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During referential communication, speaker choices regarding the syntactic encoding of their expressions can modulate the linear ordering of the properties necessary to identify the referent. We investigated whether such syntactic choices are influenced by the informativity of these properties in a given visual context, as quantified by Referential Entropy Reduction (RER). In two experiments, a maze-based sentence completion task was used to examine whether informativity of a particular property (animal or action) influenced the decision to produce pre- versus post-nominal modifications when describing animal-performing-action referents in a visual scene. While many participants used a fixed strategy, informativity did significantly influence linearization for the remaining participants, consistent with a maximal informativity strategy in which the high RER property is encoded first. This suggests that speakers who vary their encodings are indeed sensitive to the informativity of properties in a visual scene, preferring syntactic linearization in which informative properties appear early.


CogSci 2023

Does informativity modulate linearization preferences in reference production?

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The 45th Annual Meeting of the Cognitive Science Society will take place in Sydney, Australia as a modified hybrid conference. Presentations from the main-session room will be live-streamed every day for all events. This will include all invited talks and award sessions. Recordings of all CogSci 2023 sessions will be made available for on-demand viewing after the conference.<br>
The in-person component will be held at the International Convention Centre Sydney at Darling Harbour. The Conference will take place Wednesday July 26th – Saturday July 29th, 2023 (Wednesday, July 26th is the Workshops Day).

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The 45th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

This paper presents a challenge to enactivist approaches to cognition (e.g. Ward, D., Silverman, D. & Villalobos, M. 2017): how can enactivists integrate data and results from research methods used in developmental psychology and animal cognition studies that heavily rely on representations, given enactivism's aversion to representations? I discuss theoretical commitments behind forms of looking time studies that have been used to probe into the cognitive abilities of infants and animals, focusing on the Violation of Expectation (VoE) paradigm (Ginnobili & Olmos 2021), to illustrate why these methods might pose an especially difficult problem for enactivists and their re-interpretation of cognitive science in terms of dynamic coupling between organism and environment. I argue that despite the lack of clarity regarding how the notion of expectation should be applied to the minds of neonates and nonhuman animals, there is an inherently representational aspect to expectation, given that it embodies satisfaction conditions.


Expectation, Representation, and Enactivism

Recent research has identified intertemporal impulsivity as a critical cognitive variable for explaining the autocatalytic nature of socioeconomic status. But how exactly this relationship transpires has not been clearly identified, with several possible cognitive mechanisms having been proposed in the literature. We designed an experimental paradigm in which participants farmed crops under budgetary constraints and intermittently faced random resource demands. We discovered that, as a result of unpredictable resource shocks, people's preferences shifted from long-term choices to short-term ones. We also found people's self-reported sense-of-control scores to be predictive of the magnitude of their preference shifts. On the basis of these results, we argue that steep inter-temporal discounting arises as a rational adaptation to persistently experiencing long-term planning failures due to unpredictable resource shocks.


Unpredictability shortens planning horizons

Decision-making models hold a vital role in the field cognitive science, serving as a means of describing and predicting human behavior. While classical models with similar assumptions are frequently favored, there is no guarantee they provide the best accounts of behavior. Here, we evaluate BEAST, a model that has demonstrated extraordinary predictive capabilities in diverse settings, but was excluded from a recent large-scale comparison of models due to its complexity. Our evaluation of the model's performance on a large collection of experiments of decisions under risk shows it provides excellent predictions in some domains. We further show how BEAST can be adapted to increase its predictive power in contextualized settings. Our results highlight the importance of considering a more inclusive approach toward complex models to deepen our understanding of the psychological mechanisms underlying human decision making behavior.


The Importance of Complex Models in Decision Making Research: An Empirical Analysis using BEAST

Face stereotypes are prevalent and consequential. In this paper, we investigate an experience sampling account of face stereotyping. In two experiments, we show that, in an experience-sampling-based Trust Game, participants in the role of an investor were much more likely to play the game with trustees with trustworthy faces than those with untrustworthy faces (Experiment 1). Crucially, this endogenous experience sampling bias has amplified the behavioral consequences of the stereotype of facial trustworthiness. In contrast, when the information was directly described with no sampling constraint for the participants, the face stereotype had a very limited effect on investment decisions (Experiment 2). We conclude by suggesting that the description-experience gap paves a promising avenue for studying sampling-based accounts in social cognition and behavior.


A description-experience gap in face stereotyping

Computational cognitive modeling is an important tool for understanding the processes that support human and animal decision-making. Choice data in sequential decision-making tasks are always noisy, and separating noise from signal can improve the quality of computational modeling. Currently, most models assume that noise is static, typically by including a parameter (e.g., uniform ε or logistic β) to estimate the noise level. However, this assumption is not guaranteed to hold -- for example, an agent can lapse into an inattentive phase for a series of trials in the middle of otherwise low-noise performance. Assuming that noise is static could bias parameter and model identification. Here, we propose a new method to dynamically infer noise in choice behavior, under a model assumption that agents can transition between two discrete latent states (for example, attentive and noisy). Using four empirical datasets with diverse behavioral and modeling features, we demonstrate that our method improves model fit and that it can be easily incorporated into existing fitting procedures, including maximum likelihood estimation and hierarchical Bayesian modeling.


A generalized method for dynamic noise inference in modeling sequential decision-making

Despite evidence that learners are storing a lot more than simple words, it is still unclear what determines whether a phrase is stored holistically. For example, storage could be driven by either phrasal frequency or by the mutual predictability of a phrase's component parts . Further, the processing consequences of holistic storage are also unclear. Given that sentence processing is incremental, how does recognition of individual words give rise to recognition of holistically stored phrases? The present study examines these questions. Specifically, participants are presented with sentences that contain compound nouns in locally plausible or locally implausible contexts. We examine whether participants are able to overcome local implausibility effects more easily if the compound nouns are highly predictable. We find that predictability does not overcome local implausibility effects, suggesting that either predictability is not driving holistic storage or that holistic storage driven by predictability does not facilitate comprehension in our task.


Does Predictability Drive the Holistic Storage of Compound Nouns?

What determines children's production of syntactic alternations? This study takes up this question with the double PP construction in English as the test case (e.g., write [on the paper] [with this new pen]). Leveraging data of spontaneous child-parent interactions, we investigate the roles of dependency length and parent input frequency, with the latter being operationalized as lexical frequency and contextual predictability. We found that when child and parent data was combined, all three factors turned out to have significant predictive power, with dependency length having the most pronounced role. Results from the developmental trajectory of DLM as well as logistic regression analysis suggest that child production of constituent ordering preferences starts becoming more parent-like after the age of 30 months.


Double PP Constituent Ordering Preferences in English Early Child Language

Generative syntax and a consistently ordered count routine are both understood to have central roles in learning a number system. However, there has been little experimental exploration of how the diversity of each of these features alters the inductive landscape of number learning, as most empirical work has been constrained to correlational studies. We present a causal manipulation both of syntactic structures and counting procedures, using an artificial language paradigm. Our findings suggest that (1) learners have a greater facility with conjunctive over multiplicative rules of composition, (2) counting procedures help learners to recall words independently of syntax, and (3) predictable syntax helps learners to wield numerical concepts, independently of - and possibly despite - counting routines.


How does the syntax of counting affect learnability? Evidence from artificial language learning.

Languages exhibit a tremendous amount of variation in how they organise and order morphemes within words; however, regularities are also found. For example, gender and number inflectional morphology tend to appear together within a single affix, and when they appear in two separate affixes, gender marking tends to be placed closer to the stem than number. Formal theories of gender and number have been designed (in part) to explain these tendencies. However, determining whether the abstract representations hypothesised by these theories indeed drive the patterns we find cross-linguistically is difficult, if not impossible, based on the natural language data alone. In this study we use an artificial language learning paradigm to test whether the inferences learners make about the order of gender and number affixes—in the absence of any explicit information in the input—accord with formal theories of how they are represented. We test two different populations, English and Italian speakers, with substantially differ- ent gender systems in their first language. Our results suggest a clear preference for placing gender closest to the noun across these populations, across different types of gender systems, and across prefixing and suffixing morphology. These results expand the range of behavioural evidence for the role of cognitive representations in determining morpheme order.


Speakers' cognitive representations of gender and number morphology shape cross-linguistic tendencies in morpheme order

When faced with uncertainty, human observers maximize performance by integrating sensory information with learned task-relevant regularities. Does this behavior similarly occur in social settings? Here, we used a probabilistic decision-making task to explore how human behavior in an intergroup context is influenced by readily available but task-irrelevant social information (in the form of group membership) versus task-relevant reward information that could be learned over time. Across two experiments, we show that participants learned and utilized task-relevant regularities to inform their choices. We also show that human observers are not universally biased towards utilizing social information in all settings––participants learned to disregard social information when not relevant to the task at hand. However, learning about the utility of social information had a long-term influence on observers’ ability to subsequently learn and utilize available sources of information. Real-world intergroup contexts typically encompass situations and stimuli that have been previously experienced by the observer. Our findings highlight the powerful influence of learning in such contexts.


Next from CogSci 2023

Expectation, Representation, and Enactivism