Dual routes to cognitive flexibility: learning and response-conflict resolution in the dimensional change card sort task

The Effects of Linear Order in Category Learning: Some Replications of Ramscar et al. (2010) and Their Implications for Replicating Training Studies

Ramscar, Yarlett, Dye, Denny, and Thorpe (2010) showed how, consistent with the predictions of error-driven learning models, the order in which stimuli are presented in training can affect category learning. Specifically, learners exposed to artificial language input where objects preceded their labels learned the discriminating features of categories better than learners exposed to input where labels preceded objects. We sought to replicate this finding in two online experiments employing the same tests used originally: A four pictures test (match a label to one of four pictures) and a four labels test (match a picture to one of four labels). In our study, only findings from the four pictures test were consistent with the original result. Additionally, the effect sizes observed were smaller, and participants over-generalized high-frequency category labels more than in the original study. We suggest that although Ramscar, Yarlett, Dye, Denny, and Thorpe (2010) feature-label order predictions were derived from error-driven learning, they failed to consider that this mechanism also predicts that performance in any training paradigm must inevitably be influenced by participant prior experience. We consider our findings in light of these factors, and discuss implications for the generalizability and replication of training studies.

An exploration of error-driven learning in simple two-layer networks from a discriminative learning perspective

Error-driven learning algorithms, which iteratively adjust expectations based on prediction error, are the basis for a vast array of computational models in the brain and cognitive sciences that often differ widely in their precise form and application: they range from simple models in psychology and cybernetics to current complex deep learning models dominating discussions in machine learning and artificial intelligence. However, despite the ubiquity of this mechanism, detailed analyses of its basic workings uninfluenced by existing theories or specific research goals are rare in the literature. To address this, we present an exposition of error-driven learning - focusing on its simplest form for clarity - and relate this to the historical development of error-driven learning models in the cognitive sciences. Although historically error-driven models have been thought of as associative, such that learning is thought to combine preexisting elemental representations, our analysis will highlight the discriminative nature of learning in these models and the implications of this for the way how learning is conceptualized. We complement our theoretical introduction to error-driven learning with a practical guide to the application of simple error-driven learning models in which we discuss a number of example simulations, that are also presented in detail in an accompanying tutorial.

How children learn to communicate discriminatively

How do children learn to communicate, and what do they learn? Traditionally, most theories have taken an associative, compositional approach to these questions, supposing children acquire an inventory of form-meaning associations, and procedures for composing / decomposing them; into / from messages in production and comprehension. This paper presents an alternative account of human communication and its acquisition based on the systematic, discriminative approach embodied in psychological and computational models of learning, and formally described by communication theory. It describes how discriminative learning theory offers an alternative perspective on the way that systems of semantic cues are conditioned onto communicative codes, while information theory provides a very different view of the nature of the codes themselves. It shows how the distributional properties of languages satisfy the communicative requirements described in information theory, enabling language learners to align their expectations despite the vastly different levels of experience among language users, and to master communication systems far more abstract than linguistic intuitions traditionally assume. Topics reviewed include morphological development, the acquisition of verb argument structures, and the functions of linguistic systems that have proven to be stumbling blocks for compositional theories: grammatical gender and personal names.

Order Matters! Influences of Linear Order on Linguistic Category Learning

Linguistic category learning has been shown to be highly sensitive to linear order, and depending on the task, differentially sensitive to the information provided by preceding category markers (premarkers, e.g., gendered articles) or succeeding category markers (postmarkers, e.g., gendered suffixes). Given that numerous systems for marking grammatical categories exist in natural languages, it follows that a better understanding of these findings can shed light on the factors underlying this diversity. In two discriminative learning simulations and an artificial language learning experiment, we identify two factors that modulate linear order effects in linguistic category learning: category structure and the level of abstraction in a category hierarchy. Regarding category structure, we find that postmarking brings an advantage for learning category diagnostic stimulus dimensions, an effect not present when categories are non-confusable. Regarding levels of abstraction, we find that premarking of super-ordinate categories (e.g., noun class) facilitates learning of subordinate categories (e.g., nouns). We present detailed simulations using a plausible candidate mechanism for the observed effects, along with a comprehensive analysis of linear order effects within an expectation-based account of learning. Our findings indicate that linguistic category learning is differentially guided by pre- and postmarking, and that the influence of each is modulated by the specific characteristics of a given category system.

Of mice and men: Speech sound acquisition as discriminative learning from prediction error, not just statistical tracking

Despite burgeoning evidence that listeners are highly sensitive to statistical distributions of speech cues, the mechanism underlying learning may not be purely statistical tracking. Decades of research in animal learning suggest that learning results from prediction and prediction error. Two artificial language learning experiments test two predictions that distinguish error-driven from purely statistical models; namely, cue competition - specifically, Kamin's (1968) 'blocking' effect (Experiment 1) - and the predictive structure of learning events (Experiment 2). In Experiment 1, prior knowledge of an informative cue blocked learning of a second cue. This finding may help explain second language learners' difficulty in acquiring native-level perception of non-native speech cues. In Experiment 2, learning was better with a discriminative (cue-outcome) order compared to a non-discriminative (outcome-cue) order. Experiment 2 suggests that learning speech cues, including reversing effects of blocking, depends on (un)learning from prediction error and depends on the temporal order of auditory cues versus semantic outcomes. Together, these results show that (a) existing knowledge of acoustic cues can block later learning of new cues, and (b) speech sound acquisition depends on the predictive structure of learning events. When feedback from prediction error is available, this drives learners to ignore salient non-discriminative cues and effectively learn to use target cue dimensions. These findings may have considerable implications for the field of speech acquisition.

Executive functions and components of oral reading fluency through the lens of text complexity

As readers struggle to coordinate various reading- and language-related skills during oral reading fluency (ORF), miscues can emerge, especially when processing complex texts. Following a miscue, students often self-correct as a strategy to potentially restore ORF and online linguistic comprehension. Executive functions (EF) are hypothesized to play an interactive role during ORF. Yet, the role of EF in self-corrections while reading complex texts remains elusive. To this end, we evaluated the relation between students' probability of self-correcting miscues-or P(SC)-and their EF profile in a cohort of 143 participants (aged 9-15) who represented a diverse spectrum of reading abilities. Moreover, we used experimentally-manipulated passages (decoding, vocabulary, syntax, and cohesion) and employed a fully cross-classified mixed-effects multilevel regression strategy to evaluate the interplay between components of ORF, EF, and text complexity. Our results revealed that, after controlling for reading and language abilities, increased production of miscues across different passage conditions was explained by worse EF. We also found that students with better EF exhibited greater P(SC) when reading complex texts. While text complexity taxes students' EF and influences their production of miscues, findings suggest that EF may be interactively recruited to restore ORF via self-correcting oral reading errors. Overall, our results suggest that domain-general processes (e.g., EF) are associated with production of miscues and may underlie students' behavior of self-corrections, especially when reading complex texts. Further understanding of the relation between different components of ORF and cognitive processes may inform intervention strategies to improve reading proficiency and overall academic performance.

Conceptualizing syntactic categories as semantic categories: Unifying part-of-speech identification and semantics using co-occurrence vector averaging

Co-occurrence models have been of considerable interest to psychologists because they are built on very simple functionality. This is particularly clear in the case of prediction models, such as the continuous skip-gram model introduced in Mikolov, Chen, Corrado, and Dean (2013), because these models depend on functionality closely related to the simple Rescorla-Wagner model of discriminant learning in nonhuman animals (Rescorla & Wagner, 1972), which has a rich history within psychology as a model of many animal learning processes. We replicate and extend earlier work showing that it is possible to extract accurate information about syntactic category and morphological family membership directly from patterns of word co-occurrence, and provide evidence from four experiments showing that this information predicts human reaction times and accuracy for class membership decisions.

Multisensory stimuli enhance 3-year-old children's executive function: A three-dimensional object version of the standard Dimensional Change Card Sort

A three-dimensional object version of the standard Dimensional Change Card Sort (DCCS) was developed to examine the influence of multisensory stimuli on 3-year-old children's executive function. Whereas the developmental phenomenon marking 3-year-olds' difficulties with rule use in the standard DCCS can be attributed to several cognitive factors, we examined the possibility that better encoding of object features could facilitate children's rule-switching behavior. We examined whether 3-year-olds might be able to capitalize on cues available to multiple senses to create a more robust representation of object features that would enable them to overcome previous difficulties with rule switching in the standard DCCS. Participants were randomly assigned to the standard two-dimensional DCCS or the three-dimensional object version that was designed to match the rabbit and boat images used in the card version. The 3-year-olds who completed the object version outperformed those who completed the standard card version, succeeding in switching rules more accurately when provided with visual, auditory-verbal labeling, and tactile information of object features. Notably, more children achieved perfect accuracy and fewer children achieved floor-level performance in the object version than in the card version. We attribute 3-year-olds' success in the object version to greater cognitive control made possible by the enhanced encoding of the stimulus properties through multisensory input and enhanced cognitive processing of ecologically valid three-dimensional objects.

NDRA: A single route model of response times in the reading aloud task based on discriminative learning

We present the Naive Discriminative Reading Aloud (ndra) model. The ndra differs from existing models of response times in the reading aloud task in two ways. First, a single lexical architecture is responsible for both word and non-word naming. As such, the model differs from dual-route models, which consist of both a lexical route and a sub-lexical route that directly maps orthographic units onto phonological units. Second, the linguistic core of the ndra exclusively operates on the basis of the equilibrium equations for the well-established general human learning algorithm provided by the Rescorla-Wagner model. The model therefore does not posit language-specific processing mechanisms and avoids the problems of psychological and neurobiological implausibility associated with alternative computational implementations. We demonstrate that the single-route discriminative learning architecture of the ndra captures a wide range of effects documented in the experimental reading aloud literature and that the overall fit of the model is at least as good as that of state-of-the-art dual-route models.

The Role of Surprise in Learning: Different Surprising Outcomes Affect Memorability Differentially

Surprise has been explored as a cognitive-emotional phenomenon that impacts many aspects of mental life from creativity to learning to decision-making. In this paper, we specifically address the role of surprise in learning and memory. Although surprise has been cast as a basic emotion since Darwin's () The Expression of the Emotions in Man and Animals, recently more emphasis has been placed on its cognitive aspects. One such view casts surprise as a process of "sense making" or "explanation finding": metacognitive explanation-based theory proposes that people's perception of surprise is a metacognitive assessment of the cognitive work done to explain a surprising outcome. Or, to put it more simply, surprise increases with the explanatory work required to resolve it. This theory predicts that some surprises should be more surprising than others because they are harder to explain. In the current paper, this theory is extended to consider the role of surprise in learning as evidenced by memorability. This theory is tested to determine how scenarios with differentially surprising outcomes impact the memorability of those outcomes. The results show that surprising outcomes (less-known outcomes) that are more difficult to explain are recalled more accurately than less-surprising outcomes that require little (known outcomes) or no explanation (normal).

Two-year-olds' executive functioning: The influence of task-specific vocabulary knowledge

Although many executive function (EF) tasks require only nonverbal responses, the language used by experimenters to explain the task may be important for young children's EF task performance. This study investigated how the vocabulary used in explaining an EF task affects 2-year-olds' performance. Experiment 1 used the standard instructions for the Reverse Categorization Task, in which children are asked to sort different-sized blocks into different-sized buckets according to one rule and then switch to a new rule. In Experiment 2, the task remained the same, but different instructions requiring less knowledge of size words were used. Children's productive vocabulary was assessed in both experiments but was only correlated with task performance in Experiment 1. These results suggest that task-specific vocabulary knowledge may play a role in children's performance on tasks designed to measure nonverbal cognitive ability.

Are baboons learning "orthographic" representations? Probably not

The ability of Baboons (papio papio) to distinguish between English words and nonwords has been modeled using a deep learning convolutional network model that simulates a ventral pathway in which lexical representations of different granularity develop. However, given that pigeons (columba livia), whose brain morphology is drastically different, can also be trained to distinguish between English words and nonwords, it appears that a less species-specific learning algorithm may be required to explain this behavior. Accordingly, we examined whether the learning model of Rescorla and Wagner, which has proved to be amazingly fruitful in understanding animal and human learning could account for these data. We show that a discrimination learning network using gradient orientation features as input units and word and nonword units as outputs succeeds in predicting baboon lexical decision behavior-including key lexical similarity effects and the ups and downs in accuracy as learning unfolds-with surprising precision. The models performance, in which words are not explicitly represented, is remarkable because it is usually assumed that lexicality decisions, including the decisions made by baboons and pigeons, are mediated by explicit lexical representations. By contrast, our results suggest that in learning to perform lexical decision tasks, baboons and pigeons do not construct a hierarchy of lexical units. Rather, they make optimal use of low-level information obtained through the massively parallel processing of gradient orientation features. Accordingly, we suggest that reading in humans first involves initially learning a high-level system building on letter representations acquired from explicit instruction in literacy, which is then integrated into a conventionalized oral communication system, and that like the latter, fluent reading involves the massively parallel processing of the low-level features encoding semantic contrasts.

Empirical Tests of a Brain-Based Model of Executive Function Development

Executive function (EF) plays a foundational role in development. A brain-based model of EF development is probed for the experiences that strengthen EF in the dimensional change card sort task in which children sort cards by one rule and then are asked to switch to another. Three-year-olds perseverate on the first rule, failing the task, whereas 4-year-olds pass. Three predictions of the model are tested to help 3-year-olds (N = 54) pass. Experiment 1 shows that experience with shapes and the label "shape" helps children. Experiment 2 shows that experience with colors-without a label-helps children. Experiment 3 shows that experience with colors induces dimensional attention. The implications of this work for early intervention are discussed.

Words from spontaneous conversational speech can be recognized with human-like accuracy by an error-driven learning algorithm that discriminates between meanings straight from smart acoustic features, bypassing the phoneme as recognition unit

Sound units play a pivotal role in cognitive models of auditory comprehension. The general consensus is that during perception listeners break down speech into auditory words and subsequently phones. Indeed, cognitive speech recognition is typically taken to be computationally intractable without phones. Here we present a computational model trained on 20 hours of conversational speech that recognizes word meanings within the range of human performance (model 25%, native speakers 20-44%), without making use of phone or word form representations. Our model also generates successfully predictions about the speed and accuracy of human auditory comprehension. At the heart of the model is a 'wide' yet sparse two-layer artificial neural network with some hundred thousand input units representing summaries of changes in acoustic frequency bands, and proxies for lexical meanings as output units. We believe that our model holds promise for resolving longstanding theoretical problems surrounding the notion of the phone in linguistic theory.

Discrimination in lexical decision

In this study we present a novel set of discrimination-based indicators of language processing derived from Naive Discriminative Learning (ndl) theory. We compare the effectiveness of these new measures with classical lexical-distributional measures-in particular, frequency counts and form similarity measures-to predict lexical decision latencies when a complete morphological segmentation of masked primes is or is not possible. Data derive from a re-analysis of a large subset of decision latencies from the English Lexicon Project, as well as from the results of two new masked priming studies. Results demonstrate the superiority of discrimination-based predictors over lexical-distributional predictors alone, across both the simple and primed lexical decision tasks. Comparable priming after masked corner and cornea type primes, across two experiments, fails to support early obligatory segmentation into morphemes as predicted by the morpho-orthographic account of reading. Results fit well with ndl theory, which, in conformity with Word and Paradigm theory, rejects the morpheme as a relevant unit of analysis. Furthermore, results indicate that readers with greater spelling proficiency and larger vocabularies make better use of orthographic priors and handle lexical competition more efficiently.

An exploration of the role of executive functions in preschoolers' phonological development

There is limited yet compelling evidence that domain-general processes may contribute to speech sound change. This study explored whether executive functions contribute to the achievement of adult-like speech production. Children who are 4 to 5 years old, 42 with high-average speech production skills, 11 with low-average and nine with speech sound disorder (SSD), participated in a battery of executive function and speech production tasks. Performance accuracy was compared across groups and also correlated with speech sound accuracy from a single-word naming task. Children with SSD demonstrated poorer performance than other groups on forward digit span, whereas children with low-average speech skills underperformed their peers on the Flexible Item Selection Task (FIST). These preliminary results suggest that children with speech errors may have less mature working memory than peers who have mastered phonological targets earlier in development.

A meta-analysis of the Dimensional Change Card Sort: Implications for developmental theories and the measurement of executive function in children

The Dimensional Change Card Sort (DCCS) is a widely used measure of executive function in children. In the standard version, children are shown cards depicting objects that vary on two dimensions (e.g., colored shapes such as red rabbits and blue boats), and are told to sort them first by one set of rules (e.g., shape) and then by another (e.g., color). Most 3-year-olds persist in sorting by the pre-switch rules, whereas 5-year-olds switch flexibly. We conducted a meta-analysis of standard and experimental versions of the task (N = 69 reports, 426 conditions) to examine the influence of diverse task variations on performance. Age, how the test stimuli were labeled for the child, emphasis on conflict in the verbal introduction of the post-switch rules, and the number of pre-switch trials each independently predicted switching on the standard DCCS, whereas pre-switch feedback, practice, and task modality did not. Increasing the relative salience of the post-switch dimension was associated with higher rates of switching, and, conversely, decreasing post-switch salience was associated with lower rates of switching, and under both kinds of manipulation performance continued to be associated with age. Spatially separating the dimensional values was associated with higher rates of switching, and it was confirmed that the degree of spatial separation matters, with children benefiting most when the dimensional values are fully spatially segregated. Switch rates tended to be higher in versions on which children were prompted to label the stimuli compared to when the experimenter provided labels, and lower when reversal instructions were used in conjunction with the standard task stimuli. Theoretical and practical implications for the study and measurement of executive function in early childhood are discussed.

Regularization of languages by adults and children: A mathematical framework

The fascinating ability of humans to modify the linguistic input and "create" a language has been widely discussed. In the work of Newport and colleagues, it has been demonstrated that both children and adults have some ability to process inconsistent linguistic input and "improve" it by making it more consistent. In Hudson Kam and Newport (2009), artificial miniature language acquisition from an inconsistent source was studied. It was shown that (i) children are better at language regularization than adults and that (ii) adults can also regularize, depending on the structure of the input. In this paper we create a learning algorithm of the reinforcement-learning type, which exhibits patterns reported in Hudson Kam and Newport (2009) and suggests a way to explain them. It turns out that in order to capture the differences between children's and adults' learning patterns, we need to introduce a certain asymmetry in the learning algorithm. Namely, we have to assume that the reaction of the learners differs depending on whether or not the source's input coincides with the learner's internal hypothesis. We interpret this result in the context of a different reaction of children and adults to implicit, expectation-based evidence, positive or negative. We propose that a possible mechanism that contributes to the children's ability to regularize an inconsistent input is related to their heightened sensitivity to positive evidence rather than the (implicit) negative evidence. In our model, regularization comes naturally as a consequence of a stronger reaction of the children to evidence supporting their preferred hypothesis. In adults, their ability to adequately process implicit negative evidence prevents them from regularizing the inconsistent input, resulting in a weaker degree of regularization.

Why some surprises are more surprising than others: Surprise as a metacognitive sense of explanatory difficulty

Early theories of surprise, including Darwin's, argued that it was predominantly a basic emotion. Recently, theories have taken a more cognitive view of surprise, casting it as a process of "making sense of surprising events". The current paper advances the view that the essence of this sense-making process is explanation; specifically, that people's perception of surprise is a metacognitive estimate of the cognitive work involved in explaining an abnormal event. So, some surprises are more surprising because they are harder to explain. This proposal is tested in eight experiments that explore how (i) the contents of memory can influence surprise, (ii) different classes of scenarios can retrieve more/less relevant knowledge from memory to explain surprising outcomes, (iii) how partial explanations constrain the explanation process, reducing surprise, and (iv) how, overall, any factor that acts to increase the cognitive work in explaining a surprising event, results in higher levels of surprise (e.g., task demands to find three rather than one explanations). Across the present studies, using different materials, paradigms and measures, it is consistently and repeatedly found that the difficulty of explaining a surprising outcome is the best predictor for people's perceptions of the surprisingness of events. Alternative accounts of these results are considered, as are future directions for this research.

Enhancing the executive functions of 3-year-olds in the Dimensional Change Card Sort task

Executive functions enable flexible thinking, something young children are notoriously bad at. For instance, in the dimensional change card sort (DCCS) task, 3-year-olds can sort cards by one dimension (shape), but continue to sort by this dimension when asked to switch (to color). This study tests a prediction of a dynamic neural field model that prior experience with the postswitch dimension can enhance 3-year-olds' performance in the DCCS. In Experiment 1A, a matching game was used to preexpose 3-year-olds (n = 36) to color. This facilitated switching from sorting by shape to color. In , 3-year-olds (n = 18) were preexposed to shape. This did not facilitate switching from sorting by color to shape. The model was used to explain this asymmetry.

Working memory updating and the development of rule-guided behavior

The transition from middle childhood into adolescence is marked by both increasing independence and also extensive change in the daily requirements of familial demands, social pressures, and academic achievement. To manage this increased complexity, children must develop the ability to use abstract rules that guide the choice of behavior across a range of circumstances. Here, we tested children through adults in a task that requires increasing levels of rule abstraction, while separately manipulating competition among alternatives in working memory. We found that age-related differences in rule-guided behavior can be explained in terms of improvement in rule abstraction, which we suggest involves a working memory updating mechanism. Furthermore, family socioeconomic status (SES) predicted change in rule-guided behavior, such that higher SES predicted better performance with development. We discuss these results within a working memory gating framework for abstract rule-guided behavior.

Sidestepping the combinatorial explosion: an explanation of n-gram frequency effects based on naive discriminative learning

Arnon and Snider ((2010). More than words: Frequency effects for multi-word phrases. Journal of memory and language, 62, 67-82) documented frequency effects for compositional four-grams independently of the frequencies of lower-order n-grams. They argue that comprehenders apparently store frequency information about multi-word units. We show that n-gram frequency effects can emerge in a parameter-free computational model driven by naive discriminative learning, trained on a sample of 300,000 four-word phrases from the British National Corpus. The discriminative learning model is a full decomposition model, associating orthographic input features straightforwardly with meanings. The model does not make use of separate representations for derived or inflected words, nor for compounds, nor for phrases. Nevertheless, frequency effects are correctly predicted for all these linguistic units. Naive discriminative learning provides the simplest and most economical explanation for frequency effects in language processing, obviating the need to posit counters in the head for, and the existence of, hundreds of millions of n-gram representations.

Children value informativity over logic in word learning

The question of how children learn the meanings of words has long puzzled philosophers and psychologists. As Quine famously pointed out, simply hearing a word in context reveals next to nothing about its meaning. How then do children learn to understand and use words correctly? Here, we show how learning theory can offer an elegant solution to this seemingly intractable puzzle in language acquisition. From it, we derived formal predictions about word learning in situations of Quinean ambiguity, and subsequently tested our predictions on toddlers, undergraduates, and developmental psychologists. The toddlers' performance was consistent both with our predictions and with the workings of implicit mechanisms that can facilitate the learning of meaningful lexical systems. Adults adopted a markedly different and likely suboptimal strategy. These results suggest one explanation for why early word learning can appear baffling: Adult intuitions may be a poor source of insight into how children learn.

The enigma of number: why children find the meanings of even small number words hard to learn and how we can help them do better

Although number words are common in everyday speech, learning their meanings is an arduous, drawn-out process for most children, and the source of this delay has long been the subject of inquiry. Children begin by identifying the few small numerosities that can be named without counting, and this has prompted further debate over whether there is a specific, capacity-limited system for representing these small sets, or whether smaller and larger sets are both represented by the same system. Here we present a formal, computational analysis of number learning that offers a possible solution to both puzzles. This analysis indicates that once the environment and the representational demands of the task of learning to identify sets are taken into consideration, a continuous system for learning, representing and discriminating set-sizes can give rise to effective discontinuities in processing. At the same time, our simulations illustrate how typical prenominal linguistic constructions ("there are three balls") structure information in a way that is largely unhelpful for discrimination learning, while suggesting that postnominal constructions ("balls, there are three") will facilitate such learning. A training-experiment with three-year olds confirms these predictions, demonstrating that rapid, significant gains in numerical understanding and competence are possible given appropriately structured postnominal input. Our simulations and results reveal how discrimination learning tunes children's systems for representing small sets, and how its capacity-limits result naturally out of a mixture of the learning environment and the increasingly complex task of discriminating and representing ever-larger number sets. They also explain why children benefit so little from the training that parents and educators usually provide. Given the efficacy of our intervention, the ease with which it can be implemented, and the large body of research showing how early numerical ability predicts later educational outcomes, this simple discovery may have far-reaching consequences.