Modeling Option and Strategy Choices with Connectionist Networks: Towards an Integrative Model of Automatic and Deliberate Decision Making

We claim that understanding human decisions requires that both automatic and deliberate processes be considered. First, we sketch the qualitative differences between two hypothetical processing systems, an automatic and a deliberate system. Second, we show the potential that connectionism offers for modeling processes of decision making and discuss some empirical evidence. Specifically, we posit that the integration of information and the application of a selection rule are governed by the automatic system. The deliberate system is assumed to be responsible for information search, inferences and the modification of the network that the automatic processes act on. Third, we critically evaluate the multiple-strategy approach to decision making. We introduce the basic assumption of an integrative approach stating that individuals apply an all-purpose rule for decisions but use different strategies for information search. Fourth, we develop a connectionist framework that explains the interaction between automatic and deliberate processes and is able to account for choices both at the option and at the strategy level.System 1, Intuition, Reasoning, Control, Routines, Connectionist Model, Parallel Constraint Satisfaction

Theoretical Interpretations and Applications of Radial Basis Function Networks

Medical applications usually used Radial Basis Function Networks just as Artificial Neural Networks. However, RBFNs are Knowledge-Based Networks that can be interpreted in several way: Artificial Neural Networks, Regularization Networks, Support Vector Machines, Wavelet Networks, Fuzzy Controllers, Kernel Estimators, Instanced-Based Learners. A survey of their interpretations and of their corresponding learning algorithms is provided as well as a brief survey on dynamic learning algorithms. RBFNs' interpretations can suggest applications that are particularly interesting in medical domains

Handwritten Devanagari numeral recognition

Optical character recognition (OCR) plays a very vital role in today’s modern world. OCR can be useful for solving many complex problems and thus making human’s job easier. In OCR we give a scanned digital image or handwritten text as the input to the system. OCR can be used in postal department for sorting of the mails and in other offices. Much work has been done for English alphabets but now a day’s Indian script is an active area of interest for the researchers. Devanagari is on such Indian script. Research is going on for the recognition of alphabets but much less concentration is given on numerals. Here an attempt was made for the recognition of Devanagari numerals. The main part of any OCR system is the feature extraction part because more the features extracted more is the accuracy. Here two methods were used for the process of feature extraction. One of the method was moment based method. There are many moment based methods but we have preferred the Tchebichef moment. Tchebichef moment was preferred because of its better image representation capability. The second method was based on the contour curvature. Contour is a very important boundary feature used for finding similarity between shapes. After the process of feature extraction, the extracted feature has to be classified and for the same Artificial Neural Network (ANN) was used. There are many classifier but we preferred ANN because it is easy to handle and less error prone and apart from that its accuracy is much higher compared to other classifier. The classification was done individually with the two extracted features and finally the features were cascaded to increase the accuracy

The flexibly ordered brain

I investigate the human brain systems involved in the cognitive control of behaviour. Using novel cognitive paradigms and brain imaging, I identify brain systems that support the flexible structuring of behaviour. I then observe how these systems are implicated in patients with depression as they respond to psilocybin therapy. In the first of three experiments, I observe the changes in healthy adult brain activation that are associated with task-switching. This demonstrated that remapping rules introduces a switching-cost to response speed and activates the multiple-demand (MD) network. Critically, switching-costs and MD activation were greater when the rules being remapped were of an abstract and higher-order nature. Going deeper, in the second experiment, I investigate how healthy adult brains mitigate switching-costs by structuring behaviour into efficient routines. I observe that learning to optimise and structure behaviour covaries with changes in MD and default mode network (DMN) activation alongside increases in between-network connectivity. These concurrent behavioural and neural adaptations imply that cognitive demand is minimised when behavioural routines are structured. Indeed, these mechanisms are known to have broad roles in flexibly adapting behaviour and, subsequently, they have been implicated in disorders such as depression. Using these insights, in the third experiment, I examine the neural basis of the treatment response to psilocybin in patients with depression. In two clinical trials, I find that treatment response covaried with global increases in between-network connectivity. Converging functional cartography measures indicated that this global shift in network organisation related to increased dynamic flexibility and integration of the MD and DMN. Together, the findings in this thesis indicate that a ‘flexibly ordered brain’, the adaptive sequencing of neurocognitive states, is a necessary feature of well-being and for successfully navigating the demands of daily life.Open Acces

A Dynamic Approach to Rhythm in Language: Toward a Temporal Phonology

It is proposed that the theory of dynamical systems offers appropriate tools to model many phonological aspects of both speech production and perception. A dynamic account of speech rhythm is shown to be useful for description of both Japanese mora timing and English timing in a phrase repetition task. This orientation contrasts fundamentally with the more familiar symbolic approach to phonology, in which time is modeled only with sequentially arrayed symbols. It is proposed that an adaptive oscillator offers a useful model for perceptual entrainment (or `locking in') to the temporal patterns of speech production. This helps to explain why speech is often perceived to be more regular than experimental measurements seem to justify. Because dynamic models deal with real time, they also help us understand how languages can differ in their temporal detail---contributing to foreign accents, for example. The fact that languages differ greatly in their temporal detail suggests that these effects are not mere motor universals, but that dynamical models are intrinsic components of the phonological characterization of language.Comment: 31 pages; compressed, uuencoded Postscrip

Crosstalk between chromatin structure, cohesin activity and transcription

Background: A complex interplay between chromatin and topological machineries is critical for genome architec‑ ture and function. However, little is known about these reciprocal interactions, even for cohesin, despite its multiple roles in DNA metabolism. Results: We have used genome‑wide analyses to address how cohesins and chromatin structure impact each other in yeast. Cohesin inactivation in scc1‑73 mutants during the S and G2 phases causes specific changes in chromatin structure that preferentially take place at promoters; these changes include a significant increase in the occupancy of the − 1 and + 1 nucleosomes. In addition, cohesins play a major role in transcription regulation that is associated with specific promoter chromatin architecture. In scc1‑73 cells, downregulated genes are enriched in promoters with short or no nucleosome‑free region (NFR) and a fragile “nucleosome − 1/RSC complex” particle. These results, together with a preferential increase in the occupancy of nucleosome − 1 of these genes, suggest that cohesins promote transcription activation by helping RSC to form the NFR. In sharp contrast, the scc1‑73 upregulated genes are enriched in promoters with an “open” chromatin structure and are mostly at cohesin‑enriched regions, suggesting that a local accumulation of cohesins might help to inhibit transcription. On the other hand, a dramatic loss of chromatin integrity by histone depletion during DNA replication has a moderate effect on the accumulation and distribution of cohesin peaks along the genome. Conclusions: Our analyses of the interplay between chromatin integrity and cohesin activity suggest that cohesins play a major role in transcription regulation, which is associated with specific chromatin architecture and cohesin‑ mediated nucleosome alterations of the regulated promoters. In contrast, chromatin integrity plays only a minor role in the binding and distribution of cohesins.Spanish Ministry of Economy and Competitivenes BFU2012-38171, BFU2015-63698-PAndalusian Government P12-CTS-227

Sensorimotor Learning Biases Choice Behavior: A Learning Neural Field Model for Decision Making

According to a prominent view of sensorimotor processing in primates, selection and specification of possible actions are not sequential operations. Rather, a decision for an action emerges from competition between different movement plans, which are specified and selected in parallel. For action choices which are based on ambiguous sensory input, the frontoparietal sensorimotor areas are considered part of the common underlying neural substrate for selection and specification of action. These areas have been shown capable of encoding alternative spatial motor goals in parallel during movement planning, and show signatures of competitive value-based selection among these goals. Since the same network is also involved in learning sensorimotor associations, competitive action selection (decision making) should not only be driven by the sensory evidence and expected reward in favor of either action, but also by the subject's learning history of different sensorimotor associations. Previous computational models of competitive neural decision making used predefined associations between sensory input and corresponding motor output. Such hard-wiring does not allow modeling of how decisions are influenced by sensorimotor learning or by changing reward contingencies. We present a dynamic neural field model which learns arbitrary sensorimotor associations with a reward-driven Hebbian learning algorithm. We show that the model accurately simulates the dynamics of action selection with different reward contingencies, as observed in monkey cortical recordings, and that it correctly predicted the pattern of choice errors in a control experiment. With our adaptive model we demonstrate how network plasticity, which is required for association learning and adaptation to new reward contingencies, can influence choice behavior. The field model provides an integrated and dynamic account for the operations of sensorimotor integration, working memory and action selection required for decision making in ambiguous choice situations