computational cognitive systems سیستم های شناختی محاسباتی

WRITTEN REPORT ASSIGNMENTS (TASKS) 1. Explain Marr’s levels of analysis. (≈ 500 words) 10%. 2. Discuss the relevance of the Church-Turing thesis for the computational view of cognition. (≈ 500 words) 10%. 3. Describe the process of Bayesian inference for cognitive theory building in contrast to the connectionist approach. (≈1000 words) 20% 4. Compare the different associative learning structures, taking into account the current views of Pavlovian conditioning and the types of learning it accounts for, with the paradigms of learning (especially the associative learning types), defined in Rumelhart, Hinton, and McClelland (1987). A General Framework for Parallel Distributed Processing (Chapter 2). In McClelland and Rumelhart (Eds.) Parallel Distributed Processing. (≈1500 words) 30% 5. Analyse 2 of the following learning phenomena and models: (≈ 1500 words) max 30% [Choose 2] a. b. Blocking (Rescorla-Wagner and Mackintosh) 10% b. Latent Inhibition (Pearce & Hall and SOP) 10% c. Mediated Conditioning (SOP and SSCC TD) 20% d. Negative patterning discrimination (REM and Pearce) 20% e. Structural or Serial Order Discrimination (TD and SSCC TD) 20% f. Retrospective revaluation: Unovershadowing (SOP and DDA) 20% Per phenomenon, the analysis must include: Points are given for 10c designs (multiplied by 2 for 20c designs) 5.1. A description of a scenario(s), artificial or natural, where the phenomenon takes place 1% (10p task) x2=2% (20p task) 5.2. Identification of the critical stimuli: predictors and outcomes. 1% 5.3. Input of the design in relation to the scenario. E.g., in a road accident, an AB+ trial should be described as a siren (stimulus A), blue-red flashing lights (stimulus B) and an ambulance (outcome stimulus, +). 2% 5.4. A comparison of (at least) two pertinent learning models, explaining how they account (or why not) for the phenomenon theoretically, that is, identifying the relevant cognitive learning variables or processes. 2% 5.5. A simulation and comparison of (at least) two pertinent learning models, explaining how they account for the phenomenon computationally. You must show the simulated figures and describe them. 2% 5.6. Discussion of why these phenomena are relevant to understanding current principles of associative learning. What do they prove? 2% 6. Marks are given individually, therefore, please briefly explain as a mode of summary (extra 200 words) your personal leading contribution to this coursework and evaluate your partner’s contribution. EXTRA TASK Implement and explain the algorithms and code of a Rescorla-Wagner (R-W) model rendition in Python that is capable of simulating the following phenomena. You must use the same model version and settings to account for each of them. You must submit the task with the Written Report at the correct Submission Area and deadline. You must also present this task during the oral presentation, showing your code running and the material added to the slides. (≈ 700 words) 10% Phenomena: A.1. Blocking 5% A.2. Conditioned inhibition 5% (REMEMBER that presentations are random within a session!) A.3. Latent inhibition, bearing in mind that the R-W model, as originally defined, cannot account for latent inhibition. Hence, you must produce a meaningful modification (psychologically plausible). E.g., a variable attentional learning rate 5%