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The attraction effect in multi-alternative decision making reflects the context-dependent violation of axioms that are considered fundamental to rational choice. This effect is believed to depend on relatively effortless and intuitive processing (System 1) rather than on effortful and elaborative processing (System 2). To investigate the relationship between cognitive resources and the attraction effect in detail, we used a task-irrelevant probe technique, wherein task-irrelevant auditory probes were presented while participants viewed each alternative in a decision-making task, and measured the electroencephalographic responses to the probes. Thirty participants solved 48 hypothetical purchase problems with three alternatives that differed in terms of two attributes. We found that, in the second epoch of the experimental trials (possibly corresponding to the evaluation and comparison stage), the mean N1 amplitudes of the event-related potentials elicited by the auditory probes were significantly smaller when participants chose the competitor (i.e., trials in which no attraction effect occurred) than when participants chose the target (i.e., trials in which an attraction effect may have occurred). This result suggests that the allocation of more cognitive resources to the alternatives disrupts the attraction effect. This finding supports the assumption that intuitive comparisons among alternatives executed by System 1 are critical for the occurrence of the attraction effect.
Pubmed ID: 31191375
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Interactive Matlab toolbox for processing continuous and event-related EEG, MEG and other electrophysiological data incorporating independent component analysis (ICA), time/frequency analysis, artifact rejection, event-related statistics, and several useful modes of visualization of the averaged and single-trial data. First developed on Matlab 5.3 under Linux, EEGLAB runs on Matlab v5 and higher under Linux, Unix, Windows, and Mac OS X (Matlab 7+ recommended). EEGLAB provides an interactive graphic user interface (GUI) allowing users to flexibly and interactively process their high-density EEG and other dynamic brain data using independent component analysis (ICA) and/or time/frequency analysis (TFA), as well as standard averaging methods. EEGLAB also incorporates extensive tutorial and help windows, plus a command history function that eases users'' transition from GUI-based data exploration to building and running batch or custom data analysis scripts. EEGLAB offers a wealth of methods for visualizing and modeling event-related brain dynamics, both at the level of individual EEGLAB ''datasets'' and/or across a collection of datasets brought together in an EEGLAB ''studyset.'' For experienced Matlab users, EEGLAB offers a structured programming environment for storing, accessing, measuring, manipulating and visualizing event-related EEG data. For creative research programmers and methods developers, EEGLAB offers an extensible, open-source platform through which they can share new methods with the world research community by publishing EEGLAB ''plug-in'' functions that appear automatically in the EEGLAB menu of users who download them. For example, novel EEGLAB plug-ins might be built and released to ''pick peaks'' in ERP or time/frequency results, or to perform specialized import/export, data visualization, or inverse source modeling of EEG, MEG, and/or ECOG data. EEGLAB Features * Graphic user interface * Multiformat data importing * High-density data scrolling * Defined EEG data structure * Open source plug-in facility * Interactive plotting functions * Semi-automated artifact removal * ICA & time/frequency transforms * Many advanced plug-in toolboxes * Event & channel location handling * Forward/inverse head/source modeling
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