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From a phenomenological perspective, faces are perceived differently from objects as their perception always involves the possibility of a relational engagement (Bredlau, 2011). This is especially true for familiar faces, i.e., faces of people with a history of real relational engagements. Similarly, valence of emotional expressions assumes a key role, as they define the sense and direction of this engagement. Following these premises, the aim of the present study is to demonstrate that face recognition is facilitated by at least two variables, familiarity and emotional expression, and that perception of familiar faces is not influenced by orientation. In order to verify this hypothesis, we implemented a 3 × 3 × 2 factorial design, showing 17 healthy subjects three type of faces (unfamiliar, personally familiar, famous) characterized by three different emotional expressions (happy, hungry/sad, neutral) and in two different orientation (upright vs. inverted). We showed every subject a total of 180 faces with the instructions to give a familiarity judgment. Reaction times (RTs) were recorded and we found that the recognition of a face is facilitated by personal familiarity and emotional expression, and that this process is otherwise independent from a cognitive elaboration of stimuli and remains stable despite orientation. These results highlight the need to make a distinction between famous and personally familiar faces when studying face perception and to consider its historical aspects from a phenomenological point of view.

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In this paper we present an eye tracking study aimed at examining learners' behaviors while searching for the right answer in multiple-choice tests. In particular, the analysis is focused on a geometry problem characterized by four graphical solutions. The data gathered through several experiments have allowed us to find interesting relationships between answers and testers' performance (especially with regard to time spent watching the available options and gaze wavering between the two most plausible choices), as well as to identify otherwise hidden similarities among testers themselves. Providing a better understanding of students' learning processes, the obtained results can potentially be exploited to improve the design of assessment tasks and to train intelligent tutoring systems.

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