Distributional Semantics Models (DSMs) are a primary method for distilling semantic information from corpora. However,a key question remains: What types of semantic relations do DSMs detect? Prior work has addressed this question using alimited set of ratings that typically are either amorphous (association norms) or restricted to semantic similarity (SimLex,SimVerb). We tested four DSMs (SkipGram, CBOW, GloVe, PPMI) using multiple hyperparameters on a theoretically-motivated, rich set of relations involving words from multiple syntactic classes spanning the abstract-concrete continuum(21 sets of ratings). Results show wide variation in the DSMs’ ability to account for the ratings, and that hyperparametertuning buys comparatively little for improving correlations. For CBOW and SkipGram, we included word and contextembeddings. For SkipGram, there was a marked improvement in simulating the human data by averaging them. Ourresults yield important insights into the types of semantic relations that are captured by DSMs.

Biological systems are capable of acting in a shared environment to produce emergent, self-organized behavior that is the result of the constraints imposed by local interactions– such as bird flocking or ant swarming behavior. These examples present minimal demands for a shared-intention between co-actors, whereas other instances necessitate the formation of a shared goal. In these goal-directed tasks, how much of the observed complexity can be explained by the constraints imposed by both the environment and adherence to the shared task goal? This paper begins to investigate this question by presenting results from a two-person cooperative “shepherding” task first developed in Nalepka et al. (2017) but with fewer constraints. Results provide further evidence that the emergent behavior is the result of the constraints imposed by the task. The included task-dynamic model suggests a general model that can be used to understand multiagent herding behavior in a variety of contexts.

The current project examined how changes to task constraints impacted the behavioral dynamics of an interpersonal collision avoidance task previously examined and modeled by Richardson and colleagues (2015). Overall, the results demonstrate that decreasing the cost associated with colliding influences the stability and symmetry of the movement dynamics observed between co-actors in a manner consistent with those predicted by the Richardson et al. (2015), collision avoidance model. The current study therefore provides evidence that the behavioral dynamics that shape interpersonal or joint-action behavior are not only defined by the physical and informational properties of a task, but also by the strength and importance of the shared task goal.

To what degree does semantic foraging probe semantic network structure? We use a combination of foraging experiments (animals, concrete nouns) and simulations on networks based on nine approaches to semantic similarity to address this question. In data and simulations, we find a significant bias towards naming semantically similar items, and significant correlations between inter-naming time and semantic distance. In previous foraging experiments, a roughly power law distribution with a Lévy range exponent was found in the distribution of inter-naming intervals. We find the value of this exponent is not universal but is sensitive to the search space size in that the exponent decreases (moving further into the Lévy range) as the number of nameable items is exhausted. Moreover, these exponents are not unique to semantic networks but appear in censored random walks on other graphs. Our combined experimental results and simulations provide insights into the topology of semantic memory.