Thomas Andrillon, Andreas Trier Poulsen, Lars Kai Hansen, Damien Léger, and Sid Kouider, (June 15, 2016), Neural Markers of Responsiveness to the Environment in Human Sleep, The Journal of Neuroscience

Abstract :
Sleep is characterized by a loss of behavioral responsiveness. However, recent research has shown that the sleeping brain is not completely disconnected from its environment. How neural activity constrains the ability to process sensory information while asleep is yet unclear. Here, we instructed human volunteers to classify words with lateralized hand responses while falling asleep. Using an electroencephalographic (EEG) marker of motor preparation, we show how responsiveness is modulated across sleep. These modulations are tracked using classic event-related potential analyses complemented by Lempel-Ziv complexity (LZc), a measure shown to track arousal in sleep and anesthesia. Neural activity related to the semantic content of stimuli was conserved in light non-rapid eye movement (NREM) sleep. However, these processes were suppressed in deep NREM sleep and, importantly, also in REM sleep, despite the recovery of wake-like neural activity in the latter. In NREM sleep, sensory activations were counterbalanced by evoked down states, which, when present, blocked further processing of external information. In addition, responsiveness markers correlated positively with baseline complexity, which could be related to modulation in sleep depth. In REM sleep, however, this relationship was reversed. We therefore propose that, in REM sleep, endogenously generated processes compete with the processing of external input. Sleep can thus be seen as a self-regulated process in which external information can be processed in lighter stages but suppressed in deeper stages. Last, our results suggest drastically different gating mechanisms in NREM and REM sleep.

Ngon, C. & S. Peperkamp (2016), What infants know about the unsaid: Phonological categorization in the absence of auditory input. Cognition, 152, 53-60.

Abstract: Acquiring a lexicon constitutes an essential step in early language development. From an early age on, infants store words with well-specified phonological representations, and they can spontaneously activate these representations on the basis of visual information only (Mani & Plunkett, 2010a, 2011). To what extent can infants inspect and categorize phonological representations in the absence of auditory input? The present study focuses on words that infants comprehend but do not attempt to pronounce yet, and introduces a novel methodology based on anticipatory eye-movements. In two experiments, 21-month-old French-learning infants were silently presented with images of familiar objects whose labels they comprehended but did not pronounce yet. We tested whether they could activate the phonological representation of these labels and categorize them based on their length. Infants' performance exceeded chance when the target words were mono- and trisyllabic, but not when they were mono- and disyllabic. Thus, even in the absence of auditory input infants can activate the phonological representation of words they do not pronounce yet, and use this representation to perform a categorization based on word length, provided the length difference is substantial.

Stefano Palminteri, Emma J. Kilford, Giorgio Coricelli, Sarah-Jayne Blakemore (2016, June 20), The Computational Development of Reinforcement Learning during Adolescence, PLOS Computational Biology.

Abstract:
Adolescence is a period of life characterised by changes in learning and decision-making. Learning and decision-making do not rely on a unitary system, but instead require the coordination of different cognitive processes that can be mathematically formalised as dissociable computational modules. Here, we aimed to trace the developmental time-course of the computational modules responsible for learning from reward or punishment, and learning from counterfactual feedback. Adolescents and adults carried out a novel reinforcement learning paradigm in which participants learned the association between cues and probabilistic outcomes, where the outcomes differed in valence (reward versus punishment) and feedback was either partial or complete (either the outcome of the chosen option only, or the outcomes of both the chosen and unchosen option, were displayed). Computational strategies changed during development: whereas adolescents’ behaviour was better explained by a basic reinforcement learning algorithm, adults’ behaviour integrated increasingly complex computational features, namely a counterfactual learning module (enabling enhanced performance in the presence of complete feedback) and a value contextualisation module (enabling symmetrical reward and punishment learning). Unlike adults, adolescent performance did not benefit from counterfactual (complete) feedback. In addition, while adults learned symmetrically from both reward and punishment, adolescents learned from reward but were less likely to learn from punishment. This tendency to rely on rewards and not to consider alternative consequences of actions might contribute to our understanding of decision-making in adolescence.

Philippe Schlenker, Emmanuel Chemla, Cristiane Cäsar, Robin Ryder, Klaus Zuberbühler (2016), Titi semantics: Context and meaning in Titi monkey call sequences, Natural Language & Linguistic Theory

Abstract :
Cäsar et al. (2013) show that the structure of Titi monkey call sequences can, with just two call types (A and B), reflect information about predator type and predator location. Using the general methods of Schlenker et al. (2014, 2016, to appear), we ask what these observations show about the ‘linguistic’ structure of Titi calls. We first demonstrate that the simplest behavioral assumptions make it challenging to provide lexical specifications for A- and B-calls: B-calls rather clearly have the distribution of highly underspecified calls; but A-calls are also found in highly heterogeneous contexts (e.g. they are triggered by ‘cat in the canopy’ and ‘raptor on the ground’ situations). We discuss two possible solutions to the problem. One posits that entire sequences are endowed with meanings that are not compositionally derived from their individual parts (a related idea was proposed by Arnold and Zuberbühler to analyze pyow-hack sequences in Putty-nosed monkeys). The second solution, which we consider to be superior, takes sequences to have no structure besides concatenation: the B-call is a general call, the A-call is used for serious non-ground threats, and each call reflects information about the environment at the time at which it is uttered. The composition of Cäsar et al.’s sequences is seen to follow from the interaction between call meaning, rules of competition among calls, and more sophisticated assumptions about the environmental context. In the end, a detailed analysis of the division of labor between semantics, pragmatics and the environmental context yields a simple and explanatory analysis of sequences that initially seemed to display a complex mapping between syntax and semantics.

Benjamin Spector (2016), Comparing exhaustivity operators, Semantics and Pragmatics

Abstract :
In this paper, I investigate the formal relationships between two types of exhaustivity operators that have been discussed in the literature, one based on minimal worlds/models, noted exh-mw (van Rooij & Schulz 2004, Schulz & van Rooij 2006, Spector 2003, 2006, with roots in Szabolcsi 1983, Groenendijk & Stokhof 1984), and one based on the notion of innocent exclusion, noted exh-ie (Fox 2007). Among others, I prove that whenever the set of alternatives relative to which exhaustification takes place is semantically closed under conjunction, the two operators are necessarily equivalent. Together with other results, this provides a method to simplify, in some cases, the computation associated with exh-ie, and, in particular, to drastically reduce the number of alternatives to be considered.

Besides their practical relevance, these results clarify the formal relationships between both types of operators.

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