47 results about "Cognitive behaviour" patented technology

The invention discloses an individual cognitive ability prediction method and system based on dynamic function connection. The method comprises the following steps: acquiring functional magnetic resonance imaging data in a resting state; extracting an average time sequence signal of each brain region through a brain template after preprocessing of the functional magnetic resonance imaging data; calculating and constructing a dynamic function connection matrix by using a sliding time window; with the dynamic function connection matrix as input high-dimensional dynamic brain description originaldata, constructing a model combining convolution and a long-short-term memory network; and substituting a dynamic function matrix and corresponding cognitive behavior data into the model for trainingand testing so as to realize prediction of cognitive competence. According to the invention, dynamic information of brain function connection is fully utilized, the advantages of a deep learning method in big data processing are utilized, calculation is rapid, prediction effect is ideal, various behavior data of a population can be predicted, dependence on the absolute value of the amplitude of imaging signals is avoided, migrations among different MRI machines can be realized, and prediction performance is excellent.

The technology disclosed relates to a system and method for remembering content received from a patient for future use and understood by computerized natural language processing during a chatbot therapy session. The system includes a graph of entities and relationships in an artificial memory graph data structure. The entity nodes in the graph represent entities that are related to a patient root node representing the patient. The entity nodes are connected to patient root node by one or more relationship edges that have named roles. The entity nodes and/or relationship edges can be slotted for at least a distinguishing name, a patient sentiment towards the entity and an entity state.

The invention relates to a BDI modeling method and device based on a knowledge graph, equipment and a medium. The method comprises the steps that: a simulation task of a simulation model is acquired; perception elements are extracted from the simulation task, and a perception space map is established according to the perception elements; action elements are extracted from the simulation task and the sensing space map, and an action space map is established according to the action elements; cognitive elements are extracted from the simulation task, the perception space map and the action space map, and a cognitive space map is established according to the cognitive elements; according to the perception space map, a belief in BDI is constructed; according to the perception space map, the action space map and the cognitive space map, wishes and intentions in BDI are constructed; and according to the constructed belief, wish, intention and action space atlas, a cognitive behavior atlas of the simulation model agent is modeled. By adopting the method, the behaviors and the relationship between the behaviors can be described, and the method is realized in a mapping low-code mode.

A method and system are disclosed for a novel architecture in which competing suggestions, possibly generated by competing systems, are selected by a Cognitive Unit (CU). The CU observes the user context and learns which contextual circumstances affect the user's cognitive behaviour. Majority of the traditional models trained over multiple users fail to represent the individual because (1) they ignore personal bias toward certain decisions and (2) they don't have complete visibility of all options available to users (i.e. from competitive systems). The invention is ideally suited to interact with several other products as more and more modern products are using AIs to drive the user experience. That shifts traditional HCI towards a novel form of interaction that we call human-AI interaction (HAII). When applied to user experience, predictive models make decisions on users' behalf attempting to minimise user interaction while guiding them toward the completion of predefined funnels. This invention presents a novel approach using a personal model capable of partially replicating cognitive behaviours by learning personal situational biases using the CU. By leveraging the personal biases, the system allows the AI to learn situational biases otherwise not available to predictive models. Once trained cognitive unit can supplement the decision-making process of the user.

The invention discloses a research method for evaluating the cognitive performance of a driver based on driving behaviors. The method comprises the following steps: step one, carrying out data preprocessing of a collected experimental database; step two, in a regression function, selecting features by using a Pearson coefficient, constructing functions of lasso regression and rid regression, and performing cross validation to improve the accuracy and obtain predicted cognitive performance; step three, predicting cognitive behaviors of the driver through two methods; and step four, analyzing behaviors of the driver, namely analyzing an MMSE target, and performing predicting in an overall road, a residence and an intersection, especially in a residence road and an intersection.

Provided are an Eye Movement analysis with Hidden Markov Model (EMHMM) with co-clustering, an Eye Movement analysis with Switching Hidden Markov Models (EMSHMM) to analyze eye movement data in cognitive tasks involving stimuli with different feature layouts and cognitive state changes, a switching hidden Markov model (SHMM) to capture a participant's cognitive state transitions during the task and an EMSHMM to assess preference decision-making tasks with two or more cognitive states. The EMSHMM provides quantitative measures of individual differences in cognitive behavior/style, making a significant impact on the use of eye tracking to study cognitive behavior across disciplines.

The embodiment of the invention discloses a CBT(cognitive behavioral therapy)-based psychological therapy control method. The method comprises the following steps: acquiring a current learned psychological learning module of a user according to an unlocking instruction input by the user; obtaining the recent unlocking time of the learned psychological learning module, and judging whether the difference between the recent unlocking time and the current time is greater than or equal to a preset interval duration or not; if so, selecting one psychological learning module from unlearned psychological learning modules according to a preset learning sequence to serve as a psychological learning module to be learned; unlocking the psychological learning module to be learned, and displaying pathological explanation data and self-training data of the psychological learning module to be learned to the user in a man-machine conversation mode, wherein the psychological learning module is designedon the basis of the diagnosis standard and cognitive behavior therapy of the fifth-edition psychosis statistical diagnosis manual on the post-traumatic stress disorder. The method can help the user torelieve PTSD symptoms based on the cognitive behavior therapy.

The invention provides a depression cognitive behavior training system, which comprises a login module used for acquiring user login request information to realize login; a cognitive behavior training module, which is used for realizing cognitive behavior training on a user in a training interface according to the current training stage of the user and the training content corresponding to the training stage after successful login; and an emotion recording module, which is used for entering an audio recording page before each training stage is started and after each training stage is completed, and performing analysis according to the voice data recorded by the user to obtain a depression scale score of the user. In the training process, the data fed back by the user is obtained comprehensively, objectivity is high, and accuracy is high. The invention further provides a voice data processing method which can accurately recognize the emotion change of the user.

The invention provides an EBAS (Environmental Enrichment Based Acquisition System) evaluation system and discloses a comprehensive testing method for rat cognitive behaviors. The method is a comprehensive testing method for cognitive behaviors such as emotions, anxiety, society and the like of rats on the basis of EE (Environmental Enrichment), the testing is carried out under the condition of weakening the external stress environment without artificial separation, the rat cognitive behaviors are completely instinctive exploratory behaviors, and the method can be used for exploring material basis of neural circuit of hippocampus CA1/CA3 and prefrontal cortex (PFC), is scientific for evaluating animal behavior change under actions of various drugs and medical intervention and can offer assistance for investigation of influence of drugs or medical intervention on change of cranial nerve or synaptic transmitter.

To provide a an animal with schizophrenic sensorimotor and behavioral abnormalities, a specific protein factor inhibiting development of brain function is administrated to a juvenile animal in the stage of its development and an animal exhibiting sensorimotor and behavioral abnormalities is prepared. As the sensorimotor and behavioral abnormalities observed in the animal of the present invention is very similar to schizophrenia, the animal is useful for development of an anti-schizophrenic medicine and a diagnostic agent for schizophrenia.

A tinnitus management system and a computer-implemented method for managing tinnitus are described. The system comprises a survey developing unit, a progress determining unit, a performance evaluation unit, and a feedback unit. The survey developing unit develops and/or adapts, after a first training section, a subsequent, second training section or a following part of a survey based on a performance indicator determined for the first training section.