Encoding temporal regularities and information copying in hippocampal circuits

Discriminating, extracting and encoding temporal regularities is a critical requirement in the brain, relevant to sensory-motor processing and learning. However, the cellular mechanisms responsible remain enigmatic; for example, whether such abilities require specific, elaborately organized neural networks or arise from more fundamental, inherent properties of neurons. Here, using multi-electrode array technology, and focusing on interval learning, we demonstrate that sparse reconstituted rat hippocampal neural circuits are intrinsically capable of encoding and storing sub-second-order time intervals for over an hour timescale, represented in changes in the spatial-temporal architecture of firing relationships among populations of neurons. This learning is accompanied by increases in mutual information and transfer entropy, formal measures related to information storage and flow. Moreover, temporal relationships derived from previously trained circuits can act as templates for copying intervals into untrained networks, suggesting the possibility of circuit-to-circuit information transfer. Our findings illustrate that dynamic encoding and stable copying of temporal relationships are fundamental properties of simple in vitro networks, with general significance for understanding elemental principles of information processing, storage and replication

Syntactic learning by mere exposure - An ERP study in adult learners

<p>Abstract</p> <p>Background</p> <p>Artificial language studies have revealed the remarkable ability of humans to extract syntactic structures from a continuous sound stream by mere exposure. However, it remains unclear whether the processes acquired in such tasks are comparable to those applied during normal language processing. The present study compares the ERPs to auditory processing of simple Italian sentences in native and non-native speakers after brief exposure to Italian sentences of a similar structure. The sentences contained a non-adjacent dependency between an auxiliary and the morphologically marked suffix of the verb. Participants were presented four alternating learning and testing phases. During learning phases only correct sentences were presented while during testing phases 50 percent of the sentences contained a grammatical violation.</p> <p>Results</p> <p>The non-native speakers successfully learned the dependency and displayed an N400-like negativity and a subsequent anteriorily distributed positivity in response to rule violations. The native Italian group showed an N400 followed by a P600 effect.</p> <p>Conclusion</p> <p>The presence of the P600 suggests that native speakers applied a grammatical rule. In contrast, non-native speakers appeared to use a lexical form-based processing strategy. Thus, the processing mechanisms acquired in the language learning task were only partly comparable to those applied by competent native speakers.</p

AGL StimSelect: Software for automated selection of stimuli for artificial grammar learning

Artificial Grammar Learning (AGL) is an experimental paradigm that has been used extensively in cognitive research for many years to study implicit learning, associative learning, and generalization based either on similarity or rules. Without computer assistance it is virtually impossible to generate appropriate grammatical training stimuli along with grammatical or non-grammatical test stimuli that control relevant psychological variables. We present the first flexible, fully automated software for selecting AGL stimuli. The software allows users to specify a grammar of interest, and to manipulate characteristics of training and test sequences, and their relationship to each other. The user thus has direct control over stimulus features that may influence learning and generalization in AGL tasks. The software enables researchers to develop AGL designs that would not be feasible without automatic stimulus selection. It is implemented in Matlab

Modeling human performance in statistical word segmentation

The ability to discover groupings in continuous stimuli on the basis of distributional information is present across species and across perceptual modalities. We investigate the nature of the computations underlying this ability using statistical word segmentation experiments in which we vary the length of sentences, the amount of exposure, and the number of words in the languages being learned. Although the results are intuitive from the perspective of a language learner (longer sentences, less training, and a larger language all make learning more difficult), standard computational proposals fail to capture several of these results. We describe how probabilistic models of segmentation can be modified to take into account some notion of memory or resource limitations in order to provide a closer match to human performance.National Science Foundation (U.S.) (Grant BCS-0631518

The notion of contextual locking: Previously learnt items are not accessible as such when appearing in a less common context

We examined the effect of context on the learning of spatial coding in four experiments. Two partially overlapping sets of stimuli, which had the very same stimulus–response spatial coding, were presented in unique contexts. Results show contextual locking—that is, response times to the very same item in a more common context (80%) were significantly shorter than those in a less common context (20%). Contextual locking was obtained both when the context was more salient (Experiments 1 and 2) and less salient (Experiments 3 and 4). In addition, results were obtained even when contextualization seemed less necessary (Experiments 2 and 4). Binding of information to context is discussed in relation to chunking, transfer effects, and practical applications pertaining to professional training

Interference between Sentence Processing and Probabilistic Implicit Sequence Learning

During sentence processing we decode the sequential combination of words, phrases or sentences according to previously learned rules. The computational mechanisms and neural correlates of these rules are still much debated. Other key issue is whether sentence processing solely relies on language-specific mechanisms or is it also governed by domain-general principles.In the present study, we investigated the relationship between sentence processing and implicit sequence learning in a dual-task paradigm in which the primary task was a non-linguistic task (Alternating Serial Reaction Time Task for measuring probabilistic implicit sequence learning), while the secondary task were a sentence comprehension task relying on syntactic processing. We used two control conditions: a non-linguistic one (math condition) and a linguistic task (word processing task). Here we show that the sentence processing interfered with the probabilistic implicit sequence learning task, while the other two tasks did not produce a similar effect.Our findings suggest that operations during sentence processing utilize resources underlying non-domain-specific probabilistic procedural learning. Furthermore, it provides a bridge between two competitive frameworks of language processing. It appears that procedural and statistical models of language are not mutually exclusive, particularly for sentence processing. These results show that the implicit procedural system is engaged in sentence processing, but on a mechanism level, language might still be based on statistical computations