Monitoring the status of culture fish is an essential task for precision aquaculture using a smart underwater imaging device as a non-intrusive way of sensing to monitor freely swimming fish even in turbid or low-ambient-light waters. This paper developed a two-mode underwater surveillance camera system consisting of a sonar imaging device and a stereo camera. The sonar imaging device has two cloud-based Artificial Intelligence (AI) functions that estimate the quantity and the distribution of the length and weight of fish in a crowded fish school. Because sonar images can be noisy and fish instances of an overcrowded fish school are often overlapped, machine learning technologies, such as Mask R-CNN, Gaussian mixture models, convolutional neural networks, and semantic segmentation networks were employed to address the difficulty in the analysis of fish in sonar images. Furthermore, the sonar and stereo RGB images were aligned in the 3D space, offering an additional AI function for fish annotation based on RGB images. The proposed two-mode surveillance camera was tested to collect data from aquaculture tanks and off-shore net cages using a cloud-based AIoT system. The accuracy of the proposed AI functions based on human-annotated fish metric data sets were tested to verify the feasibility and suitability of the smart camera for the estimation of remote underwater fish metrics. Aquaculture, with the aim of the farmed production of fish or shellfish, has been one of the great contributors to supplying fish or seafood products for human consumption. In 1974, aquaculture contributed 7% of the fish supply and reached 50% in 2020 [Precision aquaculture requires adopting technologies such as information-based management with big data and models to guide the production process  to fullyVarious sensors such as temperature, position, humidity, flow, and photo optic or camera sensors have changed how the world accesses data from remote locations. These devices have bridged the gap in collecting data from the physical environment and transmitting wirelessly to a platform with a network of remote servers for storage, management, and data processing . Data coFor aquaculture farms, it is vital to monitor the fish growth and population as an essential parameter to approximate fish food and assess the overall wellness of the fish species. To achieve the goal of smart aquaculture, fish counting and body length estimation using underwater images are essential to estimate the fish growth curve ,10. CameIn the underwater environment where the lighting condition is poor or low, RGB cameras are limited. In contrast, sonar cameras are more robust concerning the issue of light attenuation and water turbidity that severely affects optical sensors. In terms of area to cover in capturing the underwater environment, sonar cameras have more scope and a higher range than stereo cameras, as shown in Our proposed sensor fusion comprises four steps: data collection from the aquaculture sites using our sonar and stereo camera system sensors, 3D-point cloud estimation, overlapping detection, and object detection to integrate AI functions; the details of these are discussed in the subsequent section. In this work, we used the sonar camera system as the primary sensor device for collecting depth information from the target fish objects to perform fish metric estimation, specifically for fish length and fish count. It uses its beam to collect fish information by sending multiple sound waves to the scene. Thus, it can capture the real environment’s depth information. Sonars use depth information to form an image object much different from an optical image.Although sonar devices, as mentioned earlier, provide bigger coverage, they do not have texture and color information since they just provide depth information. Due to refraction, the shape of the captured fish sonar images is affected. Thus, they can only map macro-features due to their limited resolution . The steOne of the challenges of sensor fusion is to detect the common area of each sensor or their corresponding images, and considering the environment is underwater, more problems arise. Additionally, the target objects in the sonar and stereo camera systems have different positions, so a mechanism should be devised to project the same target image into the same plane. Incorporating transformation in their corresponding rotation and translation vectors will map the features from sonar to optical coordinate system using the extrinsic sensor calibration method. Since each sensor’s range is different, the target object and its corresponding shape should be recognized by both . In thisWe proposed an AIoT system that provides sonar and stereo camera fusion that supports automatic data collection from aquaculture farms and performs artificial intelligence functions such as fish type detection, fish count, and fish length estimation. To our knowledge, combining a low-cost sonar and stereo camera system tested in various aquaculture environments with different AI monitoring functions is a novel work.We designed a methodology to perform sonar and stereo camera system fusion. However, deploying IoT can be expensive, and to limit the cost of its implementation, we employed low-cost sensors that do not entail high additional expenses for aquaculture farmers.Using a sonar camera system, we developed our mechanism to estimate the fish’s length and weight. Additional plugin AI functions can also be deployed in the cloud to meet the emerging requirements of decision-making for aquaculture management based on the collected big data sets. Agile development realizes the design of learnable digital agents to achieve the goal of precision aquaculture.The contributions of our paper are the following:The paper is structured as follows: IoT and AI have gained popularity in the past few years due to their efficiency and promising results in various fields. For example, in aquaculture production, they have been widely used to improve the accuracy and precision of farming operations, facilitate autonomous and continuous monitoring, provide a reliable decision support system, and limit manual labor demand and subjective assessment of fish conditions . In addiImaging sonar systems have been applied to many aquaculture applications, such as fish counting ,18, estiFish schools swim in the three-dimensional space;In the sonar image, fish close to the sonar system can often be incomplete, and fish away from the sonar system become blurrier;In sonar images, fish are often overlapped, and the location difference of fish in the direction perpendicular to the sonar beam is indistinguishable ;Annotators are often required to examine successive sonar images to identify fish in sonar images because they find fish by the change of the pattern and strength of echoes.Convolutional Neural Networks, or CNNs, have been applied to process sonar images for many applications, such as detecting objects on the sea floor  and counThe stereo camera system has also been extensively used in computer vision. For example, a DeepVision stereo camera system was used by Rosen et al.  for contStereo camera systems also have been widely used in fish length estimations ,40,41,42Through the years, interest in combining various sensors to achieve higher accuracy and efficiency has been widespread. Many studies regarding sensor fusions have been successfully integrated and applied in multiple fields, such as camera-lidar integration for semantic mapping , driver The experimental site has three locations representing indoor and outdoor environments and less dense and highly dense fish populations. For simultaneous data capture using the two sensors for calibration, we used a laptop computer as the sonar recording. In contrast, the data captured by the stereo camera were saved in their respective storage devices. The distance between the sonar and stereo camera was 50 cm. The Go Pro camera, which acts as the stereo camera system, used the rg174 signal line to confirm that the target object was captured. In contrast, the sonar was confirmed by the human operators. The computer device that trains the neural networks had an Intel i7-107000k 3.8. GHz CPU, NVIDIA GeForce RTX 3090 GPU, and 48 GB memory.Four markers  exist in both sonar and stereo images. Each marked point has an image coordinate of . To combine sonar and stereoscopic images using camera calibration, we used two bricks as the target object and integrated YOLOv4 to mark or capture the point coordinates provided by the bounding box of the disparity conversion. For the stereo image, the left and the right images were captured and underwent an image rectification process to obtain the correct intrinsic and extrinsic parameters using camera calibration. To find the corresponding points between a stereo pair and plot them into the 3D space, given a point in the left image, its corresponding point in the right image lies on its epipolar line. Using a stereo-image-based disparity matching algorithm in finding the correspondence of the left image to the right image, take a pixel in the left image and search on the epipolar line for that pixel in the right image. The pixel with the minimum cost is selected, and the disparity can now be computed. The point is located on the epipolar line, which would only require a one-dimensional search where cameras need to be aligned along the same axis. To obtain the depth of a stereo image pair, the disparity information is the difference in the image location of the same 3D point projected using two different cameras. The disparity of a pixel r, θ, φ) of the spherical coordinates where Similarly, the 3D point P3D  coordinate system with the z-axis of the world coordinate system; Once the corresponding pixel pairs between the sonar image and the stereo images are detected, the 3D coordinates of the matched points between sonar and stereo images are computed based on the above 3D coordinates computing scheme. Let Given the pixel coordinates of A, B, C, D in the sonar image, Equation (9) thus generates the 3D point set N is the number of frames used for parameter training.To solve the unknown parameters The area covered by the optical camera is contained by the sonar device. Once the transformation parameters Next, we compute the overlapped point cloud:w and h are the width and the height of a pixel in the sonar image, respectively; x-coordinate of the position of the sonar device in the world coordinate system. Finally, the bounding box For each 3D point The distributions of the standard length and weight of fish are essential to assessing the health and growth of the fish culture. As Apply Mask R-CNN to identify fish instances in each frame of the input sonar video. The standard length of an identified fish instance is estimated by the distance between the two farthest points on this instance.x can be expressed as follows:Apply the EM algorithm  to learnith Gaussian components, ith Gaussian component, and ith Gaussian components, denoted by ith Gaussian component The sample kurtosis  was adopThe EM algorithm was applied with the number of Gaussian components ranging from one to five. Then, the GMM with the least non-Gaussianity criterion Select the Gaussian component K-nearest neighbor regression with the training set, where the length and weight of the fish are measured manually to estimate the weight using the fish length in K for the K-nearest neighbor regression to 5.Apply The quantity There are several ways to scan the fish school to estimate their volume using the sonar system. For example, it can rotate and sideway scan the fish school. In this paper, for simplicity purposes, the fish school was analyzed without rotating the sonar beam. The sonar beam in The pattern of the fish school in the sonar image when the fish gathers and swims toward the fish surface to grab the pellets is different compared with when the fish disperses, as shown in The semantic segmentation networks’ neural network architecture to segment fish regions in the sonar image is shown in i-th type and the total count of fish detected based on the RGB image. As mentioned above, the sonar image estimates the number of fish without information on fish types. To deal with the difficulty, our two-mode fish counting algorithm estimates the count of the i-th type fish as:The fish of the left image of the input stereo image pair is detected by any object detection CNN, e.g., the YOLOv4. The object detection results can be used to annotate the fish type since the types of fish are given in the training dataset to train the object detection CNN. Let In this study, we focused on the design of the two-mode smart sensor, which consists of a sonar scanning device and a stereo optical camera. The captured images are sent to the cloud using a wireless communication network. Although the object detection CNN is not new, we can design a new CNN architecture for underwater object detection to improve the accuracy of fish distributions using (24). Note that the functionality of the smart sensor is incremental since we can add a new AI function into the cloud to provide a new service for sensor fusion.The fish objects detected in the sonar images are mapped into the stereo camera image as an area of interest. In On the other hand, the fish detection in To ensure that our mechanism detects a correct object in both sonar and stereo images, we integrated a bounding box that shows the area covered by the stereo image in the sonar image in p-value is 0.61 and the p-value for the other pair is The dataset used to train the CNN in detecting fish-gathering frames consisted of 58 fish-gathering images and 116 fish-dispersing images. The CNN was evaluated using 10-fold cross-validation and obtained an accuracy of 0.98. The object detection model utilized the pre-trained YOLOv4 with the COCO dataset  was usedOur sensor-based fusion mechanism was applied to aquaculture monitoring using optical (stereo camera) and sonar images. Each sensor system has its strength and limitations, and we took advantage of its capabilities to address the issues of the other sensors. For example, it would be difficult for an RGB camera to accurately estimate fish metrics due to poor underwater conditions addressed by the sonar camera system. Therefore, we took advantage of the texture information of the optical images to provide fish species annotation to sonar images. In addition, detecting the common area of each sensor poses a significant challenge considering the quality of images in the underwater environment.Additionally, sonar cameras have a larger area covered when compared with stereo cameras. Thus, the target objects will be in different positions or locations. We also must consider that sensors vary in terms of errors, the origin of the coordinate axis, and the types of data received. Two essential issues need to be addressed for sensor fusion, namely, opti-acoustic extrinsic calibration and opto-acoustic feature matching . To dealk-nearest-neighbor regression. Since we manually measured the fish fork length as the basis for the length estimation, our proposed estimation method is biased since the fork length is usually more significant than the standard length.One of the AI functions is to estimate the length and weight distribution of the fish in an indoor aquaculture tank. The main challenge of the first method is that the fish in the aquaculture tank are often crowded and overlapped in sonar images. Besides, sonar images of an aquaculture tank are usually noisy due to the echo from the air pumper and the bottom and wall of the tank. The Mask R-CNN  identifiFurthermore, the response of the caudal fish fin in the sonar image is usually weak, which makes the fish length measured by our proposed method close to the standard length of the fish. On the other hand, the estimated weight distribution and the ground truth were significantly different. This result could be attributed to the fish’s weight being affected by other factors, such as the thickness of the fish. In practice, it is difficult to observe both the thickness and length of the fish instance from the view of the imaging sonar system. Overall, the relative error of the estimated average fish standard length was approximately 15%, while the relative error for the estimated average weight was less than 50%.The second AI function for sonar images estimates fish quantity in an off-shore net cage, and we identified two main challenges. First, the fish considered in this paper, which were Trachinotus blochii, are often widely distributed in the net cage. Second, the view of an imaging sonar system only covers a small portion of the net cage. Since the fish of the target species can gather and swim close to the water surface to grab food pellets, the proposed method only estimates the quantity of fish in the net cage when feeding fish. The number of fish is assessed on the average fish volume and the fish school’s estimated volume and density. In this paper, a convolutional neural network was developed to determine if fishes gathered and were grabbing food pellets. Then, we supposed that the fishes gathered and were grabbing feed. In that case, a semantic segmentation network was applied to segment the fish school in the sonar image, and the volume of the fish school was estimated on the segmentation result. The visible imaging has a short imaging distance underwater due to the light attenuation caused by water absorption and scattering. Therefore, the image was more blurred, and the quantity of the image decreased as the shooting distance increased. However, the sound wave can travel far through water without attenuation. Consequently, counting based on acoustics can still work when visual counting is inappropriate .For object detection, this work used two images from the two sensors to capture a common target. The YOLOv4 , with anThe success of the data collection procedure for this study poses a difficult task. First, the assistance of the aquaculture operators is much needed during the data collection, and they must be present in every data collection activity. Second, the data can only be obtained when the aquaculture operators feed the fish, usually once a day. Third, the weather is another major factor since the net cages are in the open sea. Finally, it is essential to consider the sea current to make sure that the transducer of the imaging sonar system is steady since it greatly affects the quality of the data. After several attempts to collect data, only a few were collected due to difficulties.The 3D point clouds for each camera system were separately obtained, extracted, and matched to find each correspondence. Our sensor fusion approach detected the corresponding pixels or the bounding box of the common area. Thus, it could also detect the fish objects in the images of both sensors to be utilized for fish type annotation. In this paper, two methods were developed to estimate the quantity and the distribution of the standard length and weight of fish using a sonar imaging system. The first method was developed for estimating the distribution of the standard length and weight of fish. Using GMMs to find the distribution of the standard length of single fish instances and employing K-nearest-neighbor regression to estimate the weight of fish by the length, the relative errors of the estimated average fish standard length and weight were approximately less than 15 and 50%, respectively. Those errors can be reduced if the fish length manually measured is based on the standard length instead of the fork length. Therefore, the proposed method can be applied to monitoring the growth of culture fish. The second method estimates the fish quantity in an off-shore net cage. The preliminary experimental result showed that the quantity of fish could be within the estimate’s 68 and 95% confidence intervals. The 68 and 95% confidence interval widths were approximately 900 and 1800, respectively. Preliminary experimental results showed that the proposed method is feasible. Lastly, the fish target object detection provides an additional function to annotate fish species and offers additional information to the sonar system. For our future works, we plan to incorporate Generative Adversarial Networks to convert the optical image of the target object into a sonar image. Additionally, we will integrate sonar and stereo camera fusion for fish length and weight estimation.


Selecting appropriate defensive behaviours for threats approaching the space surrounding the body  is crucial for survival. The extent of defensive PPS is measured by recording the hand-blink reflex (HBR), a subcortical defensive response. Higher-order cortical areas involved in PPS representation exert top-down modulation on brainstem circuits subserving HBR. However, it is not yet known whether pre-existing models of social relationships  originating from early attachment experiences influence defensive responses. We hypothesized that organized IWM ensure adequate top-down regulation of brainstem activity mediating HBR, whereas disorganized IWM are associated with altered response patterns. To investigate attachment-dependent modulation on defensive responses, we used the Adult Attachment Interview to determine IWM and recorded HBR in two sessions . As expected, the HBR magnitude in individuals with organized IWM was modulated by the threat proximity to the face, regardless of the session. In contrast, for individuals with disorganized IWM, attachment system activation enhances HBR regardless of the threat position, suggesting that triggering emotional attachment experiences magnifies the threatening valence of external stimuli. Our results indicate that the attachment system exerts a strong modulation on defensive responses and the magnitude of PPS. The PPS is a multisensory representation of the space surrounding the body that uses the body itself as a reference to encode both nearby objects and approaching threats. In a threatening context, the PPS is used to protect the body. The closer to the body a threat occurs, the more likely it is to cause harm, thus reinforcing our defensive and avoidance responses5. Therefore, an accurate representation of this part of the space, namely the defensive PPS, and continuous monitoring of environmental threats approaching the body is essential for survival.In the course of our life, we develop a neural system that is specifically responsible for representing the space immediately close to the body: the peripersonal space (PPS)6. HBR is elicited by electrical stimulation of the median nerve at the wrist and recorded by electromyography (EMG) at the orbicularis oculi. The magnitude of HBR is significantly increased when the threatened hand, receiving the electrical stimulation, is close to the participants’ face as compared to when the hand is stimulated far from the face14. The position at which the first HBR enhancement can be observed in comparison to the far position delimits the imaginary boundary of the PPS of the face within which approaching threats are perceived as more threatening the closer they are. Specifically, if we conceive the PPS as a defensive cloud surrounding the face, the modulation of HBR magnitude as a function of hand position in space  can be used to map the extent of the PPS boundaries. Indeed, geometric modelling of HBR enhancement led to define the defensive PPS anchored to the face as a bubble elongated along the vertical axis15. The boundary of PPS  is located between 20 and 40 cm from the face, and within this space HBR responses are further enhanced with proximity of the threat to the face, defining a high-risk area16. Therefore, participants who show an abrupt enhancement of HBR magnitude at the nearest distance to the face  have a smaller PPS extent than participants who already show a clear enhancement at a greater distance .To measure the extent of people’s defensive PPS and the magnitude of their defensive responses, a widely used parameter is the hand blink reflex (HBR), a subcortical defensive response known to be modulated by the position of the threat in relation to the PPS of the face6. Interestingly, personality factors such as empathic7 and anxious16 traits contribute to individual variability in both the extent of defensive PPS and the magnitude of defensive responses. This is consistent with the observation that subcortical neural circuits in the brainstem can undergo significant top-down modulation by the neocortex8.It is known that neural circuits at the level of the brainstem mediate HBR and that these circuits are interconnected with higher-order cortical areas involved in the representation of PPS19, humans have an innate biological predisposition to seek the proximity of a caregiver who can provide protection and care when the need for it is aroused, for example, by fear or pain23. Bowlby attributes a protective function to this neurobehavioral system. According to this author, the need for care triggered by activation of the attachment system tends to elicit complementary caring behavior on the part of the caregiver. As a result of ongoing interactions with caregivers, each child structures interpersonal cognitive schemas or internal working models (IWM). IWM are mental representations of an individual’s attachment experiences in childhood that are formed from repeated interactions between the child and the caregiver (often the mother in the early years) and that usually remain stable into adulthood29. The main function of IWM is to predict what can be expected from the world and from interaction with it.Given these premises, in the present work we hypothesize that another dimension that could modulate the extent of defensive PPS and the magnitude of defensive responses is attachment. According to the attachment theory31 and have coherent mental states when asked to recall attachment in adulthood32. In contrast, when the structure of the IWM is disorganized, people have disoriented and/or disorganized memories of trauma or loss, incoherent expectations of attachment figures, and inconsistent behavioral strategies when asking for care32. Disorganized IWM are associated with frightening and threatening parenting styles33.Depending on the nature of parental care, the structure of mental representations of attachment experiences in childhood  may be organized or disorganized. When the structure of the IWM is organized, people develop coherent strategies when asking for care35. Converging evidence38 suggests that in people with organized IWM these hubs exert control in regulating brainstem response to environmental stimuli and promote the generation of adaptive responses. On the contrary, in people with disorganized IWM, this top-down control is much less efficient and brainstem responses to environmental stimuli are under-modulated or dysregulated.Interestingly, the neurobehavioral system of attachment has been described as part of a hierarchically organized control system in which the orbitofrontal cortex, anterior cingulate, and amygdala play the role of interconnected hubs that can regulate the activity of brainstem and midbrain nuclei39. The AAI is based on Bowlby’s attachment theory19 and is an interview that focuses on early attachment experiences and their effects and can be used to determine whether a person has organized or disorganized IWM. Previous findings41 show that in individuals classified as having organized IWM a level of organized strategies and integrative processes is maintained during activation of attachment-related memories, whereas in individuals classified as having disorganized IWM, activation of attachment-related memories could lead to a temporary failure of brain connectivity and a consequent disruption of high integrative mental functions. Furthermore, since the neurobiological attachment system exerts its own regulatory effects on brainstem activity mainly when activated, in the present study we also used an instrument known to act as a trigger of the attachment system, namely the Adult Attachment Projective (AAP). The AAP45 is a semi-structural interview that combines projective and interview techniques. The ability of the AAP to trigger attachment system has been confirmed in several research studies50. Therefore, we used the AAI to classify participants’ IWM and the AAP to experimentally trigger the neurobiological attachment system.To our knowledge, the role of mental representations related to childhood attachment experiences  in modulating PPS extent and defensive responses has never been investigated, and we aim to systematically examine this here. To this end, we used the Adult Attachment Interview (AAI)For all the above reasons, we hypothesize here that individuals with organized IWM have adequate top-down regulation of the activity of the neural circuits at the level of the brainstem that mediate HBR, and therefore will show a linear trend of HBR enhancement as the distance between the hand and the face decreases in the experimental setting. In contrast, we hypothesize that individuals with disorganized IWM will show an altered pattern of HBR responses, indicating dysregulation of control exerted on brainstem activity by higher-order structures associated with the attachment system.Thirty-two healthy participants were recruited for the study, and 26 of them  were HBR responders and thus engaged for the study. Exclusion criteria were: (1) left-handedness, (2) history of neurological and/or psychiatric disorders, (3) head trauma, (4) use of medications affecting the central nervous system in the 2 weeks prior to participation in the experiment.14. The sample size was chosen based on previous HBR studies with healthy participants51. In these studies, the sample size ranged from a minimum of 13 to a maximum of 20 participants. In the current study, because we needed to divide the sample into two groups based on the AAI classification system , we included a sample of 26 participants. All participants were naïve about the experimental procedure and the purpose of the study, and gave written informed consent to participate in the study. In accordance with the Declaration of Helsinki , all experimental procedures were approved by the Ethics Committee of the University of Turin (Prot. n. 117174).The percentage of HBR responders (81%) was consistent with previous studies39. As many studies have shown , the AAI is the instrument with greater reliability and validity for examining adult attachment and corresponding IWM. The interview classifies a person’s “state of mind” in relation to his or her attachment experiences in childhood and allows one to understand the processes that characterize the development of attachment experiences from the first months of life into adulthood.To divide the sample into individuals with organized IWM and individuals with disorganized IWM, we used the AAI of George, Kaplan, and Main56. This system is based on the person’s ability to produce a coherent narrative, actively collaborate with the interviewer, and engage in “metacognitive monitoring” while recalling and describing his or her childhood memories. The result is the identification of three main categories of states of mind related to attachment: Free, Dismissing and Entangled. Free interviewees engage in coherent and collaborative discourse while describing and evaluating their attachment-related experiences, whether described as positive or negative. Dismissing interviewees do not support or contradict a normalizing and positive description of their parents with memories of specific events. All effects of negative experiences are denied. Entangled interviewees seem preoccupied, but may also be angry, confused or passive about the experiences they are reporting. In these three categories, although in very different ways, individuals demonstrate the presence of a strategy such that their IWM are considered as organized.The AAI is conducted by a trained clinical psychologist in a quiet and comfortable room. The interview lasts an average of one hour and 30 min and consists of a sequence of questions asked in a fixed order aimed at exploring the degree of integration between episodic and semantic memory while the person retrieve childhood (0–12 years old) emotional and relational memories concerning his or her parents, moments of vulnerability , and, if present, experiences of abuse and loss. In addition, the person is asked to evaluate early attachment experiences and current attachment relationships, and to reflect on present or future dimensions of parenting. The entire interview is recorded, then transcribed, and finally coded using a scoring and classification systemWhen a traumatic event  has occurred and the narrative shows a lack of cognitive monitoring and elements of incoherence, the Unresolved category is added. If there is no strategy in the narrative and the state of mind fluctuates between Entangled and Dismissing, the category of Cannot Classify is applied. Individuals recognized as Unresolved and/or Cannot Classify are considered to have disorganized IWM.45 combines projective and interview techniques. Stability, predictive validity, and time efficiency are characteristics that make AAP one of the widely used instruments in both clinical and research settings54.The AAP45. In addition, the pictures also depict adult/adult, adult/child interactions inducing the subject to access the mental availability of attachment figures at different stages of life. Because the stimuli in the images are ambiguous, the subject must retrieve his or her own mental representations of attachment to create a narrative. The entire interview is recorded. Transcription and coding may follow if the goal is to identify attachment state of mind classifications groups. In this study, we used AAP exclusively to activate the neurobiological system of attachment before HBR stimulation.The interview takes place in a quiet room and lasts an average of 30 min. The person is asked to describe eight black and white drawings and to imagine what has preceded and what might follow in each. The sequence of images is given in a fixed order and is arranged to activate the attachment system gradually. The drawings depict situations that may recall attachment primal elements as loneliness, separation, death, illness, and abuse 51. EMG activity was recorded from the right orbicularis oculi muscle , using a pair of bipolar surface electrodes, with the active electrode placed over the mid lower eyelid and the reference electrode placed laterally to the outer canthus. Signals were amplified and digitized at 10 kHz , and stored for off-line analysis.HBR was elicited by transcutaneous electrical stimulation of the right median nerve at the wrist using a bipolar surface electrode . First, for each participant, we calibrated the stimulus intensity able to elicit a clear blink reflex by increasing the stimulus intensity until a clear and stable HBR was observed in at least five consecutive trials or the participant refused to increase the intensity further . Stimulus duration was 200 μs and, to minimize habituation, the inter-stimulus interval was ~ 30 s, as described in the literature16. The Post-AAP session aimed to test whether activation of the attachment system affects the magnitude of HBR enhancement. In both sessions, HBR was recorded while participants performed a postural manipulation task. They had to hold their stimulated hand in three different positions depending on how far away from their face they were: Far, Near, and Ultra-Near16. In the Far condition, participants sat with their forearm resting on their leg and their right hand close to the ipsilateral knee at a distance of ~ 60 cm from the ipsilateral side of their face. In the Near condition, participants sat holding their right arm with their wrist at a distance ~ 20 cm from the ipsilateral side of their face. In the Ultra-Near condition, participants sat and held their right arm with their wrist at a distance of ~ 4 cm from the ipsilateral side of their face (with fingers never touching the face), namely inside the PPS of the face . Note that the two sessions were acquired at 1 week of distance and the order was counterbalanced among participants. The Baseline session aimed to obtain a reference measure of HBR enhancement by the proximity of the hand to the face, which has been shown to be stronger the closer the hand is to the face57 is a 20-item self-report measure commonly used to assess persistent anxiety traits. Respondents are asked to rate each item on a 4-point Likert scale. The total score ranges from 20 to 80, with higher scores indicating greater trait anxiety. The BDI-II58 consists of 21 items, each rated on a 4-point Likert scale, that assess the presence of depressive symptoms. The total score ranges from 0 (no depressive symptoms) to 63 (severe depression).To better explore the trend of HBR modulation and investigate whether individual differences in trait anxiety and depression predict HBR enhancement, we also administered two self-reports, namely the subscale for trait anxiety of the State-Trait Anxiety Inventory (STAI-T) and the Beck Depression Inventory (BDI-II). The STAI-Thttp://www.mathworks.com) and Letswave 6 (http://nocions.webnode.com) software59. EMG signals from each participant were high-pass filtered (55 Hz) and full-wave rectified. HBR responses were averaged separately by position, resulting in three average HBR waveforms for each session per participant .EMG pre-processing and analysis were performed using Matlab  of each participant for each experimental session and hand position.To examine the magnitude of HBR in each group of participants , we first used a Linear Mixed Model (LMM) approach as implemented in SPSS 28.0 . We thus run LMM, three-way ANOVA with the HBR amplitude  as dependent variable. Fixed effect factors were ‘hand position’  and ‘session’ . Participants were considered as a random effect variable to properly use all observations for effect estimation. Post-hoc comparisons were performed using the Bonferroni test.Based on the results of the within-group analysis showing a peculiar pattern of HBR modulation in the disorganized group, we then adopted two different analysis approaches to better investigate the differences between the groups.In the first one, because the disorganized group showed a different pattern of HBR increase than the organized group, we calculated an index of position-dependent HBR increase expressed as a delta between HBR magnitude in the Near and Far positions (Near-Far) and between the Ultra Near and Near positions (Ultra Near-Near), regardless of session. The greater the position-dependent index the greater should be the difference between far and near and between near and ultra-near. Each index was compared between groups using an unpaired T-test (one-tailed).The second approach aimed to better investigate the different pattern of HBR increase between the disorganized and organized groups in the two sessions. To this end, we calculated an index of AAP-dependent modulation of HBR magnitude expressed as a delta between the HBR magnitude in the Post-AAP session and that in the Baseline session (AAP-dependent index = Post-AAP session − Baseline session), regardless of hand position. The greater the AAP-dependent index the greater should be the difference between the two sessions. The values obtained were compared between groups using an unpaired T-test (one-tailed).Pearson correlations were conducted between HBR enhancement indexes and individual scores on the STAI-T and the BDI-II to investigate the possible role of individual differences in trait anxiety and depression in HBR enhancement.56. The percentage of participants who were classified as disorganized is approximately double what would be expected in a non-clinical sample53. This is due to the fact that the sampling procedure was designed to increase the likelihood of using the AAI to interview individuals with a disorganized state of mind. This sampling procedure is described in detail elsewhere60.Sixteen (61.5%) and ten (38.5%) participants were classified as organized and disorganized, respectively, according to the AAI coding system = 30.839; p = 0.00001], indicating a linear trend in HBR increase regardless of the experimental session, with a significantly greater HBR magnitude in both Near (p = 0.00001) and Ultra Near (p = 0.000001) positions as compared to the Far position, and a significantly greater magnitude in the Ultra Near than in the Near position (p = 0.00001). Furthermore, we observe a significant main effect of session , suggesting that the overall magnitude of HBR was greater in the Baseline session than in the post-AAP session, regardless of the position of the hand in space  shows a significant main effect of hand position . Post-hoc comparisons show that HBR increase is significantly greater in the Ultra Near position than in the Far (p = 0.001) and the Near (p = 0.001) positions, while no difference emerged between Far and Near positions (p = 0.33). Furthermore, we observe a significant main effect of session , suggesting that the overall magnitude of HBR is greater in the Post-AAP session compared to the Baseline session, regardless of the position of the hand in space , the LMM also shows a significant main effect of hand position [F2,47.005 = 224 = 1.954; p = 0.0312; dz = 0.82) and not in the Ultra Near-Near index (p = 0.323). This suggests that, regardless of the experimental session, only organized participants show a significant HBR increase in the Near position compared to the Far position, while disorganized participants do not , indicating that HBR increase between Baseline and Post-AAP session was significantly greater in the disorganized group than in the organized group, regardless of hand position .62. It has been suggested that activation of attachment memories in individuals with disorganized state of mind may act as a trigger for disintegrative processes that impair optimal functioning of higher-level mental functions in similar ways to other trauma-related pathogenic processes involving executive and regulatory functions, self-related processes, social cognition and mentalization64. Given that the neurobiological system of attachment has been described as part of a hierarchically organized control system in which certain neural structures such as the orbitofrontal cortex, anterior cingulate and amygdala can exert top-down regulation on brainstem activity that mediates defensive PPS, one of the main aims of our work was to investigate whether the effects of disorganized attachment on higher-level mental functions already described in the literature also extend to a more automated and sub-cortical driven aspect such as defensive PPS. To do this, we used the opportunity provided by the AAP to activate the attachment system and measured the patterns of HBR responses in individuals with disorganized IWM.In recent years, there has been a growing body of literature demonstrating the medium- and long-term effects of disorganizing attachment experiences in childhood on various aspects of mental functioning. For example, a disorganized state of mind has been associated with high-level alterations in integrative mental functioning, leading to impairments in cognitive, metacognitive and emotional regulation51, nicely confirming that the closer the threatening hand is to the face, the stronger the participants’ defensive responses are. In line with our hypothesis, our new finding is that the participants’ mental state of attachment can have a great influence on the modulation of such defensive response. Indeed, a peculiar pattern of HBR enhancement in the disorganized group emerged from both the within analysis and the between group analysis.Our findings replicate the HBR enhancement induced by the proximity of the hand to the face, as widely described in previous studiesIn particular, the group of organized participants showed a linear trend in HBR enhancement, with HBR increasing the closer the hand to the face, regardless of the experimental session. In contrast, the group of disorganized participants shows a peculiar pattern of modulation of HBR enhancement. Specifically, participants with disorganized IWM do not show a linear trend in HBR magnitude as a function of hand proximity. In fact, they show an abrupt enhancement in the Ultra-Near position compared to the Far and Near position. This suggests that the extent of their defensive PPS boundaries is limited to the space immediately around the face and is not gradually shaped by the proximity of the hand to the face.As for the comparison between the Baseline and the Post-AAP sessions, the two groups show an inverse pattern. The organized group is characterised by an overall greater HBR magnitude in the Baseline session compared to the post-AAP session, while the disorganized group has a significantly overall greater HBR magnitude in the Post-AAP session compared to the Baseline session. This suggests that the triggering of emotional and relational memories of early attachment experiences by the AAP leads organized participants to experience the experimental session as less threatening than the Baseline session, while in the disorganized group the triggering of these memories leads participants to experience the Post-AAP session as more threatening than the Baseline one.65. However, the ability to gradually shape the defensive response to threats as a function of distance is likely to be built up over time and through experience. Therefore, we can speculate that early attachment experiences have an impact on how the boundaries of the representation of the defensive PPS are encoded. Thus, the frightening and threatening parental behaviour associated with disorganized IWM, leading to impairments in cognitive, metacognitive and emotional regulation, could also prevent the boundaries of the defensive PPS from being properly set in an adaptive manner. The reverse pattern observed in the overall magnitude of the HBR also seems to confirm the regulatory role of early attachment experiences in the development and functioning of the defensive PPS. In fact, when the neurobiological attachment system is activated, the presence of organized IWM seems to be a resource in situations where it is necessary to cope with a threat, while, on the contrary, disorganized IWM lead to an increase in threat perception, precisely because it was probably the very frightening and threatening parental behaviour that disorganized the IWM and consequently the defensive PPS.We can consider the defensive pattern of the organized group as the most adaptive response to threats. Indeed, their defensive response is gradually enhanced the closer the threats are , suggesting that their defensive system defines the boundaries of the potential high-risk area of their surrounding space as more extended, leading them to respond to threats at a greater distance. In contrast, our data show an alteration of the defensive pattern in the disorganized group. Indeed, they show an abrupt HBR enhancement only at a closer distance, suggesting that the boundaries of their defensive cloud are smaller. Their defensive pattern thus seems to be less adaptive. Given the evolutionary importance of responding to potential threats in space, having a mechanism for encoding space very early in life seems to be beneficialThe present study has some limitations that should be considered. Although we enrolled a number of participants consistent with previous works, further studies with a larger number of participants are needed to confirm the current results. In addition, although there is ample evidence of which brain areas are involved in the phenomena we investigated, we have no direct evidence of the effects of our experimental task on the functioning of these brain areas. Future works should replicate our study by using the same experimental procedure and employing appropriate methods to obtain direct data on the areas that are actually activated during performance of our task.66 and between psychopathology and PPS has been suggested67. The fact that in the present work we found a modulation of defensive PPS in a nonclinical sample makes this finding particularly relevant because it underscores the crucial role that high-level attachment-related mental representations play in organising PPS and associated subcortical responses, whether or not these responses are manifested by an individual with a clinical profile. This may imply that it is not psychopathology per se that alters the defensive PPS but the fact of having developed disorganized IWM. Future work should take this into account and further investigate the causal role of disorganized attachment-related state of mind in shaping the defensive PPS and the conditions under which such disorganization can sometimes determine the development of psychopathological profiles characterized by bodily self-consciousness alterations.Despite the limitations described above, our results support the hypothesis that a defensive PPS exists and show for the first time that this space and associated defensive responses are significantly modulated by individual mental representations related to early attachment experiences. Interestingly, a relationship between disorganized attachment-related state of mind and psychopathology


As the COVID-19 pandemic unfolded, questions arose as to whether the pandemic would amplify or pacify tropical deforestation. Early reports warned of increased deforestation rates; however, these studies were limited to a few months in 2020 or to selected regions. To better understand how the pandemic influenced tropical deforestation globally, this study used historical deforestation data (2004–2019) from the Terra-i pantropical land cover change monitoring system to project expected deforestation trends for 2020, which were used to determine whether observed deforestation deviated from expected trajectories after the first COVID-19 cases were reported. Time series analyses were conducted at the regional level for the Americas, Africa and Asia and at the country level for Brazil, Colombia, Peru, the Democratic Republic of Congo and Indonesia. Our results suggest that the pandemic did not alter the course of deforestation trends in some countries , while it did in others . We posit the importance of monitoring the long-term effects of the pandemic on deforestation trends as countries prioritize economic recovery in the aftermath of the pandemic.The online version contains supplementary material available at 10.1007/s11676-022-01561-7. The international community has shown concern over the possible effects of the COVID-19 pandemic on forest cover. Early press reports indicated that the global deforestation rate increased during the start of the pandemic DW News , and pubEarly studies reflect a consensus that the global deforestation rate increased in early 2020 and that this was likely due to the COVID-19 pandemic. Most of these studies, however, report results from qualitative analysis based on stakeholder interviews  a mean time series was created for each continent showing the mean deforestation trend across the tropics; (2) Discrete Fourier Transform (DFT) was applied to each series to determine if the curve is governed by cyclical behaviors and with what frequency these cycles occur; (3) the expected mean deforestation trend for 2020 for each continent was projected; and, (4) the 2020 projections were compared with the observed mean deforestation trends after the date on which active cases of COVID-19 began to be reported in each continent as well as in five key tropical countries: Brazil, Indonesia, the Democratic Republic of the Congo, Peru and Colombia. Finally, we discuss results and conclude in light of available reports on the effects of the pandemic on tropical deforestation.This study uses reports of deforestation alerts generated by Terra-i  represents one pixel with an area of 6.25 ha, according to the spatial resolution of the MODIS sensor. The system is based on the premise that vegetation follows a predictable pattern of changes in vigor due to specific geographic and climatic conditions , were used to examine the temporal variability of deforestation in the Americas, Africa and Asia. A mean time series was created for each continent showing the mean deforestation trend across the tropics. This analysis uses a statistical description of the annual deforestation variations and applies the Discrete Fourier Transform (DFT) to each series to determine if the curve is governed by cyclical behaviors and with what frequency these cycles occur  and Indonesia. These countries were chosen because their territories contain a large area of tropical forests with high biodiversity value and historically have been affected by many direct and indirect drivers of deforestation, such as illegal timber extraction, lack of governance and illicit crops  predicts the region will have the world’s slowest economic growth in 2021, with per capita GDP in many of the region’s countries not returning to pre-crisis levels until late 2025  and the Association of Southeast Asian Nations Below is the link to the electronic supplementary material.


Anthropometric variables (AV) are shown to be essential in assessing health status and to serve as markers for evaluating health-related risks in different populations. Studying the impact of physical activity (PA) on AV and its relationship with smoking is a non-trivial task from a public health perspective. In this study, a total of 107 healthy male smokers (37 ± 9.42 years) were recruited from different states in Malaysia. Standard procedures of measurement of several anthropometric indexes were carried out, and the International Physical Activity Questionnaire (IPPQ) was used to ascertain the PA levels of the participants. A principal component analysis was employed to examine the AV associated with physical activity, k-means clustering was used to group the participants with respect to the PA levels, and discriminant analysis models were utilized to determine the differential variables between the groups. A logistic regression (LR) model was further employed to ascertain the efficacy of the discriminant models in classifying the two smoking groups. Six AV out of twelve were associated with smoking behaviour. Two groups were obtained from the k-means analysis, based on the IPPQ and termed partially physically active smokers (PPAS) or physically nonactive smokers (PNAS). The PNAS were found to be at high risk of contracting cardiovascular problems, as compared with the PPAS. The PPAS cluster was characterized by a desirable AV, as well as a lower level of nicotine compared with the PNAS cluster. The LR model revealed that certain AV are vital for maintaining good health, and a partially active lifestyle could be effective in mitigating the effect of tobacco on health in healthy male smokers. Tobacco addiction remains a significant global health problem, killing over 16 million people annually . This phCigarette smoking is known to be directly implicated in several health-related problems. For instance, the ingestion of tobacco has been shown to increase the risks of hypotension ,4. On thSmoking, coupled with a sedentary lifestyle , is considered one of the major risk factors associated with non-communicable diseases. It is worth noting that not only do these factors foster the development of non-communicable diseases independently, but they also occur concurrently ,10. For Research has highlighted that an inverse association between smoking and body weight is present, suggesting that an average smoker typically weighs less than a similar-aged non-smoker . HithertRegarding energy intake, mixed findings have been reported. It was established that leptin, an adipocyte-derived signal molecule, interacts with some specific receptors located in the central neural system and peripheral tissues; these interactions often result in the decline of food intake and consequently trigger an increase in energy expenditure . This suMalaysia has made advances in tobacco control in recent years; however, the prevalence of smoking among Malaysian people aged 15 years and above remains high despite the various anti-smoking measures implemented over recent decades. It has been reported that over 5 million Malaysian adults are smokers, which is equivalent to about 22.8 per cent of the entire population . MoreoveAV are shown to be essential in assessing individuals’ health status and to serve as a marker for evaluating health-related risks in different populations ,29. NumeWhat are the most dominant AV characteristics of healthy male smokers?What are the smokers’ PA levels and the most dominant AV characteristics that differentiate the PA groups?How effective are the discriminant analysis (DA) and logistic regression models in discriminating and classifying the PPA and PNA smokers based on their AV characteristics?Gender plays a significant role in the tendency to smoke in Malaysia, where the male-to-female ratio of smokers in the country is highly skewed: A larger proportion of smokers in the country are males, which could be attributed to the social norms, which are not favourable to female smoking . Hence, An ex post facto study design was used in the current study. This design was selected because the natural characteristics of the samples are not required to be manipulated or altered . EssentiF), as suggested in a previous study [A preliminary power analysis using G*Power was carried out to ascertain the sample necessary to draw meaningful conclusions in the study . A powerus study . TherefoThe height of the participants was determined whilst the participants were in a standing position and unshod, using a portable stadiometer , while weight was assessed using a standard weighing scale. The height and weight were used to determine the body mass index of the participants (BMI). Additionally, waist and hip circumferences (WC & HC) were assessed via the usage of a measuring tape and measured from the central point between the lower rib margin and the iliac crest and at the maximal circumference over the buttocks. The measurement was performed when the participants were in a standing position. The waist-hip ratio (WHR) was determined as the ratio of WC to HC. The systolic and diastolic blood pressure were measured from the right arm of the participants while they were in a seated position using an automatic digital blood pressure measurement device . The body fat percentage (BF), total body water percentage (TBW Percentage), visceral fat (VF), bone mass, and muscle mass were measured using a digital body composition analyser  that applied the principles of bioelectrical impedance analysis (BIA).Nicotine levels were assessed using hair samples at the National Poison Centre (PRN) via gas chromatography-mass spectrometry (GCMS) . The vorThe physical activity (PA) of the participants was assessed via the short version of the International Physical Activity Questionnaire (IPAQ). The IPAQ consists of seven constructs that measure various degrees of PA concerning intensity and duration at different times of the day. This instrument was administered to the participants to obtain their ratings based on the perceived daily PA. The scores were summed used to estimate both the durations and the frequencies of the performance of the various PPA smokers. The IPAQ is shown to be valid and reliable in estimating the PA levels among an adult population .Before the commencement of the current investigation, all the procedures and protocol were endorsed by the Research Ethics Committee (Human) of Universiti Sains Malaysia, and the study was conducted in compliance with the Helsinki Declaration for the experiment with human subjects. Moreover, informed consent was obtained from all the participants. All the experiments were carried out at the laboratory of the School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian, Kelantan.A variety of multivariate analyses and machine learning methods were used to answer the research questions previously raised and to achieve the objectives of the study.PCA is a mathematical method used primarily to identify the structure of a dataset from a group of observed variables . PCA is In this study, the PCA was used to ascertain the AV associated with the smoking samples. This analysis is carried out to answer research question 1. The data retrieved via this procedure were outlined in the previous sections, i.e., age, weight, height, BMI, waist circumference, hip circumference, waist-hip ratio, fat percentage, muscle mass, total body water, bone mass, and nicotine were applied in analysing the data. All the variables were considered for PCA to identify the most important parameters for further analysis. Hence, a factor loading that is equal to or greater than 0.80, was considered important, while a variable that was smaller than the threshold was deemed less important . It is wClustering is one of the most common exploratory data processing techniques used to study the nature and patterns of datasets. Cluster analysis has been reported to be useful in identifying subsets or samples about certain observed variables ,46.Cluster analysis was applied at this stage to partition the samples of smokers into groups based on similarities and differences in the PA scale scores. In the present investigation, a k-means clustering algorithm was used, and Euclidean distance was considered a distance metric for assigning the formation of the two clusters identified, i.e., partially physically active (PPA) and physically nonactive (PNA) smokers.We employed DA in this study to determine the differences between the two clusters extracted via the clustering previously described, i.e., PPA and PNA smokers, based on their AV. At this stage, the anthropometric and health-related markers that are shown to be dominant via the PCA analysis were used. The anthropometric and health-related markers were considered the independent variables, whilst the smoking categories, i.e., PPA and PNA, were treated as the dependent variables. Three different modes of DA model, i.e., standard, backward, and forward stepwise, were developed to determine the best fit with the data.p ≤ 0.05.Moreover, a logistic regression model (LR) was employed to further ascertain the efficacy of the discriminant models in classifying the two smoking groups. A five-fold cross-validation technique was used for developing the model . The datTo examine the efficacy of the DA and LR models in discriminating as well as classifying the PPAS and PNAS, we excluded the weight and height variables in the process of developing the model as more often than not, these variables were shown to be highly correlated .p values, are shown. It could be observed from the table that fat percent and hip circumference were the highest contributors toward the prediction of group membership (PNAS or PPAS) in all three models developed. Hip circumference was observed to be a greater contributor to the model, with beta values of −3.6, −3.4, and −3.6, while fat percent had beta values of −3.1, −3.2, and −3.2 for each model, respectively. It is also evident that the best model (model c) has a comparatively lower SE as well as higher chi-square values for each variable, further demonstrating the importance of hip circumference and fat percentage in the model’s accuracy.The variables’ contributions to the efficacy of the LR models developed are tabulated in What are the most dominant AV characteristics of healthy male smokers? The findings of the current investigation from the initial objective via PCA  demonstrWhat are the smokers’ PA levels and the most dominant AV that differentiate the PA groups? The characteristics and discriminating features of PNAS and PPAS are shown in How effective are the DA and logistic regression models in discriminating and classifying the PPA and PNA smokers based on their AV characteristics? It is demonstrated from the best model of the current investigation, i.e., the forward stepwise DA feed-forward LR, that the major discriminating AV among the PA groups are the fat percentage and the hip circumference, as shown in These major findings from our study revealed that partially physically active smokers are characterized by relatively desirable anthropometric variables, in contrast with physically non-active smokers. It is evident from the current findings that certain anthropometric variables are vital for maintaining good health and that physical activity is essential for maintaining a healthy body in a sample of smokers. Furthermore, physically non-active smokers are more inclined to develop cardiovascular problems, coupled with nicotine dependence, when compared with partially physically active. Finally, it is worth highlighting that the application of multivariate analysis and machine learning may be useful in studying the underlying associations between the investigated variables.As a recommendation to diminish the prevalence of smoking among individuals, governments could decrease smoking pervasiveness through the increase of smoking expense, by methods of deploying tax increases, initiating constant social advertising efforts through which health educators could regularly encourage smokers to stop, and offering both pharmacological and humanitarian support for stopping smoking. The promotion of exercise as a daily activity could be an important mechanism through which the effects of smoking on health could be averted or controlled. Governments should embark on the creation of modern leisure and PA facilities in parks, streets, marketplaces, and institutions, as well as corporate buildings, to motivate and promote active lifestyles amongst people. From the public health and clinic perspectives, exercise as well as a variety of PA could be introduced as prescriptions as part of the cessation mechanism since exercise could assist in reducing cravings, managing other withdrawal symptoms, and diminishing stress.The current study is subject to certain limitations. For instance, the dietary intake of the participants was not assessed, which could provide more precise characteristics of the participants with respect to their PA levels. The findings of the current investigation also could not be generalized to female smokers. The lack of the use of an actual PA test may have hindered the accurate estimation of the PA levels among the participants since over- or under-reporting of the PA levels by the participants could not be completely ruled out. Moreover, the use of physical assessment batteries, as well as other motor-related functional tests, could be explored to determine the physical fitness levels of smokers in future studies.


E-mental health for depression is increasingly used in clinical practice, but patient adherence suffers as therapist involvement decreases. One reason may be the low responsiveness of existing programs: especially autonomous systems are lacking in their input interpretation and feedback-giving capabilities. Here, we explore (a) to what extent a more socially intelligent and, therefore, technologically advanced solution, namely a conversational agent, is a feasible means of collecting thought record data in dialog, (b) what people write about in their thought records, (c) whether providing content-based feedback increases motivation for thought recording, a core technique of cognitive therapy that helps patients gain an understanding of how their thoughts cause their feelings. Using the crowd-sourcing platform Prolific, 308 participants with subclinical depression symptoms were recruited and split into three conditions of varying feedback richness using the minimization method of randomization. They completed two thought recording sessions with the conversational agent: one practice session with scenarios and one open session using situations from their own lives. All participants were able to complete thought records with the agent such that the thoughts could be interpreted by the machine learning algorithm, rendering the completion of thought records with the agent feasible. Participants chose interpersonal situations nearly three times as often as achievement-related situations in the open chat session. The three most common underlying schemas were the Attachment, Competence, and Global Self-evaluation schemas. No support was found for a motivational effect of providing richer feedback. In addition to our findings, we publish the dataset of thought records for interested researchers and developers. Software systems increasingly help to prevent and treat depressive disorders. However, Richards and Richardson  have shoe-mental health for depression, delivering treatment or prevention programs via electronic devices. As a result of the COVID-19 pandemic, e-mental health for depression is increasingly finding its way into standard clinical practice , an evidence-based therapy form often used for the treatment , feedback of low richness plus process-related feedback concerning the amount of input provided (medium), and feedback of medium richness plus content-related feedback, i.e., giving an interpretation of the input with regard to possible underlying schemas (high). As dependent variables, we used the number of voluntarily completed thought records in the second session with the conversational agent as well as the engagement in self-reflection. In addition, the mediating variable insight and the moderating variable need for self-reflection were assessed. We obtained ethical approval from the Human Research Ethics Committee of Delft University of Technology  and pre-registered the study on the Open Science Framework (https://osf.io/5vucg).We developed a conversational agent for the thought recording task and let participants interact with this agent to collect thought record data and to examine the motivating effect of richer feedback. For the latter objective, we chose a double-blind, between-subjects design. The independent variable, The materials, including the informed consent, data management plan, pre- and post-questionnaires, the task instructions, the scenarios, the measures, the power analysis simulation script, as well as all data relevant for the analyses and the dataset of thought records can be found in the data repository accompanying this article .Low feedback richness entailed that it thanked the participants for completing the thought record and reminded them that completing more thought records might provide insight into thought patterns. Medium feedback richness consisted of the low-level feedback but additionally presented participants with a diagram of the number of downward arrow steps they had completed in this thought record and all previous thought records and put this number in relation to the number of people who had completed as many steps in a previous study. For high feedback richness, finally, the medium-level feedback was extended with natural language processing to determine one or multiple schemas that may have been activated. A spider diagram illustrated the degree to which the algorithm deemed the schema(s) present in the thought record using blue dots along nine schema axes. Orange dots in the same diagram depicted the aggregated results from previous thought records of this participant. The schemas for this condition were determined using a set of nine neural networks, one for each possible schema  95% CI) and acceptable . The items of each subscale were summed to obtain a summary score for the variables need for self-reflection, insight, and engagement in self-reflection. Engagement in self-reflection was negatively skewed (ceiling effect) and we consequently boxcox-tranformed the data with λ = 1.97 for use in the analyses.The reliability of the three subscales of the Self-Reflection and Insight scale was good (number of voluntarily completed thought records was expected to be Poisson-distributed and fitting a second linear model for the self-report outcome variable engagement in self-reflection. A further linear model was fit to test whether feedback richness affected insight (mediator). Finally, we fit one generalized linear model and one linear model to assess the effect of the mediator on each of the two outcome variables. Due to the lack of mediation observed in these models, we did not test for moderated mediation. However, we checked with two linear regression models whether participants' need for self-reflection (moderator) moderated the direct link between the feedback richness and either of two outcome variables.We followed the Baron and Kenny method  to test All 308 participants were able to complete thought records with the conversational agent. Of the 93 participants who chose to comment, 34 reported that they found the experiment insightful, with five participants specifically mentioning the added value of the agent and the feedback . However, another five participants also commented that they struggled with the downward arrow technique and would have liked more agent or even human support . Only two participants remarked negatively about the rich feedback  and one in the low-level feedback condition about the lack thereof .The relative frequency distribution with which the schema-labeling algorithm identified certain schemas in this dataset compared to that of the previous study by Burger et al.  correlatWe present a typical thought record situation for each content label in mean = 29.56, SD = 4.04), but completed, on average, only 1.62 (SD = 0.72) thought records in the second session. The direct effect of feedback richness on either of these measures of motivation (feedback richness on the mediator variable insight (a-path). As a consequence, partial mediation was no longer relevant. Nonetheless, a significant link between the mediating variable insight was found for both measures of motivation (b-path): for every additional scale point of insight they report, participants complete 1.03 times as many thought records  and feel 4.91 scale points more engaged  on a scale ranging from 6 to 36.Participants felt engaged in self-reflection when completing the thought records  = 3.69, p < 0.001].The moderator Attachment schema being identified by the algorithm more than the Competence schema. Despite around 4% of situations mentioning the COVID-19 pandemic, the Health schema was not more active in this dataset than in the ones collected before the pandemic. This is likely due to the training dataset of the algorithm being biased toward dieting situations for this particular schema. It can also be seen from the frequency of the Negativity label that participants were able to choose negative situations (98%) as instructed but sometimes put a positive spin on the meaning of the situation for themselves . It is important to note here, however, that the participants were subclinically depressed and these findings concerning the feasibility and content of the thought records may not generalize to a clinical population.The findings show that thought recording with a conversational agent is feasible for a subclinically depressed population: 100% of participants completed the thought records such that the machine learning algorithm trained on a similar dataset could label thoughts with regard to the underlying schemas. The distribution of the thus assigned schema labels not only closely resembles that of the dataset used for training the algorithm  but also the manually labeled dataset of Millings and Carnelley  clinica, 39, butfeedback richness influenced either the motivation of participants (direct effect) or the hypothesized mediating variable insight participants gained from the task. However, participants' gained insight positively related to both measures of motivation, and participants who reported a greater need for self-reflection also reported being more engaged in the task. When regarding these findings, limitations of the feedback on the one hand and of motivation on the other should be considered. For one, the spider plot and the academic definitions of the schemas in the rich-feedback condition might not have been as accessible as we had hoped and therefore did not add the expected value. This could be addressed in future research by following an cyclic design approach including both end users and graphical designers, simplifying the feedback, including measures of graphic literacy and health literacy as moderators, or conducting pilot studies to determine whether feedback is processed as desired. In line with this, articles concerning the design of graphical feedback in behavior change support systems argue for the importance of health literacy and usability as guiding principles  in this sample of subclinically depressed participants were the same as in previous work with healthy  and withdoi: 10.4121/20137736.The original, anonymized data that were collected in this research study are available in a publicly accessible 4TU.ResearchData repository. All materials and analyses to reproduce the results or the study can be found in the same repository. The repository is registered under The studies involving human participants were reviewed and approved by Human Research Ethics Committee Delft University of Technology. The participants provided their written informed consent to participate in this study.FB and W-PB closely collaborated on the drafting and planning of the conversational agent, the experiment, the analysis of the data, and the article. FB developed the conversational agent, conducted the experiment, analyzed the data, and created all figures. W-PB and MN supervised the entire research process, providing critical feedback and guidance throughout. FB, W-PB, and MN jointly wrote the article. All authors contributed to the article and approved the submitted version.This work was funded by two projects of the Dutch 4TU center for Humans & Technology: the Smart Social Systems and Spaces for Living Well (S4) project and the Pride and Prejudice project.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.