What Do Cognitive Networks Do? Simulations of Spoken Word Recognition Using the Cognitive Network Science Approach

Cognitive network science is an emerging approach that uses the mathematical tools of network science to map the relationships among representations stored in memory to examine how that structure might influence processing. In the present study, we used computer simulations to compare the ability of a well-known model of spoken word recognition, TRACE, to the ability of a cognitive network model with a spreading activation-like process to account for the findings from several previously published behavioral studies of language processing. In all four simulations, the TRACE model failed to retrieve a sufficient number of words to assess if it could replicate the behavioral findings. The cognitive network model successfully replicated the behavioral findings in Simulations 1 and 2. However, in Simulation 3a, the cognitive network did not replicate the behavioral findings, perhaps because an additional mechanism was not implemented in the model. However, in Simulation 3b, when the decay parameter in spreadr was manipulated to model this mechanism the cognitive network model successfully replicated the behavioral findings. The results suggest that models of cognition need to take into account the multi-scale structure that exists among representations in memory, and how that structure can influence processing.

Object-Based Trace Model for Automatic Indicator Computation in the Human Learning Environments

This paper proposes a traces model in the form of an object or class model (in the UML sense) which allows the automatic calculation of indicators of various kinds and independently of the computer environment for human learning (CEHL). The model is based on the establishment of a trace-based system that encompasses all the logic of traces collecting and indicators calculation. It is im-plemented in the form of a trace database. It is an important contribution in the field of the exploitation of the traces of apprenticeship in a CEHL because it pro-vides a general formalism for modeling the traces and allowing the calculation of several indicators at the same time. Also, with the inclusion of calculated indica-tors as potential learning traces, our model provides a formalism for classifying the various indicators in the form of inheritance relationships, which promotes the reuse of indicators already calculated. Economically, the model can allow organi-zations with different learning platforms to invest only in one traces Management System. At the social level, it can allow a better sharing of trace databases be-tween the various research institutions in the field of CEHL.

Contra assertions, feedback improves word recognition

Norris and Cutler (in press) revisit their arguments that (lexical-to-sublexical) feedback cannot improve word recognition performance, based on the assumption that feedback must boost signal and noise equally. They also argue that demonstrations that feedback improves performance (Magnuson, Mirman, Luthra, Strauss, & Harris, 2018) in the TRACE model of spoken word recognition (McClelland & Elman, 1986) were artifacts of converting activations to response probabilities. We first evaluate their claim that feedback in an interactive activation model must boost noise and signal equally. This is not true in a fully interactive activation model such as TRACE, where the feedback signal does not simply mirror the feedforward signal; it is instead shaped by joint probabilities over lexical patterns, and the dynamics of lateral inhibition. Thus, even under high levels of noise, lexical feedback will selectively boost signal more than noise. We demonstrate that feedback promotes faster word recognition and preserves accuracy under noise whether one uses raw activations or response probabilities. We then document that lexical feedback selectively boosts signal (i.e., lexically-coherent series of phonemes) more than noise by tracking sublexical (phoneme) activations under noise with and without feedback. Thus, feedback in a model like TRACE does improve word recognition, exactly by selective reinforcement of lexically-coherent signal. We conclude that whether lexical feedback is integral to human speech processing is an empirical question, and briefly review a growing body of work at behavioral and neural levels that is consistent with feedback and inconsistent with autonomous (non-feedback) architectures.

A Time-Series-Based New Behavior Trace Model for Crowd Workers That Ensures Quality Annotation

Crowdsourcing is a new mode of value creation in which organizations leverage numerous Internet users to accomplish tasks. However, because these workers have different backgrounds and intentions, crowdsourcing suffers from quality concerns. In the literature, tracing the behavior of workers is preferred over other methodologies such as consensus methods and gold standard approaches. This paper proposes two novel models based on workers’ behavior for task classification. These models newly benefit from time-series features and characteristics. The first model uses multiple time-series features with a machine learning classifier. The second model converts time series into images using the recurrent characteristic and applies a convolutional neural network classifier. The proposed models surpass the current state of-the-art baselines in terms of performance. In terms of accuracy, our feature-based model achieved 83.8%, whereas our convolutional neural network model achieved 76.6%.

Does signal reduction imply predictive coding in models of spoken word recognition?

Pervasive behavioral and neural evidence for predictive processing has led to claims that language processing depends upon predictive coding. Formally, predictive coding is a computational mechanism where only deviations from top-down expectations are passed between levels of representation. In many cognitive neuroscience studies, a reduction of signal for expected inputs is taken as being diagnostic of predictive coding. In the present work, we show that despite not explicitly implementing prediction, the TRACE model of speech perception exhibits this putative hallmark of predictive coding, with reductions in total lexical activation, total lexical feedback, and total phoneme activation when the input conforms to expectations. These findings may indicate that interactive activation is functionally equivalent or approximant to predictive coding or that caution is warranted in interpreting neural signal reduction as diagnostic of predictive coding.

Complete trace models of state and control

We consider a hierarchy of four typed call-by-value languages with either higher-order or ground-type references and with either $$\mathrm {call/cc}$$ call / cc or no control operator.Our first result is a fully abstract trace model for the most expressive setting, featuring both higher-order references and $$\mathrm {call/cc}$$ call / cc , constructed in the spirit of operational game semantics. Next we examine the impact of suppressing higher-order references and callcc in contexts and provide an operational explanation for the game-semantic conditions known as visibility and bracketing respectively. This allows us to refine the original model to provide fully abstract trace models of interaction with contexts that need not use higher-order references or $$\mathrm {call/cc}$$ call / cc . Along the way, we discuss the relationship between error- and termination-based contextual testing in each case, and relate the two to trace and complete trace equivalence respectively.Overall, the paper provides a systematic development of operational game semantics for all four cases, which represent the state-based face of the so-called semantic cube.

/dˤ/-Variation in Saudi Newscasting and Phonological Theory

In his analysis of /dˤ/-variation in Saudi Arabian newscasting, Al-Tamimi (2020) finds unpredicatble variability between the standard variant [dˤ] and the non-standard variant [ðˤ] in different in-words positions, in different phonetic environments, and in semantically ‘content’ and suprasegmentally ‘stressed’ lexical itmes assumed to favor the standard variant. He even finds in many of these lexical items an unusual realizational flucatuation between the two variants. The present exploratory and ‘theory-testing’ study aims to find a reasonable account for these findings through examining the explanatory adequacy of a number of available phonological theories, notions, models and proposals that have made different attempts to accommodate variation, and this includes Coexistent Phonemic Systems, Standard Generative Phonology, Lexical Diffusion, Variable Rules, Poly-Lectal Grammar, Articulatory Phonology, different versions of the Optimality Theory, in addition to the Multiple-Trace-Model, as represented by Al-Tamimi’s (2005) Multiple-Trace-Based Proposal. The study reveals the strengths and weaknesses of these theories in embracing the variability in the data, and concludes that the Multiple-Trace-Based Proposal can relatively offer the best insight as its allows variation to be directly encoded in the underlying representations of lexical items, a status strictly prohibited by the rest of the theories that adopt invariant lexical representations in consonance with the ‘Homogeneity Doctrine’.