Dr. Christopher Fisher
Prof. Joe Houpt
Christopher Adam Stevens
Models based on classical probability theory have difficulty accounting for order effects, which occur when the order of question presentation affects response probabilities. Recently, quantum models have garnered support as an account of order effects. In particular, the pattern of order effects is consistent with a critical property of the quantum model called the QQ equality. We investigate whether the ACT-R cognitive architecture can produce order effects and satisfy the QQ equality based on memory retrieval mechanisms. In the ACT-R model, the answer to the first question creates a new context through which spreading activation influences retrieval probabilities for the second answer. Our analysis shows that spreading activation can produce order effects and satisfy the QQ equality, depending on the composition of declarative memory. Across a wide range of conditions, violations of the QQ equality are typically small, but moderate to large in a smaller set of cases.
The similarity-based interference paradigm has been widely used to investigate the factors subserving subject-verb agreement processing. A consistent finding is facilitatory interference effects in ungrammatical sentences but inconclusive results in grammatical sentences. Existing models propose that interference is caused either by misrepresentation of the input (representation distortion-based models) or by mis-retrieval of the interfering noun phrase based on cues at the verb (retrieval-based models). These models fail to fully capture the observed interference patterns in the experimental data. We implement two new models under the assumption that a comprehender utilizes a lossy memory representation of the intended message when processing subject-verb agreement dependencies. Our models outperform the existing cue-based retrieval model in capturing the observed patterns in the data for both grammatical and ungrammatical sentences. Lossy compression models under different constraints can be useful in understanding the role of representation distortion in sentence comprehension.
It has been shown that in hand-written transcription tasks temporal micro-behavioral chunk signals hold promise as measures of competence in various domains (e.g., Cheng, 2014). But data capture under that an approach requires the use of graphics tablets which are relatively uncommon. In this paper we propose and explore an alternative method – Competence Assessment by Stimulus Matching (CASM). This new method uses simple mouse-driven interfaces to produce temporal chunk signals as measures of learner’s ability. However, it is not obvious what features of CASM will produce effective competence measures and the design space of CASM tasks is large. Thus, this paper uses GOMS modelling in order to explore the design space to find factors that will maximize the discrimination of chunk measures of competence. Results of a pilot experiment show that CASM has potential in using chunk signals to measure competence in the domain of English language.
A computational framework for modelling storage and retrieval of information in human working memory is proposed. The aim is to analyse the corresponding algebra alone, especially with regard to its congruence with empirical findings including the serial position curve. That algebra builds on the high-dimensional holographic representation of information together with two operations for computation: multiplication for binding and addition for bundling. The addition operation is inspired by basic neuronal summation and turns out to be not-associative. The non-associativity of bundling is essential. Firstly, bundling conserves sequential information; secondly, bundling implies activation gradients. Consequently, cognitive states representing a memorised list exhibit a primacy as well as a recency effect generically. The typical concave-up and asymmetrically shaped serial position curve is derived as a linear combination of those gradients. Quantitative implications of the algebra are shown to agree well with empirical data from basic cognitive tasks. This might encourage to build more full-blown models by adding further assumptions on top of this algebra.