Miller (1956) summarized evidence that people can remember about seven chunks in short-term memory (STM) tasks. However, that number was meant more as a rough estimate and a rhetorical device than as a real capacity limit. Others have since suggested that there is a more precise capacity limit, but that it is only three to five chunks. The present target article brings together a wide variety of data on capacity limits suggesting that the smaller capacity limit is real. Capacity limits (...) will be useful in analyses of information processing only if the boundary conditions for observing them can be carefully described. Four basic conditions in which chunks can be identified and capacity limits can accordingly be observed are: (1) when information overload limits chunks to individual stimulus items, (2) when other steps are taken specifically to block the recoding of stimulus items into larger chunks, (3) in performance discontinuities caused by the capacity limit, and (4) in various indirect effects of the capacity limit. Under these conditions, rehearsal and long-term memory cannot be used to combine stimulus items into chunks of an unknown size; nor can storage mechanisms that are not capacity-limited, such as sensory memory, allow the capacity-limited storage mechanism to be refilled during recall. A single, central capacity limit averaging about four chunks is implicated along with other, noncapacity-limited sources. The pure STM capacity limit expressed in chunks is distinguished from compound STM limits obtained when the number of separately held chunks is unclear. Reasons why pure capacity estimates fall within a narrow range are discussed and a capacity limit for the focus of attention is proposed. Key Words: attention; enumeration; information chunks; memory capacity; processing capacity; processing channels; serial recall; short-term memory; storage capacity; verbal recall; working memory capacity. (shrink)

Working memory resources are needed for processing and maintenance of information during cognitive tasks. Many models have been developed to capture the effects of limited working memory resources on performance. However, most of these models do not account for the finding that different individuals show different sensitivities to working memory demands, and none of the models predicts individual subjects' patterns of performance. We propose a computational model that accounts for differences in working memory capacity in terms of a quantity called (...) source activation, which is used to maintain goal‐relevant information in an available state. We apply this model to capture the working memory effects of individual subjects at a fine level of detail across two experiments. This, we argue, strengthens the interpretation of source activation as working memory capacity. (shrink)

Time underlies many interesting human behaviors. Thus, the question of how to represent time in connectionist models is very important. One approach is to represent time implicitly by its effects on processing rather than explicitly (as in a spatial representation). The current report develops a proposal along these lines first described by Jordan (1986) which involves the use of recurrent links in order to provide networks with a dynamic memory. In this approach, hidden unit patterns are fed back to themselves: (...) the internal representations which develop thus reflect task demands in the context of prior internal states. A set of simulations is reported which range from relatively simple problems (temporal version of XOR) to discovering syntactic/semantic features for words. The networks are able to learn interesting internal representations which incorporate task demands with memory demands: indeed, in this approach the notion of memory is inextricably bound up with task processing. These representations reveal a rich structure, which allows them to be highly context‐dependent, while also expressing generalizations across classes of items. These representations suggest a method for representing lexical categories and the type/token distinction. (shrink)

An English double-embedded relative clause from which the middle verb is omitted can often be processed more easily than its grammatical counterpart, a phenomenon known as the grammaticality illusion. This effect has been found to be reversed in German, suggesting that the illusion is language specific rather than a consequence of universal working memory constraints. We present results from three self-paced reading experiments which show that Dutch native speakers also do not show the grammaticality illusion in Dutch, whereas both German (...) and Dutch native speakers do show the illusion when reading English sentences. These findings provide evidence against working memory constraints as an explanation for the observed effect in English. We propose an alternative account based on the statistical patterns of the languages involved. In support of this alternative, a single recurrent neural network model that is trained on both Dutch and English sentences is shown to predict the cross-linguistic difference in the grammaticality effect. (shrink)

A key component of research on human sentence processing is to characterize the processing difficulty associated with the comprehension of words in context. Models that explain and predict this difficulty can be broadly divided into two kinds, expectation‐based and memory‐based. In this work, we present a new model of incremental sentence processing difficulty that unifies and extends key features of both kinds of models. Our model, lossy‐context surprisal, holds that the processing difficulty at a word in context is proportional to (...) the surprisal of the word given a lossy memory representation of the context—that is, a memory representation that does not contain complete information about previous words. We show that this model provides an intuitive explanation for an outstanding puzzle involving interactions of memory and expectations: language‐dependent structural forgetting, where the effects of memory on sentence processing appear to be moderated by language statistics. Furthermore, we demonstrate that dependency locality effects, a signature prediction of memory‐based theories, can be derived from lossy‐context surprisal as a special case of a novel, more general principle called information locality. (shrink)

All other things being equal the parser favors attaching an ambiguous modifier to the most recent possible site. A plausible explanation is that locality preferences such as this arise in the service of minimizing memory costs—more distant sentential material is more difficult to reactivate than more recent material. Note that processing any sentence requires linking each new lexical item with material in the current parse. This often involves the construction of long‐distance dependencies. Under a resource‐limited view of language processing, lengthy (...) integrations should induce difficulty even in unambiguous sentences. To date there has been little direct quantitative evidence in support of this perspective. This article presents 2 self‐paced reading studies, which explore the hypothesis that dependency distance is a fundamental determinant of reading complexity in unambiguous constructions in English. The evidence suggests that the difficulty associated with integrating a new input item is heavily determined by the amount of lexical material intervening between the input item and the site of its target dependents. The patterns observed here are not straightforwardly accounted for within purely experience‐based models of complexity. Instead, this work supports the role of a memory bottleneck in language comprehension. This constraint arises because hierarchical linguistic relations must be recovered from a linear input stream. (shrink)