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opencc-by-4.0Sep 2022View details →

The theory of planned behavior and the prediction of pre-service biology teachers' intention to teach evolution

<p>We developed the project to identify and analyze variables that promote or hinder prospective biology teachers’ intentions to teach evolution. We adopted the model of the theory of planned behavior (TPB). We extended it to include additional variables described by teacher education research as key determinants of behavioral intention to teach evolution. We initially hypothesized that attitudes toward teaching evolution, subjective norms, perceived behavioral control, personal religious beliefs, perceived usefulness, and knowledge about evolution would determine a person’s behavioral intentions. To test the hypotheses, we developed an online questionnaire and conducted a quantitative cross-sectional survey in the field of teacher education. The data included information on <em>N</em> = 309 participants. Because we initially analyzed the data using a two-stage structural equation model (SEM), we uploaded two data files that were created in subprocesses of our original analyses (for more information, see the original publication). The dataset “data3” contains 77 variables and has missing values. Since we wanted to use complete data for the SEM, we trimmed the data set “data3” to include only the 67 variables necessary for the SEM, then applied an expectation-maximum (EM) algorithm with multiple imputations, and obtained the data set “data4”. </p>

opencc-by-4.0Jul 2024View details →

A Service Robot in the Wild: Analysis of Users Intentions, Robot Behaviors, and Their Impact on the Interaction

<p>This file contains human-robot interaction data acquired during an  experiment conducted at the University of Applied Sciences and Arts of Southern Switzerland (SUPSI). The campaign focuses on collecting non-identifying data, such as torso trajectories and the internal state of the system, from people in the proximity of a robot. The study spans three days in two different environments at the University Campus Est in Lugano, Switzerland.</p> <div> <div> <div> <div> <p>The campaign adheres to ethical guidelines and is approved by SUPSI's local ethics committee.</p> <p>Duration: Total of 5 hours and 7 minutes.</p> <p>Participants: 1777 individuals tracked.</p> <p><strong>Environments:</strong></p> <ul> <li>Entrance to the campus canteen (demographically diverse, including students and staff).</li> <li>Corridor between classrooms (mainly attended by students).</li> </ul> <p><strong>Data Types</strong>:</p> <ul> <li>Robot Sensor: Timestamps, user ID, 3D torso pose in Robot Sensor frame, interaction intention detector output.</li> <li>Environment Sensor: Timestamps, user ID, 3D poses of torso and hands in Environment Sensor frame, 2D torso positions in the sensor’s field of view.</li> <li>Robot State: Currently selected behavior, state (idle or performing an offering motion).</li> </ul> <p><strong>Key Events</strong>:</p> <ul> <li>Pick Motion: User's hand movement within 0.3 meters of the box.</li> <li>Robot Offer: Robot begins an offering motion.</li> <li>Successful Offer: Pick Motion within 6 seconds of a Robot Offer.</li> </ul> </div> </div> </div> </div> <div> <div> <div> </div> </div> </div>

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

<p>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:<br> 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.<br> 16<br> HomA(F(X),A) &cong; Het(X,A)</p>

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

<p>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)<br> and Het(A,X) &cong; HomA(A,F(X)).<br> 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.</p>

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

<p>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'.</p>

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

<p>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.</p>

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

<p>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.</p>

Figure 8: Language production through a sending universal-Brain Functors: A mathematical model of intentional perception and action

<p>The dual to "language understanding" is language production or linguistic action (e.g., "speech acts"). The role of the specific het is played by some auditory output such as utterances (Humboldt’s "vocal stimulus"). But the corresponding internal specific hom is the speech act (i.e., internal speech with intentionality) that through the language faculty produces the same outputs but as intentional speech.</p>

Figure 1: Het d: X→ A-Brain Functors: A mathematical model of intentional perception and action

<p>In the body of this paper, I will try to keep the mathematics at a minimal conceptual level– which the mathematical formulations restricted to the Appendix. Category theory lends itself to visualization in diagrams so that non-mathematical style of presentation is emphasized by an abundant use of diagrams. A category is intuitively a set of objects of the same type. Morphisms between objects should be thought of as a type of determining relation or cause-effect relation between the objects. When a morphism is between objects of the same category, it is called a homomorphism or hom, and when between objects of different categories it is a heteromorphism or het.4 One of the problems in the conventional treatment of category theory5 is that it tries to ignore heteromorphisms even though hets are a natural part of working mathematics. This leads to certain definitions being rather contrived (to avoid mentioning hets), the usual treatment of the universal mapping properties in adjunctions being the case in point. Adjunctions will be introduced informally and in the natural manner using hets. The general setting is how the objects in one category (e.g., the "environment" in a life sciences context), the "sending" category, will "affect" or "determine" objects in another category (e.g., "organisms"), the "receiving" category. We start with an object X in the sending category, an object A in the receiving category, and a specific het determination d : X → A from X to A.6</p>

Figure 4: The Adjunctive Square Diagram-Brain Functors: A mathematical model of intentional perception and action

<p>Dually, we can define the above situation, given by the association of the sending universal G(A) with each object A in the receiving category along with the canonical isomorphism Het(X,A) &cong; Homsending(X,G(A)), as a right semiadjunction. Now we are prepared to define an adjunction essentially as:<br> adjunction = left semiadjunction + right semiadjunction Homreceiving(F(X),A) &cong; Het(X,A) &cong; Homsending(X,G(A)).</p>

Figure 7: -"Action" as determination through a sending universal-Brain Functors: A mathematical model of intentional perception and action

<p>Dual to the generic model of "perception" is the generic model of "action"–which is the determinative scheme given by a right semiadjunction. In the model of perception, there is the uninterpreted message as just a sensory input (the external het), and then there is the second level where the factorization (the internal hom) through the receiving universal recognizes the interpretation, meaning, or intentionality of the message. In the dual model of "action," the external het specifies the external behavior (which could be even a reflex behavior) while internal hom factoring through a sending universal that supplies the "intentionality" of the "action" (where an "action" is a "behavior" plus the second level of "intentionality"). In each case, we end up with a certain behavior but determined by two different means.</p>

Figure 6: "Perception" as determination through a receiving universal-Brain Functors: A mathematical model of intentional perception and action

<p>Before turning to right semiadjunctions, it might be useful to present a rather generic version of determination through a receiving universal as model of "recognition" or "perception" that captures many of the common features of the various examples. The determination through the receiving universal is the active internal process that supplies the "interpretation" or "intentionality" to the raw sense data. The red blotch is seen as a tomato; the sound "ya" is understood as indicating agreement, and so forth. In the passive/direct alternative, the raw sensory input supplies Lockean "perception" like writing on a blank slate or a stamp making an impression on wax.</p>

Figure 3: Scheme for determination by a sending universal G(A)-Brain Functors: A mathematical model of intentional perception and action

<p>The universal mapping property is: for every het d : X→A, there is a unique hom g(d) : X⇒G(A) in the sending category such that: eAg(d) = X⇒G(A)→A = X→A = d i.e., such that the determination through the universal sending het eA : G(A)→A preceded by the hom g(d) : X⇒G(A) is the same as the original het d : X→A.</p>

Intentional Forgetting in Organizations: The Positive Effects of Decision Support Systems on Mental Resources and Well-being

<p>This dataset contains raw data collected in an experimental study at the University of Muenster, Germany. The study is part of a larger research project and examined “intentional forgetting” effects in a simulated sales planning scenario. Intentional forgetting was operationalized via computer-based decision support system that enabled users to forget decision-relevant background information. We assumed that such intentional forgetting not only enhances decision quality but also decreases strain of decision makers and releases memory capacities for additional tasks.</p>

Intention Reconsideration in Wumpus World And Intentional Inference in Adolescents-Figure 5. Failures percentage in the CWW condition

<p>In the CWW condition, the cautious failures, for [N = 34], there were 139 out of a total of 304 wrong movements (46%) and the mean was 4.09 (SD = 2.22), 95% CI [3.31, 4.86] . On the other hand, the bold failures were 165 out of a total of 304 wrong movements (54%) and the mean was 4.85 (SD = 1.48), 95% CI [4.34, 5.37]. See the Figure 5.</p>

Intention Reconsideration in Wumpus World And Intentional Inference in Adolescents-Figure 3. Screen showing the agent finding the gold.

<p>The novelty of a map, that is, an 8x8 board added to the world, is introduced in the MCWW design, as opposed to the CWW version. This map is responsible for showing on the screen the agent's knowledge base or the inferences she makes as she moves and receives the different perceptions on the board. In this way, it is easier for the experimental subjects to predict the possible movements of the agent. As an example, observe a sequence of screens of the board of our version MCWW in which it is seen that the agent kills the Wumpus (the goal of Intention 2). See the figures 1-3.</p>

Intention Reconsideration in Wumpus World And Intentional Inference in Adolescents-Figure 4. Failures percentage in the CMWW condition.

<p>Now let us analyze what kind of failures have occurred in each of the versions of Wumpus World. In the CMWW condition, the cautious failures were 258 over a total of 738 wrong movements (35%) and we have that, for [N = 34], the mean was 7.59 (SD = 2.59), 95% CI [6.68, 8.49]. The bold failures were 480 out of a total of 738 wrong movements (65%) and the mean was 13.82 (SD = 9.33), 95% CI [10.57, 17.08]. See the figure 4.</p>

Intention Reconsideration in Wumpus World And Intentional Inference in Adolescents-Figure 2. Screen showing the agent hunting the Wumpus.

Intention Reconsideration in Wumpus World And Intentional Inference in Adolescents-Figure 1. Screen showing the agent in the cave.