The role of motion in learning new faces

Lander, Karen; Bruce, Vicki

doi:10.1080/13506280344000149

Cited by 90 publications

(96 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Others have failed to observe advantages for faces viewed in motion over static pictures (e.g., Lee, Habak & Wilson, 2010;Bonner, Burton, & Bruce, 2003;Lander & Bruce, 2003). Christie and Bruce (1998) found no improvement in the recognition of unfamiliar faces exhibiting rigid motion (shaking and nodding) compared to multiple static views.…”

Section: Introductionmentioning

confidence: 99%

Categorizing identity from facial motion

Girges

Spencer

O’Brien

2015

Quarterly Journal of Experimental Psychology

View full text Add to dashboard Cite

Advances in marker--less motion capture technology now allow the accurate replication of facial motion and deformation in computer--generated imagery (CGI). A forced--choice discrimination paradigm using such CGI facial animations showed that human observers can categorise identity solely from facial motion cues. Animations were generated from motion captures acquired during natural speech, thus eliciting both rigid (head rotations and translations) and nonrigid (expressional changes) motion. To limit interferences from individual differences in facial form, all animations shared the same appearance. Observers were required to discriminate between different videos of facial motion and between the facial motions of different people. Performance was compared to the control condition of orientation--inverted facial motion. The results show that observers are able to make accurate discriminations of identity in the absence of all cues except facial motion. A clear inversion effect in both tasks provided consistency with previous studies, supporting the configural view of human face perception. The accuracy of this motion capture technology thus allowed stimuli to be generated which closely resembled real moving faces. Future studies may wish to implement such methodology when studying human face perception.

show abstract

Section: Introductionmentioning

confidence: 99%

Categorizing identity from facial motion

Girges

Spencer

O’Brien

2015

Quarterly Journal of Experimental Psychology

View full text Add to dashboard Cite

show abstract

“…The fact that the memory advantage for moving scenes is unaffected by dividing attention during encoding argues against a strong version of the hypothesis that the moving advantage reflects greater engagement with moving stimuli (Lander & Bruce, 2003). The same conclusion is urged by the fact that even those participants for whom tone-monitoring was better during the Memory for moving scenes 18 static stimuli showed the dynamic superiority effect.…”

Section: Attention Content and Timementioning

confidence: 67%

“…One suggestion is that moving images are better remembered because they are better at engaging attention or are more deeply processed than static images (Lander & Bruce, 2003). A strong version of this idea would hold that the dynamic superiority effect results from a type or depth of encoding that is only possible when the observer fully attends to the stimulus, in which case dividing attention will eliminate the memory advantage for moving scenes.…”

Section: Methodsmentioning

confidence: 99%

“…In recent years there has been a surge of interest in the effects of movement on memory for faces (Knight & Johnston, 1997;Lander & Bruce, 2003;Lander & Chuang, 2005;O'Toole, Roark, & Abdi, 2002;Pike, Kemp, Towell, & Phillips, 1997) and objects (Balas & Sinha, 2009;Koban & Cook, 2009;Stone, 1998Stone, , 1999Vuong & Tarr, 2004). These studies have shown that dynamic information can be stored in long-term memory; people encode the temporal sequence of views of a moving stimulusits "spatiotemporal signature" -so that the information extracted from dynamic stimuli is more than just a set of static views, particularly when the motion is non-rigid (Knight & Johnston, 1997;Lander & Chuang, 2005;Stone, 1999).…”

Section: Exploring the Memory Advantage For Moving Scenesmentioning

confidence: 99%

See 1 more Smart Citation

Exploring the memory advantage for moving scenes

2010

View full text Add to dashboard Cite

Recognition memory is better for moving images than for static images (the dynamic superiority effect), and performance is best when the mode of presentation at test matches that at study (the study-test congruence effect). We investigated the basis for these effects. In Experiment 1, dividing attention during encoding reduced overall performance but had little effect on the dynamic superiority or study-test congruence effects. In addition, these effects were not limited to scenes depicting faces. In Experiment 2, movement improved both old-new recognition and scene orientation judgments. In Experiment 3, movement improved the recognition of studied scenes but also increased the spurious recognition of novel scenes depicting the same people as studied scenes, suggesting that movement improves the identification of individual objects or actors without necessarily improving the retrieval of associated information. We discuss the theoretical implications of these results and highlight directions for future investigation. Memory for moving scenes 2 Exploring the memory advantage for moving scenesThe visual world is intrinsically dynamic and our visual and mnemonic systems have developed to encode and retrieve information about a moving environment (Gibson, 1979). In recent years there has been a surge of interest in the effects of movement on memory for faces (Knight & Johnston, 1997;Lander & Bruce, 2003;Lander & Chuang, 2005;O'Toole, Roark, & Abdi, 2002;Pike, Kemp, Towell, & Phillips, 1997) and objects (Balas & Sinha, 2009;Koban & Cook, 2009;Stone, 1998Stone, , 1999Vuong & Tarr, 2004). These studies have shown that dynamic information can be stored in long-term memory; people encode the temporal sequence of views of a moving stimulusits "spatiotemporal signature" -so that the information extracted from dynamic stimuli is more than just a set of static views, particularly when the motion is non-rigid (Knight & Johnston, 1997;Lander & Chuang, 2005;Stone, 1999).To date, there has been surprisingly little research on memory for moving scenes -natural images depicting complex agglomerations of objects and people. In an early study, Goldstein, Chance, Hoisington, and Buescher (1982) had participants study film clips or still images taken from those clips, and administered a recognition memory test a few minutes later. They reported a dynamic superiority effect: Recognition was better for moving images than for static images, although Goldstein et al.'s comparison was not well controlled as the durations of moving and static stimuli were not equal. More recently, Matthews, Benjamin, and Osborne (2007) presented participants with moving and static scenes of equal duration drawn from a wide variety of sources, and tested recognition memory at intervals ranging from 3 days to 1 month. Recognition of moving scenes was better than that of static scenes, and this moving advantage was independent of chromaticity. Matthews et al. also examined performance in a "multistatic" condition in which single frames drawn from regu...

show abstract

“…In the case of newly-learned or unfamiliar faces, the data are not clear as to whether motion improves face recognition. Some studies report a motion benefit [65,96,98], whereas other studies find no benefit of motion [23,24,35,54]. A closer inspection of these results suggest that the benefits of facial motion for unfamiliar face recognition tasks may be tied to the specific parameters of the learning and test conditions used across these different studies.…”

Section: Recognizing Moving Facesmentioning

confidence: 80%

Predicting Human Performance for Face Recognition

2006

Face Processing

View full text Add to dashboard Cite

The ability of humans to recognize faces provides an implicit benchmark for gauging the performance of automatic face recognition algorithms. IntroductionHuman face recognition abilities are impressive by comparison to the performance of many current automatic face recognition systems. Such is the belief of many psychologists and computer scientists. This belief, however, is supported more by anecdotal impression than by scientific evidence. In fact, there are relatively few systematic and direct comparisons between the performance of humans and automatic face recognition algorithms, (though see [84], for exceptions). Comparisons of these sorts can be carried out both at a quantitative and qualitative level. On the qualitative side, as the field of automatic face recognition develops, the assessment of working systems has expanded to consider, not only overall levels of performance, but performance under more restricted and targeted conditions. It may thus be important to know, for example, whether an algorithm performs accurately recognizing "male faces of a certain age", "female faces of a certain ethnicity", or faces that are "typical" in the context

show abstract

The role of motion in learning new faces

Cited by 90 publications

References 20 publications

Categorizing identity from facial motion

Categorizing identity from facial motion

Exploring the memory advantage for moving scenes

Predicting Human Performance for Face Recognition

Contact Info

Product

Resources

About