fMRI Investigation on Gradual Change of Awareness States in Implicit Sequence Learning

The establishment of the general microexpression recognition ability and its relevant brain activity

Microexpressions are very transitory expressions lasting about 1/25∼1/2 s, which can reveal people's true emotions they try to hide or suppress. The PREMERT (pseudorandom ecological microexpression recognition test) could test the individual's microexpression recognition ability with six microexpression Ms (the mean of accuracy rates of a microexpression type under six expression backgrounds), and six microexpression SDs (the standard deviation of accuracy rates of this microexpression type under six expression backgrounds), but it and other studies did not explore the general microexpression recognition ability (the GMERA) or could not test the GMERA effectively. Therefore, the current study put forward and established the GMERA with the behavioral data of the PREMERT. The spontaneous brain activity in the resting state is a stable index to measure individual cognitive characteristics. Therefore, the current study explored the relevant resting-state brain activity of the GMERA indicators to prove that GMERA is an individual cognitive characteristic from brain mechanisms with the neuroimaging data of the PREMERT. The results showed that (1) there was a three-layer hierarchical structure in human microexpression recognition ability: The GMERA (the highest layer); recognition of a type of microexpression under different expression backgrounds (the second layer); and recognition of a certain microexpression under a certain expression background (the third layer). A common factor GMERA was extracted from the six microexpression types recognition in PREMERT. Four indicators of the GMERA were calculated from six microexpression Ms and six microexpression SDs, such as GMERAL (level of GMERA), GMERAF (fluctuation of GMERA), GMERAB (background effect of GMERA), and GMERABF (fluctuation of GMERAB), which had good parallel-forms reliability, calibration validity, and ecological validity. The GMERA provided a concise and comprehensive overview of the individual's microexpression recognition ability. The PREMERT was proved as a good test to measure the GMERA. (2) ALFFs (the amplitude of low-frequency fluctuations) in both eyes-closed and eyes-opened resting-states and ALFFs-difference could predict the four indicators of the GMERA. The relevant resting-state brain areas were some areas of the expression recognition network, the microexpression consciousness and attention network, and the motor network for the change from expression backgrounds to microexpression. (3) The relevant brain areas of the GMERA and different types of microexpression recognition belonged to the three cognitive processes, but the relevant brain areas of the GMERA were the "higher-order" areas to be more concise and critical than those of different types of microexpression recognition.

Exploring an online method of measuring implicit sequence-learning consciousness

Existing methods for measuring implicit sequence-learning consciousness are conducted offline. Based on the traditional measurement of cued-generation task, this study implemented an online measurement method by converting a generation task into a forced-choice task to observe the dynamic changes of consciousness in the implicit sequence-learning process. In this study, we compared the performance of online measurement task and traditional sequence-learning tasks in 31 university students. The results revealed that the online indicators were significantly correlated with classic consciousness indicators and typical ERP components of consciousness. Without affecting the development of consciousness, the online measurement indicators were found to promptly and effectively reflect the gradually changing progression of consciousness in implicit sequence learning.

Dynamic development of intuitions and explicit knowledge during implicit learning

Implicit learning refers to learning without conscious awareness of the content acquired. Theoretical frameworks of human cognition suggest that intuitions develop based on incomplete perceptions of regularity during implicit learning and, in turn, lead to the development of more explicit, consciously-accessible knowledge. Surprisingly, however, this putative information processing pathway (i.e., implicit learning ➔ intuition ➔ explicit knowledge) has yet to be empirically demonstrated. The present study investigated the relationship between implicit learning, intuitions, and explicit knowledge using a modified Serial Reaction Time Task. Results indicate that intuitions of implicitly-learned patterns emerge prior to the development of explicit knowledge. Moreover, intuition timing and accuracy were significantly associated with accuracy of explicit reports. We did not, however, find that stronger implicit learners developed more accurate intuitions. Our findings suggest a crucial role of intuition in the formation of explicit knowledge from implicit learning.

The gradual subjective consciousness fluctuation in implicit sequence learning and its relevant brain activity

Existing studies have investigated gradual subjective consciousnesses, guess, intuition, fluency, rule, and memory, and their fluctuation behavioral characteristics in implicit learning, but they did not investigate or elucidate the underlying brain mechanisms. Therefore, the current study asked participants to report subjective consciousnesses in each trial of inclusion and exclusion tasks after implicit sequence learning and used the eyes-closed and eyes-opened resting-states' fMRI to examine the relevant brain areas of the five gradual subjective consciousnesses and their fluctuation. The results showed that: (1) There were many relevant resting-state brain areas of the five gradual subjective consciousnesses to reveal their brain mechanisms. In the eyes-closed and eyes-opened resting states, as the participants' consciousness level was gradually increasing from guess to intuition, to fluency, to rule, and to memory, the positively-relevant brain areas correspondingly changed from somatic motor to a mixture of somatic motor, consciousness, emotion feeling, and implicit learning; and then to a mixture of visual, somatic motor, and consciousness; and then to a mixture of visual, somatic motor, and consciousness; and then to a mixture of visual, somatic motor, and consciousness. The negatively-relevant brain areas correspondingly changed from a mixture of visual, consciousness, somatic sensory, and implicit learning to a mixture of visual, somatic motor, somatic sensory, and other consciousness; and then to memory; and then to a mixture of other somatic motors; and then to a mixture of other consciousness and other somatic motors. However, in the amplitude of low frequency fluctuations (ALFFs)-difference, the relative directions of the guess and intuition were almost opposite to those in the eyes-closed and eyes-opened resting states. But the relative directions of the fluency, rule, and memory were consistent with those in the eyes-closed and eyes-opened resting states. (2) There were significant gradual subjective consciousness fluctuations, including the gradual subjective consciousness fluctuation-all M and SD. There were many relevant resting-state brain areas of gradual subjective consciousness fluctuations to reveal their brain mechanisms. The gradual subjective consciousness fluctuation M was positively related to Calcarine_R, Lingual_R, Lingual_R, Temporal_Pole_Mid_L, ParaHippocampal_L, Vermis_1_2, and Vermis_7; but was negatively related to Calcarine_R. The gradual subjective consciousness fluctuation-all SD was positively related to Parietal_Inf_L, Thalamus_L, Temporal_Mid_L, Vermis_9, Parietal_Inf_L, and Thalamus_L and Thalamus_R; but was negatively related to Rolandic_Oper_R, Rolandic_Oper_R, Insula_L, Insula_R, Cingulum_Post_L, and Temporal_Mid_L. The detailed function of the relevant brain areas of consciousness fluctuations needs further investigation. (3) ALFFs in eyes-closed and eyes-opened resting states and their ALFFs-difference could differently predict the five gradual subjective consciousnesses and their fluctuations, indicating that using the two resting states was necessary, and the ALFFs-difference was a new quantitative sensitivity index of the gradual subjective consciousnesses and their fluctuations.

The ecological validity of MET was favourable in sitting implicit sequence learning consciousness by eyes closed and eyes open resting states fMRI

The current study made participants sit to complete both the implicit sequence learning and the inclusion/exclusion tasks with the latter just after the former, and used eyes-closed and eyes-open resting states fMRI and their difference to test the ecological validity of the mutually exclusive theory (MET) in implicit-sequence-learning consciousness. (1) The behavioral and neuroimaging data did not support the process dissociation procedure, but did fit well with the MET. The correct inclusion-task response and the incorrect exclusion-task response were mutually exclusive with each other. The relevant brain areas of the two responses were either different or opposite in the eyes-closed and eyes-open resting-states and their difference. (2) ALFFs in eyes-closed and eyes-open resting-states and their difference were diversely related to the four MET knowledge in implicit sequence learning. The relevant brain areas of the four MET knowledge in the eyes-closed and eyes-open resting-state were the cerebral cortex responsible for vision, attention, cognitive control and consciousness, which could be called the upper consciousness network, and there were more relevant brain areas in the eyes-open resting-state than in the eye-closed resting-state.The relevant brain areas in ALFFs-difference were the subcortical nucleus responsible for sensory awareness, memory and implicit sequence learning, which could be called the lower consciousness network. ALFFs-difference could predict the four MET knowledge as a quantitative transition sensitivity index from internal feeling to external stimulus. (3) The relevant resting-state brain areas of the four MET knowledge were either different (for most brain areas, if some brain areas were related to one MET knowledge, they were not related to the other three MET knowledge) or opposite (for some brain areas, if some brain areas were positively related to one MET knowledge, they were negatively related to other MET knowledge). With the participants' control/consciousness level increasing from no-acquisition to controllable knowledge step by step, the positively relevant resting-state brain areas of the four MET knowledge changed from some consciousness network and the motor network, to some consciousness network and the implicit learning network, and then to some consciousness network; and the negatively relevant resting-state brain areas of the four MET knowledge changed from some consciousness network and visual perception network, to some consciousness network, then to some consciousness network and the motor network, and then to some consciousness network, the implicit learning network, and the motor network. In conclusion, the current study found the ecological validity of the MET was good in sitting posture and eyes-closed and eyes-open resting-states, ALFFs in eyes-closed and eyes-open resting-states and their difference could predict the four MET knowledge diversely, and the four MET knowledge had different or opposite relevant resting-state brain areas.

The relevant resting-state brain activity of ecological microexpression recognition test (EMERT)

Zhang, et al. (2017) established the ecological microexpression recognition test (EMERT), but it only used white models’ expressions as microexpressions and backgrounds, and there was no research detecting its relevant brain activity. The current study used white, black and yellow models’ expressions as microexpressions and backgrounds to improve the materials ecological validity of EMERT, and it used eyes-closed and eyes-open resting-state fMRI to detect relevant brain activity of EMERT for the first time. The results showed: (1) Two new recapitulative indexes of EMERT were adopted, such as microexpression M and microexpression SD. The participants could effectively identify almost all the microexpressions, and each microexpression type had a significantly background effect. The EMERT had good retest reliability and calibration validity. (2) ALFFs (Amplitude of Low-Frequency Fluctuations) in both eyes-closed and eyes-open resting-states and ALFFs-difference could predict microexpression M. The relevant brain areas of microexpression M were some frontal lobes, insula, cingulate cortex, hippocampus, parietal lobe, caudate nucleus, thalamus, amygdala, occipital lobe, fusiform, temporal lobe, cerebellum and vermis. (3) ALFFs in both eyes-closed and eyes-open resting-states and ALFFs-difference could predict microexpression SD, and the ALFFs-difference was more predictive. The relevant brain areas of microexpression SD were some frontal lobes, insula, cingulate cortex, cuneus, amygdala, fusiform, occipital lobe, parietal lobe, precuneus, caudate lobe, putamen lobe, thalamus, temporal lobe, cerebellum and vermis. (4) There were many similarities and some differences in the relevant brain areas between microexpression M and SD. All these brain areas can be trained to enhance ecological microexpression recognition ability.

Testing the Process Dissociation Procedure by Behavioral and Neuroimaging Data: The Establishment of the Mutually Exclusive Theory and the Improved PDP

The process dissociation procedure (PDP) of implicit sequence learning states that the correct inclusion-task response contains the incorrect exclusion-task response. However, there has been no research to test the hypothesis. The current study used a single variable (Stimulus Onset Asynchrony SOA: 850 ms vs. 1350 ms) between-subjects design, with pre-task resting-state fMRI, to test and improve the classical PDP to the mutually exclusive theory (MET). (1) Behavioral data and neuroimaging data demonstrated that the classical PDP has not been validated. In the SOA = 850 ms group, the correct inclusion-task response was at chance, but the incorrect exclusion-task response occurred greater than chance. In the SOA = 850 ms group, the two responses were not correlated, but in the SOA = 1,350 ms group and putting the two groups together, the two responses were in contrast to each other. In each group, brain areas whose amplitude of low frequency fluctuations (ALFFs) in the resting-state related to the two responses were either completely different or opposite to one another. However, the results were perfectly consistent with the MET proposed by the present study which suggests that the correct inclusion-task response is equal to the correct exclusion-task response is equal to C + A₁, and the incorrect exclusion-task response is equal to A₂. C denotes the controlled response and A₁ and A₂ denote two different automatic responses. (2) The improved PDP was proposed to categorize the 12 kinds of triplets as delineating four knowledge types, namely non-acquisition of knowledge, uncontrollable knowledge, half-controllable knowledge, and controllable knowledge with the MET. ALFFs in the resting-state could predict the four knowledge types of the improved PDP among two groups. The participants’ control of the four knowledge types (degree of consciousness) gradually improved. Correspondingly, the brain areas in the resting-state positively related to the four knowledge types, gradually changed from the sensory and motor network to the somatic sensorimotor network, and then to the implicit learning network, and then to the consciousness network. The brain areas in the resting-state negatively related to the four knowledge types gradually changed from the consciousness network to the sensory and motor network. As SOA increased, the brain areas associated with almost all the four knowledge types changed. (3) The inhomogeneous hypothesis of the MET is best suited to interpret behavioral and neuroimaging data; it states that the same components among the four knowledge types are not homogeneous, and the same knowledge types are not homogeneous between the two SOA groups.

The Establishment of Pseudorandom Ecological Microexpression Recognition Test (PREMERT) and Its Relevant Resting-State Brain Activity

The EMERT (ecological microexpression recognition test) by Zhang et al. (2017) used between-subjects Latin square block design for backgrounds; therefore, participants could not get comparable scores. The current study used within-subject pseudorandom design for backgrounds to improve EMERT to PREMERT (pseudorandom EMERT) and used eyes-closed and eyes-open resting-state functional magnetic resonance imaging to detect relevant brain activity of PREMERT for the first time. The results showed (1) two new recapitulative indexes of PREMERT were adopted, such as microexpression M and microexpression SD. Using pseudorandom design, the participants could effectively identify almost all the microexpressions, and each microexpression type had significant background effect. The PREMERT had good split-half reliability, parallel-forms reliability, criterion validity, and ecological validity. Therefore, it could stably and efficiently detect the participants' microexpression recognition abilities. Because of its pseudorandom design, all participants did the same test; their scores could be compared with each other. (2) amplitude of low-frequency fluctuations (ALFF; 0.01-0.1 Hz) in both eyes-closed and eyes-open resting states and ALFF difference could predict microexpression M, and the ALFF difference was less predictive. The relevant resting-state brain areas of microexpression M were some frontal lobes, insula, cingulate cortex, hippocampus, amygdala, fusiform gyrus, parietal lobe, caudate nucleus, precuneus, thalamus, putamen, temporal lobe, and cerebellum. (3) ALFFs in both eyes-closed and eyes-open resting states and ALFF difference could predict microexpression SD, and the ALFF difference was more predictive. The relevant resting-state brain areas of microexpression SD were some frontal lobes, central anterior gyrus, supplementary motor area, insula, hippocampus, amygdala, cuneus, occipital lobe, fusiform gyrus, parietal lobe, caudate nucleus, pallidum, putamen, thalamus, temporal lobe, and cerebellum. (4) There were many similar relevant resting-state brain areas, such as brain areas of expression recognition, microexpressions consciousness and attention, and the change from expression backgrounds to microexpression, and some different relevant resting-state brain areas, such as precuneus, insula, and pallidum, between microexpression M and SD. The ALFF difference was more sensitive to PREMERT fluctuations.