Finding Answers to Questions: Bridging between Type-based and Computational Neuroscience Approaches (2025)

Abstract: The paper outlines an account of how the brain might process questions and answers in linguistic interaction, focusing on accessing answers in memory and combining questions and answers into propositions. To enable this, we provide an approximation of the lambda calculus implemented in the Semantic Pointer Architecture (SPA), a neural implementation of a Vector Symbolic Architecture. The account builds a bridge between the type-based accounts of propositions in memory (as in the treatments of belief by Ranta (1994) and Cooper (2023) and the suggestion for question answering made by Eliasmith (2013) question answering is described in terms of transformations of structured representations in memory providing an answer. We will take such representations to correspond to beliefs of the agent. On Cooper’s analysis, beliefs are considered to be types which have a record structure closely related to the structure which Eliasmith codes in vector representations (Larsson et al, 2023). Thus the act of answering a question can be seen to have a neural base in a vector transformation translatable in Eliasmith’s system to activity of spiking neurons and to correspond to using an item in memory (abelief) to provide an answer to the question.

## introduction understanding how semantic representations are instantiated in biological neural networks remains a fundamental challenge in cognitive science. the semantic pointer architecture (spa) has been used to build what is currently the world's largest functional brain model (spaun; @xcite @xcite , which includes perception, decision making, and motor control systems integrated in a cognitive model that is implemented in spiking neurons and captures detailed anatomical and physiological characteristics of the mammalian brain. the spa's structured representations are a neural implementation of a vector symbolic architecture (vsa; @xcite . the basic strategy of the spa, as we explain below, is to combine a vsa's algebraic structure on a vector space, with coding and decoding operations into ensembles of neurons. in this way, one can retain compositional analyses of natural language in a transparent way that contrasts with llm approaches, while retaining the robustness and the continuity vectors provide in semantic space. in this paper, we begin to address this challenge by focussing on the processing of questions and answers within a vsa approach, using a vsa approximation of the lambda calculus, and how it can be imlemented in neural network simulations. @xcite ) and eliasmith (2013, §4.4) discuss how structured representations encoded as vectors can be manipulated to support reasoning. in particular, on pp. 135ff eliasmith gives a simple suggestion of how some aspects of question answering could work. in terms of vectors it involves a convolution of a proposition expressed as superpositions of role-filler pairs with another vector corresponding to the question in order to obtain a vector which approximately encodes the answer. however, as @xcite himself notes, this model "does not have a solid linguistic justification". accordingly, vsa approaches to date lack a semantically motivated representation of question and question answering (qa). in particular, some aspects of question and answer processing are still missing, specifically (1) how a proposition containing an answer to a question can be found in memory and (2) how a question and an (elliptical) answer can be combined into a proposition upon hearing the answer. here we attempt to fill this gap by bridging between a type-based semantic theory of questions and the computational neuroscience vsa approach of the spa. the memory which is being probed for answers can be thought of as a collection of proposition encodings -in some cases long term memory (e.g., (1a)), in others working memory, at times a combination thereof (e.g., (1b)). of course, we may not have precisely the proposition we need in memory but may need to reason from a proposition we have to the proposition we need to answer the question. in this paper we will first explain the formal and conceptual backgrounds that are synthesized in this paper (section 2), including spa and type theory with records, the semantic framework whose entities underpin our discussion here. we will then explain the previous work on question answering in terms of flat role-filler structures (section 3). finally, we propose a more comprehensive account of questions and answers using our type-based approach, where we use a spa approximation of the lambda calculus to handle both question answering and semantic ellipsis resolution (section 4). section 5 presents a toy model implementation that illustrates and evaluates some features of the spa-ttr hybrid approach to qa. we conclude in section 6. ## background in this section we describe vector symbolic architectures that mediate between symbolic and distributive representations, neural networks that implement such representations, and finally typetheoretic semantics, specifically the framework type theory with records. in the following, we briefly discuss each of these backgrounds. ## previous work on question answering in hrr and spa modelling questions in the nef-spa means constructing a (biocognitive inspired) network that models a question-related task. @xcite illustrates such a network with question answering (qa). the idea is that the visual cortex provides statements and questions, both supplied as semantic pointers. statement pointers (e.g., "red ⊛ circle") represent "the world" and are sent to a memory population of neurons (working memory). question pointers pose questions to memory content (e.g., "red ′ " ≈ "what is red?"). basal ganglia monitors input and determines what kind of routing is appropriate to answer the question. the answer is sent to the clean-up memory and the memory item with highest similarity is sent to motor cortex (i.e., the answer is given). for instance, the simple statement "dog 52 chases cat 43 " could be represented as a flat role-filler structure as follows: a fixed set of semantic role labels provides information about where to find the desired information in semantic pointers and can be used for modeling questions. for example, who-questions address entities (persons, or animate beings in general) associated with a certain role in role-filler represen-tations, where addressing is captured in terms of unbinding. accordingly, asking the given statement (4) "who does dog 52 chase?" amounts to unbinding (4) with theme ′ and thereby retrieving an answer: secondly, a long-term memory along with the working memory will be used. routing through basal ganglia will then decide when to look into working or long-term memory to find an answer to a given question. qa with flat role-filler structures in the spa according to the pattern outlined above offers two insights: the first issue seems to be a limiting consequence of using the inverses of "label pointers" within flat role-filler structures as models for questions. this assumption is not shared in linguistic semantics, which instead uses functions. linguistic semantics in turn ignores the dynamicity of knowledge source retrieval and qa rehearsal in working memory. here we aim to reconcile this gap between neuro-computational and formal semantics. the role-filler approach to simple question answering outlined in section 3 assumes that you have already found the proposition which provides the answer and tells you where to look to find the answer within the proposition. a natural language question has to in addition give you material to find the proposition in memory. also, since answers are often semantically underspecified (and syntactically elliptical) and thus need to be understood in the context of a specific question, it must be possible to combine questions and (underspecified) answers into full propositions. an account of question and answer processing thus needs to address the following: we address these questions in sections 4.1 and 4.5, respectively. along the way, we also cover typechecking of answer candidates in section 4.2, double abstraction ("who chases who?") in section 4.3, and as a preparation for section 4.5 we also adapt the role-filler approach to extracting an answer from a question and a ptype, in section 4.4. we will represent questions as functions. the body of the function tells you what would be an appropriate proposition to find in memory. the abstraction in the function tells you where to look in that proposition. ## a simple proof-of-concept model to see if λ -abstracted question answering is feasible with neurons, we implemented the key steps from section 4.5 in nengo ( https://www.neng o.ai/ ). 5 the "knowledge base" m from which an answer is to be found consists of six role-filler semantic pointers (vectors of 128 dimensions) that correspond to the statements @xmath0, @xmath1, @xmath2, @xmath3, @xmath4, @xmath5. the inputs to the network are three questions, posed one after the other: 1. who does the cat like? 2. who does the dog chase? 3. who likes the cow? an answer for 1. can be found in p5 (i.e., mouse), an answer for 3. can be found in p6 (i.e., dog). 2., however, is ambiguous: possible answers are provided by p1 and p2 (i.e., cat or cow). the networks unbinds the body and the lambdapath from the question, substracts the lambdapath, and involutes both to retrieve the fragment answer (i.e., compares the resulting vector to the semantic pointers in clean-up memory). the result is shown in the bottom row in fig. 1 ("answer without memory clean-up") and shows that the model does not perform well: the semantic pointer corresponding to mouse is always returned as the answer, which is wrong in all but one case. apparently, the convolution operations introduce too much noise. to compensate for the noise, we introduced a memory clean-up step over the ptypes p1-p6 after unbinding the questions' bodies. the vector that is fed into an autoassociative clean-up memory over time is most similar to the propositions shown in the top row of fig. 1 ("memory input"). the clean-up step reinforces this input (see "memory output"). as a consequence, the network now returns vectors that are indeed most similar to the expected items (i.e., mouse, cow or cat, and dog, see "answer with memory clean-up"). note that the last answer is in close competition with the wrong fragment mouse, so a final cleanup may be required. ## conclusions and future work in this paper we have sketched an approach to abstraction, questions, and answering in spa which allows us to maintain compositional semantic analyses within a biologically plausible cognitive framework. this is of course just the first step for such an account which one should enhance with networks assessing whether a proposition resolves a question or constitutes a partial or indirect answer. work in vsas has not to date addressed quantification, but we take this as a first step to showing that this can be done. we hypothesize that the non-generalized quantifier ttr-based approach to quantification developed in @xcite affords a feasible path to this aim. another important step involves providing an account of working memory, in order to integrate the current insights of dynamic dialogical semantic frameworks such as kos @xcite , msdrt @xcite , and sdrt @xcite .