Sophie(Xing) Su

Sophie(Xing) Su

WUSTL PBS Grad,Haskins, Cornell

A Deep Dive into the Construction-Integration Model and Event Representation Theories

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Delving deeper into the Construction-Integration (C-I) model(Kintsch, 1988), a framework proposed by Kintsch, we explore its multifaceted approach to understanding text comprehension. This model raises a fundamental question: How do we form cognitive representations based on textual descriptions? Kintsch proposed a bottom-up process, examining its connection to the representation of visual events and contemplating its implications for discourse comprehension.

At the heart of discourse comprehension lies the concept of constructing a coherent representation of the discourse, enabling various computational analyses. After successfully comprehending a text, one might reasonably expect to perform a range of cognitive tasks: answering questions about the text, recalling or summarizing its content, verifying statements as true or false, or rephrasing the text in one’s own words.

But how are these intricate representations formed? The Construction-Integration model provides a two-tiered approach: construction at the propositional level followed by the modification of feature relationships during the integration process.

In the initial construction phase, the process unfolds in four meticulous steps:

  1. Formation of Concepts and Propositions: Directly corresponding to the linguistic input. For instance, in the sentence “The lawyer discussed the case with the judge,” the concepts of discussion, lawyer, judge, and case are represented as discuss[lawyer, judge, case].
  2. Elaboration of Elements: Selecting a few closely associated neighbors from the general knowledge network to elaborate on each element.
  3. Inference of Additional Propositions: Expanding the representation by inferring logical propositions based on the context and content.
  4. Assignment of Connection Strengths: Defining the strength of connections between pairs of elements to form a coherent structure.

The outcome of these steps is a raw and, initially, incoherent set of representations. At this stage, elements associated with the text are included indiscriminately, leading to inconsistencies and irrelevant details. It is during the integration phase that the model refines this raw material. Integration is assumed to be a cyclical process, predominantly occurring at the level of short sentences or phrases. Within each cycle, a new set of propositional representations is constructed, incorporating elements from the short-term memory buffer of the previous cycle. Once construction is complete, integration commences, allowing activation to spread throughout the system until equilibrium is achieved. This process of integration is realized through the repeated multiplication of an activation vector, representing the initial activation values of all nodes in the network, with a connectivity matrix—a process akin to Hebbian learning.

How does this relate to event representations? Prominent theories suggest that the comprehension process includes both bottom-up feature extraction and top-level goal representations. For instance, Kuperberg’s model of event representation(Kuperberg, 2021) posits that, in addition to representing events, there are goal dimensions that constrain the building of representations. The Event Segmentation Theory(Zacks et al., 2007) proposes that individuals construct working event models and continually predict future events; significant deviations from these predictions trigger the update of the model. However, a key characteristic of the Construction-Integration model is its purely bottom-up nature, devoid of predictive elements. As a result, high-level situational representations are not deemed necessary.

Despite this, it seems improbable that an efficient model of event representation would defer integration until the conclusion of a series of events. The question then arises: Is an additional representation necessary at the end of this process, or can some form of priming, akin to lexical priming prevalent in lexical detection tasks, suffice?

By dissecting these processes and their interconnectedness with event representation theories, the Construction-Integration model offers a comprehensive framework for understanding the intricacies of text and discourse comprehension.

References:

Kintsch, W. (1988). The Role of Knowledge in Discourse Comprehension: A Construction-Integration Model. Kuperberg, G. R. (2021). Tea With Milk? A Hierarchical Generative Framework of Sequential Event Comprehension. Topics in Cognitive Science, 13(1), 256–298. https://doi.org/10.1111/tops.12518 Zacks, J. M., Speer, N. K., Swallow, K. M., Braver, T. S., & Reynolds, J. R. (2007). Event perception: A mind-brain perspective. Psychological Bulletin, 133(2), 273–293. https://doi.org/10.1037/0033-2909.133.2.273

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Specific Aims: Action recognition, a pivotal component of computer vision, finds diverse applications across several fields such as smart security (Hu et al., 2007), human-robot interaction (Akkaladevi & Heindl, 2015) and virtual reality (Bates et al., 2017). This technology plays a crucial role in enhancing surveillance systems by enabling efficient monitoring and security through the prompt detection of unusual behaviors. Despite significant advancements in human action recognition, state-of-the-art algorithms still face challenges, such as misclassifications caused by background noise mistaken for signals and the scarcity of annotated data. Moreover, two foundational issues call for non-technical solutions: intra- and inter-class variations in action labels, where the same action may be performed differently depending on motor capabilities, and the similarities across different action categories. For example, “running” and “walking” involve similar human motion patterns. Another challenge is regarding to the action vocabulary. Actions can also be categorized into different levels—movements, atomic actions, composite actions, events—creating an action hierarchy. Complex actions at higher levels of the hierarchy can be decomposed into a combination of actions at lower levels. Defining and analyzing these different types of actions is crucial. Additionally, humans can often effortlessly solve these challenges, highlighting the importance of examining the discrepancies between machine performance and human capabilities to improve the design of machine action recognition systems. Aim 1: Understand and quantify the Action Invariance attributes in State-of-the-Art Action Recognition Algorithms. This project will be examining the last layers’ embeddings from various SOTA action recognition networks with different kind of architectures ranging from supervised to unsupervised and self-supervised learning algorithms. These networks will be analyzed based on their performance when the input videos depict the same action executed from different angles and in diverse contexts with varying levels of abstraction. We will leverage the META dataset(Bezdek et al., 2022)—a large-scale, well-characterized collection of stimuli representative of such activities. This dataset consists of a structured and thoroughly instrumented set of extended event sequences performed in naturalistic settings, complete with hand-annotated timings of high-level actions. Additionally, it includes sequences of humans performing similar actions in a highly controlled manner, devoid of actual projects. Specifically, a Representation Dissimilarity Matrix analysis will be applied to assess the action invariance capabilities of these state-of-the-art algorithms. This approach will provide a quantitative measure of the action invariance features within the SOTA action recognition models. Furthermore, we will explore the relationship between these invariance abilities and the success of action classification. Aim 2: Examine the Correspondence Between Artificial Neural Networks and Biological Neural Representations of Actions. Participants will watch movies depicting everyday activities while undergoing simultaneous neural recording in an MRI scanner. The activation patterns in the brain networks will be compared with those produced by state-of-the-art action recognition algorithms. This comparison aims to investigate both the convergence and divergence of these patterns. Significance and Innovation: One of the principal objectives of artificial intelligence research is to develop machines that can accurately comprehend human actions and intentions, thereby enhancing their ability to assist us. Consider a scenario where a patient performs rehabilitation exercises at home, monitored by a robotic assistant. This robot is not only capable of recognizing the patient’s movements but also evaluates the accuracy of the exercises and prevents potential injuries. Such advanced technology could significantly reduce the need for in-person therapy visits, decrease medical expenses, and facilitate the feasibility of remote rehabilitation. Action recognition algorithms are pivotal to numerous practical applications, particularly in the realms of sports and entertainment where they enhance viewer engagement through detailed analytics and enriched interactive experiences. In healthcare, these algorithms are integral to precise patient monitoring and support physical therapy by ensuring movements are performed correctly. Cutting-edge technologies in this field (Feichtenhofer et al., 2017; Wang et al., 2016) have substantially minimized the need for manual video data analysis, providing insights into both present and predicted future activities within video sequences. 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A primary objective of this research is to provide a detailed and precise quantification of the challenges current state-of-the-art action recognition technologies face in handling high intra- and inter-class variations. In addition, by exploring how biological systems overcome these issues, we aim to enhance and advance the development of future action recognition technologies. The invariance problem is not exclusive to machine models for action recognition.. Convolutional Neural Networks (CNNs) have significantly advanced the field of image recognition, yet they encounter notable challenges with the object invariance problem. This issue arises when objects are presented in slightly different ways or when negligible, seemingly insignificant features are introduced. In such scenarios, CNNs often struggle to maintain consistent classification accuracy. For instance, minor variations in object orientation, scale, or background changes can disproportionately affect the CNN’s ability to recognize the object correctly (Kar et al., 2019).
A key innovation of this research proposal is the attempt to address this computational vision model challenge by examining human behavior and biological systems’ solutions to similar problems. As a social species, humans rely on recognizing the actions of others in their everyday lives. We quickly and effortlessly extract action information from rich dynamic stimuli, despite variations in the visual appearance of action sequences due to transformations such as changes in size, position, actor, and viewpoint (e.g., determining whether a person is running or walking towards us, regardless of the direction they are coming from). This ability emerges early in development. Studies involving eye-gazing in four-month-old infants(Woodward & Sommerville, 2000) have demonstrated that infants possess an innate understanding of actions. They tend to gaze longer at the conclusion of action sequences that end unexpectedly compared to those that conclude as anticipated. Even when actions are minimally represented, such as through joint-position skeleton representations, Johansson discovered that individuals could recognize the action plans quickly and without error. Those behavioral findings indicated that challenges deemed significant in machine models are not as problematic for human models. This project aims to first quantify the action invariance capabilities of machine models and then explore human neural representations and strategies that could potentially address these challenges. Existing literature suggests that the existence of mirror neurons could be a potential solution to the action invariance problem. Mirror neurons(Gallese & Goldman, 1998), first discovered in the premotor cortex of macaques, are known to fire both when the animal performs an action and when it observes the same action performed by others. This discovery has been extrapolated to humans, suggesting a neurological basis for action understanding and imitation. Research indicates that mirror neurons may play a crucial role in not just recognizing but also predicting and interpreting the actions of others, thereby bridging the gap in action recognition across different perspectives and contexts. This neural mechanism underscores the potential for mirror neurons to facilitate a more robust understanding of action invariance, enhancing our ability to develop more accurate and adaptable action recognition technologies.

Adaptive Behavior and Environmental Influence

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The central debate between Davachi s and Zack’s stimulus types is not merely about one being more naturalistic than the other. Consider, for instance, the inherent oddity in signing up for a psychological experiment—our study primarily involves participants labeled as ‘WIRED,’ consisting of undergraduate students. Conversely, sequences of pictures with varying colored backgrounds might deviate from our everyday experiences, yet one might argue that, from a process perspective, both types of stimuli are processed by similar biological computations and could generalize to more naturalistic stimuli.