Brains, it has recently been argued, are essentially prediction machines. They are bundles of cells that support perception and action by constantly attempting to match incoming sensory inputs with top-down expectations or predictions. This is achieved using a hierarchical generative model that aims to minimize prediction error within a bidirectional cascade of cortical processing. Such accounts offer a unifying model of perception and action, illuminate the functional role of attention, and may neatly capture the special contribution of cortical processing to adaptive success. This target article critically examines this "hierarchical prediction machine" approach, concluding that it offers the best clue yet to the shape of a unified science of mind and action. Sections 1 and 2 lay out the key elements and implications of the approach. Section 3 explores a variety of pitfalls and challenges, spanning the evidential, the methodological, and the more properly conceptual. The paper ends (sections 4 and 5) by asking how such approaches might impact our more general vision of mind, experience, and agency.

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Whatever next? Predictive brains, situated agents, and the future of cognitive science

Doing Qualitative Research

In this review, we consider the potential functional role of beta-band oscillations, which at present is not yet well understood. We discuss evidence from recent studies on top-down mechanisms involved in cognitive processing, on the motor system and on the pathophysiology of movement disorders that suggest a unifying hypothesis: beta-band activity seems related to the maintenance of the current sensorimotor or cognitive state. We hypothesize that beta oscillations and/or coupling in the beta-band are expressed more strongly if the maintenance of the status quo is intended or predicted, than if a change is expected. Moreover, we suggest that pathological enhancement of beta-band activity is likely to result in an abnormal persistence of the status quo and a deterioration of flexible behavioural and cognitive control.

Beta-band oscillations--signalling the status quo?

Talk of linguistic universals has given cognitive scientists the impression that languages are all built to a common pattern. In fact, there are vanishingly few universals of language in the direct sense that all languages exhibit them. Instead, diversity can be found at almost every level of linguistic organization. This fundamentally changes the object of enquiry from a cognitive science perspective. This target article summarizes decades of cross-linguistic work by typologists and descriptive linguists, showing just how few and unprofound the universal characteristics of language are, once we honestly confront the diversity offered to us by the world's 6,000 to 8,000 languages. After surveying the various uses of "universal," we illustrate the ways languages vary radically in sound, meaning, and syntactic organization, and then we examine in more detail the core grammatical machinery of recursion, constituency, and grammatical relations. Although there are significant recurrent patterns in organization, these are better explained as stable engineering solutions satisfying multiple design constraints, reflecting both cultural-historical factors and the constraints of human cognition. Linguistic diversity then becomes the crucial datum for cognitive science: we are the only species with a communication system that is fundamentally variable at all levels. Recognizing the true extent of structural diversity in human language opens up exciting new research directions for cognitive scientists, offering thousands of different natural experiments given by different languages, with new opportunities for dialogue with biological paradigms concerned with change and diversity, and confronting us with the extraordinary plasticity of the highest human skills.

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The myth of language universals: Language diversity and its importance for cognitive science

Infants’ speech perception skills show a dual change towards the end of the first year of life. Not only does non-native speech perception decline, as often shown, but native language speech perception skills show improvement, reflecting a facilitative effect of experience with native language. The mechanism underlying change at this point in development, and the relationship between the change in native and non-native speech perception, is of theoretical interest. As shown in new data presented here, at the cusp of this developmental change, infants’ native and non-native phonetic perception skills predict later language ability, but in opposite directions. Better native language skill at 7.5 months of age predicts faster language advancement, whereas better non-native language skill predicts slower advancement. We suggest that native language phonetic performance is indicative of neural commitment to the native language, while non-native phonetic performance reveals uncommitted neural circuitry. This paper has three goals: (i) to review existing models of phonetic perception development, (ii) to present new event-related potential data showing that native and nonnative phonetic perception at 7.5 months of age predicts language growth over the next 2 years, and (iii) to describe a revised version of our previous model, the native language magnet model, expanded (NLM-e). NLM-e incorporates five new principles. Specific testable predictions for future research programmes are described.

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Phonetic learning as a pathway to language: new data and native language magnet theory expanded (NLM-e)

Gradient effects of within-category phonetic variation on lexical access.

Classic approaches to word learning emphasize referential ambiguity: In naming situations, a novel word could refer to many possible objects, properties, actions, and so forth. To solve this, researchers have posited constraints, and inference strategies, but assume that determining the referent of a novel word is isomorphic to learning. We present an alternative in which referent selection is an online process and independent of long-term learning. We illustrate this theoretical approach with a dynamic associative model in which referent selection emerges from real-time competition between referents and learning is associative (Hebbian). This model accounts for a range of findings including the differences in expressive and receptive vocabulary, cross-situational learning under high degrees of ambiguity, accelerating (vocabulary explosion) and decelerating (power law) learning, fast mapping by mutual exclusivity (and differences in bilinguals), improvements in familiar word recognition with development, and correlations between speed of processing and learning. Together it suggests that (a) association learning buttressed by dynamic competition can account for much of the literature; (b) familiar word recognition is subserved by the same processes that identify the referents of novel words (fast mapping); (c) online competition may allow the children to leverage information available in the task to augment performance despite slow learning; (d) in complex systems, associative learning is highly multifaceted; and (e) learning and referent selection, though logically distinct, can be subtly related. It suggests more sophisticated ways of describing the interaction between situation- and developmental-time processes and points to the need for considering such interactions as a primary determinant of development.

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Word learning emerges from the interaction of online referent selection and slow associative learning.

Infants in the early stages of word learning have difficulty learning lexical neighbors (ie word pairs that differ by a single phoneme), despite their ability to discriminate the same contrast in a purely auditory task While prior work has focused on top-down explanations for this failure (eg task demands, lexical competition), none has examined if bottom-up acoustic-phonetic factors play a role We hypothesized that lexical neighbor learning could be improved by incorporating greater acoustic variability in the words being learned, as this may buttress still-developing phonetic categories, and help infants identify the relevant contrastive dimension Infants were exposed to pictures accompanied by labels spoken by either a single or multiple speakers At test, infants in the single-speaker condition failed to recognize the difference between the two words, while infants who heard multiple speakers discriminated between them

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Speaker Variability Augments Phonological Processing in Early Word Learning.

Most theories of categorization emphasize how continuous perceptual information is mapped to categories. However, equally important are the informational assumptions of a model, the type of information subserving this mapping. This is crucial in speech perception where the signal is variable and context dependent. This study assessed the informational assumptions of several models of speech categorization, in particular, the number of cues that are the basis of categorization and whether these cues represent the input veridically or have undergone compensation. We collected a corpus of 2,880 fricative productions (Jongman, Wayland, & Wong, 2000) spanning many talker and vowel contexts and measured 24 cues for each. A subset was also presented to listeners in an 8AFC phoneme categorization task. We then trained a common classification model based on logistic regression to categorize the fricative from the cue values and manipulated the information in the training set to contrast (a) models based on a small number of invariant cues, (b) models using all cues without compensation, and (c) models in which cues underwent compensation for contextual factors. Compensation was modeled by computing cues relative to expectations (C-CuRE), a new approach to compensation that preserves fine-grained detail in the signal. Only the compensation model achieved a similar accuracy to listeners and showed the same effects of context. Thus, even simple categorization metrics can overcome the variability in speech when sufficient information is available and compensation schemes like C-CuRE are employed.

https://kuscholarworks.ku.edu/bitstream/handle/1808/13410/McMuray_Jongman_2011.pdf;sequence=1

What information is necessary for speech categorization? Harnessing variability in the speech signal by integrating cues computed relative to expectations

During the second year of life, the rate at which children acquire new words accelerates dramatically. This has led the field of language acquisition to posit specialized mechanisms that leverage the few words learned in the initial slow phase for faster vocabulary growth later. Simulations and mathematical analysis demonstrate that specialized cognitive changes are unnecessary. The acceleration in lexical acquisition is a necessary by-product of learning if (i) multiple words are learned in parallel and (ii) words are distributed such that there are few words that can be acquired quickly and many difficult ones.

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Bob McMurray

Papers

Gradient effects of within-category phonetic variation on lexical access.

Word learning emerges from the interaction of online referent selection and slow associative learning.

Speaker Variability Augments Phonological Processing in Early Word Learning.

What information is necessary for speech categorization? Harnessing variability in the speech signal by integrating cues computed relative to expectations

Defusing the Childhood Vocabulary Explosion