scholarly journals Cognitive plausibility in voice-based AI health counselors

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Thomas Kannampallil ◽  
Joshua M. Smyth ◽  
Steve Jones ◽  
Philip R. O. Payne ◽  
Jun Ma
2017 ◽  
Vol 10 (1) ◽  
pp. 26-55 ◽  
Author(s):  
K. M. JASZCZOLT

abstractI discuss the perspectival nature of temporality in discourse and argue that the human concept of time can no more be dissociated from the perspectival thought than the concept of the self can. The corollary of this observation is that perspectival temporality can no more be excluded from the semantic representation than the notion of the self can: neither can be reduced to the bare referent for the purpose of semantic representation if the latter is to retain cognitive plausibility. I present such a semantic qua conceptual approach to temporal reference developed within my theory of Default Semantics. I build upon my theory of time as epistemic modality according to which, on the level of conceptual qua semantic building blocks, temporality reduces to degrees of detachment from the certainty of the here and the now. I also address the questions of temporal asymmetry between the past and the future, and the relation between metaphysical time (timeM), psychological time (timeE, where ‘E’ marks the domain of epistemological enquiry), and time in natural language (timeL), concluding that the perspective-infused timeE and timeL are compatible with timeM of mathematical models of spacetime: all are definable through possibility and perspectivity.


2000 ◽  
Vol 12 (4) ◽  
pp. 237-259 ◽  
Author(s):  
Richard Butterworth ◽  
Ann Blandford ◽  
David Duke

2016 ◽  
Vol 50 (2) ◽  
Author(s):  
Jane Klavan ◽  
Dagmar Divjak

AbstractUsage-based linguistics abounds with studies that use statistical classification models to analyze either textual corpus data or behavioral experimental data. Yet, before we can draw conclusions from statistical models of empirical data that we can feed back into cognitive linguistic theory, we need to assess whether the text-based models are cognitively plausible and whether the behavior-based models are linguistically accurate. In this paper, we review four case studies that evaluate statistical classification models of richly annotated linguistic data by explicitly comparing the performance of a corpus-based model to the behavior of native speakers. The data come from four different languages (Arabic, English, Estonian, and Russian) and pertain to both lexical as well as syntactic near-synonymy. We show that behavioral evidence is needed in order to fine tune and improve statistical models built on data from a corpus. We argue that methodological pluralism is the key for a cognitively realistic linguistic theory.


2010 ◽  
Vol 18 (1) ◽  
pp. 136-164 ◽  
Author(s):  
Odette Scharenborg ◽  
Lou Boves

Computational modelling has proven to be a valuable approach in developing theories of spoken-word processing. In this paper, we focus on a particular class of theories in which it is assumed that the spoken-word recognition process consists of two consecutive stages, with an ‘abstract’ discrete symbolic representation at the interface between the stages. In evaluating computational models, it is important to bring in independent arguments for the cognitive plausibility of the algorithms that are selected to compute the processes in a theory. This paper discusses the relation between behavioural studies, theories, and computational models of spoken-word recognition. We explain how computational models can be assessed in terms of the goodness of fit with the behavioural data and the cognitive plausibility of the algorithms. An in-depth analysis of several models provides insights into how computational modelling has led to improved theories and to a better understanding of the human spoken-word recognition process.


2008 ◽  
Vol 20 (12) ◽  
pp. 2298-2307 ◽  
Author(s):  
Janet Hui-wen Hsiao ◽  
Danke X. Shieh ◽  
Garrison W. Cottrell

Anatomical evidence shows that our visual field is initially split along the vertical midline and contralaterally projected to different hemispheres. It remains unclear at which processing stage the split information converges. In the current study, we applied the Double Filtering by Frequency (DFF) theory (Ivry & Robertson, 1998) to modeling the visual field split; the theory assumes a right-hemisphere/low-frequency bias. We compared three cognitive architectures with different timings of convergence and examined their cognitive plausibility to account for the left-side bias effect in face perception observed in human data. We show that the early convergence model failed to show the left-side bias effect. The modeling, hence, suggests that the convergence may take place at an intermediate or late stage, at least after information has been extracted/encoded separately in the two hemispheres, a fact that is often overlooked in computational modeling of cognitive processes. Comparative anatomical data suggest that this separate encoding process that results in differential frequency biases in the two hemispheres may be engaged from V1 up to the level of area V3a and V4v, and converge at least after the lateral occipital region. The left-side bias effect in our model was also observed in Greeble recognition; the modeling, hence, also provides testable predictions about whether the left-side bias effect may also be observed in (expertise-level) object recognition.


2020 ◽  
Author(s):  
Héctor Otero Mediero ◽  
Neil R Bramley

A number of recent studies have used ideal observer models to capture human physical learning and reasoning as based on approximate mental simulation. While these approaches can match human competence in specific tasks, they are still relatively far from cognitive plausibility and are limited in their ability to capture patterns of human errors. In the current work, we train a recurrent neural network on the same physical reasoning task explored in Bramley, Gerstenberg, Tenenbaum, and Gureckis (2018) (passive condition), finding a closer match to human patterns than the ideal observer model previously used to make sense of the human judgement patterns.


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