Cleeremans, Axel

Reuter, Robert

REUTER, Robert

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What can evaluative learning tell us about implicit learning?Robert A.P. REUTER & Axel CLEEREMANSCognitive Science Research Unit, Université Libre de Bruxelles, BelgiumSubjects. 18 students from the ULB participated for a small fee.Each subject was tested individually.Material. 70 pictures of human faces (35 male and 35 femalefaces) were used as stimulus material. They varied in contentand form (age, colour palette, size, orientation). Stimulusdimensions were set to 276 x 208 pixels.Procedure. Our experimental procedure was based on ELstudies conducted by Baeyens et al. (1988). Stage 1 - Baseline Assessment:Ss were presented with 70randomly ordered images of human faces with a rating scalepositioned on the right. The scale ranged from -100 (dislike)through 0 (neutral) to +100 (liked), in intervals of 10. Ss wereinstructed to give their spontaneous affective evaluation (ER1)of the human faces by pointing the mouse cursor at the scoreson the scale and then clicking. After giving task instructions, theexperimenter left the room, so that Ss would rate the facesunobserved. Stage 2 - Acquisition:The 6 (2 x 3) pictures that had receivedthe highest and lowest baseline scores were used as Best (B)and Worst (W) stimuli respectively. The 18 pictures that wereevaluated closest to 0 were used as Neutral stimuli (NSi). 12stimulus pairs (6 Neutral-Neutral, 3 Neutral-Liked, 3 Neutral-Disliked) were constructed by randomly assigning an NS to anNS (N1-N2), B (NB-B) or W (NW-W). Each stimulus pair washown 10 times in random order. Ss were told to attend to thecentre of the computer screen were they would see pairs ofhuman faces, sequentially presented, and to evaluate theperceptual similarity of the face pairs on a 7-point Likert scale.Stage 3 - Post-Acquisition Assessment:Finally, Ss were told togive their current, spontaneous affective evaluation of thewhole set of 70 pictures again, using the same responsesystem as in stage 1. This measure is called ER2.Henceforth, we will use the following labels:NB: neutral stimuli followed by best stimuliNW: neutral stimuli followed by worst stimuliN1: neutral stimuli followd by neutral stimuliB: best stimuli following NBW: worst stimuli following NWN2: neutral stimuli following N1 stimuliFor each stimulus Pair Type (NSi associated with W, N2 andB) mean evaluative responses were calculated, i.e., ER1and ER2. Statistics were computed on mean evaluativeshifts (ER2-ER1) by Pair Type (N1, NB and NW).  In additionto the analysis of Evaluative Shifts of NSi, we computed theMean Evaluative Shifts for ASi (N2, B and W) as well.Results in figure1 clearly showthat no evaluativeshift in NSi occursas a function ofthe nature of thea s s o c i a t e dstimulus. Data in figure 2display a patternof "reduction-to-the mean" for Best and Worst liked stimulifrom ER1 to ER2. The slight increase in evaluative valencefor NSi (N1 and N2) is likely to reflect a "mere exposureeffect" (Zajonc, 1986), i.e., repeated exposure of a stimulusleading to increased liking for it. Bas d on Field's (1999) account of evaluative shift resultingfrom perceptual similarity between NSi and exemplars of theBest and Worst categories, we analysed evaluative shiftdata as a function of perceptual similarity. If we merelyassume that meanevaluative shift islinked to PS by am o n o t o n i c a l l yi n c r e a s i n gfunction, it makessense to recode1-7 Likert-scalescores into "-7" to"+7" scores, suchthat PS scores ofNSi associatedwith Worst stimuli are negative while PS scores of NSiassociated with Best stimuli are positive. Increasingabsolute values of the PS scores reflect the strength ofexpected evaluative shifts, and their sign reflects thedirection of expected shifts. For neutral stimuli PS scoreswere all set to 0, since similarity between N1 and N2 couldnot be expected to give rise to consistent evaluative shifts. Figure 3separately plots evaluative shifts against perceivedsimilarity for NW,N1 and NBstimuli. A l t h o u g hhigh meansimilarity scoresfor all types ofstimulus pairs arerather infrequent(only 18.1% of|PS| scores areabove 4, while43.5% are below2), we observe a general tendency for evaluative shifts to beconsistently related to perceived similarity of NSi with theirassociated stimulus (see regression data on figure 3).What is Evaluative Learning?Evaluative Learning (EL) can be defined (see De Houwer et al.,1997) as the transfer of affective value from one stimulus toanother, resulting from their mere contingent (e.g., visual)presentation. Typically, an affectively neutral stimulus (NS) ispaired with a (strongly) liked or disliked stimulus (associatedstimulus or AS). This "causes" the NS to acquire the sameaffective valence as the AS it was paired with, while the valenceof NSi paired with other NSi remains unchanged. ELhas beencharacterised as a "Contamination by Contact" phenomenon.What are the advantages of this paradigm for research onImplicit Learning (IL)?Learning is truly incidental because Ss are not instructed toexplicitly learn stimulus pairs. Further, Baeyens et al. (1992)have shown that ELseems not to be influenced by Ss havingexplicit knowledge of the NS-AS contingencies. Thus, ELappears to be a promising experimental paradigm for the studyof IL phenomena in terms of associative learning. In addition,E L involves learning of numerous simple associations ofsemantically and emotionally relevant stimuli, while other ILp ra igms involve a single complex rule (e.g., an artificialgrammar) implemented in rather artificial and arbitrary stimuli.E L also seems to be resistant to the general criticismaddressed at ILstudies on grounds of their failing to meeti formation and sensitivity criteria. There are actually nocorrelated rules Ss could rely on; hence direct and indirectmeasures are likely to reflect the same kind of (associative)knowledge. Contingency awareness can be measured for eachstimulus pair and compared to the corresponding evaluativeshift.What is the aim of the present study?In a previous study (Reuter & Cleeremans, 2000) we had failedat replicating Baeyens et al.'s (1998) ELstudy. We observed noevaluative shifts of NSi at all. This led us to examine ELi  thelight of a recent alternative account. Field et al. (1999), haveindeed shown that evaluative shift depends on perceptualimilarity of NSi to exemplars of either positive or negativestimuli rather than on their temporal association. In the presents t u d y, we aimed to measure perceptual similarity on anindividual and per stimulus pair basis in order to directly assesswhether evaluative shifts depend on PERCEIVED similaritybetween NSi with liked/disliked ASi.Our results, in accordance with Field et al.’s (1999),suggest that evaluative learning effects - if they areobserved - may be explained as the result ofnonassociative artifacts inherent to the stimulusmaterial and the common lack of control for similaritybetween associated stimuli. Moreover, ourmeasures of the similarity structure of our currentstimulus material (high ratings were ratherinfrequent) may give us a hint at why our previousand present study failed to replicate the classicassociative evaluative shift results.Our data thus sharply contrast with those ofBaeyens et al. (1989) concerning the influence ofperceptual similarity/dissimilarity on evaluativelearning. However, we think that our direct measuresof perceived similarity of the associated stimuli andtheir demonstrable effect on evaluative shifts, takentogether with Field et al's (1999) results suggest thatevaluative shifts are more likely to correspond to a"generalise - to - close - neighbours" than a"contamination - by - contact" phenomenon. Field etal. (1999) did observe conditioning-like effects withneutral stimuli that, for statistical analysis, werecon idered to be "coupled" to either positive ornegative stimuli by perceptual similarity, whileactually never presented together with these verystimuli. Future research is necessary to furtherdistinguish between the proposed opposinghypotheses about the nature of evaluative learningme hanisms. We have shown that measuringsimilarity of stimulus pairs represents an appropriatebut incomplete way to do so. We thus propose todesign experimental conditions that go beyond thetraditional ELparadigm, where Ss are not askedtheir own evaluations of stimuli, but rather have tolearn a “pretend” person's more-or-less complexcriteria for liking/disliking faces. This should enableus to exert better control on similarity-based vs.association-induced learning of evaluativeresponses to novel items.Robert Reuterrreuter@ulb.ac.beAxel Cleeremansaxcleer@ulb.ac.beCognitive ScienceResearch UnitUniversité libre deBruxellesBelgiumBaeyens, F., Eelen, P., Van den Bergh, O., & Cromez, G. (1989) Theinfluence of CS-UCS Perceptual Similarity/Dissimilarity on HumanEvaluative Learning and Signal Learning. Learning and Motivation. 20, 322-33.Baeyens, F., Eelen, P., Van den Bergh, O., & Cromez, G. (1992) The Contentof Learning in Human Evaluative Conditioning: Acquired Valence is Sensitiveto US-Revaluation. Learning and Motivation. 23, 200-224.De Houwer, J., Baeyens, F., & Hendrickx H. (1997) Implicit Learning ofEvaluative Associations. Psychologica Belgica. 37-1/2, 115-130.Field, A. & Davey, G.C.L. (1999) Reevaluating Evaluative Conditioning: ANonassociative Explanation of Conditioning Effects in the Visual EvaluativeConditioning Paradigm. Journal of Experimental Psychology: A n i m a lBehavior Processes. Vol. 25, No. 2, 211-224.Reuter, R.A.P. & Cleeremans, A. (2000) Reverse Evaluative Learning:Paradoxical Contamination of Liked/Disliked Stimuli by Neutral Stimuli.Proceedings of the Annual Meeting of the Belgian Psychological Society.May 12th, Liège, Belgium.Zajonc, R.B. (1968) Attitudinal effects of mere exposure. J. Personality soc.Psychol. Monogr. 9 (2,Pt.2), 1-27.I N T R O D U C T I O NM AT E R I A L and METHODR E S U LT SD I S C U S S I O N R E F E R E N C E SC O N TA C Tr2= 0.691

What can Evaluative Learning tell us about Implicit Learning?

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What can Evaluative Learning tell us about Implicit Learning?

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