Recent advancements in deep learning have achieved significant progress by increasing the number of parameters in a given model. However, this comes at the cost of computing resources, prompting researchers to explore model compression techniques that reduce the number of parameters while maintaining or even improving performance. Convolutional neural networks (CNN) have been recognized as more efficient and effective than fully connected (FC) networks. We propose a column row convolutional neural network (CRCNN) in this letter that applies 1D convolution to image data, significantly reducing the number of learning parameters and operational steps. The CRCNN uses column and row local receptive fields to perform data abstraction, concatenating each direction's feature before connecting it to an FC layer. Experimental results demonstrate that the CRCNN maintains comparable accuracy while reducing the number of parameters and compared to prior work. Moreover, the CRCNN is employed for one-class anomaly detection, demonstrating its feasibility for various applications.
The human brain's remarkable motor adaptability stems from the formation of context representations and the use of a common context representation (e.g., an invariant task structure across task contexts) derived from structural learning. However, direct evaluation of context representations and structural learning in sensorimotor tasks remains limited. This study aimed to rigorously distinguish neural representations of visual, movement, and context levels crucial for multi-context visuomotor adaptation and investigate the association between representation commonality across task contexts and adaptation performance using multivariate decoding analysis with fMRI data. Here, we focused on three distinct task contexts, two of which share a rotation structure (i.e., visuomotor rotation contexts with -90° and +90° rotations, in which the mouse cursor's movement was rotated 90 degrees counterclockwise and clockwise relative to the hand-movement direction, respectively) and the remaining one does not (i.e., mirror-reversal context where the horizontal movement of the computer mouse was inverted). This study found that visual representations (i.e., visual direction) were decoded in the occipital area, while movement representations (i.e., hand-movement direction) were decoded across various visuomotor-related regions. These findings are consistent with prior research and the widely recognized roles of those areas. Task-context representations (i.e., either -90° rotation, +90° rotation, or mirror-reversal) were also distinguishable in various brain regions. Notably, these regions largely overlapped with those encoding visual and movement representations. This overlap suggests a potential intricate dependency of encoding visual and movement directions on the context information. Moreover, we discovered that higher task performance is associated with task-context representation commonality, as evidenced by negative correlations between task performance and task-context-decoding accuracy in various brain regions, potentially supporting structural learning. Importantly, despite limited similarities between tasks (e.g., rotation and mirror-reversal contexts), such association was still observed, suggesting an efficient mechanism in the brain that extracts commonalities from different task contexts (such as visuomotor rotations or mirror-reversal) at multiple structural levels, from high-level abstractions to lower-level details. In summary, while illuminating the intricate interplay between visuomotor processing and context information, our study highlights the efficiency of learning mechanisms, thereby paving the way for future exploration of the brain's versatile motor ability.
The current study aimed to investigate the possibility of rapid and accurate diagnoses of Panic disorder (PD) and Major depressive disorder (MDD) using machine learning. The support vector machine method was applied to 2-channel EEG signals from the frontal lobes (Fp1 and Fp2) of 149 participants to classify PD and MDD patients from healthy individuals using non-linear measures as features. We found significantly lower correlation dimension and Lempel-Ziv complexity in PD patients and MDD patients in the left hemisphere compared to healthy subjects at rest. Most importantly, we obtained a 90% accuracy in classifying MDD patients vs. healthy individuals, a 68% accuracy in classifying PD patients vs. controls, and a 59% classification accuracy between PD and MDD patients. In addition to demonstrating classification performance in a simplified setting, the observed differences in EEG complexity between subject groups suggest altered cortical processing present in the frontal lobes of PD patients that can be captured through non-linear measures. Overall, this study suggests that machine learning and non-linear measures using only 2-channel frontal EEGs are useful for aiding the rapid diagnosis of panic disorder and major depressive disorder.
Depressive Disorder, Major , Panic Disorder , Humans , Depressive Disorder, Major/diagnosis , Panic Disorder/diagnosis , Electroencephalography/methods , Frontal Lobe , Machine Learning
Human fingerprints are randomly created during fetal activity in the womb, resulting in unique and physically irreproducible fingerprint patterns that are applicable as a biological cryptographic primitive. Similarly, stochastically knitted single-walled carbon nanotube (SWNT) network surfaces exhibit inherently random and unique electrical characteristics that can be exploited as a physical unclonable function (PUF) in the authentication. In this study, filamentous M13 bacteriophages are used as a biological gluing template to create a random SWNT network surface with mechanical flexibility, with electrical properties determined by random variation during fabrication. The resistance profile between two adjacent electrodes was mapped for these M13-mediated SWNT network surfaces, with the results demonstrating a unique resistance profile for each M13-SWNT device, similar to that of human fingerprints. Randomness and uniqueness measures were evaluated as respectively 50.5% and 50% using generated challenge-response pairs. Min-entropy for unpredictability evaluation of the M13-SWNT based PUFs resulted in 0.98. Our results showed that M13-SWNT random network exhibits cryptographic characteristics when used in a bio-inspired PUF device.
Bacteriophages , Biomimetic Materials , Nanotubes, Carbon , Humans , Electronics , Electrodes
Virtual reality (VR) is a rapidly developing technology that simulates the real world. However, for some cybersickness-susceptible people, VR still has an unanswered problem-cybersickness-which becomes the main obstacle for users and content makers. Sensory conflict theory is a widely accepted theory for cybersickness. It proposes that conflict between afferent signals and internal models can cause cybersickness. This study analyzes the brain states that determine cybersickness occurrence and related uncomfortable feelings. Furthermore, we use the electroencephalogram (EEG) microstates and functional connectivity approach based on the sensory conflict theory. The microstate approach is a time-space analysis method that allows signals to be divided into several temporarily stable states, simultaneously allowing for the exploration of short- and long-range signals. These temporal dynamics can show the disturbances in mental processes associated with neurological and psychiatric conditions of cybersickness. Furthermore, the functional connectivity approach gives us in-depth insight and relationships between the sources related to cybersickness. We recruited 40 males (24.1 ± 2.3 years), and they watched a VR video on a curved computer monitor for 10 min to experience cybersickness. We recorded the 5-min resting state EEG (baseline condition) and 10-min EEG while watching the VR video (task condition). Then, we performed a microstate analysis, focusing on two temporal parameters: mean duration and global explained variance (GEV). Finally, we obtained the functional connectivity data using eLoreta and lagged phase synchronization (LPS). We discovered five sets of microstates (A-E), including four widely reported canonical microstates (A-D), during baseline and task conditions. The average duration increased in microstates A and B, which is related to the visual and auditory networks. The GEV and duration decreased in microstate C, whereas those in microstate D increased. Microstate C is related to the default mode network (DMN) and D to the attention network. The temporal dynamics of the microstate parameters are from cybersickness disturbing the sensory, DMN, and attention networks. In the functional connectivity part, the LPS between the left and right parietal operculum (OP) significantly decreased (p < 0.05) compared with the baseline condition. Furthermore, the connectivity between the right OP and V5 significantly decreased (p < 0.05). These results also support the disturbance of the sensory network because a conflict between the visual (V5) and vestibular system (OP) causes cybersickness. Changes in the microstates and functional connectivity support the sensory conflict theory. These results may provide additional information in understanding brain dynamics during cybersickness.
Objective. Reaching hand movement is an important motor skill actively examined in the brain-computer interface (BCI). Among the various components of movement analyzed is the hand's trajectory, which describes the hand's continuous positions in three-dimensional space. While a large body of studies have investigated the decoding of real movements and the reconstruction of real hand movement trajectories from neural signals, fewer studies have attempted to decode the trajectory of the imagined hand movement. To develop BCI systems for patients with hand motor dysfunctions, the systems essentially have to achieve movement-free control of external devices, which is only possible through successful decoding of purely imagined hand movement.Approach. To achieve this goal, this study used a machine learning technique (i.e. the variational Bayesian least square) to analyze the electrocorticogram (ECoG) of 18 epilepsy patients obtained from when they performed movement execution (ME) and kinesthetic movement imagination (KMI) of the reach-and-grasp hand action.Main results. The variational Bayesian decoding model was able to successfully predict the imagined trajectories of the hand movement significantly above the chance level. The Pearson's correlation coefficient between the imagined and predicted trajectories was 0.3393 and 0.4936 for the KMI (KMI trials only) and MEKMI paradigm (alternating trials of ME and KMI), respectively.Significance. This study demonstrated a high accuracy of prediction for the trajectories of imagined hand movement, and more importantly, a higher decoding accuracy of the imagined trajectories in the MEKMI paradigm compared to the KMI paradigm solely.
Brain-Computer Interfaces , Bayes Theorem , Electroencephalography/methods , Hand , Humans , Movement
To understand, predict, and help correct each other's actions we need to maintain accurate, up-to-date knowledge of people, and communication is a critical means by which we gather and disseminate this information. Yet the conditions under which we communication social information remain unclear. Testing hypotheses generated from our theoretical framework, we examined when and why social information is disseminated about an absent third party: i.e., gossiped. Gossip scenarios presented to participants (e.g., "Person-X cheated on their exam") were based on three key factors: (1) target (ingroup, outgroup, or celebrity), (2) valence (positive or negative), and (3) content. We then asked them (a) whether they would spread the information, and (b) to rate it according to subjective valence, ordinariness, interest level, and emotion. For ratings, the scenarios participants chose to gossip were considered to have higher valence (whether positive or negative), to be rarer, more interesting, and more emotionally evocative; thus showing that the paradigm was meaningful to subjects. Indeed, for target, valence, and content, a repeated-measures ANOVA found significant effects for each factor independently, as well as their interactions. The results supported our hypotheses: e.g., for target, more gossiping about celebrities and ingroup members (over strangers); for valence, more about negative events overall, and yet for ingroup members, more positive gossiping; for content, more about moral topics, with yet all domains of social content communicated depending on the situation-context matters, influencing needs. The findings suggest that social knowledge sharing (i.e., gossip) involves sophisticated calculations that require our highest sociocognitive abilities, and provide specific hypotheses for future examination of neural mechanisms.
Communication , Famous Persons , Emotions , Humans , Knowledge , Morals
Prior experience of social hierarchy is known to modulate emotional contagion, a basic form of affective empathy. However, it is not known whether this behavioral effect occurs through changes in an individual's traits due to their experience of social hierarchy or specific social interrelationships between the individuals. Groups of four mice with an established in-group hierarchy were used to address this in conjunction with a tube test. The rank-1 and rank-4 mice were designated as the dominant or subordinate groups, respectively. The two individuals in between were designated as the intermediate groups, which were then used as the observers in observational fear learning (OFL) experiments, an assay for emotional contagion. The intermediate observers showed greater OFL responses to the dominant demonstrator than the subordinate demonstrators recruited from the same home-cage. When the demonstrators were strangers from different cages, the intermediate observers did not distinguish between dominant and subordinate, displaying the same level of OFL. In a reverse setting in which the intermediate group was used as the demonstrator, the subordinate observers showed higher OFL responses than the dominant observers, and this occurred only when the demonstrators were cagemates of the observers. Furthermore, the bigger the rank difference between a pair, the higher the OFL level that the observer displayed. Altogether, these results demonstrate that the hierarchical interrelationship established between a given pair of animals is critical for expressing emotional contagion between them rather than any potential changes in intrinsic traits due to the experience of dominant/subordinate hierarchy. PRACTITIONER POINTS: Subordinate observer or dominant demonstrator resulted in higher affective empathic response in familiar pairs but not unfamiliar pairs. The relative social rank of the observer with respect to the demonstrator had a negative linear correlation with the affective empathic response of the observer in familiar pairs but not unfamiliar pairs. The effect of social rank on affective empathy is attributed to the prior social hierarchical interrelationship between them and is not due to intrinsic attributes of an individual based on one's dominance rank.
Emotions , Empathy , Animals , Emotions/physiology , Fear , Hierarchy, Social , Learning , Mice
Evidence that the brain combines different value learning strategies to minimize prediction error is accumulating. However, the tradeoff between bias and variance error, which imposes different constraints on each learning strategy's performance, poses a challenge for value learning. While this tradeoff specifies the requirements for optimal learning, little has been known about how the brain deals with this issue. Here, we hypothesize that the brain adaptively resolves the bias-variance tradeoff during reinforcement learning. Our theory suggests that the solution necessitates baseline correction for prediction error, which offsets the adverse effects of irreducible error on value learning. We show behavioral evidence of adaptive control using a Markov decision task with context changes. The prediction error baseline seemingly signals context changes to improve adaptability. Critically, we identify multiplexed representations of prediction error baseline within the ventrolateral and ventromedial prefrontal cortex, key brain regions known to guide model-based and model-free reinforcement learning.
Brain/physiology , Decision Making , Learning , Prefrontal Cortex/physiology , Reinforcement, Psychology , Reward , Adult , Female , Humans , Male , Young Adult
Dendritic network implementable organic neurofiber transistors with enhanced memory cyclic endurance for spatiotemporal iterative learning are proposed. The architecture of the fibrous organic electrochemical transistors consisting of a double-stranded assembly of electrode microfibers and an iongel gate insulator enables the highly sensitive multiple implementation of synaptic junctions via simple physical contact of gate-electrode microfibers, similar to the dendritic connections of a biological neuron fiber. In particular, carboxylic-acid-functionalized polythiophene as a semiconductor channel material provides stable gate-field-dependent multilevel memory characteristics with long-term stability and cyclic endurance, unlike the conventional poly(alkylthiophene)-based neuromorphic electrochemical transistors, which exhibit short retention and unstable endurance. The dissociation of the carboxylic acid of the polythiophene enables reversible doping and dedoping of the polythiophene channel by effectively stabilizing the ions that penetrate the channel during potentiation and depression cycles, leading to the reliable cyclic endurance of the device. The synaptic weight of the neurofiber transistors with a dendritic network maintains the state levels stably and is independently updated with each synapse connected with the presynaptic neuron to a specific state level. Finally, the neurofiber transistor demonstrates successful speech recognition based on iterative spiking neural network learning in the time domain, showing a substantial recognition accuracy of 88.9%.
The Electrophysiology Professional Interest Area (EPIA) and Global Brain Consortium endorsed recommendations on candidate electroencephalography (EEG) measures for Alzheimer's disease (AD) clinical trials. The Panel reviewed the field literature. As most consistent findings, AD patients with mild cognitive impairment and dementia showed abnormalities in peak frequency, power, and "interrelatedness" at posterior alpha (8-12 Hz) and widespread delta (< 4 Hz) and theta (4-8 Hz) rhythms in relation to disease progression and interventions. The following consensus statements were subscribed: (1) Standardization of instructions to patients, resting state EEG (rsEEG) recording methods, and selection of artifact-free rsEEG periods are needed; (2) power density and "interrelatedness" rsEEG measures (e.g., directed transfer function, phase lag index, linear lagged connectivity, etc.) at delta, theta, and alpha frequency bands may be use for stratification of AD patients and monitoring of disease progression and intervention; and (3) international multisectoral initiatives are mandatory for regulatory purposes.
Alzheimer Disease/physiopathology , Clinical Trials as Topic , Electroencephalography/standards , Brain/physiopathology , Cognitive Dysfunction/physiopathology , Disease Progression , Humans
Vascular contribution to cognitive impairment (VCI) and dementia is related to etiologies that may affect the neurophysiological mechanisms regulating brain arousal and generating electroencephalographic (EEG) activity. A multidisciplinary expert panel reviewed the clinical literature and reached consensus about the EEG measures consistently found as abnormal in VCI patients with dementia. As compared to cognitively unimpaired individuals, those VCI patients showed (1) smaller amplitude of resting state alpha (8-12 Hz) rhythms dominant in posterior regions; (2) widespread increases in amplitude of delta (< 4 Hz) and theta (4-8 Hz) rhythms; and (3) delayed N200/P300 peak latencies in averaged event-related potentials, especially during the detection of auditory rare target stimuli requiring participants' responses in "oddball" paradigms. The expert panel formulated the following recommendations: (1) the above EEG measures are not specific for VCI and should not be used for its diagnosis; (2) they may be considered as "neural synchronization" biomarkers to enlighten the relationships between features of the VCI-related cerebrovascular lesions and abnormalities in neurophysiological brain mechanisms; and (3) they may be tested in future clinical trials as prognostic biomarkers and endpoints of interventions aimed at normalizing background brain excitability and vigilance in wakefulness.
Brain/physiopathology , Cognitive Dysfunction/diagnosis , Dementia, Vascular/diagnosis , Electroencephalography/methods , Cognitive Dysfunction/etiology , Cognitive Dysfunction/physiopathology , Dementia, Vascular/etiology , Dementia, Vascular/physiopathology , Evoked Potentials/physiology , Humans , Rest/physiology
Humans organize sequences of events into a single overall experience, and evaluate the aggregated experience as a whole, such as a generally pleasant dinner, movie, or trip. However, such evaluations are potentially computationally taxing, and so our brains must employ heuristics (i.e., approximations). For example, the peak-end rule hypothesis suggests that we average the peaks and end of a sequential event vs. integrating every moment. However, there is no general model to test viable hypotheses quantitatively. Here, we propose a general model and test among multiple specific ones, while also examining the role of working memory. The models were tested with a novel picture-rating task. We first compared averaging across entire sequences vs. the peak-end heuristic. Correlation tests indicated that averaging prevailed, with peak and end both still having significant prediction power. Given this, we developed generalized order-dependent and relative-preference-dependent models to subsume averaging, peak and end. The combined model improved the prediction power. However, based on limitations of relative-preference-including imposing a potentially arbitrary ranking among preferences-we introduced an absolute-preference-dependent model, which successfully explained the remembered utilities. Yet, because using all experiences in a sequence requires too much memory as real-world settings scale, we then tested "windowed" models, i.e., evaluation within a specified window. The windowed (absolute) preference-dependent (WP) model explained the empirical data with long sequences better than without windowing. However, because fixed-windowed models harbor their own limitations-including an inability to capture peak-event influences beyond a fixed window-we then developed discounting models. With (absolute) preference-dependence added to the discounting rate, the results showed that the discounting model reflected the actual working memory of the participants, and that the preference-dependent discounting (PD) model described different features from the WP model. Taken together, we propose a combined WP-PD model as a means by which people evaluate experiences, suggesting preference-dependent working-memory as a significant factor underlying our evaluations.
It is important to maintain attention when carrying out significant daily-life tasks that require high levels of safety and efficiency. Since degradation of attention can sometimes have dire consequences, various brain activity measurement devices such as electroencephalography (EEG) systems have been used to monitor attention states in individuals. However, conventional EEG instruments have limited utility in daily life because they are uncomfortable to wear. Thus, this study was designed to investigate the possibility of discriminating between the attentive and resting states using in-ear EEG signals for potential application via portable, convenient earphone-shaped EEG instruments. We recorded both on-scalp and in-ear EEG signals from 6 subjects in a state of attentiveness during the performance of a visual vigilance task. We have designed and developed in-ear EEG electrodes customized by modelling both the left and right ear canals of the subjects. We use an echo state network (ESN), a powerful type of machine learning algorithm, to discriminate attention states on the basis of in-ear EEGs. We have found that the maximum average accuracy of the ESN method in discriminating between attentive and resting states is approximately 81.16% with optimal network parameters. This study suggests that portable in-ear EEG devices and an ESN can be used to monitor attention states during significant tasks to enhance safety and efficiency.
In 1999, the International Federation of Clinical Neurophysiology (IFCN) published "IFCN Guidelines for topographic and frequency analysis of EEGs and EPs" (Nuwer et al., 1999). Here a Workgroup of IFCN experts presents unanimous recommendations on the following procedures relevant for the topographic and frequency analysis of resting state EEGs (rsEEGs) in clinical research defined as neurophysiological experimental studies carried out in neurological and psychiatric patients: (1) recording of rsEEGs (environmental conditions and instructions to participants; montage of the EEG electrodes; recording settings); (2) digital storage of rsEEG and control data; (3) computerized visualization of rsEEGs and control data (identification of artifacts and neuropathological rsEEG waveforms); (4) extraction of "synchronization" features based on frequency analysis (band-pass filtering and computation of rsEEG amplitude/power density spectrum); (5) extraction of "connectivity" features based on frequency analysis (linear and nonlinear measures); (6) extraction of "topographic" features (topographic mapping; cortical source mapping; estimation of scalp current density and dura surface potential; cortical connectivity mapping), and (7) statistical analysis and neurophysiological interpretation of those rsEEG features. As core outcomes, the IFCN Workgroup endorsed the use of the most promising "synchronization" and "connectivity" features for clinical research, carefully considering the limitations discussed in this paper. The Workgroup also encourages more experimental (i.e. simulation studies) and clinical research within international initiatives (i.e., shared software platforms and databases) facing the open controversies about electrode montages and linear vs. nonlinear and electrode vs. source levels of those analyses.
Electroencephalography/methods , Mental Disorders/physiopathology , Nervous System Diseases/physiopathology , Rest/physiology , Artifacts , Biomedical Research , Brain Mapping/methods , Brain Waves/physiology , Databases as Topic , Electrodes , Electroencephalography/instrumentation , Electroencephalography/standards , Electroencephalography Phase Synchronization/physiology , Environment , Humans , Information Storage and Retrieval/methods , Neurophysiology , Scalp , Simulation Training , Software , Wakefulness/physiology
Electrophysiology provides a real-time readout of neural functions and network capability in different brain states, on temporal (fractions of milliseconds) and spatial (micro, meso, and macro) scales unmet by other methodologies. However, current international guidelines do not endorse the use of electroencephalographic (EEG)/magnetoencephalographic (MEG) biomarkers in clinical trials performed in patients with Alzheimer's disease (AD), despite a surge in recent validated evidence. This position paper of the ISTAART Electrophysiology Professional Interest Area endorses consolidated and translational electrophysiological techniques applied to both experimental animal models of AD and patients, to probe the effects of AD neuropathology (i.e., brain amyloidosis, tauopathy, and neurodegeneration) on neurophysiological mechanisms underpinning neural excitation/inhibition and neurotransmission as well as brain network dynamics, synchronization, and functional connectivity, reflecting thalamocortical and corticocortical residual capacity. Converging evidence shows relationships between abnormalities in EEG/MEG markers and cognitive deficits in groups of AD patients at different disease stages. The supporting evidence for the application of electrophysiology in AD clinical research as well as drug discovery pathways warrants an international initiative to include the use of EEG/MEG biomarkers in the main multicentric projects planned in AD patients, to produce conclusive findings challenging the present regulatory requirements and guidelines for AD studies.
Alzheimer Disease/diagnosis , Alzheimer Disease/physiopathology , Brain/physiopathology , Electrophysiology/methods , Alzheimer Disease/pathology , Animals , Brain/pathology , Drug Discovery , Electroencephalography , Evoked Potentials , Humans , Magnetoencephalography
The thalamus has been implicated in fear extinction, yet the role of the thalamic reticular nucleus (TRN) in this process remains unclear. Here, in mice, we show that the rostroventral part of the TRN (TRNrv) is critically involved in the extinction of tone-dependent fear memory. Optogenetic excitation of TRNrv neurons during extinction learning dramatically facilitated, whereas the inhibition disrupted, the fear extinction. Single unit recordings demonstrated that TRNrv neurons selectively respond to conditioned stimuli but not to neutral stimuli. TRNrv neurons suppressed the spiking activity of the medial part of the dorsal midline thalamus (dMTm), and a blockade of this inhibitory pathway disrupted fear extinction. Finally, we found that the suppression of dMTm projections to the central amygdala promotes fear extinction, and TRNrv neurons have direct connections to this pathway. Our results uncover a previously unknown function of the TRN and delineate the neural circuit for thalamic control of fear memory.
Fear , Freezing Reaction, Cataleptic , Thalamic Nuclei/physiology , Animals , Behavior, Animal , Limbic System/physiology , Male , Mice , Mice, Inbred C57BL
Our decisions have a temporally distributed order, and different choice orders (e.g., choosing preferred items first or last) can lead to vastly different experiences. We previously found two dominant strategies (favorite-first and favorite-last) in a preference-based serial choice setting (the 'sushi problem'). However, it remains unclear why these two opposite behavioral patterns arise: i.e., the mechanisms underlying them. Here we developed a novel serial-choice task, using pictures based on attractiveness, to test for a possible shared mechanism with delay discounting, the 'peak-end' bias (i.e., preference for experienced sequences that end well), or working-memory capacity. We also collected psychological and clinical metric data on personality, depression, anxiety, and emotion regulation. We again found the two dominant selection strategies. However, the results of the delay, peak-end bias, and memory capacity tasks were not related to serial choice, while two key psychological metrics were: emotion regulation and conscientiousness (with agreeableness also marginally related). Favorite-first strategists actually regulated emotions better, suggesting better tolerance of negative outcomes. Whereas participants with more varied strategies across trials were more conscientious (and perhaps agreeable), suggesting that they were less willing to settle for a single, simpler strategy. Our findings clarify mechanisms underlying serial choice and show that it may reflect a unique ability to organize choices into sequences of events.
Delay Discounting/physiology , Emotional Regulation/physiology , Intuition/physiology , Adult , Healthy Volunteers , Humans , Male , Young Adult
Apologizing is an effective interpersonal conflict resolution strategy, but whether, and if so how, organizations should issue public apologies after crises remains less clear. To assuage the fear of possible crisis reoccurrence, public apologies may be effective when they provide a comprehensive account of what happened and clarify actions taken by the company to address the problems. If this is so, public apologies may be most effective when the crisis source resides within the organization itself, suggesting that the company has control over it. In the current study, we first tested this hypothesis by presenting participants with multiple crisis scenarios (e.g., ignition failures in a new car model) followed by one of two written apologies: one stating that the crisis source was internal to and controllable by the organization, and the other external and uncontrollable. The internal-controllable (IC) public apology proved most effective. We then examined the neural basis of this public apology assessment and found that the frontal polar cortex appears to mediate the assessment of organizational control, and the angular gyrus uses the information for the apology assessment. Examination of complex social interactions, such as the public's reaction to corporate crises, helps to elucidate high-level brain function.
Real-life decisions often require a comparison of multi-attribute options with various benefits and costs, and the evaluation of each option depends partly on the others in the choice set (i.e., the choice context). Although reinforcement learning models have successfully described choice behavior, how to account for multi-attribute information when making a context-dependent decision remains unclear. Here we develop a computational model of attention control that includes context effects on multi-attribute decisions, linking a context-dependent choice model with a reinforcement learning model. The overall model suggests that the distinctiveness of attributes guides an individual's preferences among multi-attribute options via an attention-control mechanism that determines whether choices are selectively biased toward the most distinctive attribute (selective attention) or proportionally distributed based on the relative distinctiveness of attributes (divided attention). To test the model, we conducted a behavioral experiment in rhesus monkeys, in which they made simple multi-attribute decisions over three conditions that manipulated the degree of distinctiveness between alternatives: (1) four foods of different size and calorie; (2) four pieces of the same food in different colors; and (3) four identical pieces of food. The model simulation of the choice behavior captured the preference bias (i.e., overall preference structure) and the choice persistence (repeated choices) in the empirical data, providing evidence for the respective influences of attention and memory on preference bias and choice persistence. Our study provides insights into computations underlying multi-attribute decisions, linking attentional control to decision-making processes.
