How trial-to-trial learning shapes mappings in the mental lexicon: Modelling lexical decision with linear discriminative learning

Learning or Boredom? Task Adaptation Effects in Sentence Processing Experiments

Task adaptation, characterized by a progressive increase in speed throughout experimental trials, has been extensively observed across various paradigms. Yet, the underlying mechanisms driving this phenomenon remain unclear. According to the learning-based explanation, participants are implicitly learning, becoming more proficient over time. Conversely, a motivation-based view suggests that participants' drive wanes gradually, prompting quicker pace and reduced task engagement. These explanations offer distinct predictions. The learning-based view anticipates not only accelerated speed but also improved response accuracy. In contrast, the motivation-based view assumes that participants lose their focus, their pace increases, but their response accuracy tends to decline. The present study tests these implications in a series of six self-paced reading experiments investigating the interplay between reaction times, immediate recall, and trial order. Robust learning effects are documented. Participants not only read progressively faster during the experiments, but they also get better in responding. Moreover, an analysis of recall accuracy reveals systematic differences between different types of information, with nouns yielding substantially higher recall accuracy than adjectives. These findings are explained through attentional mechanisms: prolonged processing of specific words correlates with improved recall. Furthermore, the differential recall patterns are modulated by the task's question structure, with adjectives recalled more effectively in experiments with a higher proportion of adjective-targeting questions. This underscores participants' strategic allocation of attention to sentence components deemed crucial for task performance, highlighting the dynamic interplay between learning, motivation, and attentional mechanisms in task adaptation.

Emerging Roots: Investigating Early Access to Meaning in Maltese Auditory Word Recognition

In Semitic languages, the consonantal root is central to morphology, linking form and meaning. While psycholinguistic studies highlight its importance in language processing, the role of meaning in early lexical access and its representation remain unclear. This study investigates when meaning becomes accessible during the processing of Maltese verb forms, using a computational model based on the Discriminative Lexicon framework. Our model effectively comprehends and produces Maltese verbs, while also predicting response times in a masked auditory priming experiment. Results show that meaning is accessible early in lexical access and becomes more prominent after the target word is fully processed. This suggests that semantic information plays a critical role from the initial stages of lexical access, refining our understanding of real-time language comprehension. Our findings contribute to theories of lexical access and offer valuable insights for designing priming studies in psycholinguistics. Additionally, this study demonstrates the potential of computational models in investigating the relationship between form and meaning in language processing.

Language with vision: A study on grounded word and sentence embeddings

Grounding language in vision is an active field of research seeking to construct cognitively plausible word and sentence representations by incorporating perceptual knowledge from vision into text-based representations. Despite many attempts at language grounding, achieving an optimal equilibrium between textual representations of the language and our embodied experiences remains an open field. Some common concerns are the following. Is visual grounding advantageous for abstract words, or is its effectiveness restricted to concrete words? What is the optimal way of bridging the gap between text and vision? To what extent is perceptual knowledge from images advantageous for acquiring high-quality embeddings? Leveraging the current advances in machine learning and natural language processing, the present study addresses these questions by proposing a simple yet very effective computational grounding model for pre-trained word embeddings. Our model effectively balances the interplay between language and vision by aligning textual embeddings with visual information while simultaneously preserving the distributional statistics that characterize word usage in text corpora. By applying a learned alignment, we are able to indirectly ground unseen words including abstract words. A series of evaluations on a range of behavioral datasets shows that visual grounding is beneficial not only for concrete words but also for abstract words, lending support to the indirect theory of abstract concepts. Moreover, our approach offers advantages for contextualized embeddings, such as those generated by BERT (Devlin et al, 2018), but only when trained on corpora of modest, cognitively plausible sizes. Code and grounded embeddings for English are available at ( https://github.com/Hazel1994/Visually_Grounded_Word_Embeddings_2 ).

Comparing Functional Trend and Learning among Groups in Intensive Binary Longitudinal Eye-Tracking Data using By-Variable Smooth Functions of GAMM

This paper presents a model specification for group comparisons regarding a functional trend over time within a trial and learning across a series of trials in intensive binary longitudinal eye-tracking data. The functional trend and learning effects are modeled using by-variable smooth functions. This model specification is formulated as a generalized additive mixed model, which allowed for the use of the freely available mgcv package (Wood in Package 'mgcv.' https://cran.r-project.org/web/packages/mgcv/mgcv.pdf , 2023) in R. The model specification was applied to intensive binary longitudinal eye-tracking data, where the questions of interest concern differences between individuals with and without brain injury in their real-time language comprehension and how this affects their learning over time. The results of the simulation study show that the model parameters are recovered well and the by-variable smooth functions are adequately predicted in the same condition as those found in the application.

The Keys to the Future? An Examination of Statistical Versus Discriminative Accounts of Serial Pattern Learning

Sequence learning is fundamental to a wide range of cognitive functions. Explaining how sequences-and the relations between the elements they comprise-are learned is a fundamental challenge to cognitive science. However, although hundreds of articles addressing this question are published each year, the actual learning mechanisms involved in the learning of sequences are rarely investigated. We present three experiments that seek to examine these mechanisms during a typing task. Experiments 1 and 2 tested learning during typing single letters on each trial. Experiment 3 tested for "chunking" of these letters into "words." The results of these experiments were used to examine the mechanisms that could best account for them, with a focus on two particular proposals: statistical transitional probability learning and discriminative error-driven learning. Experiments 1 and 2 showed that error-driven learning was a better predictor of response latencies than either n-gram frequencies or transitional probabilities. No evidence for chunking was found in Experiment 3, probably due to interspersing visual cues with the motor response. In addition, learning occurred across a greater distance in Experiment 1 than Experiment 2, suggesting that the greater predictability that comes with increased structure leads to greater learnability. These results shed new light on the mechanism responsible for sequence learning. Despite the widely held assumption that transitional probability learning is essential to this process, the present results suggest instead that the sequences are learned through a process of discriminative learning, involving prediction and feedback from prediction error.

Frequency effects in linear discriminative learning

Word frequency is a strong predictor in most lexical processing tasks. Thus, any model of word recognition needs to account for how word frequency effects arise. The Discriminative Lexicon Model (DLM) models lexical processing with mappings between words' forms and their meanings. Comprehension and production are modeled via linear mappings between the two domains. So far, the mappings within the model can either be obtained incrementally via error-driven learning, a computationally expensive process able to capture frequency effects, or in an efficient, but frequency-agnostic solution modeling the theoretical endstate of learning (EL) where all words are learned optimally. In the present study we show how an efficient, yet frequency-informed mapping between form and meaning can be obtained (Frequency-informed learning; FIL). We find that FIL well approximates an incremental solution while being computationally much cheaper. FIL shows a relatively low type- and high token-accuracy, demonstrating that the model is able to process most word tokens encountered by speakers in daily life correctly. We use FIL to model reaction times in the Dutch Lexicon Project by means of a Gaussian Location Scale Model and find that FIL predicts well the S-shaped relationship between frequency and the mean of reaction times but underestimates the variance of reaction times for low frequency words. FIL is also better able to account for priming effects in an auditory lexical decision task in Mandarin Chinese, compared to EL. Finally, we used ordered data from CHILDES to compare mappings obtained with FIL and incremental learning. We show that the mappings are highly correlated, but that with FIL some nuances based on word ordering effects are lost. Our results show how frequency effects in a learning model can be simulated efficiently, and raise questions about how to best account for low-frequency words in cognitive models.