Subject-verb agreement in the presence of an attractor noun located between the main noun and the verb elicits complex behavior: judgments of grammaticality are modulated by the grammatical features of the attractor.For example, in the sentence "The girl near the boys likes climbing", the attractor (boys) disagrees in grammatical number with the verb (likes), creating a locally implausible transition probability.Here, we parametrically modulate the distance between the attractor and the verb while keeping the length of the sentence equal.We evaluate the performance of both humans and two artificial neural network models: both make more mistakes when the attractor is closer to the verb, but neural networks get close to the chance level while humans are mostly able to overcome the attractor interference.Additionally, we report a linear effect of attractor distance on reaction times.We hypothesize that a possible reason for the proximity effect is the calculation of transition probabilities between adjacent words.Nevertheless, classical models of attraction such as the cue-based model might suffice to explain this phenomenon, thus paving the way for new research.Data and analyses available at https://osf.io/d4g6k

We present a system for inductive program synthesis called DreamCoder, which inputs a corpus of synthesis problems each specified by one or a few examples, and automatically derives a library of program components and a neural search policy that can be used to efficiently solve other similar synthesis problems. The library and search policy bootstrap each other iteratively through a variant of "wake-sleep" approximate Bayesian learning. A new refactoring algorithm based on E-graph matching identifies common sub-components across synthesized programs, building a progressively deepening library of abstractions capturing the structure of the input domain. We evaluate on eight domains including classic program synthesis areas and AI tasks such as planning, inverse graphics, and equation discovery. We show that jointly learning the library and neural search policy leads to solving more problems, and solving them more quickly.

Successful approaches to program induction require a hand-engineered domain-specific language (DSL), constraining the space of allowed programs and imparting prior knowledge of the domain. We contribute a program induction algorithm that learns a DSL while jointly training a neural network to efficiently search for programs in the learned DSL. We use our model to synthesize functions on lists, edit text, and solve symbolic regression problems, showing how the model learns a domain-specific library of program components for expressing solutions to problems in the domain.