The conjoint-recognition model (CRM) implements fuzzy-trace theory's opponent process conception of false memory. Within the family of measurement models that separate the memory effects of recollection and familiarity, CRM is the only one that accomplishes this for false as well as true memory. We assembled a corpus of 537 sets of…

Automatic essay scoring (AES) is an essential educational application in natural language processing. This automated process will alleviate the burden by increasing the reliability and consistency of the assessment. With the advances in text embedding libraries and neural network models, AES systems achieved good results in terms of accuracy.…

The contents and structure of semantic memory have been the focus of much recent research, with major advances in the development of distributional models, which use word co-occurrence information as a window into the semantics of language. In parallel, connectionist modeling has extended our knowledge of the processes engaged in semantic…

We demonstrate that the key components of cognitive architectures (declarative and procedural memory) and their key capabilities (learning, memory retrieval, probability judgment, and utility estimation) can be implemented as algebraic operations on vectors and tensors in a high-dimensional space using a distributional semantics model.…

Although there is a broad consensus that both the procedural and declarative memory systems play a crucial role in language learning, use, and knowledge, the mapping between linguistic types and memory structures remains underspecified: by default, a dual-route mapping of language systems to memory systems is assumed, with declarative memory…

Reading comprehension is key to knowledge acquisition and to reinforcing memory for previous information. While reading, a mental representation is constructed in the reader's mind. The mental model comprises the words in the text, the relations between the words, and inferences linking to concepts in prior knowledge. The automated model of…

Two broad approaches characterize the type of evidence that mediates recognition memory: discrete state and continuous. Discrete-state models posit a thresholded memory process that provides accurate information about an item (it is detected) or, failing that, no mnemonic information about the item. Continuous models, in contrast, posit the…

To account for natural variability in cognitive processing, it is standard practice to optimize a model's parameters by fitting it to behavioral data. Although most language-related theories acknowledge a large role for experience in language processing, variability reflecting that knowledge is usually ignored when evaluating a model's fit to…

Abstraction is a core principle of Distributional Semantic Models (DSMs) that learn semantic representations for words by applying dimensional reduction to statistical redundancies in language. Although the posited learning mechanisms vary widely, virtually all DSMs are prototype models in that they create a single abstract representation of a…

Ten years ago, researchers using event-related brain potentials (ERPs) to study language comprehension were puzzled by what looked like a "Semantic Illusion": Semantically anomalous, but structurally well-formed sentences did not affect the N400 component--traditionally taken to reflect semantic integration--but instead produced a P600…

Event-based prospective memory (PM) tasks require participants to substitute an atypical PM response for an ongoing task response when presented with PM targets. Responses to ongoing tasks are often slower with the addition of PM demands ("PM costs"). Prominent PM theories attribute costs to capacity-sharing between the ongoing and PM…

The goal of this article is to make the case for a radical exemplar account of child language acquisition, under which unwitnessed forms are produced and comprehended by on-the-fly analogy across multiple stored exemplars, weighted by their degree of similarity to the target with regard to the task at hand. Across the domains of (1) word meanings,…

Recent semantic space models learn vector representations for word meanings by observing statistical redundancies across a text corpus. A word's meaning is represented as a point in a high-dimensional semantic space, and semantic similarity between words is quantified by a function of their spatial proximity (typically the cosine of the angle…

The RI-Val model of comprehension includes a validation process in which linkages formed by integration are matched against active memory. In five experiments, we investigated factors that influence validation. Reading times were measured on target sentences that contained either correct information or semantically related, but incorrect content.…

Most current models of sentence comprehension assume that the human parsing mechanism (HPM) algorithmically computes detailed syntactic representations as basis for extracting sentence meaning. These models share the assumption that the representations computed by the HPM accurately reflect the linguistic input. This assumption has been challenged…