Mutual Gaze During Early Mother-Infant Interactions Promotes Attention Control Development.

Efficient attention control is fundamental for infant cognitive development, but its early precursors are not well understood. This study investigated whether dyadic visual attention during parent-infant interactions at 5 months of age predicts the ability to control attention at 11 months of age (N = 55). Total duration of mutual gaze (MG) was assessed during free play at 5 months, while infant attention control was measured in a gap-and-overlap task at 5 and 11 months. MG predicted attention disengagement at 11 months. Infants who spent more time in MG at 5 months showed better attention control at 11 months. These results provide important insights into developmental pathways linking visual behavior in dyadic interactions with infants' subsequent attention skills.

Most quantifiers have many meanings.

In this paper, we investigate, by means of a computational model, how individuals map quantifiers onto numbers and how they order quantifiers on a mental line. We selected five English quantifiers (few, fewer than half, many, more than half, and most) which differ in truth conditions and vagueness. We collected binary truth value judgment data in an online quantifier verification experiment. Using a Bayesian three-parameter logistic regression model, we separated three sources of individual differences: truth condition, vagueness, and response error. Clustering on one of the model's parameter that corresponds to truth conditions revealed four subgroups of participants with different quantifier-to-number mappings and different ranges of the mental line of quantifiers. Our findings suggest multiple sources of individual differences in semantic representations of quantifiers and support a conceptual distinction between different types of imprecision in quantifier meanings. We discuss the consequence of our findings for the main theoretical approaches to quantifiers: the bivalent truth-conditional approach and the fuzzy logic approach. We argue that the former approach neither can explain inter-individual differences nor intra-individual differences in truth conditions of vague quantifiers. The latter approach requires further specification to fully account for individual differences demonstrated in this study.

Uncovering the Structure of Semantic Representations Using a Computational Model of Decision-Making.

According to logical theories of meaning, a meaning of an expression can be formalized and encoded in truth conditions. Vagueness of the language and individual differences between people are a challenge to incorporate into the meaning representations. In this paper, we propose a new approach to study truth-conditional representations of vague concepts. For a case study, we selected two natural language quantifiers most and more than half. We conducted two online experiments, each with 90 native English speakers. In the first experiment, we tested between-subjects variability in meaning representations. In the second experiment, we tested the stability of meaning representations over time by testing the same group of participants in two experimental sessions. In both experiments, participants performed the verification task. They verified a sentence with a quantifier (e.g., "Most of the gleerbs are feezda.") based on the numerical information provided in the second sentence, (e.g., "60% of the gleerbs are feezda"). To investigate between-subject and within-subject differences in meaning representations, we proposed an extended version of the Diffusion Decision Model with two parameters capturing truth conditions and vagueness. We fit the model to responses and reaction times data. In the first experiment, we found substantial between-subject differences in representations of most as reflected by the variability in the truth conditions. Moreover, we found that the verification of most is proportion-dependent as reflected in the reaction time effect and model parameter. In the second experiment, we showed that quantifier representations are stable over time as reflected in stable model parameters across two experimental sessions. These findings challenge semantic theories that assume the truth-conditional equivalence of most and more than half and contribute to the representational theory of vague concepts. The current study presents a promising approach to study semantic representations, which can have a wide application in experimental linguistics.

Working together to orient faster: The combined effects of alerting and orienting networks on pupillary responses at 8 months of age.

Multiple visual attention mechanisms are active already in infancy, most notably one supporting orienting towards stimuli and another, maintaining appropriate levels of alertness, when exploring the environment. They are thought to depend on separate brain networks, but their effects are difficult to isolate in existing behavioural paradigms. Better understanding of the contribution of each network to individual differences in visual orienting may help to explain their role in attention development. Here, we tested whether alerting and spatial cues differentially modulate pupil dilation in 8-month-old infants in a visual orienting paradigm. We found differential effects in the time course of these responses depending on the cue type. Moreover, using Principal Component Analysis (PCA) we identified two main components of pupillary response, which may reflect the alerting and orienting network activity. In a regression analysis, these components together explained nearly 40 % of variance in saccadic latencies in the spatial cueing condition of the task. These results likely demonstrate that both networks work together in 8-month-old infants and that their activity can be indexed with pupil dilation combined with PCA, but not with raw changes in pupil diameter.