scholarly journals Bumblebees Use Sequential Scanning of Countable Items in Visual Patterns to Solve Numerosity Tasks

2020 ◽  
Vol 60 (4) ◽  
pp. 929-942 ◽  
Author(s):  
HaDi MaBouDi ◽  
H Samadi Galpayage Dona ◽  
Elia Gatto ◽  
Olli J Loukola ◽  
Emma Buckley ◽  
...  
Keyword(s):  
Working Memory ◽  
Parallel Processing ◽  
Comparative Cognition ◽  
Rapid Assessment ◽  
Sequential Pattern ◽  
Processing Capacity ◽  
Numerical Information ◽  
Visual Patterns ◽  
Scanning Strategies ◽  
Sequential Scanning

Abstract Most research in comparative cognition focuses on measuring if animals manage certain tasks; fewer studies explore how animals might solve them. We investigated bumblebees’ scanning strategies in a numerosity task, distinguishing patterns with two items from four and one from three, and subsequently transferring numerical information to novel numbers, shapes, and colors. Video analyses of flight paths indicate that bees do not determine the number of items by using a rapid assessment of number (as mammals do in “subitizing”); instead, they rely on sequential enumeration even when items are presented simultaneously and in small quantities. This process, equivalent to the motor tagging (“pointing”) found for large number tasks in some primates, results in longer scanning times for patterns containing larger numbers of items. Bees used a highly accurate working memory, remembering which items have already been scanned, resulting in fewer than 1% of re-inspections of items before making a decision. Our results indicate that the small brain of bees, with less parallel processing capacity than mammals, might constrain them to use sequential pattern evaluation even for low quantities.

scholarly journals Counting insects

2018 ◽  
Vol 373 (1740) ◽  
pp. 20160513 ◽  
Author(s):  
Peter Skorupski ◽  
HaDi MaBouDi ◽  
Hiruni Samadi Galpayage Dona ◽  
Lars Chittka
Keyword(s):  
Neural Network ◽  
Working Memory ◽  
Parallel Processing ◽  
Neural Circuits ◽  
Working Memory Capacity ◽  
Memory Capacity ◽  
Small Fish ◽  
Network Analyses ◽  
Outstanding Question ◽  
Sequential Scanning

When counting-like abilities were first described in the honeybee in the mid-1990s, many scholars were sceptical, but such capacities have since been confirmed in a number of paradigms and also in other insect species. Counter to the intuitive notion that counting is a cognitively advanced ability, neural network analyses indicate that it can be mediated by very small neural circuits, and we should therefore perhaps not be surprised that insects and other small-brained animals such as some small fish exhibit such abilities. One outstanding question is how bees actually acquire numerical information. For perception of small numerosities, working-memory capacity may limit the number of items that can be enumerated, but within these limits, numerosity can be evaluated accurately and (at least in primates) in parallel. However, presentation of visual stimuli in parallel does not automatically ensure parallel processing. Recent work on the question of whether bees can see ‘at a glance’ indicates that bees must acquire spatial detail by sequential scanning rather than parallel processing. We explore how this might be tested for a numerosity task in bees and other animals. This article is part of a discussion meeting issue ‘The origins of numerical abilities’.

The mental time travel continuum: on the architecture, capacity, versatility and extension of the mental bridge into the past and future

2016 ◽  
Vol 27 (4) ◽  
pp. 421-434 ◽  
Author(s):  
Prescott Breeden ◽  
Dorothea Dere ◽  
Armin Zlomuzica ◽  
Ekrem Dere
Keyword(s):  
Working Memory ◽  
Time Travel ◽  
Mental Time Travel ◽  
Memory Model ◽  
Processing Capacity ◽  
High Complexity ◽  
The Past ◽  
Decision Making Processes ◽  
Future Events ◽  
Working Memory Model

AbstractMental time travel (MTT) is the ability to remember past events and to anticipate or imagine events in the future. MTT globally serves to optimize decision-making processes, improve problem-solving capabilities and prepare for future needs. MTT is also essential in providing our concept of self, which includes knowledge of our personality, our strengths and weaknesses, as well as our preferences and aversions. We will give an overview in which ways the capacity of animals to perform MTT is different from humans. Based on the existing literature, we conclude that MTT might represent a quantitative rather than qualitative entity with a continuum of MTT capacities in both humans and nonhuman animals. Given its high complexity, MTT requires a large processing capacity in order to integrate multimodal stimuli during the reconstruction of past and/or future events. We suggest that these operations depend on a highly specialized working memory subsystem, ‘the MTT platform’, which might represent a necessary additional component in the multi-component working memory model by Alan Baddeley.

Processing capacity defined by relational complexity: Implications for comparative, developmental, and cognitive psychology

1998 ◽  
Vol 21 (6) ◽  
pp. 803-831 ◽  
Author(s):  
Graeme S. Halford ◽  
William H. Wilson ◽  
Steven Phillips
Keyword(s):  
Working Memory ◽  
Sentence Comprehension ◽  
Computational Cost ◽  
Processing Capacity ◽  
Neural Net ◽  
Processing Load ◽  
Unary Relation ◽  
Sources Of Variation ◽  
Experimental Findings ◽  
Relational Complexity

Working memory limits are best defined in terms of the complexity of the relations that can be processed in parallel. Complexity is defined as the number of related dimensions or sources of variation. A unary relation has one argument and one source of variation; its argument can be instantiated in only one way at a time. A binary relation has two arguments, two sources of variation, and two instantiations, and so on. Dimensionality is related to the number of chunks, because both attributes on dimensions and chunks are independent units of information of arbitrary size. Studies of working memory limits suggest that there is a soft limit corresponding to the parallel processing of one quaternary relation. More complex concepts are processed by “segmentation” or “conceptual chunking.” In segmentation, tasks are broken into components that do not exceed processing capacity and can be processed serially. In conceptual chunking, representations are “collapsed” to reduce their dimensionality and hence their processing load, but at the cost of making some relational information inaccessible. Neural net models of relational representations show that relations with more arguments have a higher computational cost that coincides with experimental findings on higher processing loads in humans. Relational complexity is related to processing load in reasoning and sentence comprehension and can distinguish between the capacities of higher species. The complexity of relations processed by children increases with age. Implications for neural net models and theories of cognition and cognitive development are discussed.

Algorithm Improvement of Two-Way Merge Sort Based on OpenMP

2014 ◽  
Vol 701-702 ◽  
pp. 24-29
Author(s):  
Jun Zhang ◽  
Yong Ping Gao ◽  
Yue Shun He ◽  
Xue Yuan Wang
Keyword(s):  
Parallel Processing ◽  
Time Complexity ◽  
Good Time ◽  
Processing Capacity ◽  
Time Efficiency ◽  
Merge Sort ◽  
Sort Algorithm ◽  
Programming Interface ◽  
Multi Core Processor

Two-way merge sort algorithm has a good time efficiency which has been used widely. The sort algorithm can be improved on speed and efficient based on its own potential parallelism via the parallel processing capacity of multi-core processor and the convenient programming interface of OpenMP. The time complexity is improved to O(nlog2n/TNUM) and inversely proportional to the number of parallel threads. The experiment results show that the improved two-way merge sort algorithm become much more efficient compared to the traditional one.

Hemispheric Differences in Processing Visual Patterns

1976 ◽  
Vol 28 (4) ◽  
pp. 667-681 ◽  
Author(s):  
John L. Bradshaw ◽  
Anne Gates ◽  
Kay Patterson
Keyword(s):  
Parallel Processing ◽  
Experimental Design ◽  
Right Hemisphere ◽  
The Other ◽  
Hemispheric Differences ◽  
Hemispheric Processing ◽  
Visual Patterns ◽  
Experimental Parameters ◽  
Identity Matching ◽  
Visuospatial Processing

The dichotomies verbal/visuospatial, serial/parallel and analytic/holistic are reviewed with respect to differences in hemispheric processing. A number of experimental parameters may be varied in such tasks, and together with certain frequently-occurring weaknesses of experimental design may account for the often discrepant results hitherto reported. The above factors are systematically reviewed, and three further experiments are reported which attempt to fill in the missing designs. Further evidence is given in support of the hypothesis that right-hemisphere superiority is most apparent in processes leading to identity matching. It is quantitative rather than qualitative, and may depend upon operations on the entire gestalt, such as holistic matching, mental rotation, reflection, distortion, etc., rather than, e.g., simultaneous (parallel) processing of discretely analysed or isolated features or elements. On the other hand left-hemisphere involvement in visuospatial processing is thought to reflect analysis of the configuration into its separable components; such processing may be either serial or parallel, and may frequently lead to a judgement different.

scholarly journals On Solving the Decycling Problem in a Torus Network

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Antoine Bossard
Keyword(s):  
Parallel Processing ◽  
High Performance ◽  
Massively Parallel ◽  
Processing Capacity ◽  
3 Dimensional ◽  
Massively Parallel Systems ◽  
The World ◽  
High Performance Systems ◽  
Computing Performance ◽  
Torus Network

Modern supercomputers are massively parallel systems: they embody thousands of computing nodes and sometimes several millions. The torus topology has proven very popular for the interconnect of these high-performance systems. Notably, this network topology is employed by the supercomputer ranked number one in the world as of November 2020, the supercomputer Fugaku. Given the high number of compute nodes in such systems, efficient parallel processing is critical to maximise the computing performance. It is well known that cycles harm the parallel processing capacity of systems: for instance, deadlocks and starvations are two notorious issues of parallel computing that are directly linked to the presence of cycles. Hence, network decycling is an important issue, and it has been extensively discussed in the literature. We describe in this paper a decycling algorithm for the 3-dimensional k -ary torus topology and compare it with established results, both theoretically and experimentally. (This paper is a revised version of Antoine Bossard (2020)).

Focus Switching in Working Memory

2018 ◽  
Vol 65 (3) ◽  
pp. 115-127 ◽  
Author(s):  
Rocío Linares ◽  
Santiago Pelegrina
Keyword(s):  
Working Memory ◽  
Focus Of Attention ◽  
Numerical Information ◽  
Content Retrieval ◽  
Information Contents ◽  
Types Of Information

Abstract. Focus switching in working memory involves accessing an object in the focus of attention in order to retrieve its content. Objects in working memory can be viewed as consisting of two types of information: contents (e.g., numerical information) and contexts (e.g., cues to retrieve the contents). This study examined the extent to which content retrieval and context access may be separated. Three experiments were carried out in which object switching and content retrieval were manipulated. In addition, the alternation between the retrieval operations was also manipulated. The main result was that content retrieval required time over and above that needed to access the object. This finding supports the idea that contexts and their contents may be accessed independently when an object is brought into the focus.

Sign in / Sign up

Export Citation Format

Share Document