Continuity in cognition

Designing for continuous interaction requires designers to consider the way in which human users can perceive and evaluate an artefact’s observable behaviour, in order to make inferences about its state and plan, and execute their own continuous behaviour. Understanding the human point of view in continuous interaction requires an understanding of human causal reasoning, of the way in which humans perceive and structure the world, and of human cognition. We present a framework for representing human cognition, and show briefly how it relates to the analysis of structure in continuous interaction, and the ways in which it may be applied in design

Soros’s Reflexivity Concept in a Complex World: Cauchy Distributions, Rational Expectations, and Rational Addiction

George Soros makes an important analytical contribution to understanding the concept of reflexivity in social science by explaining reflexivity in terms of how his cognitive and manipulative causal functions are connected to one another by a pair of feedback loops (Soros, 2013). Fallibility, reflexivity and the human uncertainty principle. Here I put aside the issue of how the natural sciences and social sciences are related, an issue he discusses, and focus on how his thinking applies in economics. I argue that standard economics assumes a ‘classical’ view of the world in which knowledge and action are independent, but that we live in a complex reflexive world in which knowledge and action are interdependent. I argue that Soros\u27s view provides a reflexivity critique of the efficient market hypothesis seen as depending on untenable claims about the nature of random phenomena and the nature of economic agents. Regarding the former, I develop this critique in terms of Cauchy distributions; regarding the latter I develop it in terms of rational expectations and rational addiction reasoning

Representation and reasoning: a causal model approach

How do we represent our world and how do we use these representations to reason about it? The three studies reported in this thesis explored different aspects of the answer to this question. Even though these investigations offered diverse angles, they all originated from the same psychological theory of representation and reasoning. This is the idea that people represent the world and reason about it by constructing dynamic qualitative causal networks. The first study investigated how mock jurors represent criminal evidence and reason with such representations. The second study examined how people represent the causes of a complex environmental problem and how their individual representations are directly linked to how they reason about the issue. The third and final study inspected how people represent causal loops and reason in accordance with these cyclical representations. These studies suggest that people do represent the world by arranging evidence, causes, or pieces of information into a causal network. In addition, the studies support the idea that these networks are of a qualitative nature. All three studies also indicated that people update their representations in accordance to a dynamic world. The studies specifically explored how reasoning, and therefore judgment is linked to these representations. The thesis discusses the theoretical implications of these and other findings for the causal model framework as well as for cognitive science more generally. Related practical implications include the importance of understanding naïve causal models for applied fields such as legal decision-making and environmental psychology

Mechanisms as Modal Patterns

Philosophical discussions of mechanisms and mechanistic explanation (e.g., Bechtel 2006; Bechtel and Abrahamsen 2005; Craver 2007; Craver and Darden 2014; Darden 2006) have often been framed by contrast to laws and deductive-nomological explanation. A more adequate conception of lawfulness and nomological necessity, emphasizing the role of modal considerations in scientific reasoning, circumvents such contrasts and enhances understanding of mechanisms and their scientific significance. The first part of the paper sketches this conception of lawfulness, drawing upon Haugeland (1998), Lange (2000, 2007), and Rouse (2015). This conception emphasizes the role of lawful stability under relevant counterfactual suppositions in scientific reasoning across the sciences, in place of traditional conceptions of law that are primarily confined to the physical sciences. It also extends lawful stability beyond verbally or mathematically expressed law-statements, to encompass other ways of conjoining patterns in the world with scientific pattern recognition. The remainder of this paper shows how and why mechanisms constructively exemplify this conception of lawfulness in scientific practice: • Mechanisms are robust, counterfactually stable and inductively projectible patterns, even though they are not exceptionless “laws of nature”. • Mechanistic explanations often take non-verbal forms, which consequently resist philosophical inclinations to semantic ascent, but understanding lawfulness in terms of counterfactually stable pattern recognition accounts for these ways in which scientific understanding outruns the expressive capacities of natural languages; • Mechanisms are sometimes characterized as real (“ontic”) patterns in the world, and sometimes as epistemic representations; understanding mechanisms as modal patterns shows why both conceptions are needed, as mutually supportive. • Mechanisms are typically open-ended, and only partially specified, in ways open to and directive toward further articulation and revision (“discovery”). Understanding mechanisms as modal patterns incorporates this aspect of mechanistic understanding within a broader conception of scientific understanding as embedded in research practice, rather than in bodies of knowledge extracted from it. • Mechanistic explanation has often been placed on the causal side of an opposition between causal and nomological explanation, but understanding mechanisms as modal patterns helps overcome that opposition, and contributes to a pluralist conception of causal relations and their characteristic forms of counterfactual invariance. • The recognition of mechanisms as modal patterns allows for a new way to think about the relations among distinct levels of a mechanistic hierarchy, and the broader scientific significance of mechanistic understanding

EgoTaskQA: Understanding Human Tasks in Egocentric Videos

Understanding human tasks through video observations is an essential capability of intelligent agents. The challenges of such capability lie in the difficulty of generating a detailed understanding of situated actions, their effects on object states (i.e., state changes), and their causal dependencies. These challenges are further aggravated by the natural parallelism from multi-tasking and partial observations in multi-agent collaboration. Most prior works leverage action localization or future prediction as an indirect metric for evaluating such task understanding from videos. To make a direct evaluation, we introduce the EgoTaskQA benchmark that provides a single home for the crucial dimensions of task understanding through question-answering on real-world egocentric videos. We meticulously design questions that target the understanding of (1) action dependencies and effects, (2) intents and goals, and (3) agents' beliefs about others. These questions are divided into four types, including descriptive (what status?), predictive (what will?), explanatory (what caused?), and counterfactual (what if?) to provide diagnostic analyses on spatial, temporal, and causal understandings of goal-oriented tasks. We evaluate state-of-the-art video reasoning models on our benchmark and show their significant gaps between humans in understanding complex goal-oriented egocentric videos. We hope this effort will drive the vision community to move onward with goal-oriented video understanding and reasoning.Comment: Published at NeurIPS Track on Datasets and Benchmarks 202

EgoTV: Egocentric Task Verification from Natural Language Task Descriptions

To enable progress towards egocentric agents capable of understanding everyday tasks specified in natural language, we propose a benchmark and a synthetic dataset called Egocentric Task Verification (EgoTV). EgoTV contains multi-step tasks with multiple sub-task decompositions, state changes, object interactions, and sub-task ordering constraints, in addition to abstracted task descriptions that contain only partial details about ways to accomplish a task. We also propose a novel Neuro-Symbolic Grounding (NSG) approach to enable the causal, temporal, and compositional reasoning of such tasks. We demonstrate NSG's capability towards task tracking and verification on our EgoTV dataset and a real-world dataset derived from CrossTask (CTV). Our contributions include the release of the EgoTV and CTV datasets, and the NSG model for future research on egocentric assistive agents

What are the essential cognitive requirements for prospection (thinking about the future)?

Placing the future center stage as a way of understanding cognition is gaining attention in psychology. The general modern label for this is “prospection” which refers to the process of representing and thinking about possible future states of the world. Several theorists have claimed that episodic and prospective memory, as well as hypothetical thinking (mental simulation) and conditional reasoning are necessary cognitive faculties that enable prospection. Given the limitations in current empirical efforts connecting these faculties to prospection, the aim of this mini review is to argue that the findings show that they are sufficient, but not necessary for prospection. As a result, the short concluding section gives an outline of an alternative conceptualization of prospection. The proposal is that the critical characteristics of prospection are the discovery of, and maintenance of goals via causal learning

Beliefs as inner causes: the (lack of) evidence

Many psychologists studying lay belief attribution and behavior explanation cite Donald Davidson in support of their assumption that people construe beliefs as inner causes. But Davidson’s influential argument is unsound; there are no objective grounds for the intuition that the folk construe beliefs as inner causes that produce behavior. Indeed, recent experimental work by Ian Apperly, Bertram Malle, Henry Wellman, and Tania Lombrozo provides an empirical framework that accords well with Gilbert Ryle’s alternative thesis that the folk construe beliefs as patterns of living that contextualize behavior