Judgment and Embodied Cognition of Lawyers. Moral Decision-Making and Interoceptive Physiology in the Legal Field

EEG Correlates of Moral Decision-Making: Effect of Choices and Offers Types

BACKGROUND

Moral decision-making consists of a complex process requiring individuals to evaluate potential consequences of personal and social decisions, including applied organizational contexts.

METHODS

This research aims to investigate the behavioral (offer responses and reaction times, RTs) and electrophysiological (EEG) correlates underlying moral decision-making during three different choice conditions (professional fit, company fit, and social fit) and offers (fair, unfair, and neutral).

RESULTS

An increase of delta and theta frontal activity (related to emotional behavior and processes) and beta frontal and central activity (linked to cognitive and attentional processes) was found. A left beta, delta, and theta frontal activity was observed for fair offers in professional fit conditions, while increased right frontal delta and theta activity was found in response to unfair offers in company fit conditions. Also, an increase of left delta and theta parietal activity for unfair offers in social fit condition was detected. Finally, higher accepted responses were found for fair and neutral offers in professional and social fit conditions, with increased RTs for unfair offers suggesting decisions' cognitive load and complexity.

CONCLUSIONS

By revealing a greater involvement of left and right frontal areas in decision-making processes based on choices and offers, personal interest evaluations and emotional values, and of parietal areas in more prosocial and altruistic moral behavior, current findings provide information about the neural and behavioral correlates underlying company moral behavior.

Can Professionals Resist Cognitive Bias Elicited by the Visual System? Reversed Semantic Prime Effect and Decision Making in the Workplace: Reaction Times and Accuracy

Information that comes from the environment reaches the brain-and-body system via sensory inputs that can operate outside of conscious awareness and influence decision processes in different ways. Specifically, decision-making processes can be influenced by various forms of implicit bias derived from individual-related factors (e.g., individual differences in decision-making style) and/or stimulus-related information, such as visual input. However, the relationship between these subjective and objective factors of decision making has not been investigated previously in professionals with varying seniority. This study explored the relationship between decision-making style and cognitive bias resistance in professionals compared with a group of newcomers in organisations. A visual "picture-picture" semantic priming task was proposed to the participants. The task was based on primes and probes' category membership (animals vs. objects), and after an animal prime stimulus presentation, the probe can be either five objects (incongruent condition) or five objects and an animal (congruent condition). Behavioural (i.e., accuracy-ACC, and reaction times-RTs) and self-report data (through the General Decision-Making Scale administration) were collected. RTs represent an indirect measure of the workload and cognitive effort required by the task, as they represent the time it takes the nervous system to receive and integrate incoming sensory information, inducing the body to react. For both groups, the same level of ACC in both conditions and higher RTs in the incongruent condition were found. Interestingly, for the group of professionals, the GDMS-dependent decision-making style negatively correlates with ACC and positively correlates with RTs in the congruent condition. These findings suggest that, under the incongruent decision condition, the resistance to cognitive bias requires the same level of cognitive effort, regardless of seniority. However, with advancing seniority, in the group of professionals, it has been demonstrated that a dependent decision-making style is associated with lower resistance to cognitive bias, especially in conditions that require simpler decisions. Whether this result depends on age or work experience needs to be disentangled from future studies.

Physiology of Risk-Taking and Risk Management in Realistic Decision-Making Scenarios

Decisions under risk are a particular case of decisional skills taking place in complex and mostly unpredictable situations, where affective connotation of deciding is highly relevant. We aimed at investigating decisional processes under risk by outlining individual risk-taking (RT) and risk management (RM) attitudes via realistic decision-making and, in keeping with the risk-as-feeling hypothesis, at exploring implicit physiological correlates of such processes. 35 participants were presented with realistic situations where they had to make decisions by choosing between alternatives connoted by different levels of riskiness. Concurrently, autonomic physiological activation (cardiovascular and electrodermal activity) was recorded. Data analysis highlighted that: (i) participants showed higher propensity towards risk management than risk-taking; (ii) the propensity towards both risk taking and risk management was significantly determined by physiological markers of autonomic activity; and (iii) risk taking and risk management indices showed associations with different autonomic measures, respectively heart rate and skin conductance metrics.

Decisional brain of lawyers at the workplace. A neurolaw pilot study

This pilot study investigated legal and non-legal professionals' decision process during a typical working day. During self-evaluated highly relevant decisions (rated through a daily diary), the two groups were asked to wear the Muse™ Headband to record their electrophysiological (EEG) activity in terms of frequency bands (delta, theta, alpha and beta). EEG cognitive findings displayed a generally increased beta power in the anterior frontal region (mainly in the right than left) for both groups during highly relevant decisions. Significantly results were also found for the legal professionals' group, for which a decrease of alpha power was found in the left compared to right frontal cortex. Furthermore, a decreased alpha power and increased delta and theta power in the right compared to left Temporo-Parietal Junction was observed in the legal professionals when taking highly relevant decisions. This pilot study suggested a specific EEG pattern for legal professionals while taking highly relevant decisions.

The application of cognitive neuroscience to judicial models: recent progress and trends

Legal prediction presents one of the most significant challenges when applying artificial intelligence (AI) to the legal field. The legal system is a complex adaptive system characterized by the ambiguity of legal language and the diversity of value functions. The imprecision and procedural knowledge inherent in law makes judicial issues difficult to be expressed in a computer symbol system. Current semantic processing and machine learning technologies cannot fully capture the complex nature of legal relations, thereby raising doubts about the accuracy of legal predictions and reliability of judicial models. Cognitive computing, designed to emulate human brain functions and aid in enhancing decision-making processes, offers a better understanding of legal data and the processes of legal reasoning. This paper discusses the advancements made in cognitive methods applied to legal concept learning, semantic extraction, judicial data processing, legal reasoning, understanding of judicial bias, and the interpretability of judicial models. The integration of cognitive neuroscience with law has facilitated several constructive attempts, indicating that the evolution of cognitive law could be the next frontier in the intersection of AI and legal practice.

Deep Learning-Based Text Emotion Analysis for Legal Anomie

Text emotion analysis is an effective way for analyzing the emotion of the subjects’ anomie behaviors. This paper proposes a text emotion analysis framework (called BCDF) based on word embedding and splicing. Bi-direction Convolutional Word Embedding Classification Framework (BCDF) can express the word vector in the text and embed the part of speech tagging information as a feature of sentence representation. In addition, an emotional parallel learning mechanism is proposed, which uses the temporal information of the parallel structure calculated by Bi-LSTM to update the storage information through the gating mechanism. The convolutional layer can better extract certain components of sentences (such as adjectives, adverbs, nouns, etc.), which play a more significant role in the expression of emotion. To take advantage of convolution, a Convolutional Long Short-Term Memory (ConvLSTM) network is designed to further improve the classification results. Experimental results show that compared with traditional LSTM model, the proposed text emotion analysis model has increased 3.3 and 10.9% F1 score on psychological and news text datasets, respectively. The proposed CBDM model based on Bi-LSTM and ConvLSTM has great value in practical applications of anomie behavior analysis.