Visual prediction and perceptual expertise

Abstract

Making accurate predictions about what may happen in the environment requires analogies between perceptual input and associations in memory. These elements of predictions are based on cortical representations, but little is known about how these processes can be enhanced by experience and training. On the other hand, studies on perceptual expertise have revealed that the acquisition of expertise leads to strengthened associative processing among features or objects, suggesting that predictions and expertise may be tightly connected. Here we review the behavioral and neural findings regarding the mechanisms involving prediction and expert processing, and highlight important possible overlaps between them. Future investigation should examine the relations among perception, memory and prediction skills as a function of expertise. The knowledge gained by this line of research will have implications for visual cognition research, and will advance our understanding of how the human brain can improve its ability to predict by learning from experience.

Fig. 1

A schematic depiction of prediction generation via analogies and associations, as proposed in Bar (2007). An input (A′) activates an analogous representation in memory (A), which leads to the co-activation of associated representations (B, C, D) to generate predictions. The input may either be an external, sensory input, or an internally generated thought. Moreover, the input can be of different degrees of complexity, which may result in predictions that are of various levels of elaboration, encompassing the range from perceptual to executive predictions. Copyright © by Elsevier Ltd. Reproduced with permission.

Fig. 2

Examples of inconsistent pairing of a salient item and a scene, used in Davenport and Potter (2004). Top: A clergyman in a football field. Bottom: A football player in a church. Copyright © by American Psychological Society. Reproduced with permission.

Fig. 3

A. The model for the top–down contextual facilitation of object recognition proposed in Bar (2004). According to this model, low spatial frequencies (LSF) from an input are extracted rapidly to activate an association set with possible interpretations of a target object (e.g., the target object may be an umbrella or a lamp, but it is certainly not a lighthouse or a dog). The later arrival of high spatial frequencies determines the exact representation of the specific exemplar (e.g., the target object is indeed a beach umbrella). For simplicity, only the relevant cortical connections and flow directions of the proposed mechanisms are illustrated here. ITC, inferior temporal cortex; LSF, low spatial frequencies; PFC, prefrontal cortex; PHC, parahippocampal cortex, V2 and V4, early visual areas. ‘Lightening strike’ symbols represent activation of representations. B. Individual members of basic-level categories tend to look similar to each other and look different from members in other categories (e.g., dog vs. cat). LSF representations are often sufficient for distinguishing basic-level object categories. Copyrights © Nature Publishing Group and MIT Press. Adapted with permission.

Fig. 4

Sample composite faces, made from combining different bottom halves with the same top half, appear to be two completely different faces. It is difficult to selectively attend to the top halves only and recognize that the top halves of the two composite faces are identical, presumably because the strong associative nature of face processing leads to ‘fusion’ of facial features within a face context, and to the expectation that these faces are of different people (as from our extensive experience, different features can only be found on different faces). The face halves were taken from the Max Planck Institute face database.

Fig. 5

Different emphases in the investigation of neural mechanisms for associative prediction vs. perceptual expertise. A: Medial view of the typical contextual association network (e.g., Bar and Aminoff, 2003) and its overlap with the default network (e.g., Raichle et al., 2001). The context network activations are obtained from the contrast between strongly contextually associative objects (e.g., a tennis racket) and weakly contextually associative objects (e.g., a jacket). The default network regions are those that are more active during fixation rest than during task performance. MPC, medial parietal cortex. MTL, middle temporal lobe. MPFC, medial prefrontal cortex. B: An axial oblique slice through the right ‘fusiform face area’ (FFA, marked with a red circle), one of the local regions emphasized in several perceptual expertise studies (for subordinate-level expertise, e.g., Gauthier et al., 1999, 2000a,b; Wong et al., 2009b), for a car expert and a bird expert. The FFA was first defined by more robust activations for faces than objects. In Gauthier et al. (2000a), this area was found to be more activated for car experts when viewing cars compared to other objects, and for bird experts when viewing birds compared to other objects. Copyrights © by Elsevier Ltd. and Nature America Inc. respectively. Adapted with permission.