Preference Oriented Mining Techniques for Location based Point Search

With the development of internet and wireless technologies, location based search is among the most discussed topic in current era. To address issues of location based search a lot of research has been done but it mainly focused on the specific aspects of the domain like most of the studies focused, on the search of nearby restaurants, shopping malls, hospitals, stores etc., by utilizing location of users as searching criteria. Problem with these studies is that users might not be satisfied by their results and the sole reason behind this might be the absence of user preferences in the search criteria. There exists some studies which focused user preferences along with user location and query time and proposed some frameworks but they are only limited to stores and their research cannot be scaled to other points like schools, hospitals, doctors , petrol pumps, gas station etc. Moreover there exist scalability issues in their recommended algorithms along with some data credibility issues in their public evaluations strategies. Our proposed research is going to present a novel location based searching technique not only for stores but for any point. The presented solution has overcome issues faced in previous research studies and possesses capability to search for “K” nearest points which are most preferable by user, by utilizing searching time as well as query location. Our research has proposed two feedback learning algorithms and one ranking algorithm. To increase the credibility of public evaluation score, system have utilized Google ranking approach while calculating the score of the point. To make user recommendations nonvolatile along with improving recommendations algorithm efficiency, proposed system have introduced item to item collaborative filtering algorithm. Through experimental evaluations on real dataset of yelp.com presented research have shown significant gain in performance and accuracy.

Hippocampal Contribution to Probabilistic Feedback Learning: Modeling Observation- and Reinforcement-based Processes

Simple probabilistic reinforcement learning is recognized as a striatum-based learning system, but in recent years, has also been associated with hippocampal involvement. The present study examined whether such involvement may be attributed to observation-based learning processes, running in parallel to striatum-based reinforcement learning. A computational model of observation-based learning (OL), mirroring classic models of reinforcement-based learning (RL), was constructed and applied to the neuroimaging dataset of Palombo, Hayes, Reid, & Verfaellie (2019). Hippocampal contributions to value-based learning: Converging evidence from fMRI and amnesia. Cognitive, Affective & Behavioral Neuroscience, 19(3), 523–536. Results suggested that observation-based learning processes may indeed take place concomitantly to reinforcement learning and involve activation of the hippocampus and central orbitofrontal cortex (cOFC). However, rather than independent mechanisms running in parallel, the brain correlates of the OL and RL prediction errors indicated collaboration between systems, with direct implication of the hippocampus in computations of the discrepancy between the expected and actual reinforcing values of actions. These findings are consistent with previous accounts of a role for the hippocampus in encoding the strength of observed stimulus-outcome associations, with updating of such associations through striatal reinforcement-based computations. Additionally, enhanced negative prediction error signaling was found in the anterior insula with greater use of OL over RL processes. This result may suggest an additional mode of collaboration between OL and RL systems, implicating the error monitoring network.

Decision Making Under Chronic Stress and Anxiety: State and Trait Anxiety Impact Contextual Updating but not Feedback Learning

Stress may alter executive functioning by causing structural and functional changes to the brain. Sub-optimal decisions made under high levels of stress and anxiety may act as a mediator for stress-related health effects. We examined the effect of three personality traits–chronic stress, state anxiety, and trait anxiety–on updating working memory and feedback learning across 330 participants, using electroencephalography (EEG). We hypothesized a decrease in P300 (updating working memory) and reward positivity (feedback learning) amplitudes with increasing chronic stress and anxiety scores. The three personality traits were not correlated with reward positivity amplitudes. Additionally, chronic stress had no effect on P300 amplitudes. However, state and trait anxiety were negatively correlated with P300 amplitudes. Anxiety appears to impact working memory processes, and this effect was amplified with decreasing anxiety score quantiles to reflect the tails of the distribution. Our results are evidence of the beginnings of a correlation between anxiety and the neural correlates of decision-making, offering insight into anxiety-related adverse health outcomes.

Accumbofrontal tract integrity is related to early life adversity and feedback learning

Abuse, neglect, exposure to violence, and other forms of early life adversity (ELA) are incredibly common and significantly impact physical and mental development. While important progress has been made in understanding the impacts of ELA on behavior and the brain, the preponderance of past work has primarily centered on threat processing and vigilance while ignoring other potentially critical neurobehavioral processes, such as reward-responsiveness and learning. To advance our understanding of potential mechanisms linking ELA and poor mental health, we center in on structural connectivity of the corticostriatal circuit, specifically accumbofrontal white matter tracts. Here, in a sample of 77 youth (Mean age = 181 months), we leveraged rigorous measures of ELA, strong diffusion neuroimaging methodology, and computational modeling of reward learning. Linking these different forms of data, we hypothesized that higher ELA would be related to lower quantitative anisotropy in accumbofrontal white matter. Furthermore, we predicted that lower accumbofrontal quantitative anisotropy would be related to differences in reward learning. Our primary predictions were confirmed, but similar patterns were not seen in control white matter tracts outside of the corticostriatal circuit. Examined collectively, our work is one of the first projects to connect ELA to neural and behavioral alterations in reward-learning, a critical potential mechanism linking adversity to later developmental challenges. This could potentially provide windows of opportunity to address the effects of ELA through interventions and preventative programming.

Reward learning and working memory: Effects of massed versus spaced training and post-learning delay period

Neuroscience research has illuminated the mechanisms supporting learning from reward feedback, demonstrating a critical role for the striatum and midbrain dopamine system. However, in humans, short-term working memory that is dependent on frontal and parietal cortices can also play an important role, particularly in commonly used paradigms in which learning is relatively condensed in time. Given the growing use of reward-based learning tasks in translational studies in computational psychiatry, it is important to understand the extent of the influence of working memory and also how core gradual learning mechanisms can be better isolated. In our experiments, we manipulated the spacing between repetitions along with a post-learning delay preceding a test phase. We found that learning was slower for stimuli repeated after a long delay (spaced-trained) compared to those repeated immediately (massed-trained), likely reflecting the remaining contribution of feedback learning mechanisms when working memory is not available. For massed learning, brief interruptions led to drops in subsequent performance, and individual differences in working memory capacity positively correlated with overall performance. Interestingly, when tested after a delay period but not immediately, relative preferences decayed in the massed condition and increased in the spaced condition. Our results provide additional support for a large role of working memory in reward-based learning in temporally condensed designs. We suggest that spacing training within or between sessions is a promising approach to better isolate and understand mechanisms supporting gradual reward-based learning, with particular importance for understanding potential learning dysfunctions in addiction and psychiatric disorders.