scholarly journals The minimalist grammar of action

2012 ◽  
Vol 367 (1585) ◽  
pp. 103-117 ◽  
Author(s):  
Katerina Pastra ◽  
Yiannis Aloimonos
Keyword(s):  
Computational Models ◽  
Reference Model ◽  
Action Learning ◽  
Human Action ◽  
Human Cognition ◽  
Neural Basis ◽  
Biologically Inspired ◽  
Language Structure ◽  
Minimalist Grammar ◽  
Temporal Sequences

Language and action have been found to share a common neural basis and in particular a common ‘syntax’, an analogous hierarchical and compositional organization. While language structure analysis has led to the formulation of different grammatical formalisms and associated discriminative or generative computational models, the structure of action is still elusive and so are the related computational models. However, structuring action has important implications on action learning and generalization, in both human cognition research and computation. In this study, we present a biologically inspired generative grammar of action, which employs the structure-building operations and principles of Chomsky's Minimalist Programme as a reference model. In this grammar, action terminals combine hierarchically into temporal sequences of actions of increasing complexity; the actions are bound with the involved tools and affected objects and are governed by certain goals. We show, how the tool role and the affected-object role of an entity within an action drives the derivation of the action syntax in this grammar and controls recursion, merge and move, the latter being mechanisms that manifest themselves not only in human language, but in human action too.

scholarly journals Biologically-Inspired Computational Neural Mechanism for Human Action/activity Recognition: A Review

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1169
Author(s):  
Bardia Yousefi ◽  
Chu Kiong Loo
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Activity Recognition ◽  
Computational Models ◽  
Neural Mechanism ◽  
Human Action ◽  
Action Perception ◽  
Biologically Inspired ◽  
Knowledge Based ◽  
Massive Information

Theoretical neuroscience investigation shows valuable information on the mechanism for recognizing the biological movements in the mammalian visual system. This involves many different fields of researches such as psychological, neurophysiology, neuro-psychological, computer vision, and artificial intelligence (AI). The research on these areas provided massive information and plausible computational models. Here, a review on this subject is presented. This paper describes different perspective to look at this task including action perception, computational and knowledge based modeling, psychological, and neuroscience approaches.

Sequential Difficulty Effects During Strategy Execution

2012 ◽  
Vol 59 (5) ◽  
pp. 295-301 ◽  
Author(s):  
Kim Uittenhove ◽  
Patrick Lemaire
Keyword(s):  
Computational Models ◽  
Preceding Trial ◽  
Human Cognition ◽  
Strategy Execution ◽  
Strategy Performance ◽  
Empirical Implications

In two experiments, we tested the hypothesis that strategy performance on a given trial is influenced by the difficulty of the strategy executed on the immediately preceding trial, an effect that we call strategy sequential difficulty effect. Participants’ task was to provide approximate sums to two-digit addition problems by using cued rounding strategies. Results showed that performance was poorer after a difficult strategy than after an easy strategy. Our results have important theoretical and empirical implications for computational models of strategy choices and for furthering our understanding of strategic variations in arithmetic as well as in human cognition in general.

scholarly journals Distributed Code for Semantic Relations Predicts Neural Similarity

2019 ◽  
Author(s):  
Jeffrey N. Chiang ◽  
Yujia Peng ◽  
Hongjing Lu ◽  
Keith J. Holyoak ◽  
Martin M. Monti
Keyword(s):  
Computational Models ◽  
Semantic Network ◽  
Building Blocks ◽  
Semantic Relations ◽  
Human Cognition ◽  
Sequential Presentation ◽  
Specific Content ◽  
Distributed Representations ◽  
Relational Similarity ◽  
The Brain

AbstractThe ability to generate and process semantic relations is central to many aspects of human cognition. Theorists have long debated whether such relations are coded as atomistic links in a semantic network, or as distributed patterns over some core set of abstract relations. The form and content of the conceptual and neural representations of semantic relations remains to be empirically established. The present study combined computational modeling and neuroimaging to investigate the representation and comparison of abstract semantic relations in the brain. By using sequential presentation of verbal analogies, we decoupled the neural activity associated with encoding the representation of the first-order semantic relation between words in a pair from that associated with the second-order comparison of two relations. We tested alternative computational models of relational similarity in order to distinguish between rival accounts of how semantic relations are coded and compared in the brain. Analyses of neural similarity patterns supported the hypothesis that semantic relations are coded, in the parietal cortex, as distributed representations over a pool of abstract relations specified in a theory-based taxonomy. These representations, in turn, provide the immediate inputs to the process of analogical comparison, which draws on a broad frontoparietal network. This study sheds light not only on the form of relation representations but also on their specific content.SignificanceRelations provide basic building blocks for language and thought. For the past half century, cognitive scientists exploring human semantic memory have sought to identify the code for relations. In a neuroimaging paradigm, we tested alternative computational models of relation processing that predict patterns of neural similarity during distinct phases of analogical reasoning. The findings allowed us to draw inferences not only about the form of relation representations, but also about their specific content. The core of these distributed representations is based on a relatively small number of abstract relation types specified in a theory-based taxonomy. This study helps to resolve a longstanding debate concerning the nature of the conceptual and neural code for semantic relations in the mind and brain.

Evolutionary Cognitive Archaeology

Anthropology ◽  
2020 ◽  
Author(s):  
Frederick L. Coolidge ◽  
Thomas Wynn
Keyword(s):  
Evolutionary Psychology ◽  
Cognitive Abilities ◽  
Homo Sapiens ◽  
Human Cognition ◽  
Evolutionary Development ◽  
Last Common Ancestor ◽  
Archaeological Remains ◽  
Neural Basis ◽  
Modern Humans ◽  
Cognitive Archaeology

Cognitive archaeology may be divided into two branches. Evolutionary cognitive archaeology (ECA) is the discipline of prehistoric archaeology that studies the evolution of human cognition. Practitioners are united by a methodological commitment to the idea that archaeological traces of past activity provide access to the minds of the agents responsible. The second branch, ideational cognitive archaeology, encompasses archaeologists who strive to discover the meaning of symbolic system, primarily through the analysis of iconography. This approach differs from ECA in its epistemology, historical roots, and citation universes, and focuses on comparatively recent time periods (after 10,000 years ago). Evolutionary cognitive archaeologists are concerned with the nature of cognition itself, and its evolutionary development from the time of the last common ancestor with chimpanzees to the final ascendancy of modern humans at the end of the Pleistocene. Although ECA methods are primarily archaeological, its theoretical grounding is in the cognitive sciences, including cognitive psychology, neuropsychology, and cognitive neuroscience. It is by its nature interdisciplinary. ECA differs from the allied discipline of evolutionary psychology in several important respects. Methodologically, ECA is a macroevolutionary science that studies physical evidence of past human cognition, including archaeological and fossil remains. Evolutionary psychology relies heavily on reverse engineering from controlled experiments on living humans. Theoretically, ECA is more eclectic, drawing on a variety of cognitive and evolutionary models; evolutionary psychology is committed to a neo-Darwinian, selectionist understanding of evolutionary change. The two approaches tend to study different components of human mental life, but are not inherently contradictory. ECA practitioners reconstruct prehistoric activities using well-established archaeological methods and techniques, including morphological analysis of artifacts to identify action sequences and decision patterns, functional analyses (e.g., microwear) to identify use patterns, and spatial patterns within sites to recognize activity loci (e.g., hearths). An increasingly important method is the actualistic recreation of prehistoric technologies to identify features not preserved in the archaeological remains. Neuroarchaeologists enhance such actualistic research by imaging the brains of the participants (most typically using fMRI), an approach that also contributes directly to cognitive science’s understanding of the neural basis of technical cognition. ECA practitioners take two non-mutually exclusive approaches to documenting human cognitive evolution. The first approach enriches the understanding of specific hominin taxa (i.e., Homo sapiens and their direct ancestors since 6 million years ago) by providing accounts of their cognitive life worlds, or by contrasting two taxa with one another. This approach is famously exemplified by attempts to contrast the abilities of Neandertals with those of modern humans. The second approach traces the evolution of specific cognitive abilities from the first appearance of stone tools 3.3 million years ago to the emergence of city-states 5,000 years ago. The range of accessible cognitive abilities is limited by the nature of archaeological remains, but evolutionary cognitive archaeologists have been able to trace developments in spatial cognition, memory, cognitive control, technical expertise, theory of mind, aesthetic cognition, symbolism, language, and numeracy.

Distributed Code for Semantic Relations Predicts Neural Similarity during Analogical Reasoning

2020 ◽  
pp. 1-13 ◽  
Author(s):  
Jeffrey N. Chiang ◽  
Yujia Peng ◽  
Hongjing Lu ◽  
Keith J. Holyoak ◽  
Martin M. Monti
Keyword(s):  
Analogical Reasoning ◽  
Computational Models ◽  
Semantic Network ◽  
Semantic Relations ◽  
Human Cognition ◽  
Sequential Presentation ◽  
Frontoparietal Network ◽  
Neural Activities ◽  
Core Set ◽  
Superior Parietal Cortex

The ability to generate and process semantic relations is central to many aspects of human cognition. Theorists have long debated whether such relations are coarsely coded as links in a semantic network or finely coded as distributed patterns over some core set of abstract relations. The form and content of the conceptual and neural representations of semantic relations are yet to be empirically established. Using sequential presentation of verbal analogies, we compared neural activities in making analogy judgments with predictions derived from alternative computational models of relational dissimilarity to adjudicate among rival accounts of how semantic relations are coded and compared in the brain. We found that a frontoparietal network encodes the three relation types included in the design. A computational model based on semantic relations coded as distributed representations over a pool of abstract relations predicted neural activities for individual relations within the left superior parietal cortex and for second-order comparisons of relations within a broader left-lateralized network.

Models of Semantic Memory

2015 ◽  
Author(s):  
Michael N. Jones ◽  
Jon Willits ◽  
Simon Dennis
Keyword(s):  
Semantic Memory ◽  
Computational Models ◽  
Human Cognition ◽  
Perceptual Information ◽  
Formal Models ◽  
Compositional Semantics ◽  
Semantic Modeling ◽  
Fundamental Component ◽  
Future Developments ◽  
Semantic Models

Meaning is a fundamental component of nearly all aspects of human cognition, but formal models of semantic memory have classically lagged behind many other areas of cognition. However, computational models of semantic memory have seen a surge of progress in the last two decades, advancing our knowledge of how meaning is constructed from experience, how knowledge is represented and used, and what processes are likely to be culprit in disorders characterized by semantic impairment. This chapter provides an overview of several recent clusters of models and trends in the literature, including modern connectionist and distributional models of semantic memory, and contemporary advances in grounding semantic models with perceptual information and models of compositional semantics. Several common lessons have emerged from both the connectionist and distributional literatures, and we attempt to synthesize these themes to better focus future developments in semantic modeling.

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