CN103530505B - Human brain language cognition modeling method - Google Patents

Abstract

The invention discloses a human brain language cognition modeling method, which comprises the following steps: initializing a cognitive state instance; mapping a probability distribution between the activation features and the observed data; defining a brain hidden state cognition model; and analyzing parameters of the implicit cognitive model. In the cognitive modeling process, input stimulation, observation results and a hidden state cognitive state are defined as a dynamic event related triple time sequence, namely: the cognitive stimulation task time sequence, the observation characteristic time sequence and the recessive cognitive state time sequence are connected through a group of probability distributions, and the collected brain data is not taken as static information to be counted, so that the cognitive modeling method provided by the invention does not need to meet basic assumption based on statistics, is still established under the condition of small sample data, ensures the correctness of a cognitive analysis result, and solves the cognitive modeling problem under the small sample data. The invention improves the accuracy of cognitive modeling and provides an effective way for complex cognitive analysis.

Description

A method for modeling human brain language cognition

technical field

The invention relates to a human brain language cognition technology, in particular to a human brain language cognition modeling method.

Background technique

The most direct reflection of cognition in the human brain is the degree of activation or inhibition of brain regions. From the perspective of neuroinformatics, the key to the study of language cognition is to study the state of brain regions in the process of language cognition in the human brain and the process of these state migrations. , one of the most important methods is to design experiments according to the requirements of neuroinformatics, stimulate the cognitive functions of the subjects (mostly volunteers from a specific group), and collect functional data, and then use certain modeling techniques Analyze these data. Brain imaging techniques developed in recent years (positron emission tomography, functional nuclear magnetic resonance) provide means for people to directly observe and analyze the functional activities of the human brain. The current brain imaging analysis technology that cognitive research relies on is mainly to generate corresponding t-test brain maps through statistics-based modeling tools such as statistical parametric map systems and functional neuroimaging analysis systems to discover which brain regions control a certain cognitive function. . These statistical-based cognitive modeling methods are based on a basic assumption for statistical analysis of sample data. This assumption holds that: given a certain cognitive stimulus, the cognitive state of the stimulus must appear in the human brain cognition. In fact, the observed cognitive activation features are event-related, and the cognitive state has the characteristics of superposition and small data samples, which do not have strict statistical significance and do not meet the prerequisites of statistical cognitive modeling methods. It is easy to lead to wrong cognitive results.

Contents of the invention

In order to solve the above-mentioned problems in the prior art, the present invention is to design a human brain language cognition modeling method suitable for small samples and having dynamic time characteristics.

In order to achieve the above object, the technical solution of the present invention is as follows: a method for modeling human brain language cognition, comprising the following steps:

A. Cognitive state instance initialization

Use h to identify the cognitive state in the human brain. Since h cannot be directly observed by instruments and equipment, h is called the implicit cognitive state, and the implicit cognitive state h is expressed as a quadruple <W,d, Θ,Ω>, where: d represents the continuous scalar of h in time; W is the reflection moment of space-time dimension d×V, where V is the dimension of the brain area involved in h, and its spatial dimension component is v; Θ is the In the parameter space, Ω is the value range of the parameter space; in the process of cognitive observation, when a stimulus Δ is input, there will be a certain time lag in the activation of the cognitive state of the brain; for the cognitive state in the activated state, it is an instance of the cognitive state , marked by ξ, considering the time factor of the implicit cognitive state h, a cognitive state instance is a triplet <h,λ,o>, where: h is the corresponding implicit cognitive state, λ is the experimental stimulus Input the time point, o is the time offset of the cognitive state, the starting time of the cognitive state instance ξ is λ+o, and its duration d is the same as the duration of the corresponding implicit cognitive state h, both use d( h) means;

B. Mapping the probability distribution between activation features and observed data

Assume that in the course of the cognitive experiment, the observation of the subject’s brain activation characteristics b _t through the imaging device during the t period is the result of the joint action of L cognitive state instances, and denote the L by ξ ₁ , ξ ₂ ,…,ξ _L cognition state instance, then record b _t ={ξ ₁ ,ξ ₂ ,…,ξ _L }; b _t and the component s _tv of time period t and space dimension component v in the observed time series S follow normal distribution, namely:

s _tv ～N(μ _tv (b _t ),σ _v ) (1)

Among them, σ _v is the standard deviation, which reflects the noise distribution related to the observed time series and has time independence; μ _tv (b _t ) reflects the superposition effect of all relevant cognitive states in the corresponding period, that is, it satisfies the following formula:

${\mathrm{<span\; class="notranslate">\; \&mu;</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}\mathrm{<span\; class="notranslate">\; v</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; \&xi;</span>}<span\; class="notranslate">\; \&Element;</span>{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}\stackrel{\mathrm{<span\; class="notranslate">\; d</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&xi;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}\mathrm{<span\; class="notranslate">\; \&delta;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&lambda;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&xi;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; o</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&xi;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; \&tau;</span>}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; w</span>}}_{\mathrm{<span\; class="notranslate">\; \&tau;</span>}\mathrm{<span\; class="notranslate">\; v</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&xi;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>$

Where δ(.) is an indicator function, if the independent variable is true, the function result is 1, otherwise it is 0; h(ξ) identifies the implicit cognitive state corresponding to the cognitive state instance ξ; w _τv (h(ξ) ) is the v component of the implicit cognitive state reflection moment W(h(ξ)) at the τth time step of the identification period t.

C. Define the implicit cognitive model of the brain

Define the brain implicit cognitive model as a six-tuple <H, Φ, I, S, Ξ, Γ>, marked by HCM, where: H is the set of implicit cognitive states; Φ is the parameter of the implicit cognitive model Vectors, they depend on the experimental design parameters, namely: the type of stimulus, the time point of the input stimulus; S is the observation time series; Ξ is the stimulus sequence composed of the input stimulus Δ; I is the time series of cognitive state instances, due to cognitive The activation time point of the state instance corresponds to the observation time series, so the element b _t in I is abbreviated as b; Γ is the variance set <σ ₁ ,σ ₂ ,…,σ _v >, where σ _v is the formula (1) The standard deviation in , reflects the noise distribution of the time series; HCM defines a triple time series in which I, S, and Ξ constitute probability correlations, is a random variable defined by I, then the probability distribution between the observed brain activation features and the implicit cognitive state is:

$\mathrm{<span\; class="notranslate">\; P</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; S</span>}<span\; class="notranslate">\; |</span>\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; \&Delta;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; b</span>}<span\; class="notranslate">\; \&Element;</span>\mathrm{<span\; class="notranslate">\; I</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}\mathrm{<span\; class="notranslate">\; P</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; S</span>}<span\; class="notranslate">\; |</span>\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; ,</span>\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; I</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; b</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; P</span>}<span\; class="notranslate">\; (</span>\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; I</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; b</span>}<span\; class="notranslate">\; |</span>\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; \&Delta;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; )</span>$

in,

$\mathrm{<span\; class="notranslate">\; P</span>}<span\; class="notranslate">\; (</span>\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; I</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; b</span>}<span\; class="notranslate">\; |</span>\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; \&Delta;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{\underset{\mathrm{<span\; class="notranslate">\; \&xi;</span>}<span\; class="notranslate">\; \&Element;</span>\mathrm{<span\; class="notranslate">\; b</span>}}{\mathrm{<span\; class="notranslate">\; \&Pi;</span>}}\mathrm{<span\; class="notranslate">\; P</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&xi;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; |</span>\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; \&Delta;</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; P</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; o</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&xi;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; |</span>\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&xi;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; \&Delta;</span>}<span\; class="notranslate">\; )</span>}{\underset{{\mathrm{<span\; class="notranslate">\; b</span>}}^{<span\; class="notranslate">\; ,</span>}<span\; class="notranslate">\; \&Element;</span>\mathrm{<span\; class="notranslate">\; I</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}\underset{{\mathrm{<span\; class="notranslate">\; \&xi;</span>}}^{<span\; class="notranslate">\; ,</span>}<span\; class="notranslate">\; \&Element;</span>{\mathrm{<span\; class="notranslate">\; b</span>}}^{<span\; class="notranslate">\; ,</span>}}{\mathrm{<span\; class="notranslate">\; \&Pi;</span>}}\mathrm{<span\; class="notranslate">\; P</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&xi;</span>}}^{<span\; class="notranslate">\; ,</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; |</span>\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; \&Delta;</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; P</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; o</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&xi;</span>}}^{<span\; class="notranslate">\; ,</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; |</span>\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&xi;</span>}}^{<span\; class="notranslate">\; ,</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; \&Delta;</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 4</span>}<span\; class="notranslate">\; )</span>$

In the formula, P(h(ξ)|HCM,Δ) is the conditional probability that the conditional stimulus Δ activates the implicit cognitive state h and the corresponding cognitive state instance ξ in the process of Chinese cognitive experiment, P(o(ξ)| h(ξ), HCM, Δ) is the corresponding time-delay conditional probability; the modeling process of the hidden cognitive model needs to determine its conditional probability for a specific cognitive process, and at the same time determine the parameter vector of the hidden cognitive model , perform maximum likelihood estimation on the parameters of HCM; for HCM, according to Bayes' theorem:

$\mathrm{<span\; class="notranslate">\; P</span>}<span\; class="notranslate">\; (</span>\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; I</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; b</span>}<span\; class="notranslate">\; |</span>\mathrm{<span\; class="notranslate">\; S</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; \&Delta;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; P</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; S</span>}<span\; class="notranslate">\; |</span>\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; I</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; b</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; P</span>}<span\; class="notranslate">\; (</span>\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; I</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; b</span>}<span\; class="notranslate">\; |</span>\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; \&Delta;</span>}<span\; class="notranslate">\; )</span>}{\underset{{\mathrm{<span\; class="notranslate">\; b</span>}}^{<span\; class="notranslate">\; \&prime;</span>}<span\; class="notranslate">\; \&Element;</span>\mathrm{<span\; class="notranslate">\; I</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}\mathrm{<span\; class="notranslate">\; P</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; S</span>}<span\; class="notranslate">\; |</span>\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; I</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; b</span>}}^{<span\; class="notranslate">\; \&prime;</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; P</span>}<span\; class="notranslate">\; (</span>\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; I</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; b</span>}}^{<span\; class="notranslate">\; \&prime;</span>}<span\; class="notranslate">\; |</span>\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; \&Delta;</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 5</span>}<span\; class="notranslate">\; )</span>$

D. Analysis of implicit cognitive model parameters

The hidden cognitive model determines the hidden cognitive state probability and its parameters that match the observed time series through learning and training, that is, minimizes the objective function:

$\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&Phi;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; v</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; V</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}\underset{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; T</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}\underset{\mathrm{<span\; class="notranslate">\; b</span>}<span\; class="notranslate">\; \&Element;</span>\mathrm{<span\; class="notranslate">\; I</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}\frac{\mathrm{<span\; class="notranslate">\; P</span>}<span\; class="notranslate">\; (</span>\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; I</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; b</span>}<span\; class="notranslate">\; |</span>\mathrm{<span\; class="notranslate">\; S</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; \&Phi;</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; 2</span>}{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; v</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}\mathrm{<span\; class="notranslate">\; v</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; \&mu;</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}\mathrm{<span\; class="notranslate">\; v</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; b</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 6</span>}<span\; class="notranslate">\; )</span>$

In the formula, Φ represents the mapping probability parameter among the triple time series of cognitive stimulus task time series, observation feature time series, and implicit cognitive state time series in the hidden cognitive model, and T is the total time of the hidden cognitive model.

D1. Initialize the search group:

The population formed by n search individuals is used to perform parallel search in the space determined by Φ, and the maximum speed of the search individual v _max =r; when time step t=0, the n search individuals are randomly initialized, that is, The position p _ij of the jth dimension of the i search individual =Rand(-r,r) and the velocity v _ij of the jth dimension of the i search individual =Rand(-v _max ,v _max ); r is the domain of definition, t is the time step;

D2. If the predetermined maximum number of iterations is met or the result of 10 iterations is not improved, output the results p* and f(p*) and end the calculation; otherwise, go to step D3;

In the formula, p* is the best individual state in the group composed of search individuals, and f(p*) is the fitness value determined by the best individual state in the group composed of search individuals.

D3. Calculate the fitness value of the search individual

Calculate the fitness value of the search individual according to formula (6);

D4. Optimal preservation

Let t=t+1, implement the optimal preservation strategy, namely:

${\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}^{<span\; class="notranslate">\; \#</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; arg</span>}\underset{\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; m</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}^{<span\; class="notranslate">\; \#</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>$

${\mathrm{<span\; class="notranslate">\; p</span>}}^{<span\; class="notranslate">\; *</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; arg</span>}\underset{\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; m</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; p</span>}}^{<span\; class="notranslate">\; *</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span><span\; class="notranslate">\; ...</span><span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>$

In the formula, p* is the best individual state in the group composed of search individuals, p _i ^# is the iterative state of the ith search individual from t=0 to the current best state, f(p*) is the best state of the search individual The fitness value determined by the best individual state in the formed group;

D5. State transfer joint operation

Introduce the group advantages of search individuals to search dynamically, and perform state transition joint operations according to formulas (7) and (8) for each dimension of each search individual:

${\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; wv</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}^{<span\; class="notranslate">\; \#</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}^{<span\; class="notranslate">\; *</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 7</span>}<span\; class="notranslate">\; )</span>$

x _ij (t) = v _ij (t) + x _ij (t-1) (8)

Go to step D2.

Compared with the prior art, the present invention has the following beneficial effects:

1. In the process of cognitive modeling, the present invention defines input stimuli, observation results, and hidden cognitive states as triple time series related to dynamic events, namely: cognitive stimulation task time series, observation characteristic time series, recessive Cognitive state time series, and these triple time series are connected through a set of probability distributions, the collected brain data is not regarded as static information for statistics, so the cognitive modeling method of the present invention does not need to meet the basic assumptions based on statistics, It is still established under the condition of small sample data, which ensures the correctness of cognitive analysis results, thus solving the problem of cognitive modeling under small sample data.

2. The present invention corresponds the time series brain activation data observed in the cognitive experiment to the reflection moment set of the Chinese cognitive state instance, wherein each state instance plays a role according to the probability at a certain time interval. Each state instance during the time period influences the observed activation characteristics at the time of data collection. If multiple state instances act almost simultaneously, the observed activation features are the superposition of effects produced by all related state instances, which provides a basis for cognitive state subdivision, thereby improving the accuracy of cognitive modeling, and providing a basis for complex cognitive states. Knowledge analysis provides an effective way.

3. The brain hidden state cognitive model proposed by the present invention introduces the overall T of the cognitive state of the model, the observation period t of the cognitive state of the model and its time step τ, the input time point λ of the experimental stimulus, and the time offset of the cognitive state oThe amount of time, so that the model has a cognitive state time effect, is an event-related time-series dynamic observation method based on the cognitive experimental stimulus series, and the observed cognitive activation characteristics are used during the cognitive detection process The sequence is mapped to the influence of one or more hidden cognitions on brain activation at the same time period, and the observation sequence is not in one-to-one correspondence with the hidden cognitive state sequence, but is related to the stimulus and activates different hidden cognitions with a certain probability. Cognitive state provides the basis for the analysis of cognitive dynamics.

Description of drawings

The present invention has 2 accompanying drawings, wherein:

Fig. 1 is a flow chart of the present invention.

Fig. 2 is an example diagram of triple time series mapping in the present invention.

detailed description

The present invention will be further described below in conjunction with the accompanying drawings. As shown in Figures 1-2, the model method of the present invention includes triple time series, namely: the upper layer stimulus time series Ξ, the middle layer hidden state cognition time series I, and the observation time series S, and there is a hidden state cognition superposition in the observation sequence , that is, when the middle-level implicit cognitive sequence I is mapped to the observation sequence S, there are multiple arrows pointing to the same mapping area, but this triple time series has a basic mapping relationship, and this relationship is determined by the probability parameter Φ, through step D1 ~D5 analyzes the probability mapping relationship determined by its probability parameters according to formula (6). Give a simple example: the input stimulus time series Ξ is {Δ ₁ , Δ ₂ , Δ ₃ }; the observation time series S is {s ₁ , s ₂ , s ₃ }; the hidden cognitive instance time series I is {b ₁ ,b ₂ ,b ₃ }, where b ₁ ={ξ ₁ ⁽¹⁾ ,ξ ₂ ⁽¹⁾ ,ξ _L3 ⁽¹⁾ },b ₂ ={ξ ₁ ⁽²⁾ ,ξ ₂ ⁽²⁾ ,ξ ₃ ⁽²⁾ , ξ ₄ ⁽²⁾ }, b ₃ = {ξ ₁ ⁽³⁾ , ξ ₂ ⁽³⁾ , ξ ₃ ⁽³⁾ }, ξ with a parenthesized superscript indicates that it belongs to the corresponding cognitive instance ; The hidden cognitive state set H corresponding to I is {h ₁ , h ₂ , h ₃ , h ₄ }. Determine the probability parameter Φ according to formula (6) through steps D1~D5, the main results are: ξ ₁ ⁽¹⁾ →s ₁ (0.82),ξ ₂ ⁽¹⁾ →s ₁ (0.91),ξ ₃ ⁽¹⁾ →s ₂ (0.86);

ξ ₁ ⁽²⁾ → s ₂ (0.90), ξ ₂ ⁽²⁾ → s ₂ (0.76), ξ ₃ ⁽²⁾ → s ₃ (0.84), ξ ₄ ⁽²⁾ → s ₃ (0.69);

ξ ₁ ⁽³⁾ → s ₃ (0.78), ξ ₂ ⁽³⁾ → s ₃ (0.85), ξ ₃ ⁽³⁾ → s ₃ (0.80);

ξ ₁ ⁽¹⁾ → h ₁ (0.90), ξ ₂ ⁽¹⁾ → h ₂ (0.74), ξ ₃ ⁽¹⁾ → h ₃ (0.92);

ξ ₁ ⁽²⁾ → h ₁ (0.97), ξ ₂ ⁽²⁾ → h ₂ (0.88), ξ ₃ ⁽²⁾ → h ₃ (0.79), ξ ₄ ⁽²⁾ → h ₄ (0.85);

ξ ₁ ⁽³⁾ → h ₁ (0.93), ξ ₂ ⁽³⁾ → h ₃ (0.87), ξ ₃ ⁽³⁾ → h ₄ (0.91).

In the formula: "→" indicates that there is a mapping relationship, and the numbers in brackets indicate the corresponding mapping probability.

Claims (1)

1. a human brain language cognition modeling method, is characterized in that: comprise the following steps: A. Cognitive state instance initialization Use h to identify the cognitive state in the human brain. Since h cannot be directly observed by instruments and equipment, h is called the implicit cognitive state, and the implicit cognitive state h is expressed as a quadruple <W,d, Θ,Ω>, where: d represents the continuous scalar of h in time; W is the reflection moment of space-time dimension d×V, where V is the dimension of the brain area involved in h, and its spatial dimension component is v; Θ is the In the parameter space, Ω is the value range of the parameter space; in the process of cognitive observation, when a stimulus Δ is input, there will be a certain time lag in the activation of the cognitive state of the brain; for the cognitive state in the activated state, it is an instance of the cognitive state , marked by ξ, considering the time factor of the implicit cognitive state h, a cognitive state instance is a triplet <h,λ,o>, where: h is the corresponding implicit cognitive state, λ is the experimental stimulus Input the time point, o is the time offset of the cognitive state, the starting time of the cognitive state instance ξ is λ+o, and its duration d is the same as the duration of the corresponding implicit cognitive state h, both use d( h) means; B. Mapping the probability distribution between activation features and observed data Assume that in the course of the cognitive experiment, the observation of the subject’s brain activation characteristics b _t through the imaging device during the t period is the result of the joint action of L cognitive state instances, and denote the L by ξ ₁ , ξ ₂ ,…,ξ _L cognition state instance, then record b _t ={ξ ₁ ,ξ ₂ ,…,ξ _L }; b _t and the component s _tv of time period t and space dimension component v in the observed time series S follow normal distribution, namely: s _tv ～N(μ _tv (b _t ),σ _v ) (1) Among them, σ _v is the standard deviation, which reflects the noise distribution related to the observed time series and has time independence; μ _tv (b _t ) reflects the superposition effect of all relevant cognitive states in the corresponding period, that is, it satisfies the following formula: ${\mathrm{<span\; class="notranslate">\; \&mu;</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}\mathrm{<span\; class="notranslate">\; v</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; \&xi;</span>}<span\; class="notranslate">\; \&Element;</span>{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}}{<span\; class="notranslate">\; \&Sigma;</span>}\stackrel{\mathrm{<span\; class="notranslate">\; d</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&xi;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}\mathrm{<span\; class="notranslate">\; \&delta;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&lambda;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&xi;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; o</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&xi;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; \&tau;</span>}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; w</span>}}_{\mathrm{<span\; class="notranslate">\; \&tau;</span>}\mathrm{<span\; class="notranslate">\; v</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&xi;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>$ Where δ(.) is an indicator function, if the independent variable is true, the function result is 1, otherwise it is 0; h(ξ) identifies the implicit cognitive state corresponding to the cognitive state instance ξ; w _τv (h(ξ) ) is the v component of the implicit cognitive state reflection moment W(h(ξ)) on the τth time step of the identification period t; C. Define the implicit cognitive model of the brain Define the brain implicit cognitive model as a six-tuple <H, Φ, I, S, Ξ, Γ>, marked by HCM, where: H is the set of implicit cognitive states; Φ is the parameter of the implicit cognitive model Vectors, they depend on the experimental design parameters, namely: the type of stimulus, the time point of the input stimulus; S is the observation time series; Ξ is the stimulus sequence composed of the input stimulus Δ; I is the time series of cognitive state instances, due to cognitive The activation time point of the state instance corresponds to the observation time series, so the element b _t in I is abbreviated as b; Γ is the variance set <σ ₁ ,σ ₂ ,…,σ _v >, where σ _v is the formula (1) The standard deviation in , reflects the noise distribution of the time series; HCM defines a triple time series in which I, S, and Ξ constitute probability correlations, is a random variable defined by I, then the probability distribution between the observed brain activation features and the implicit cognitive state is: $\mathrm{<span\; class="notranslate">\; P</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; S</span>}<span\; class="notranslate">\; |</span>\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; \&Delta;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; b</span>}<span\; class="notranslate">\; \&Element;</span>\mathrm{<span\; class="notranslate">\; I</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}\mathrm{<span\; class="notranslate">\; P</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; S</span>}<span\; class="notranslate">\; |</span>\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; ,</span>\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; I</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; b</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; P</span>}<span\; class="notranslate">\; (</span>\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; I</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; b</span>}<span\; class="notranslate">\; |</span>\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; \&Delta;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; )</span>$ in, $\mathrm{<span\; class="notranslate">\; P</span>}<span\; class="notranslate">\; (</span>\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; I</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; b</span>}<span\; class="notranslate">\; |</span>\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; \&Delta;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{\underset{\mathrm{<span\; class="notranslate">\; \&xi;</span>}<span\; class="notranslate">\; \&Element;</span>\mathrm{<span\; class="notranslate">\; b</span>}}{<span\; class="notranslate">\; \&Pi;</span>}\mathrm{<span\; class="notranslate">\; P</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&xi;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; |</span>\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; \&Delta;</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; P</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; o</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&xi;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; |</span>\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&xi;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; \&Delta;</span>}<span\; class="notranslate">\; )</span>}{\underset{{\mathrm{<span\; class="notranslate">\; b</span>}}^{<span\; class="notranslate">\; ,</span>}<span\; class="notranslate">\; \&Element;</span>\mathrm{<span\; class="notranslate">\; I</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}\underset{{\mathrm{<span\; class="notranslate">\; \&xi;</span>}}^{<span\; class="notranslate">\; ,</span>}<span\; class="notranslate">\; \&Element;</span>{\mathrm{<span\; class="notranslate">\; b</span>}}^{<span\; class="notranslate">\; ,</span>}}{<span\; class="notranslate">\; \&Pi;</span>}\mathrm{<span\; class="notranslate">\; P</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&xi;</span>}}^{<span\; class="notranslate">\; ,</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; |</span>\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; \&Delta;</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; P</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; o</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&xi;</span>}}^{<span\; class="notranslate">\; ,</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; |</span>\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&xi;</span>}}^{<span\; class="notranslate">\; ,</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; \&Delta;</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 4</span>}<span\; class="notranslate">\; )</span>$ In the formula, P(h(ξ)|HCM,Δ) is the conditional probability that the conditional stimulus Δ activates the implicit cognitive state h and the corresponding cognitive state instance ξ in the process of Chinese cognitive experiment, P(o(ξ)| h(ξ), HCM, Δ) is the corresponding time-delay conditional probability; the modeling process of the hidden cognitive model needs to determine its conditional probability for a specific cognitive process, and at the same time determine the parameter vector of the hidden cognitive model , perform maximum likelihood estimation on the parameters of HCM; for HCM, according to Bayes' theorem: $\mathrm{<span\; class="notranslate">\; P</span>}<span\; class="notranslate">\; (</span>\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; I</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; b</span>}<span\; class="notranslate">\; |</span>\mathrm{<span\; class="notranslate">\; S</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; \&Delta;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; P</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; S</span>}<span\; class="notranslate">\; |</span>\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; I</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; b</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; P</span>}<span\; class="notranslate">\; (</span>\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; I</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; b</span>}<span\; class="notranslate">\; |</span>\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; \&Delta;</span>}<span\; class="notranslate">\; )</span>}{\underset{{\mathrm{<span\; class="notranslate">\; b</span>}}^{<span\; class="notranslate">\; \&prime;</span>}<span\; class="notranslate">\; \&Element;</span>\mathrm{<span\; class="notranslate">\; I</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}\mathrm{<span\; class="notranslate">\; P</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; S</span>}<span\; class="notranslate">\; |</span>\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; I</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; b</span>}}^{<span\; class="notranslate">\; \&prime;</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; P</span>}<span\; class="notranslate">\; (</span>\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; I</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; b</span>}}^{<span\; class="notranslate">\; \&prime;</span>}<span\; class="notranslate">\; |</span>\mathrm{<span\; class="notranslate">\; h</span>}\mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; \&Delta;</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 5</span>}<span\; class="notranslate">\; )</span>$ D. Analysis of implicit cognitive model parameters The hidden cognitive model determines the hidden cognitive state probability and its parameters that match the observed time series through learning and training, that is, minimizes the objective function: $\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&Phi;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; v</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; V</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}\underset{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; T</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}\underset{\mathrm{<span\; class="notranslate">\; b</span>}<span\; class="notranslate">\; \&Element;</span>\mathrm{<span\; class="notranslate">\; I</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}\frac{\mathrm{<span\; class="notranslate">\; P</span>}<span\; class="notranslate">\; (</span>\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; I</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; b</span>}<span\; class="notranslate">\; |</span>\mathrm{<span\; class="notranslate">\; S</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; \&Phi;</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; 2</span>}{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; v</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}\mathrm{<span\; class="notranslate">\; v</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; \&mu;</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}\mathrm{<span\; class="notranslate">\; v</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; b</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 6</span>}<span\; class="notranslate">\; )</span>$ In the formula, Φ represents the mapping probability parameter among the triple time series of cognitive stimulus task time series, observation feature time series, and implicit cognitive state time series in the implicit cognitive model, and T is the total time of the implicit cognitive model; D1. Initialize the search group: The population formed by n search individuals is used to perform parallel search in the space determined by Φ, and the maximum speed of the search individual v _max =r; when time step t=0, the n search individuals are randomly initialized, that is, The position p _ij of the jth dimension of the i search individual =Rand(-r,r) and the velocity v _ij of the jth dimension of the i search individual =Rand(-v _max ,v _max ); r is the domain of definition, t is the time step; D2. If the predetermined maximum number of iterations is met or the result of 10 iterations is not improved, output the results p* and f(p*) and end the calculation; otherwise, go to step D3; In the formula, p* is the best individual state in the group composed of searching individuals, and f(p*) is the fitness value determined by the best individual state in the group composed of searching individuals; D3. Calculate the fitness value of the search individual Calculate the fitness value of the search individual according to formula (6); D4. Optimal preservation Let t=t+1, implement the optimal preservation strategy, namely: ${\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}^{<span\; class="notranslate">\; \#</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; arg</span>}\underset{\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; m</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}^{<span\; class="notranslate">\; \#</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>$ ${\mathrm{<span\; class="notranslate">\; p</span>}}^{<span\; class="notranslate">\; *</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; arg</span>}\underset{\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; m</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; p</span>}}^{<span\; class="notranslate">\; *</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span><span\; class="notranslate">\; ...</span><span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>$ In the formula, p* is the best individual state in the group composed of search individuals, p _i ^# is the iterative state of the ith search individual from t=0 to the current best state, f(p*) is the best state of the search individual The fitness value determined by the best individual state in the formed group; D5. State transfer joint operation Introduce the dynamic search of group advantages composed of search individuals, and perform state transition joint operations according to formulas (7) and (8) for each dimension of each search individual: ${\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; wv</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}^{<span\; class="notranslate">\; \#</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; r</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}^{<span\; class="notranslate">\; *</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 7</span>}<span\; class="notranslate">\; )</span>$ x _ij (t) = v _ij (t) + x _ij (t-1) (8) Go to step D2.