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No description available.

Designing user-interfaces so that first-time or infrequent users can accomplish their goals by exploration has been an enduring challenge in Human-Computer Interaction. Iterative user-testing is an effective but costly method to develop user-interfaces that support use through exploration. A complementary method is to use modeling tools that can generate predictions of user exploration given a user-interface and a goal description. Recent computational models of goal-directed user exploration have focused on predicting user exploration of websites and demonstrated how predictions can inform user-interface design. These models employ the common concepts of label following and information scent: that the user's choice is partly determined by the semantic relevance between the user's goal and the options presented in the user-interface. However, in addition to information scent, other factors including the layout position and grouping of options in the user-interface also affect user exploration and the likelihood of success. This dissertation contributes a new model of goal-directed user exploration, called CogTool- Explorer, which considers the layout position and the grouping of options in the user-interface in concert with a serial evaluation visual search process and information scent. Tests show that predictions from CogTool-Explorer match participant data better than alternative models that do not consider layout position and grouping. This dissertation work has also integrated the CogTool- Explorer model into an existing modeling tool, called CogTool, making it easier for other researchers and practitioners to setup and generate predictions of likely user exploration paths and task performance using CogTool-Explorer.

Designs of human-computer systems intended for time-critical multitasking can benefit from an understanding of the human factors that support or limit multitasking performance and a detailed account of the human-machine interactions that unfold in a given task environment. An integrated, computational cognitive model can test and provide such an understanding of the human factors related to multitasking and reveal the dynamic interactions that occur in the task at the level of hundreds of milliseconds. This dissertation provides such a detailed computation model of human multitasking, built for a time-critical, multimodal dual task experiment and validated by the eye tracking data collected from the experiment. This dissertation also develops new approaches to conducting cognitive modeling, which enable efficient and systematical exploration of multitasking strategies, as well as principled model comparisons. The dual task experiment captures many key aspects of real-world multitasking scenarios such as driving. In the experiment, the participant interleaved two tasks: one requires tracking a constantly-moving target with a joystick, and the other requires keying-in responses to objects moving across a radar display. Peripheral visibility and auditory conditions of the experiment were manipulated to assess the influence of peripheral visual information and auditory information on multitasking performance. Detailed eye tracking data were collected, and this dissertation presents a detailed analysis of this set of data, which provides the bases for model development and validation. The cognitive model presented in this dissertation, built based on the Executive Processes-Interactive Control cognitive architecture, accurately accounted for the eye movement data and other behavioral data of each participant using systematic explorations of task strategies and parameters configured for each individual participant. A parallelized cognitive modeling system was developed to accommodate the much increased computational demand of strategy exploration and individualized model building. New model comparison techniques were proposed to determine which strategy best accounts for the empirical data. Payoff analyses were applied, and they revealed people’s tendency to locally optimize task performance based on task payoff as well as instantaneous feedback. The results point to new approaches for building a priori models that predict multitasking performance. This dissertation includes previously published coauthored material.

Arithmetic abilities are required when solving problems such as “3x4” or “24+33” and are important for many everyday life situations: for instance, to make a recipe or to pay at the store. Given the high incidence of arithmetic difficulties throughout development, the acquisition of arithmetic abilities received much attention in field of numerical cognition. However, although fundamental, only a handful of studies (e.g. Geary & Wiley, 1991) investigated the impact of normal aging on arithmetic problem solving.Therefore the question of whether and how arithmetic abilities are affected by the decline of cognitive processing remains poorly understood. The aim of the current thesis was to fill in this gap and to investigate whether and how arithmetic abilities as well as the related executive functions change when aging. Our results showed that the deficit with age in arithmetic abilities and related executive functions is not general but specific to the executive function under investigation. Moreover, our findings suggest that these executive functions could be specific to the arithmetic domain and not an instance of a more general executive system.In our view, this thesis constitutes an initial step towards enhancing our comprehension of the impact of age on executive functions in the arithmetic domain and its potential specificity with other domains. However, especially because of the lack of studies directly addressing these issues, the results reported here need to be replicated to strengthen our conclusions. In this respect, we believed that the DDM could help to specify exactly which cognitive processes remain intact with age and which become deficient. / Doctorat en Sciences psychologiques et de l'éducation / info:eu-repo/semantics/nonPublished

<p>According to the heuristics and biases approach, the representativeness heuristic (RH) is one of the heuristics available for assessing subjective probabilities (A. Tversky & D. Kahneman, 1974). A subjective probability assessed by the RH is determined by how representative the target object is of the target category. Several aspects of the RH are argued to cause systematic biases, for example: (<i>i</i>) When the RH is used, the category is represented by one single prototypical exemplar. This feature is argued to cause biases such as misperception of chance and insensitivity to sample size. (<i>ii</i>) The RH assesses the inverse rather than the conditional probability. This feature is argued to cause biases such as the conjunction fallacy and base-rate neglect.</p><p>The present thesis focuses on the cognitive aspects of the RH. Three studies were conducted. Overall, data indicated that the RH does not play a major role when subjective probabilities are assessed. Study I indicated that subjective probabilities are not typically determined by how representative the target object is of the target category. Study II indicated that the category is not represented by one single prototypical exemplar when subjective probabilities are assessed. Study III indicated that conjunction fallacies are not caused by the RH. </p><p>The results presented in Studies I-III cast serious doubts on the claim that subjective probabilities are routinely assessed using the RH. Rather, Studies I-II suggested that subjective probabilities are based on exemplar memory and Study III suggested that the conjunction fallacy is caused by people combining component probabilities in a an inappropriate way. In the General Discussion, it is suggested that people use a weighted average rule when combining component probabilities into conjunction probabilities. A simulation showing the ecological relevance of the weighted average rule is presented.</p>

One reason Qualitative Spatial Reasoning (QSR) is becoming increasingly important to Artificial Intelligence (AI) is the need for a smooth ‘human-like’ communication between autonomous agents and people. The selected, yet general, task motivating the work presented here is the scenario of an object configuration that has to be described by an observer on the ground using only relational object positions. The description provided should enable a second agent to create a map-like picture of the described configuration in order to recognize the configuration on a representation from the survey perspective, for instance on a geographic map or in the landscape itself while observing it from an aerial vehicle. Either agent might be an autonomous system or a person. Therefore, the particular focus of this work lies on the necessity to develop description and reconstruction methods that are cognitively easy to apply for a person. This thesis presents the representation scheme QuaDRO (Qualitative Description and Reconstruction of Object configurations). Its main contributions are a specification and qualitative classification of information available from different local viewpoints into nine qualitative equivalence classes. This classification allows the preservation of information needed for reconstruction nto a global frame of reference. The reconstruction takes place in an underlying qualitative grid with adjustable granularity. A novel approach for representing objects of eight different orientations by two different frames of reference is used. A substantial contribution to alleviate the reconstruction process is that new objects can be inserted anywhere within the reconstruction without the need for backtracking or rereconstructing. In addition, an approach to reconstruct configurations from underspecified descriptions using conceptual neighbourhood-based reasoning and coarse object relations is presented.

According to the heuristics and biases approach, the representativeness heuristic (RH) is one of the heuristics available for assessing subjective probabilities (A. Tversky &amp; D. Kahneman, 1974). A subjective probability assessed by the RH is determined by how representative the target object is of the target category. Several aspects of the RH are argued to cause systematic biases, for example: (i) When the RH is used, the category is represented by one single prototypical exemplar. This feature is argued to cause biases such as misperception of chance and insensitivity to sample size. (ii) The RH assesses the inverse rather than the conditional probability. This feature is argued to cause biases such as the conjunction fallacy and base-rate neglect. The present thesis focuses on the cognitive aspects of the RH. Three studies were conducted. Overall, data indicated that the RH does not play a major role when subjective probabilities are assessed. Study I indicated that subjective probabilities are not typically determined by how representative the target object is of the target category. Study II indicated that the category is not represented by one single prototypical exemplar when subjective probabilities are assessed. Study III indicated that conjunction fallacies are not caused by the RH. The results presented in Studies I-III cast serious doubts on the claim that subjective probabilities are routinely assessed using the RH. Rather, Studies I-II suggested that subjective probabilities are based on exemplar memory and Study III suggested that the conjunction fallacy is caused by people combining component probabilities in a an inappropriate way. In the General Discussion, it is suggested that people use a weighted average rule when combining component probabilities into conjunction probabilities. A simulation showing the ecological relevance of the weighted average rule is presented.