Early Identification of Language Disorders Using Natural Language Processing and Machine Learning: Challenges and Emerging Approaches.

Purpose: Recent advances in artificial intelligence provide opportunities to capture and represent complex features of human language in a more automated manner, offering potential means of improving the efficiency of language assessment. This review article presents computerized approaches for the analysis of narrative language and identification of language disorders in children.

Method: We first describe the current barriers to clinicians' use of language sample analysis, narrative language sampling approaches, and the data processing stages that precede analysis.

Personal likelihood and event familiarity influence the simulation of future events.

Episodic future thinking is the ability to project the self forward in time to preexperience a potential future event. It has been hypothesized that two components enhance simulations of future events: personal likelihood and event familiarity. Personal likelihood varies depending on the dynamics of personal goals throughout an individual's lifetime.

Committing to emotion regulation: Factors impacting the choice to implement a reappraisal after its generation.

Cognitive reappraisal, changing the way one thinks about an emotional event, is one of the most effective and extensively studied emotion regulation strategies. Previous research has dissociated the generation of reappraisals (i.e.

Enhancing cognitive control of our decisions: Making the most of humor during the IGT in females and males.

We studied the impact of humor on the Iowa Gambling Task (IGT) decision-making performance and the cognitive control exerted during this task, considering sex as a moderator, and examined whether cognitive control mediated the influence of humor on decision-making. Sixty participants (30 females) performed an extended version of the IGT (500 trials divided into 20 blocks). We randomly assigned them to either an experimental group (Humor Group; Hg; n = 30), where humorous videos were interspersed in the decision-making trials or a control group (Non-Humor Group; NHg; n = 30), where nonhumorous videos were interspersed in the decision-making trials.