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Whereas most adults can repeat about seven digits in correct order, some individuals have shown impressive enlargements of their digit span—up to 80 digits. This feat is possible by extensive training on an encoding strategy by which the digits in a list are grouped (usually in groups of three to five) and these groups are encoded as a single unit (a chunk). For this to succeed, participants must be able to recognize the groups as some known string of digits. One person studied by Ericsson and his colleagues, for example, used an extensive knowledge of racing times from the history of sports in the process of coding chunks: several such chunks could then be combined into a higher-order chunk, forming a hierarchy of chunks. In this way, only some chunks at the highest level of the hierarchy must be retained in working memory, and for retrieval the chunks are unpacked. That is, the chunks in working memory act as retrieval cues that point to the digits they contain. Practicing memory skills such as these does not expand working memory capacity proper: it is the capacity to transfer (and retrieve) information from long-term memory that is improved, according to Ericsson and Kintsch (1995; see also Gobet & Simon, 2000).

Whereas most adults can repeat about seven digits in correct order, some individuals have shown impressive enlargements of their digit span—up to 80 digits. This feat is possible by extensive training on an encoding strategy by which the digits in a list are grouped (usually in groups of three to five) and these groups are encoded as a single unit (a chunk). For this to succeed, participants must be able to recognize the groups as some known string of digits. One person studied by Ericsson and his colleagues, for example, used an extensive knowledge of racing times from the history of sports in the process of coding chunks: several such chunks could then be combined into a higher-order chunk, forming a hierarchy of chunks. In this way, only some chunks at the highest level of the hierarchy must be retained in working memory, and for retrieval the chunks are unpacked. That is, the chunks in working memory act as retrieval cues that point to the digits they contain. Practicing memory skills such as these does not expand working memory capacity proper: it is the capacity to transfer (and retrieve) information from long-term memory that is improved, according to Ericsson and Kintsch (1995; see also Gobet & Simon, 2000).

大多数成年人能够正确地重复大约7个数字，但有些个体显示出显著增加的数字记忆规模——高达80个数字。这种技术可以通过对编码策略的广泛训练来实现。按编码策略将列表中的数字分组（通常分3到5组）并将这些组编码为一个独立单元（一个组块）。要实现这一点，参与者必须能够将组块识别为某些已知的数字字符串。例如，埃里克森 Ericsson 和他的同事研究的一位研究对象利用了体育历史中比赛时间的广泛知识来编写代码组块: 几个这样的组块可组合成一个更高级的组块，形成组块层次结构。如此，必须保持在工作记忆中的只有层次结构最高级别的一些组块，且这些组块是打开的用于检索。也就是说，工作记忆中的组块作为提取线索发挥作用，提取它们所指向的数字内容。埃里克森 Ericsson 和 金茨 Kintsch （1995; 参见 Gobet & Simon，2000）认为，练习这种记忆技术并不能真正提高工作记忆容量，所提高的是从长期记忆中传递（和检索）信息的容量。

大多数成年人能够正确地重复大约7个数字，但有些个体显示出显著扩大的数字记忆规模——高达80个数字。这种技术可以通过对编码策略的广泛训练来实现。按编码策略将列表中的数字分组（通常分3到5组）并将这些组编码为一个独立单元（一个组块）。要实现这一点，参与者必须能够将组块识别为某些已知的数字字符串。例如，埃里克森 Ericsson 和他的同事的研究对象利用了体育历史中比赛时间的广泛知识来编写代码组块: 几个这样的组块可组合成一个更高级的组块，形成组块层次结构。如此，只有层次结构最高级别的一些组块必须保持在工作记忆中，且这些组块是开放于检索的。也就是说，工作记忆中的组块作为提取线索发挥作用，提取它们所指向的数字内容。埃里克森 Ericsson 和 金茨 Kintsch （1995; 参见 Gobet & Simon，2000）认为，练习这种记忆技术并不能真正提高工作记忆容量，所提高的是从长期记忆中传递（和检索）信息的容量。

工作记忆容量可以通过一系列任务来测试。一个常用的度量方法是双任务范例，它将'''<font color="#ff8000">记忆广度测度 Memory Span Measure</font>'''与并发处理任务（有时称为“复杂规模”）结合起来。1980年，丹曼 Daneman 和 卡朋特 Carpenter 发明了这类任务的第一个版本——“阅读广度”。受试者阅读大量的句子（通常2至6个） ，并努力记住每个句子的最后一个单词。句子阅读完后他们按照自己认为正确的顺序复述单词。还有一些其他非双重任务性质的任务也是测量工作记忆容量的好办法。丹曼 Daneman 和 卡朋特 Carpenter 相信“存储”（维护）和加工的结合是测量工作记忆容量所必须的，现在我们知道工作记忆的容量即可以用没有额外处理组件的短时记忆任务来测量。也可以用不涉及信息维护的某些处理任务来衡量。用于测量工作记忆容量的好的任务应当具备那些特征，是一个尚在研究中的课题。

工作记忆容量可以通过一系列任务来测试。一个常用的度量方法是双任务范例，它将'''<font color="#ff8000">记忆广度测度 Memory Span Measure</font>'''与并发处理任务（有时称为“复杂规模”）结合起来。1980年，丹曼 Daneman 和 卡朋特 Carpenter 发明了这类任务的第一个版本——“阅读广度”。受试者阅读大量的句子（通常2至6个） ，并努力记住每个句子的最后一个单词。句子阅读完后他们按照自己认为正确的顺序复述单词。还有一些其他非双重任务性质的任务也是测量工作记忆容量的好办法。丹曼 Daneman 和 卡朋特 Carpenter 相信“存储”（维护）和加工的结合是测量工作记忆容量所必须的，现在我们知道工作记忆的容量既可以用没有额外处理组件的短时记忆任务来测量，也可以用不涉及信息维护的某些处理任务来衡量。至于用于测量工作记忆容量的好的任务方案应当具备哪些特征，是一个尚在研究中的课题。

Measures of working-memory capacity are strongly related to performance in other complex cognitive tasks, such as reading comprehension, problem solving, and with measures of intelligence quotient.

Measures of working-memory capacity are strongly related to performance in other complex cognitive tasks, such as reading comprehension, problem solving, and with measures of intelligence quotient.

The assumption that the contents of short-term or working memory decay over time, unless decay is prevented by rehearsal, goes back to the early days of experimental research on short-term memory. It is also an important assumption in the multi-component theory of working memory. The most elaborate decay-based theory of working memory to date is the "time-based resource sharing model". This theory assumes that representations in working memory decay unless they are refreshed. Refreshing them requires an attentional mechanism that is also needed for any concurrent processing task. When there are small time intervals in which the processing task does not require attention, this time can be used to refresh memory traces. The theory therefore predicts that the amount of forgetting depends on the temporal density of attentional demands of the processing task—this density is called "cognitive load". The cognitive load depends on two variables, the rate at which the processing task requires individual steps to be carried out, and the duration of each step. For example, if the processing task consists of adding digits, then having to add another digit every half second places a higher cognitive load on the system than having to add another digit every two seconds. In a series of experiments, Barrouillet and colleagues have shown that memory for lists of letters depends neither on the number of processing steps nor the total time of processing but on cognitive load.

The assumption that the contents of short-term or working memory decay over time, unless decay is prevented by rehearsal, goes back to the early days of experimental research on short-term memory. It is also an important assumption in the multi-component theory of working memory. The most elaborate decay-based theory of working memory to date is the "time-based resource sharing model". This theory assumes that representations in working memory decay unless they are refreshed. Refreshing them requires an attentional mechanism that is also needed for any concurrent processing task. When there are small time intervals in which the processing task does not require attention, this time can be used to refresh memory traces. The theory therefore predicts that the amount of forgetting depends on the temporal density of attentional demands of the processing task—this density is called "cognitive load". The cognitive load depends on two variables, the rate at which the processing task requires individual steps to be carried out, and the duration of each step. For example, if the processing task consists of adding digits, then having to add another digit every half second places a higher cognitive load on the system than having to add another digit every two seconds. In a series of experiments, Barrouillet and colleagues have shown that memory for lists of letters depends neither on the number of processing steps nor the total time of processing but on cognitive load.

该理论假设短期记忆或工作记忆的内容会随着时间的推移而衰退，除非通过刷新来防止衰退，这种理论可追溯到短期记忆早期的实验研究。这也是工作记忆多分量理论中的一个重要假设。迄今为止，最详尽的基于衰减假设的工作记忆理论是“基于时间的资源共享模型”。这个理论假设工作记忆中不断衰退的表征需要刷新维持，而刷新需要注意力机制，而注意力又对于任何并发进程任务都是必需的。当进程任务存在不需要注意力的微小时间间隔时，该时间可刷新记忆痕迹。因此，该理论预测遗忘量取决于进程任务临时所需注意力的密度，这种密度叫做“认知负荷”。认知负荷取决于两个变量，一是进程任务需要单个步骤执行的速率，二是每个步骤的持续时间。例如，如果处理任务包括添加数字，那么每半秒添加一个数字会比每两秒添加一个数字给系统带来更大的认知负荷。在一系列的实验中，巴鲁耶 Barrouillet 及其同事已证明字母列表的记忆并不取决于处理步骤数量或者处理总时间，而是取决于认知负荷。

该理论假设短期记忆或工作记忆的内容会随着时间的推移而衰退，除非通过刷新来防止衰退，这种理论可追溯到短期记忆早期的实验研究。这也是工作记忆多元理论中的一个重要假设。迄今为止，最详尽的基于衰减假设的工作记忆理论是“基于时间的资源共享模型”。该理论假设工作记忆中不断衰退的表征需要刷新维持，而刷新需要注意力机制，而注意力又对于任何并发进程任务都是必需的。当进程任务存在不需要注意力的微小时间间隔时，该时间可刷新记忆痕迹。因此，该理论预测遗忘量取决于进程任务临时所需注意力的密度，这种密度叫做“认知负荷”。认知负荷取决于两个变量，一是进程任务需要单个步骤执行的速率，二是每个步骤的持续时间。例如，如果处理任务包括添加数字，那么每半秒添加一个数字会比每两秒添加一个数字给系统带来更大的认知负荷。在一系列的实验中，巴鲁耶 Barrouillet 及其同事已证明字母列表的记忆并不取决于处理步骤数量或者处理总时间，而是取决于认知负荷。

==== 资源理论 Resource theories====

==== 资源理论 Resource theories====