Christopher Baldassano

Tal Golan, Matthew Siegelman, Nikolaus Kriegeskorte et al.

Neural network language models can serve as computational hypotheses about how humans process language. We compared the model-human consistency of diverse language models using a novel experimental approach: controversial sentence pairs. For each controversial sentence pair, two language models disagree about which sentence is more likely to occur in natural text. Considering nine language models (including n-gram, recurrent neural networks, and transformer models), we created hundreds of such controversial sentence pairs by either selecting sentences from a corpus or synthetically optimizing sentence pairs to be highly controversial. Human subjects then provided judgments indicating for each pair which of the two sentences is more likely. Controversial sentence pairs proved highly effective at revealing model failures and identifying models that aligned most closely with human judgments. The most human-consistent model tested was GPT-2, although experiments also revealed significant shortcomings of its alignment with human perception.

Catherine Chen, Qihong Lu, Andre Beukers et al.

Through specific experiences, humans learn relationships underlying the structure of events in the world. Schema theory suggests that we organize this information in mental frameworks called "schemata," which represent our knowledge of the structure of the world. Generalizing knowledge of structural relationships to new situations requires role-filler binding, the ability to associate specific "fillers" with abstract "roles." For instance, when we hear the sentence "Alice ordered a tea from Bob," the role-filler bindings "Alice:customer," "tea:drink," and "Bob:barista" allow us to understand and make inferences about the sentence. We can perform these bindings for arbitrary fillers -- we understand this sentence even if we have never heard the names "Alice," "tea," or "Bob" before. In this work, we define a model as capable of performing role-filler binding if it can recall arbitrary fillers corresponding to a specified role, even when these pairings violate correlations seen during training. Previous work found that models can learn this ability when explicitly told what the roles and fillers are, or when given fillers seen during training. We show that networks with external memory can learn these relationships with fillers not seen during training and without explicitly labeled role-filler bindings, and show that analyses inspired by neural decoding can provide a means of understanding what the networks have learned.

Kiran Vodrahalli, Po-Hsuan Chen, Yingyu Liang et al.

Several research groups have shown how to correlate fMRI responses to the meanings of presented stimuli. This paper presents new methods for doing so when only a natural language annotation is available as the description of the stimulus. We study fMRI data gathered from subjects watching an episode of BBCs Sherlock [1], and learn bidirectional mappings between fMRI responses and natural language representations. We show how to leverage data from multiple subjects watching the same movie to improve the accuracy of the mappings, allowing us to succeed at a scene classification task with 72% accuracy (random guessing would give 4%) and at a scene ranking task with average rank in the top 4% (random guessing would give 50%). The key ingredients are (a) the use of the Shared Response Model (SRM) and its variant SRM-ICA [2, 3] to aggregate fMRI data from multiple subjects, both of which are shown to be superior to standard PCA in producing low-dimensional representations for the tasks in this paper; (b) a sentence embedding technique adapted from the natural language processing (NLP) literature [4] that produces semantic vector representation of the annotations; (c) using previous timestep information in the featurization of the predictor data.

Michelle R. Greene, Christopher Baldassano, Andre Esteva et al.

How do we know that a kitchen is a kitchen by looking? Relatively little is known about how we conceptualize and categorize different visual environments. Traditional models of visual perception posit that scene categorization is achieved through the recognition of a scene's objects, yet these models cannot account for the mounting evidence that human observers are relatively insensitive to the local details in an image. Psychologists have long theorized that the affordances, or actionable possibilities of a stimulus are pivotal to its perception. To what extent are scene categories created from similar affordances? Using a large-scale experiment using hundreds of scene categories, we show that the activities afforded by a visual scene provide a fundamental categorization principle. Affordance-based similarity explained the majority of the structure in the human scene categorization patterns, outperforming alternative similarities based on objects or visual features. We all models were combined, affordances provided the majority of the predictive power in the combined model, and nearly half of the total explained variance is captured only by affordances. These results challenge many existing models of high-level visual perception, and provide immediately testable hypotheses for the functional organization of the human perceptual system.