manipulation, not just storage.
2. Overlooking the episodic buffer as a separate component of working memory.
3. Assuming long-term memory is always permanent; it can decay or be forgotten.
4. Misunderstanding serial position effects as solely due to rehearsal, ignoring retrieval processes.
5. Confusing explicit and implicit memory; explicit involves conscious recall, implicit does not.
6. Believing procedural memory is part of declarative memory—it's implicit.
7. Ignoring the distinction between encoding (initial learning) and retrieval (accessing stored info).
8. Overgeneralizing memory phenomena without considering context or task differences.

✅ Exam Checklist

• Know Baddeley & Hitch's (1974) model of working memory and its components: phonological loop, visuospatial sketchpad, central executive, episodic buffer.
• Understand Tulving's (1972) distinction between episodic and semantic memory.
• Be able to explain serial position effect and primacy/recency phenomena (Murdock, 1962).
• Recognize Atkinson & Shiffrin's (1968) multi-store model of memory.
• Differentiate between explicit (episodic, semantic) and implicit (procedural, priming) memory (Tulving, Squire).
• Know Squire's (1992) findings on procedural memory and its independence from hippocampal function.
• Understand the concepts of encoding, storage, and retrieval.
• Recall the levels of processing theory (Craik & Lockhart, 1972).
• Be familiar with memory phenomena like the spacing effect and testing effect.
• Recognize memory distortions such as false memories and the misinformation effect.
• Know Ebbinghaus's (1885) forgetting curve and the importance of rehearsal.
• Understand the neural basis of memory, including hippocampus role in episodic memory.
• Be able to describe amnesia types (anterograde, retrograde) and their implications.

📊 Synthesis Tables

⚠️ Common Pitfalls & Confusions

1. Confusing deductive with inductive reasoning—deductive guarantees, inductive is probabilistic.
2. Overlooking heuristics as shortcuts that can lead to errors.
3. Assuming all reasoning is purely logical; often influenced by biases.
4. Misidentifying cognitive biases as reasoning errors rather than heuristics.
5. Ignoring the role of problem space and mental set in problem-solving.
6. Overgeneralizing algorithmic solutions; many problems require heuristics.
7. Confusing confirmation bias with general skepticism.
8. Underestimating the impact of heuristics on everyday decision-making.

✅ Exam Checklist

• Know Johnson-Laird's (1983) distinction between deductive, inductive, and abductive reasoning.
• Understand Tversky & Kahneman's (1974) research on cognitive biases like confirmation bias and anchoring.
• Be familiar with Newell & Simon's (1972) model of problem-solving and the concept of problem space.
• Recognize different heuristics and their advantages/disadvantages.
• Understand heuristics like availability, representativeness, and anchoring.
• Know Peirce's (1878) concept of abductive reasoning.
• Be able to identify fallacies and evaluate arguments critically.
• Recognize how biases influence decision-making and reasoning accuracy.
• Understand the difference between algorithmic and heuristic approaches.
• Recall examples of heuristics in real-life decision-making scenarios.
• Be familiar with problem-solving strategies and mental set effects.
• Know heuristics' role in everyday reasoning and their potential pitfalls.

📊 Synthesis Tables

⚠️ Common Pitfalls & Confusions

1. Confusing syntax with semantics—syntax is structure, semantics is meaning.
2. Overlooking pragmatics as the use of language in context.
3. Assuming innate grammar applies universally without considering cultural differences.
4. Misinterpreting aphasia types; e.g., speech production vs comprehension deficits.
5. Believing language is solely innate; some aspects are learned.
6. Confusing displacement with simple communication.
7. Overgeneralizing language properties without considering context or modality.
8. Ignoring the role of pragmatics in understanding implied meaning.

✅ Exam Checklist

• Know Chomsky's (1957) theory of Universal Grammar and the concept of innate language structures.
• Understand Lenneberg's (1967) critical period hypothesis for language acquisition.
• Recognize Broca's and Wernicke's aphasia and their respective deficits.
• Be familiar with Hockett's (1958) design features of language, especially displacement and productivity.
• Understand Saussure's distinction between langue and parole.
• Know displacement as the ability to talk about absent or non-present entities.
• Recognize productivity as the capacity to generate novel sentences.
• Understand duality of patterning: meaningful units are composed of smaller, meaningless units.
• Recall the importance of pragmatics in language comprehension.
• Be able to explain language development theories and disorders.
• Know the neural basis of language, including Broca's and Wernicke's areas.
• Recognize language universals and variations across cultures.

📊 Synthesis Tables