Maître de Conférences
Psychologie générale – Psychologie de l’éducation
- Faculté : Univ Lille Nord de France – Université de Lille 3
- Département : Unité de Formation et Recherche en Psychologie
- Laboratoire : PSITEC – EA 4072
- Membre de l’Axe de Recherche (D)REC : (Dys)Régulations Emotionnelles et Cognitives
- Adresse postale :
Université Lille 3
Domaine Universitaire du Pont de Bois
59653 Villeneuve d’Ascq Cedex
♦ Domaine de recherche ♦
His current research interests focus on proposing a model of associative learning based on variations of attention. Attention is suggested to be a determinant variable, not only during acquisition of the associative strength between two events, but also at the moment of test. A paper presenting this model is currently in preparation in collaboration with Mikael Molet (University of Lille) and Ralph Miller (S.U.N.Y. Binghampton). The model is called A.A.P.V. (Attention as an Acquisition and Performance Variable) and can be simulated using two main equations. A simulator has been programmed in Python and is available on one of the links. Another recent research interest (again in collaboration with Molet and Miller) was to introduce a technique allowing the study of associative strength between two events in humans paper submitted). Participants had to learn which cues were paired with a specific outcome. The reaction time (RT) to indicate if ‘yes’ or ‘no’ a cue predicted the outcome was measured and a rule combining in one single measure RT and type of response given (i.e., ‘yes’ or ‘no’) was introduced. We tested the rule using well established Pavlovian preparations such as overshadowing, blocking and the summation tests, to test if the results transformed with our rule were correlated with the expected levels of associative strength in the different Pavlovian conditions.
- AAPV SimulatorTrials
Once you have downloaded and executed the file ‘AAPV setup’, the simulator will start inviting you to indicate on a chart the number of trials you need for specific cues or pair of cues.
- The tested cue is ‘X’
- Empty boxes in the chart are treated as 0 trial (e.g., you don’t have to fill up all the boxes if you want to simulate overshadowing [i.e., AX+]).
- ‘+’ means that the cue or pair of cues is followed by the US. ‘-‘ means that the cue or pair of cues is not followed by the US.
- For intermixed trials (i.e., different cues during the same phase), trials are in a random order.
Obviously the user needs to put the right number of trials in the right boxes. For example, if you want to simulate forward blocking (i.e., A+ | AX+), and you present the A-US pairings 25 times during Phase 1, and the AX-US pairings 15 times during Phase 2, all you have to do is to put ’25’ in the Phase 1 A+ box, and 15 in the Phase 2 AX+ box. Then press ‘ENTER’.
Of course one could also test forward blocking using the B+ and BX+ boxes…
Note that you can even make a simulation for complex designs (up to four phases).
Once you have pressed ‘Enter’, the next step consists of entering the salience value for each cue required. You just have to indicate a value between 0 and 1 in the appropriate rectangle and press enter. We advise the user to choose a low salience for the context (e.g., 0.1) and a high salience for the US (e.g., 0.7). For the simulations introduced in the present paper we chose those specific values and the cues’ salience was 0.4.
The result for RX immediately appears on the bottom part of the screen. If Rx is strong (near t 1), a high CR is expected. In like manner, if Rx is weak (near 0), a weak CR is expected.
A curve will also appear showing the latent evolution of RX (i.e., the value RX would have on each trial if X were tested) through all the trials you have put in your design. Click on the button ‘next step’ and you will have a choice between running another simulation and leaving the program. You can run up to four simulations in a row. If you run several simulations in a row, you will be offered the possibility of comparing the current result with prior estimations of RX.
- DowloadAAPV-simulator version 1.1: Download
♦ Publications ♦
Attenuating social affective learning effects with Memory Suppression manipulations Article de journal
In: Acta Psychologica, (164), p. 136–143, 2016.
Attenuating Evaluative Conditioning Effects by Reducing Memory for CSs-USs Associations. Inproceedings
In: on Learning, Summer School Emotional; in Health, Memory; Psychopathology, Leuven (Belgique). (Ed.): 2015.
In: Learning and Motivation, 51 , p. 43 – 49, 2015, ISSN: 0023-9690.
The impact of motion on the likeability of a stimulus. Inproceedings
In: Consortium of European Research on Emotion Conference (CERE), Berlin (Allemagne). (Ed.): 2014.
Attention as an acquisition and performance variable (AAPV) Article de journal
In: Learn Behav, 42 (2), p. 105–122, 2014, ISSN: 1543-4494, 1543-4508.
Integration of multiple memories in sensory preconditioning Article de journal
In: Behavioural Processes, 108 , p. 94 – 97, 2014, ISSN: 0376-6357.
Attenuating Evaluative Conditioning : A Theoretical Issue With Clinical Implications. Inproceedings
In: Congrès annuel de Psychonomic Society, Toronto (Canada). (Ed.): 2013.
Spatial integration of boundaries in a 3D virtual environment Article de journal
In: Acta Psychologica, 144 (2), p. 316 – 323, 2013, ISSN: 0001-6918.
Reaction time as a measure of human associative learning Article de journal
In: Behavioural processes, 90 (2), p. 189–197, 2012.
Tolman et le Conditionnement Pavlovien Article de journal
In: Acta Comportamentalia, 18 (2), p. 243–255, 2010.
Pour une approche cognitive du conditionnement pavlovien Article de journal
In: Année psychologique, 109 (2), p. 333, 2009.
In: Psychologie Française, 53 (3), p. 411–436, 2008.