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Cheating forms part of a complex emotional and cognitive process. However, although a relatively mundane phenomenon, instruments to evaluate cheating and its effects socially are scarce. This paper presents a five-stage approach aimed at providing validity to an instrument designed to assess cheating — specifically, its detection, and emotional reactions towards it once detected. An instrument was designed after (1) reviewing the relevant literature on cheating, in order to (2) design a bank of stimuli, (3) formulate a Delphi panel to judge the most coherent and pertinent ones, and (4) perform three pilot studies to adjust the final version of the instrument. Results from Stages 1 to 4 show that content validity was achieved for the Instrument for Detecting Cheating and its Emotional Reactions (INDETRAE, in Spanish: Instrumento para la Detección de Trampa y sus Reacciones Emocionales). Stimuli were grouped into five categories of 18 different scenarios, for a total of 90 vignettes: meaning, the INDETRAE is a 5-category, vignette-based questionnaire consisting of contrasting social cost-benefit scenarios, where the cheating situation affects an undefined, a first or a third person, and also a neutral category with no cheating. In Stage 5, several chi-squared tests (p < 0.0005) revealed significant differences between categories, proving that the instrument can indeed be used to detect cheating and to identify differentiated emotional reactions – for example, anger when there was detriment to a third person as opposed to neutral situations, or glad when there was a case of cheating which benefited the first person. The last stage counts as the first approximation to support construct validity of the INDETRAE. The most important contribution of this work consists in developing an instrument to detect cheating, confirmed by the resulting emotional reactions, which therefore demonstrate its validity.

In Older Adults (OAs), Electroencephalogram (EEG) slowing in frontal lobes and a diminished muscle atonia during Rapid Eye Movement sleep (REM) have each been effective tracers of Mild Cognitive Impairment (MCI), but this relationship remains to be explored by non-linear analysis. Likewise, data provided by EEG, EMG (Electromyogram) and EOG (Electrooculogram)—the three required sleep indicators—during the transition from REM to Non-REM (NREM) sleep have not been related jointly to MCI. Therefore, the main aim of the study was to explore, with results for Detrended Fluctuation Analysis (DFA) and multichannel DFA (mDFA), the Color of Noise (CN) at the NREM to REM transition in OAs with MCI vs. subjects with good performances. The comparisons for the transition from NREM to REM were made for each group at each cerebral area, taking bilateral derivations to evaluate interhemispheric coupling and anteroposterior and posterior networks. In addition, stationarity analysis was carried out to explore if the three markers distinguished between the groups. Neuropsi and the Mini-Mental State Examination (MMSE) were administered, as well as other geriatric tests. One night polysomnography was applied to 6 OAs with MCI (68.1 ± 3) and to 7 subjects without it (CTRL) (64.5 ± 9), and pre-REM and REM epochs were analyzed for each subject. Lower scores for attention, memory and executive funcions and a greater index of arousals during sleep were found for the MCI group. Results confirmed that EOGs constituted significant markers of MCI, increasing the CN for the MCI group in REM sleep. The CN of the EEG from the pre-REM to REM was higher for the MCI group vs. the opposite for the CTRL group at frontotemporal areas. Frontopolar interhemispheric scaling values also followed this trend as well as right anteroposterior networks. EMG Hurst values for both groups were lower than those for EEG and EOG. Stationarity analyses showed differences between stages in frontal areas and right and left EOGs for both groups. These results may demonstrate the breakdown of fractality of areas especially involved in executive functioning and the way weak stationarity analyses may help to distinguish between sleep stages in OAs.