Year 7 Cambridge Psychology: Formulas & Theorems Quick Reference Handbook | 剑桥7年级心理学：公式定理速查手册

📚 Year 7 Cambridge Psychology: Formulas & Theorems Quick Reference Handbook | 剑桥7年级心理学：公式定理速查手册

Welcome to your go-to quick reference handbook for Year 7 Cambridge Psychology. Although psychology is not a subject of mathematical formulas in the same way as physics, it is full of powerful principles, models, and ‘equations’ that summarise how the mind works. This handbook presents key psychological ‘formulas’ – from the memory equation to social influence theorems – that you must know. Each concept is written as a memorable formula with a clear explanation.

欢迎使用这本七年级剑桥心理学速查手册。尽管心理学不像物理学那样充满数学公式，但它包含大量概括心智运作的强大原理、模型和“方程式”。本手册以必须掌握的公式形式呈现了关键心理学概念——从记忆方程到社会影响定理。每个概念都配以清晰的解释，并用易记的公式表示。

1. The Memory Equation: Encoding + Storage + Retrieval | 记忆方程：编码 + 存储 + 提取

Memory is not a single event but a system with three essential stages: encoding (transforming sensory input into a form the brain can use), storage (maintaining that information over time), and retrieval (accessing the stored information when needed). The foundational ‘memory equation’ declares that all three stages must succeed: Memory = Encoding + Storage + Retrieval. If any component breaks down, the memory cannot be formed or recovered.

记忆不是单一事件，而是一个包含三个关键阶段的系统：编码（将感觉信息转换为大脑可用的形式）、存储（随时间保持该信息）和提取（在需要时访问存储的信息）。基本“记忆方程”宣告，所有三个阶段都必须成功：记忆 = 编码 + 存储 + 提取。如果任何一个环节出问题，记忆就无法形成或唤醒。

Going deeper, researchers Craik and Lockhart introduced the levels-of-processing idea. Memory strength depends on how deeply we process information. We can express this as: Memory Strength = Depth of Processing × Rehearsal. The deeper the processing (e.g., thinking about meaning rather than just sound) and the more we rehearse, the stronger the memory trace.

进一步来看，研究者 Craik 和 Lockhart 提出了加工层次的观点。记忆强度取决于我们加工信息的深度。我们可以表达为：记忆强度 = 加工深度 × 复述量。加工越深（例如思考意义而非仅仅听音）且复述越多，记忆痕迹就越强。

2. Multi-Store Model Formula: Sensory → STM → LTM | 多存储模型公式：感觉记忆 → 短时记忆 → 长时记忆

Atkinson and Shiffrin’s multi-store model describes memory as a flow of information through three distinct stores: sensory memory, short-term memory (STM), and long-term memory (LTM). The ‘formula’ illustrates the transfer process: Sensory Input → Sensory Store → (Attention) → STM → (Rehearsal) → LTM. Information that is not rehearsed in STM is lost through decay or displacement.

Atkinson 和 Shiffrin 的多存储模型将记忆描述为信息流经三个独立的存储器：感觉记忆、短时记忆（STM）和长时记忆（LTM）。这个“公式”展示了传递过程：感觉输入 → 感觉存储 → （注意）→ 短时记忆 → （复述）→ 长时记忆。在短时记忆中未经复述的信息会因衰退或替代而丢失。

The table below summarises the key features of each store according to the model:

下表总结了模型中各存储器的关键特征：

3. STM Capacity & Duration Theorem: 7 ± 2 Chunks | 短时记忆容量与持续时间定理：7 ± 2 组块

George Miller’s classic research revealed that short-term memory has a limited capacity. The ‘capacity theorem’ states: STM Capacity = 7 ± 2 chunks. A chunk is a meaningful grouping of information. By organising information into chunks, we can significantly expand the amount of information held in STM. For instance, the letter sequence ‘FBICIAUSA’ is 9 individual items and exceeds the limit, but chunked as ‘FBI CIA USA’ it becomes 3 chunks and fits easily. STM duration without active rehearsal is around 18–30 seconds, after which information decays unless transferred to LTM.

乔治·米勒的经典研究揭示了短时记忆的容量有限。“容量定理”指出：STM容量 = 7 ± 2 组块。组块是指有意义的信息组合。通过将信息组织成组块，我们可以显著扩大短时记忆中可容纳的信息量。例如，字母序列“FBICIAUSA”是9个单独项，超出了容量限制，但分组为“FBI CIA USA”后变成3个组块，轻松容纳。若无主动复述，短时记忆的持续时间约为18–30秒，此后信息会衰退，除非转入长时记忆。

The chunking advantage can be expressed as: Effective STM Capacity = Number of Chunks × Chunk Size. This theorem underpins many revision techniques, such as using acronyms or grouping digits in a phone number.

组块优势可表示为：有效短时容量 = 组块数 × 每块大小。这一定理支撑了许多复习技巧，如使用首字母缩写或把电话号码数字分组。

4. Ebbinghaus Forgetting Curve Law: Retention Declines Exponentially | 艾宾浩斯遗忘曲线定律：保留率指数下降

Hermann Ebbinghaus discovered that forgetting follows a predictable pattern: it is very rapid immediately after learning and then gradually levels off. The ‘forgetting law’ can be approximated by an exponential decay function: Retention ≈ 100 × e^(-k × t), where t is the elapsed time since learning and k is a decay constant that varies with memory strength. Spaced repetition and overlearning increase k, flattening the curve.

赫尔曼·艾宾浩斯发现遗忘遵循可预测的模式：学习后最初遗忘迅速，然后逐渐平缓。这个“遗忘定律”可以用指数衰减函数近似：保留率 ≈ 100 × e^(-k × t)，其中t为学习后经过的时间，k为随记忆强度而变化的衰减常数。间隔复述和过度学习会增大k值，减缓曲线下降。

Retention % ≈ 100 × e^(-k × t)

In practical terms, reviewing material after one day, one week, and one month dramatically boosts long-term retention. The law reminds us that a single study session is never enough; distributed practice is the key to durable memory.

在实际应用中，一天后、一周后和一个月后复习材料会极大地提升长期保留。这个定律提醒我们，单次学习永远不够；分散练习才是持久记忆的关键。

5. Retrieval Cue Dependency Principle: Recall = Encoding Specificity + Cues | 提取线索依赖原理：回忆 = 编码特异性 + 线索

Endel Tulving’s encoding specificity principle states that recall is improved when the retrieval context overlaps with the encoding context. We can model recall as: Recall Success = Encoding Specificity + Available Cues. Cues can be external (the room, smells, music) or internal (mood, physical state). This explains context-dependent memory (better recall in the same room where you learned) and state-dependent memory (information learned while happy is better recalled when happy).

Endel Tulving 的编码特异性原则指出，当提取情境与编码情境重叠时，回忆效果更好。我们可以将回忆建模为：回忆成功 = 编码特异性 + 可用线索。线索可以是外部的（房间、气味、音乐）

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