Further Explorations of Expert Object Recognition

1. Further Explorations of Expert Object Recognition Assaf Harel Department of Psychology, Hebrew University, Jerusalem

2. Acknowledgements Prof. Shlomo Bentin Prof. Rafael Malach Yulia Golland

3. What is Expert Object Recognition? Experts have more experience with and are more knowledgeable about objects in their domain of expertise. Theoretical question: What role does experience play in the object recognition system? 1) Cognitive substrate 2) Neural substrate

4. Expertise: a downward shift to subordinate level.But, what type of processing is needed for subordinate level (expert) identification?

5. And Then Came Faces… The Face Expertise Model Are faces special? • Object recognition is domain general. Face and object processing should not be functionally independent. Faces are an example of stimuli which observers have gained natural expertise with. • Therefore, according to this hypothesis experts in any kind of object recognition will show “face-specific” effects.

6. Neuroimaging Findings • Gauthier et al. (2000) suggested that FFA, a region which shows preferential activation for faces, can also be activated while bird and car experts viewed objects in their domain of expertise. • FFA activation was also found for laboratory created expertise with Greebles (Gauthier et al., 1999).

7. Focus of Present Research • Expert object recognition in and of itself, independent of the domain specificity/expertise debate. • Research question: How is expert object recognition expressed in the brain? • Are there any other “expertise” areas, except for the FFA? • How early in the visual stream can we find expertise effects? • Expertise-specific areas or a network of expertise?

8. Selection of Car experts 14 car experts (all males, mean age 27) were selected based on their performance on a car discrimination task. Participants had to determine whether two cars were of the same model (within maker) varying in year, color, and orientation. Their accuracy (d’) on this task was1.39compared with a group of 20 novices whose accuracy was 0.57 (t(32)=7.72, p<0.01). In a similar recognition task, with a different object category (airplanes), experts were as accurate as novices (0.67 and 0.43, respectively, t(32)=1.72, p=0.09).

11. Methods • Participants: 9 car experts (males, mean age 23) and 10 novices (males, mean age 25). • 1.5-T Signa Horizon LX 8.25 GE scanner of the Tel Aviv Sourasky Medical Center. • fMRI parameters: Gradient EPI sequence (TR=3000 ms, TE=55 ms, FOV=240x240 mm, matrix size= 80x80, slice thickness=4 mm, 1 mm gap, 27 axial slices). • T1-weighted high resolution (1x1x1 mm) anatomic images and a whole-brain SPGR sequence.

12. fMRI Experiment Three scans: 2 experimental scans (faces, cars, airplanes) One-back memory task External localizer (faces, houses, tools, geometric patterns) One-back covert memory task

13. All stimuli were equiluminant

14. ExperimentalDesign

15. Results: Group Activation Maps Experts Novices Faces > Houses

16. Novices Cars > Airplanes

17. Experts Cars > Airplanes

18. Experts Novices Cars > Airplanes

19. FFA was activated by faces in experts and novices. FFA was not preferentially activated in car experts while viewing cars. • In contrast to faces, the pattern of activation elicited by cars was different for experts and novices. • Whereas in novices, activation was limited to medio-occipital regions, in experts the car-activation was wide-spread, distributed over a large portion of the occipital cortex and extending to posterior regions of the inferior temporal lobe.

20. ROI Analysis Four ROIs were defined using the external localizer: • FFA – defined by the contrast Faces vs. Houses. • LO - defined by the contrast Tools vs. Textures. • CoS - defined by the contrast Houses vs. Faces. • Early visual areas - defined by the contrast Textures vs. all other object categories.

21. ROI Analysis: Results * *

22. * *

23. The analysis of the pre-defined ROIs revealed no difference between car-activation in experts and novices neither in the FFA nor in the LO. • In early visual areas, equivalent activation was found across categories in novices, while in experts cars elicited significantly higher activation than faces and airplanes. A similar trend was found in the CoS.

24. What Can We Learn About Expert Object Recognition? • The neural substrates of car expertise are not equivalent to those of face expertise. • Expert object recognition is distributed and not restricted to a specific “hot spot” such as the FFA. • Expert object recognition in different domains recruits different brain regions. Is face recognition the right model for expert object recognition?

25. Alternative explanation: the extent of activity for objects of expertise (such as cars), which is not seen for faces, might indicate general alertness/arousal (emotional reaction?) superimposed on peculiar perceptual processes.

26. Mourao-Miranda et al. (Neuroimage, 2003)

27. What Can We Learn About Expert Object Recognition? • The results suggest a notion of a dedicated expert object recognition network, whereby early vision is top-down modulated according to differential recognition goals. • Theoretical framework: Reverse Hierarchy Theory (Ahissar & Hochstein, TiCS 2004)

28. Basic Level Categorization (generalization) Subordinate Level Categorization (specificity) Hochstein & Ahissar, 2002Ahissar & Hochstein, 2004

29. In situations requiring better signal-to-noise ratio (such as discriminating among similar members of the same class) highly-trained performers, who have had a great deal of training experience, base their performance on low-level representations guided by top-down activated pathways. • In the present study, this model manifests in the car selective activation found in early visual’ areas of car experts and in the extensive pattern of activation for objects of expertise.

30. Future Research • Neuroimaging studies manipulating alertness/interest/arousal. • Behavioral measures manipulating low- level processing. • Temporal dynamics of low-level processing vs. high-level processing in experts.

31. RHT and Perceptual Learning Perceptual Learning = practice-induced improvement in the ability to perform specific perceptual tasks. • Perceptual learning improvement largely stems from a gradual top-down guided increase in usability in first high and then lower-level task-relevant information. • This process is subserved by a cascade of top-to-bottom level modifications that enhance task-relevant, and prune irrelevant, information.