Strengthening semantic and orthographic associations among words in a lexicon may help to improve memory processes related to fluent organizing and retrieval of language. In the present study, we examined how training in several different word games impacts later retrieval access for the words. Games included a word-stem completion task (orthographic), a free association task (semantic), and a crossword paradigm task (orthographic+semantic). A within-subject experiment was used to compare the relative effectiveness of these three training methods on a lexical association task performed prior to and following training. Results showed that the games were able to improve participants’ decision times, and the increased fluency in the lexical association task due to the free association task was greater than the other games. We will further apply and examine this study with non-native English speakers.

Word games such as crossword puzzles are widely used in ed-ucation to help familiarize students with technical vocabulary.Despite an extensive literature discussing their use, few pub-lished research articles have established their effectiveness onmemory retention and retrieval, especially in comparison tocontrol study methods. We report two experiments in whichuniversity introductory psychology students studied materialsrelevant to their coursework using an on-line interactive wordgame. Results showed that word games improved later tests ofthe material, both on immediate test and after a delay, and re-tention was most enhanced in comparison to control when theclues were solved repeatedly and given with difficult ortho-graphic hints. Importantly, easy clues were not retained overtime even with multiple repetitions. Results suggest that wordgames can be effective in this domain, but it depends on howthey are implemented, and several factors predicted by existingcognitive theory can guide implementation choices.

Standard behavioral economic games assume that rational actors have stable, well-defined preferences. Two experimentswere created to simulate various priming factors within a standard dictator game. Throughout the first experiment nearly50% of the participants gave an equal distribution of value between themselves and the recipient. This trend persistedwhen the recipient was clearly labelled as a computer. The second study evaluated whether or not the equal distributionobserved in the first experiment was due to an automatic response, where the default mode is to allocate resources equitably.After providing participants with a time delay and critical thinking prompt, there was a 6% shift in the number of equaldistributions given. These results indicate that equal distributions may be the result of an automatic thinking process.Methodological implications pertaining to past studies in which automatic behavior was not considered during the use ofdictator games may arise.

Explainability is assumed to be a key factor for theadoption of Artificial Intelligence systems in a wide rangeof contexts (Hoffman, Mueller, & Klein, 2017; Hoffman,Mueller, Klein, & Litman, 2018; Doran, Schulz, & Besold,2017; Lipton, 2018; Miller, 2017; Lombrozo, 2016).The use of AI components in self-driving cars, medicaldiagnosis, or insurance and financial services has shownthat when decisions are taken or suggested by automatedsystems it is essential for practical, social, and increasinglylegal reasons that an explanation can be provided tousers, developers or regulators.1Moreover, the reasons forequipping intelligent systems with explanation capabilitiesare not limited to user rights and acceptance. Explainabilityis also needed for designers and developers to enhancesystem robustness and enable diagnostics to prevent bias,unfairness and discrimination, as well as to increase trust byall users in why and how decisions are made. Against thatbackground, increased efforts are directed towards studyingand provisioning explainable intelligent systems, both inindustry and academia, sparked by initiatives like the DARPAExplainable Artificial Intelligence Program (DARPA, 2016).In parallel, scientific conferences and workshops dedicated toexplainability are now regularly organised, such as the ‘ACMConference on Fairness, Accountability, and Transparency(ACM FAT)’ (Friedler & Wilson, n.d.) or the ‘Workshop onExplainability in AI’ at the 2017 and 2018 editions of theInternational Joint Conference on Artificial Intelligence.However, one important question remains hithertounanswered: What are the criteria for a good explanation?