What does semantic tiling of the cortex tell us about semantics?

Recent use of voxel-wise modeling in cognitive neuroscience suggests that semantic maps tile the cortex. Although this impressive research establishes distributed cortical areas active during the conceptual processing that underlies semantics, it tells us little about the nature of this processing. While mapping concepts between Marr's computational and implementation levels to support neural encoding and decoding, this approach ignores Marr's algorithmic level, central for understanding the mechanisms that implement cognition, in general, and conceptual processing, in particular. Following decades of research in cognitive science and neuroscience, what do we know so far about the representation and processing mechanisms that implement conceptual abilities? Most basically, much is known about the mechanisms associated with: (1) features and frame representations, (2) grounded, abstract, and linguistic representations, (3) knowledge-based inference, (4) concept composition, and (5) conceptual flexibility. Rather than explaining these fundamental representation and processing mechanisms, semantic tiles simply provide a trace of their activity over a relatively short time period within a specific learning context. Establishing the mechanisms that implement conceptual processing in the brain will require more than mapping it to cortical (and sub-cortical) activity, with process models from cognitive science likely to play central roles in specifying the intervening mechanisms. More generally, neuroscience will not achieve its basic goals until it establishes algorithmic-level mechanisms that contribute essential explanations to how the brain works, going beyond simply establishing the brain areas that respond to various task conditions

Translating Neuralese

Several approaches have recently been proposed for learning decentralized deep multiagent policies that coordinate via a differentiable communication channel. While these policies are effective for many tasks, interpretation of their induced communication strategies has remained a challenge. Here we propose to interpret agents' messages by translating them. Unlike in typical machine translation problems, we have no parallel data to learn from. Instead we develop a translation model based on the insight that agent messages and natural language strings mean the same thing if they induce the same belief about the world in a listener. We present theoretical guarantees and empirical evidence that our approach preserves both the semantics and pragmatics of messages by ensuring that players communicating through a translation layer do not suffer a substantial loss in reward relative to players with a common language.Comment: Fixes typos and cleans ups some model presentation detail

Probabilistic Programming Concepts

A multitude of different probabilistic programming languages exists today, all extending a traditional programming language with primitives to support modeling of complex, structured probability distributions. Each of these languages employs its own probabilistic primitives, and comes with a particular syntax, semantics and inference procedure. This makes it hard to understand the underlying programming concepts and appreciate the differences between the different languages. To obtain a better understanding of probabilistic programming, we identify a number of core programming concepts underlying the primitives used by various probabilistic languages, discuss the execution mechanisms that they require and use these to position state-of-the-art probabilistic languages and their implementation. While doing so, we focus on probabilistic extensions of logic programming languages such as Prolog, which have been developed since more than 20 years

On staying grounded and avoiding Quixotic dead ends

The 15 articles in this special issue on The Representation of Concepts illustrate the rich variety of theoretical positions and supporting research that characterize the area. Although much agreement exists among contributors, much disagreement exists as well, especially about the roles of grounding and abstraction in conceptual processing. I first review theoretical approaches raised in these articles that I believe are Quixotic dead ends, namely, approaches that are principled and inspired but likely to fail. In the process, I review various theories of amodal symbols, their distortions of grounded theories, and fallacies in the evidence used to support them. Incorporating further contributions across articles, I then sketch a theoretical approach that I believe is likely to be successful, which includes grounding, abstraction, flexibility, explaining classic conceptual phenomena, and making contact with real-world situations. This account further proposes that (1) a key element of grounding is neural reuse, (2) abstraction takes the forms of multimodal compression, distilled abstraction, and distributed linguistic representation (but not amodal symbols), and (3) flexible context-dependent representations are a hallmark of conceptual processing