There have been some suggestions in linguistics and cognitive science that humans process continuous speech by routinely chunking it up into smaller units. The nature of the process is open to debate, which is complicated by the apparent existence of two entirely different chunking processes, both of which seem to be warranted by the limitations of working memory. To overcome them, humans seem to both combine items into larger units for future retrieval (usage-based chunking), and partition incoming streams into temporal groups (perceptual chunking). To determine linguistic properties and cognitive constraints of perceptual chunking, most previous research has employed short-constructed stimuli modeled on written language. In contrast, we presented linguistically naïve listeners with excerpts of natural speech from corpora and collected their intuitive perceptions of chunk boundaries. We then used mixed-effects logistic regression models to find out to what extent pauses, prosody, syntax, chunk duration, and surprisal predict chunk boundary perception. The results showed that all cues were important, suggesting cue degeneracy, but with substantial variation across listeners and speech excerpts. Chunk duration had a strong effect, supporting the cognitive constraint hypothesis. The direction of the surprisal effect supported the distinction between perceptual and usage-based chunking.

A longstanding question in the second language acquisition literature is whether late second language (L2) learners process grammatical structures in a native-like manner. Here, we use Time Frequency Representation (TFR) analysis to test morpho-syntactic processing of clitic pronouns in native and late L2 learners of Spanish. The TFR results show overall similar power decreases in theta, alpha, and beta frequencies in both groups. Critically, the observed oscillatory effects persisted in time for native Spanish speakers, but declined earlier for L2 learners. We discuss the results using cell-assembly theory models for language processing (e.g., Pulvermüller, 1999) that posit a biphasic time-course for neural assemblies consisting of an early ignition (recognition) and a later reverberation (re-processing) phase. We propose a working hypothesis for L2 processing in tune with a cell-assembly theory suggesting that the length of the reverberation phase could be a distinguishing feature between native and L2 processing.

Human agents draw a variety of inferences effortlessly, spontaneously, and with remarkable efficiency – as though these inferences were a reflexive response of their cognitive apparatus. Furthermore, these inferences are drawn with reference to a large body of background knowledge. This remarkable human ability seems paradoxical given the complexity of reasoning reported by researchers in artificial intelligence. It also poses a challenge for cognitive science and computational neuroscience: How can a system of simple and slow neuronlike elements represent a large body of systemic knowledge and perform a range of inferences with such speed? We describe a computational model that takes a step toward addressing the cognitive science challenge and resolving the artificial intelligence paradox. We show how a connectionist network can encode millions of facts and rules involving n-ary predicates and variables and perform a class of inferences in a few hundred milliseconds. Efficient reasoning requires the rapid representation and propagation of dynamic bindings. Our model (which we refer to as SHRUTI) achieves this by representing (1) dynamic bindings as the synchronous firing of appropriate nodes, (2) rules as interconnection patterns that direct the propagation of rhythmic activity, and (3) long-term facts as temporal pattern-matching subnetworks. The model is consistent with recent neurophysiological evidence that synchronous activity occurs in the brain and may play a representational role in neural information processing. The model also makes specific psychologically significant predictions about the nature of reflexive reasoning. It identifies constraints on the form of rules that may participate in such reasoning and relates the capacity of the working memory underlying reflexive reasoning to biological parameters such as the lowest frequency at which nodes can sustain synchronous oscillations and the coarseness of synchronization.