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opencc-by-4.0Dec 2019View details →

Figure 1 in Public risk perceptions associated with Asian carp introduction and corresponding response actions

Figure 1. Proportion of participants perceiving five different types of environmental impacts from a potential invasion of Asian Carp in Michigan as being low, medium or high risk, 2017 (n = 2,788).

opencc-by-4.0Dec 2021View details →

Figure 4 in Fishers' perceptions of river resources: case study of French Guiana native populations using contextual cognitive mapping

Figure 4. – Cognitive maps focused on a minimum common overview (gray concepts and edges (incoming arrows)) and village-specific overviews (white concepts and blue (5 Amerindian villages) or brown (2 Aluku villages) edges) of threats to the fish resource and environment.

Figure 1 in Perception of Amazonian fishers regarding environmental changes as causes of drastic events of fish mortality

Figure 1. Image of the Ilha do Careiro, immediately below the confluence of the Negro and Solimões rivers (Amazonas state), area of black and whitewaters mixing and, inside, the huge floodplain system known as Lago do Rei.

Figure 4. A - Sentinel 2 in Perception of Amazonian fishers regarding environmental changes as causes of drastic events of fish mortality

Figure 4. A - Sentinel 2 satellite image of Lago do Rei on 20th November 2018. B - Sentinel 2 satellite image of the Lago do Rei on 20th June 2018. C - Sentinel 2 satellite image of the Lago do Rei on 15th November 2019. D - Sentinel 2 satellite image of Lago do Rei on 6th January 2020.

Figure 2. A in Perception of Amazonian fishers regarding environmental changes as causes of drastic events of fish mortality

Figure 2. A biplot is showing the years by the number of days with river level below 18 meters and the amplitude (meters) of the annual flood pulse.

Figure 5 in Perception of Amazonian fishers regarding environmental changes as causes of drastic events of fish mortality

Figure 5. Relationship between the river level, measured in the Port of Manaus – Station 14990000, and the Oceanic Niño Index (ONI), from 2009 to 2020, taking as reference the level of disconnection between Lago do Rei and the Amazon River.

Figure 3 in Perception of Amazonian fishers regarding environmental changes as causes of drastic events of fish mortality

Figure 3. Analysis of the water surface of Lago do Rei using the modified normalized difference water index for the years 2015 to 2020.

Figure 3 in Perception of fungi by farmers in the Cerrado

Figure 3. Non-metric multidimensional scaling (nMDS), based on the Gower similarity index, that was carried out with the answers given by the research participants about their ability to identify fungi or fungal structures. Most respondents noted specimens with characteristics that reminded them of something they had already seen on their property and/or cultures, such as corticoid fungus (No. 9, in the images in detail), vegetative mycelium (No. 13), fungus lichenized (no. 16), and endophytic fungi (no. 5 and 10). These were recognized either because of plant diseases or the fungi they saw growing in compost or wood, which were more easily associated with being fungi. Organisms that did not clearly show these characteristics (such as non-fungal or crusted lichens) were mistakenly classified as fungi or received the "I don't know" option as an answer.

Figure 1 in Perception of fungi by farmers in the Cerrado

Figure 1. Setup of the organism plates presented to the research participants to assess knowledge of the Fungi Kingdom. The plate was composed of different biological structures, both belonging to the Fungi Kingdom (c), as well as to other groups (a, b), in order to encourage participants to reason and bring up previous knowledge about the group. The background knowledge was accumulated in different capacities by the participant (lectures, courses, conversations with acquaintances/family members, television programs, among other communication/learning vehicles). Numbers 1 to 20 represent the different materials collected. Source: prepared by the authors.

Figure 2 in Perception of fungi by farmers in the Cerrado

Figure 2. Different moments from the interviews about mycological knowledge carried out between agricultural producers of settlement projects in the City of Goiás, Goiás. (a-d) research participants are presented to the organism plates; (e) participant showing places where he has already found fungi; (f-h) moments of explanations about fungi; (i-l) different places where the interviews were conducted, that is, where the interviewees market their products: Municipal market (i) and the Organic Products Fair (j-l).

Figure 4 in Perception of fungi by farmers in the Cerrado

Figure 4. Comparison of the total number of correct and wrong answers in distinguishing fungal representatives between different living for conventional and in transition farmers with respect to their agroecological practices. Different letters in each set of responses represent statistically significant differences (P <0.05) after analysis of variance (Fanova: 30.34, P-value: <0.0001).

opencc-by-4.0Oct 2013View details →

BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 14. Implementation of Overall Architecture of the Perception Model in AnyLogic 4.3

To perform complex functions, individual neuro-symbols are then connected to networks. Figure 14 shows a screenshot of the AnyLogic implementation of the overall system at the beginning of the learning phase. The lowest neuro-symbolic levels receive the direct sensor information as input. The higher neuro-symbolic levels are originally not interconnected amongst each other. Instead, they are connected to so-called “learning ports”, which additionally receive control information needed for the supervised learning process. Details about the multi-stage multilevel learning process can be found in.

opencc-by-4.0Aug 2015View details →

Figure 11. Cognitive architecture of the process of social signals perception-Gestalt Processing in Human-Robot Interaction: A Novel Account for Autism Research

A possible cognitive architecture and formalization of the process of learning via multisensory integration is presented in figure 11. The formal description of the proposed cognitive architecture, capable of interpreting social-communication signals, signs and symbols, is based on multisensory integration at the level of perception, parallel processing at the level of interpretation and decision making followed by verbalization, as well as performing an action (eye contact, gesture, mimicking) at the level of behaviour.

opencc-by-4.0Jan 2018View details →

Students' perceptions and attitudes towards science in PERFORM: Survey template

This document contains the survey instrument developed to measure the impact of the PERFORM project RRI approach in students’ attitudes and pro-scientific behaviour and learning. It was a self-administered questionnaire combining close-ended with some open-ended qüestions. It was handled to students before and after the development of PERFORM participatory workshops to the participant students and a control group in order to: <ul> <li>Obtain basic demographic data (those compatible with PERFORM ethical guidelines)</li> <li>Explore initial attitudes and perceptions towards science and STEM careers, with an emphasis on RRI-related dimensions (gender stereotypes, ethical issues, inclusiveness, engagement and critical/creative thinking) and potential changes after the implementation of participative performances (PERSEIAS in PERFORM jergon)</li> <li>Explore participants’ perceptions towards the PERSEIAS process, also as an input to inform the design of focus groups</li> </ul>

opencc-by-4.0Jan 2018View details →

Data set for Plos One Article "Force sharing and other collaborative strategies in a dyadic force perception task"

Data set for Plos One Article : Tatti, F., Baud-Bovy G. (2018) "Force sharing and other collaborative strategies in a dyadic force perception task". doi: 10.1371/journal.pone.0192754 This study investigates how people might interact to extract information from the forces experienced while holding an object together.   More specifically, the dyads (i.e. pairs formed two persons) participating to the study had to identify the direction of a small force applied to a jointly held object by a haptic device. This study included a condition where each participant responded independently and another one where the two participants had to agree upon a single negotiated response. The dataset (data.csv) contains the force produced by the haptic device and the average and standard deviation of the interaction force for all trials together with the responses of the participants. We also included the initial and final position of the haptic device and total distance traveled for each trial. The data are in comma separated text format and its description in a PDF document (readme.pdf).

Brain Functors: A mathematical model of intentional perception and action-Figure 15: Mathematical butterfly diagram for a brain functor

HomA(F(X),A) &cong; Het(X,A) &cong; HomX(X,G(A)). If the functor F also has a left adjoint H : A→X, then: HomX(H(A),X) &cong; Het(A,X) &cong; HomA(A,F(X)). Then taking the isomorphisms that do not involve G or H gives: and Het(A,X) &cong; HomA(A,F(X)), i.e., F is a brain functor. Hence all functors that have both right and left adjoints are brain functors. 16 HomA(F(X),A) &cong; Het(X,A)

Figure 14: Adjunctive square diagram-Brain Functors: A mathematical model of intentional perception and action

Finally, a brain functor is a functor F: X→A that is a left semiadjunction for Het(X, A) and a right semiadjunction for Het(A, X), i.e., HomA(F(X),A) &cong; Het(X,A) and Het(A,X) &cong; HomA(A,F(X)). For each d in Het(X, A), there is a unique hom f(d) in HomA(F(X), A) so that the upper triangular ‘wing’ in the butterfly diagram commutes. For each d' in Het(A, X), there is a unique hom g(d') in HomA(A, F(X)) so that the lower triangular ‘wing’ commutes.

Figure 13: Composition of hets and homs-Brain Functors: A mathematical model of intentional perception and action

The cross-category object-to-object hets d : X→A will be indicated by thin arrows (→) rather than thick arrows (⇒). The first question is how do heteromorphisms compose with one another? But that is not necessary. Chimera do not need to ‘mate’ with other chimera to form a ‘species’ or category; they only need to mate with the intra-category morphisms on each side to form other chimera.8 Given a het d : X→A from an object in a category X to an object in a category A, and homs h : X'⇒X in X and k : A⇒A' in A, the composition dh : X'⇒X→A is another het X'→A and the composition kd : X→A⇒A'  is another het X→A'.

Figure 12: Language faculty as two-way determination through a universal-Brain Functors: A mathematical model of intentional perception and action

A brain functor, broadly put, is any universal mechanism of determination that can factor determination either way through a universal–rather than an adjunction that factors one way determination through two (receiving and sending) universals. In some contexts in the life sciences, determination is strictly one way so one might expect to find a semiadjunction but not a two-way system like a brain functor. An application of the scheme for a brain functor in the cognitive sciences is to model the language faculty where there is two way determination between vocal stimuli and internal representations. The previous semiadjunctions for language understanding and language action can be merged to arrive at the brain-like function of the language faculty.

Figure 11: Coding and decoding Cartesian coordinates of geometrical points-Brain Functors: A mathematical model of intentional perception and action

The simplest form of a brain "functor" is just a two-way representation or coding system that constructs and implements a set of codes. Given some set of objects, it is encoded using some isomorphic set of representations or codes for the objects, and then given an instance of the code, it is decoded to determine the object. Coordinatizing is a form of coding. The geometrical plane is a collection of points, and the Cartesian coordinate system represents each point P by a pair (xP, yP) of coordinates. Given a point P , the "coordinate" function selects the coordinates (xP, yP) of the point which is the recognized or coded output, and given the coordinates or code for a point (xP, yP) as an input, the "plot" function designates the point.