Speech-language pathologists’ approaches to augmentative and alternative communication (AAC) assessments

Bilingual Aphasia Assessment and Treatment

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.


Learning and Information Use in an Intergroup Context

Multimodal binding as mereological co-constituency

Spatial reasoning is an inherent aspectof everyday life. For example, spatial reasoning supports young children playing with blocks, older children learning about positioning on a sports field,and adults navigatingwhere they need to go. Spatial reasoning is also a critical aspect of STEM (science, technology, engineering,mathematics,e.g., Wai, Lubinski, & Benbow, 2009). For example, chemists imagine molecules rotating to compare structure, and physicistsreason aboutthe influence of invisible forces on magnetic fields. As can be observed in the examples above, there are a wide range of spatial tasks. Spatial reasoning in real-world contexts can be complex and multifaceted(Atit et al., 2020). How individuals engage in spatial reasoning is highly context-specific, influenced by task demands, novelty of task, and prior knowledge (Uttal & Cohen, 2012). Unfortunately, most of what we know about spatial reasoning is driven by psychological research using psychometric tasks designed to isolate specific spatial skills,independent of context (for review see Atitet al.,2020). Without examining variability across contexts, models of spatial reasoning are incomplete and limited in scope (Resnick & Stieff, in press). In this symposium, we willexplore different contexts in which spatial reasoning is learned. In doing so, we will raise theoretical questions around universal aspects of spatial cognition as well as practical considerations, such as how and when spatial learning should be included in education. This work is important because spatial reasoning can be learned and improved (Uttal et al., 2013), and improvements in spatial reasoning causally support improvements in STEM (e.g., Hawes et al., 2022; Sorby, 2009).Although all four talks center on the role of context in spatial learning, the presenters are drawn from different disciplines, methodologies, and perspectives.Mary Hegartywillbegin by characterizingthe disconnect between psychological research in spatial reasoning and the spatial reasoning involved in complex STEM tasks. The next three talks unpack specific everyday STEM contexts where spatial reasoningis learned. Ilyse Resnick will present work in the learning sciences on shared book reading. David Uttal, a cognitive and developmental psychologist, will present on geographic information systems. Tom Lowrie, a mathematics education researcher, will present on the importance of embedded authentic tasks in spatial and STEM learning.

Spatial Reasoning: The Role of Context in Learning

Helping is an inherently cooperative behavior, but the cognitive mechanisms underlying this behavior remain relatively underexplored. In this paper, we introduce a novel gamified paradigm for understanding a variety of cognitive behaviors associated with helping. Principals are assigned secret goals in a block-based grid (e.g., move all blue blocks to room C), and helpers can either pass their turn or make a move that could help the principal. We show that principals make useful and pragmatic first moves and helpers accumulate evidence over time before initiating a helpful move. We also introduce a preliminary set of computational models based on recursive pragmatic inference and utility maximization that attempt to account for these behavioral findings.


Help me help you: A computational model for goal inference and action planning

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

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

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

Modeling Rule Learning via Analogical Generalization

This symposia brings together researchers, with backgrounds in both academia and industry, to discuss how to best foster theory development, basic science, and tackle applied problems.

How should industry influence cognitive science?

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.


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Speech-language pathologists’ approaches to augmentative and alternative communication (AAC) assessments

Bilingual Aphasia Assessment and Treatment

Learning and Information Use in an Intergroup Context

Multimodal binding as mereological co-constituency

Spatial Reasoning: The Role of Context in Learning

Help me help you: A computational model for goal inference and action planning

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

Double PP Constituent Ordering Preferences in English Early Child Language

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

Modeling Rule Learning via Analogical Generalization

How should industry influence cognitive science?

A description-experience gap in face stereotyping

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