Processing Speed for Action and Semantic Memory

Previous research suggests that the processing of affordances may require more perceptually relevant information than words can provide (Surber et al., 2018; Chainay & Humphreys, 2002). The present study investigates this hypothesis with the shoebox task used in Bowers and Turner (2003). A list of 81 object nouns (targets) and associated features (primes: affordance, semantic, and non-associates) was compiled from the McRae, Cree, Seidenberg, and McNorgan (2005) norms. Affordances denote possibilities for action in relation to the object (e.g. chair – sit), whereas semantic features indicate definitional characteristics (e.g. chair – has legs). Affordances and semantic features served as primes in the present experiments. Primes were presented as words in all experiments. Participants decided if primes and targets could fit inside of a shoebox across three experiments. Experiment 1 presented target objects as words (i.e. the name of the object) or photographs (Brodeur, Dionne-Dostie, Montreuil, & Lepage, 2010). Experiment 2 presented target objects as photographs degraded by 1 of 3 levels (clear, medium blur, maximum blur). Experiment 3 presented target objects as photographs that began degraded and slowly became clear. Results for Experiments 1 and 2 showed a significant priming effect for affordances (i.e. affordance primed objects were responded to faster than objects primed with non-associate, as well as a significant effect of accuracy for affordance primed objects. Experiment 3 results showed a marginally significant effect of prime type on reaction time. These results are consistent with the idea that affordance perception is optimized for real-world stimuli

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

Deep Affordance-grounded Sensorimotor Object Recognition

It is well-established by cognitive neuroscience that human perception of objects constitutes a complex process, where object appearance information is combined with evidence about the so-called object "affordances", namely the types of actions that humans typically perform when interacting with them. This fact has recently motivated the "sensorimotor" approach to the challenging task of automatic object recognition, where both information sources are fused to improve robustness. In this work, the aforementioned paradigm is adopted, surpassing current limitations of sensorimotor object recognition research. Specifically, the deep learning paradigm is introduced to the problem for the first time, developing a number of novel neuro-biologically and neuro-physiologically inspired architectures that utilize state-of-the-art neural networks for fusing the available information sources in multiple ways. The proposed methods are evaluated using a large RGB-D corpus, which is specifically collected for the task of sensorimotor object recognition and is made publicly available. Experimental results demonstrate the utility of affordance information to object recognition, achieving an up to 29% relative error reduction by its inclusion.Comment: 9 pages, 7 figures, dataset link included, accepted to CVPR 201

INTUITIVE USER INTERFACES

The paper outlines an approach to the development of intuitively understandable on-screen user interfaces. Users have been found to explain the operation of equipment with screen based user interfaces in terms of handling “objects” and interacting with “agents” in a virtual “space”. These metaphorical descriptions may reflect general and fundamental principles of cognition that are rooted in the evolution of the human species. It is postulated that presentation of information on the interface as scenes, objects and actors can call upon instinctive capacities for direct perceptual information pickup, intuitive cognitive functions and natural behavioral tendencies. In order to initiate learning of complex functions that cannot be perceived directly may necessitate the use of symbolic information. This must be based on an analysis of the most appropriate way to map the new functions to the users’ prior conceptual understanding of technological objects and functions

The Emotional Facet of Subjective and Neural Indices of Similarity.

Emotional similarity refers to the tendency to group stimuli together because they evoke the same feelings in us. The majority of research on similarity perception that has been conducted to date has focused on non-emotional stimuli. Different models have been proposed to explain how we represent semantic concepts, and judge the similarity among them. They are supported from behavioural and neural evidence, often combined by using Multivariate Pattern Analyses. By contrast, less is known about the cognitive and neural mechanisms underlying the judgement of similarity between real-life emotional experiences. This review summarizes the major findings, debates and limitations in the semantic similarity literature. They will serve as background to the emotional facet of similarity that will be the focus of this review. A multi-modal and overarching approach, which relates different levels of neuroscientific explanation (i.e., computational, algorithmic and implementation), would be the key to further unveil what makes emotional experiences similar to each other

Archaeological knowledge and its representation an inter-disciplinary study of the problems of knowledge representation

The thesis is a study of archaeology viewed from a perspective informed by (a) social constructionist theory and pragmatism; (b) techniques of Belief and Knowledge Representation developed by Artificial Intelligence research and (c) the conception of history and historical practice propounded by the philosopher, historian and archaeologist, R.G. Collingwood. It is argued that Gibsonian affordances and von Uexkull's notion of the Umwelt, recently discussed by Rom Harré, provide the basis for a description and understanding of human action and agency. Further, belief and knowledge representation techniques embodied in Expert Systems and Intelligent Tutoring Systems provide a means of implementing models of human action which may bridge intentionality and process and thereby provide a unifying learning environment in which the relationships of language, social action and material transformation of the physical world can be explored in a unified way. The central claim made by the thesis is that Collingwood's logic (dialectic) of Question & Answer developed in 1917 as a hermeneutic procedure, may be seen as a fore-runner of Newell and Simon's Heuristic Search, and thereby amenable to modem approaches to problem solving. Collingwood's own approach to History/ Archaeology is grounded on many shared ideas with pragmatism and a social constructionist conception of mind and is conducted within a problem solving framework. Collingwood is therefore seen as a three-way bridge between Social Psychology, Artificial Intelligence and Archaeology. The thesis concludes that Social Psychology, Artificial Intelligence and Archaeology can be integrated through the use of Intelligent Tutoring Systems informed by a Collingwoodian perspective on Archaeology, Mind and History - construed as Mind's self-knowledge

A New Semantic-Based Tool Detection Method for Robots

Home helper robots have become more acceptable due to their excellent image recognition ability. However, some common household tools remain challenging to recognize, classify, and use by robots. We designed a detection method for the functional components of common household tools based on the mask regional convolutional neural network (Mask-R-CNN). This method is a multitask branching target detection algorithm that includes tool classification, target box regression, and semantic segmentation. It provides accurate recognition of the functional components of tools. The method is compared with existing algorithms on the dataset UMD Part Affordance dataset and exhibits effective instance segmentation and key point detection, with higher accuracy and robustness than two traditional algorithms. The proposed method helps the robot understand and use household tools better than traditional object detection algorithms

Prototypes, Location, and Associative Networks (PLAN): Towards a Unified Theory of Cognitive Mapping

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/98111/1/s15516709cog1901_1.pd

Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age

Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors' take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved