When pragmatics matters more for truth-value judgments: An investigation of quantifier scope ambiguity

Investigations of linguistic meaning rely crucially on truth-value judgments, which assess whether a sentence can truthfully describe a given scenario. In investigations of language acquisition, truth-value judgments are used to assess both the target knowledge adults have and the developing knowledge children have at different ages. On the basis of truth-value judgments, researchers have concluded that differences between how children resolve ambiguous utterances and how adults do so persist until at least age five. Current explanations compatible with the experimental data attribute these differences to both grammatical processing and pragmatic factors. Here, we use computational cognitive modeling to formally articulate the ambiguity-resolution process that underlies child and adult judgments in a truth-value judgment task; crucially, the model can separate out the individual contributions of specific grammatical processing and pragmatic factors to the resulting judgment behavior. We find that pragmatic factors play a larger role than grammatical processing factors in explaining children’s non-adult-like ambiguity resolution behavior, and the computational modeling framework allows us to understand exactly why that is. Interestingly, the model predicts qualitative similarity between child and adult ambiguity resolution. Given this prediction, we then extend our model to show how the same processes are active in adult ambiguity resolution. This result supports continuity in the development of ambiguity resolution, where children do not qualitatively change how they resolve ambiguity in order to become adult-like. We discuss the implications of our results for acquisition more generally, including both theories of development and methods for assessing that development.

When a gain becomes a loss: The effect of wealth predictions on financial decisions

When people make financial decisions, they need not only think about their current financial situation, but also about changes in future wealth. This work investigates people's beliefs about their future wealth and how these beliefs impact financial decisions. Using a joint experimental and computational cognitive modeling approach, we show that people's future beliefs serve as reference points when making investment decisions. These results are further supported by data from a large-scale cross-sectional survey (n = 4,606) showing that people's beliefs about the future value of their assets are related to investment decisions between risky (i.e., stock market index) and safe (i.e., bond earning a fixed amount per year) options. In both the experiments and survey, we hypothesize that outcomes that are nominally stated as sure gains can become coded as losses due to belief-based reference points. This pattern leads to an increase in riskier choices across positive outcomes for individuals with optimistic beliefs about their future wealth.

Cognitive neurorobotics and self in the shared world, a focused review of ongoing research

Through brain-inspired modeling studies, cognitive neurorobotics aims to resolve dynamics essential to different emergent phenomena at the level of embodied agency in an object environment shared with human beings. This article is a review of ongoing research focusing on model dynamics associated with human self-consciousness. It introduces the free energy principle and active inference in terms of Bayesian theory and predictive coding, and then discusses how directed inquiry employing analogous models may bring us closer to representing the sense of self in cognitive neurorobots. The first section quickly locates cognitive neurorobotics in the broad field of computational cognitive modeling. The second section introduces principles according to which cognition may be formalized, and reviews cognitive neurorobotics experiments employing such formalizations. The third section interprets the results of these and other experiments in the context of different senses of self, both “minimal” and “narrative” self. The fourth section considers model validity and discusses what we may expect ongoing cognitive neurorobotics studies to contribute to scientific explanation of cognitive phenomena including the senses of minimal and narrative self.

Neural Correlates of Causal Confounding

As scientists, we are keenly aware that if putative causes perfectly covary, the independent influence of neither can be discerned—a “no confounding” constraint on inference, fundamental to philosophical and statistical perspectives on causation. Intriguingly, a substantial behavioral literature suggests that naïve human reasoners, adults and children, are tacitly sensitive to causal confounding. Here, a combination of fMRI and computational cognitive modeling was used to investigate neural substrates mediating such sensitivity. While being scanned, participants observed and judged the influences of various putative causes with confounded or nonconfounded, deterministic or stochastic, influences. During judgments requiring generalization of causal knowledge from a feedback-based learning context to a transfer probe, activity in the dorsomedial pFC was better accounted for by a Bayesian causal model, sensitive to both confounding and stochasticity, than a purely error-driven algorithm, sensitive only to stochasticity. Implications for the detection and estimation of distinct forms of uncertainty, and for a neural mediation of domain-general constraints on causal induction, are discussed.