Turning the dial on object perception.

One of the more challenging aspects of neuroimaging studies of higher cortical function is the isolation of a mental operation of interest. An experimental condition that evokes the cognitive process of working memory, semantic retrieval, or spatial representation, for example, can be expected to evoke a number of other processes as well (e.g., stimulus perception, eye movements, response preparation, etc.). The “cognitive subtraction” method tackles this problem by comparing the neural activity evoked by matched experimental and control conditions that putatively differ only in the cognitive process of interest. The assumptions, however, that cognitive subtraction requires are themselves problematic (Friston et al. 1996xFriston, K.J., Price, C.J., Fletcher, P., Moore, C., Frackowiak, R.S.J., and Dolan, R.J. Neuroimage. 1996; 4: 97–104Crossref | PubMed | Scopus (311)See all References, Zarahn et al. 1997xZarahn, E., Aguirre, G.K., and D'Esposito, M. Neuroimage. 1997; 6: 122–138Crossref | PubMed | Scopus (320)See all References) and stem in part from the need to evoke the mental operation under study in an all-or-nothing fashion. Parametric manipulations of cognitive processes (in which the level of the operation, as opposed to its presence or absence, is varied) have less stringent assumptions and therefore provide for stronger inference.In this issue of Neuron, Bar and colleagues present an fMRI study of object recognition that is parametric in a clever way (Bar et al., 2001xBar, M., Tootell, R.B.H., Schacter, D.L., Greve, D.N., Fischl, B., Mendola, J.D., Rosen, B.R., and Dale, A.M. Neuron. 2001; 29: 529–535Abstract | Full Text | Full Text PDF | PubMed | Scopus (291)See all References(Bar et al., 2001). Subjects were asked to identify simple objects briefly flashed on a screen and to rate the confidence of their recognition on a scale of 1 to 4. The speed of presentation and a poststimulus mask made the task rather difficult, with the result that sometimes stimuli were clearly identified by the subject, but sometimes the subject was uncertain, or worse. The authors reasoned that if a region of the brain contributes in a computationally meaningful way to object recognition, then there should be a systematic relationship between the magnitude of neural activity at that location and the subject's report of recognition confidence. Consider the two regions with the patterns of neural activity illustrated by the solid and dashed lines in Figure 1Figure 1. The solid line represents the pattern of neural activity found by the authors in retinotopic, “early” visual areas, while the dashed line represents the pattern found in the anterior fusiform gyrus (interestingly, other patterns of response, such as a “U” function, were found in other areas).Figure 1Object PerceptionSubjects viewed briefly presented line drawings of objects on each trial and reported on a scale of 1-4 how confident they were of their ability to identify the object. The text below each object offers an apocryphal view of the subject's thought process. Below, the relationship between neural activity and reported confidence is shown for the anterior fusiform gyrus (dashed line on the graph and dashed arrow) and earlier visual areas (solid line on the graph and solid arrow)View Large Image | View Hi-Res Image | Download PowerPoint SlideWhat can be said about the role that these regions play in object recognition given their different patterns of response? Clearly, we cannot take these results as evidence of the necessity of any given region for object recognition, as lesions to any of the identified areas might impair object recognition (area V1, for example). Instead, the inferences to be drawn are on the level of information processing. Areas with flat responses (solid line) support visual computations that are agnostic (in terms of bulk neural activity) to the ultimate identification of a stimulus as a particular object. This tells us something about the “modularity” of processing in these locations, in that the region engages in computations that seem insensitive to the status of the input as an object. We might imagine that interruption of this computation would impair aspects of visual processing beyond object recognition. This is in contrast to those regions that demonstrated a relationship between neural activity and confidence of object recognition (dotted line). Such regions potentially support computations that are relevant to object recognition per se, so that local manipulations of neural activity would impact object recognition performance more precisely.Additional information was provided by a second manipulation performed by the authors, in which stimulus properties (i.e., whether the stimulus was masked or not) were varied as well. Interestingly, activity in early visual areas was found to be sensitive to the presence or absence of a mask (even in the setting of equivalent levels of recognition confidence), while the fusiform area reflected only the confidence of object recognition. Also of interest was a frontal gyrus site that demonstrated increasing degrees of neural activity for increasing confidence of recognition, but had reduced responses when the masking stimulus was removed (i.e., when the task was made much “easier”). The interpretation of this pattern of response is still uncertain, but the authors consider intriguing possibilities in terms of attention and search effort.Excepting the manipulation of the mask presence or absence, subjects were presented with identical stimuli on all trials. It therefore cannot be argued that the results reflect some systematic difference in stimulus property that is more present in object stimuli than other types of stimuli. This encourages the authors to argue that the activation seen in the fusiform gyrus is the result of object processing and illustrates the power of parametric designs that manipulate the process of interest itself, as opposed to designs that attempt to isolate the process by subtraction. Despite the uniform nature of the stimuli and the cogent arguments of the authors, however, it is still possible that the regions identified as having a linear relationship with confidence of object recognition are engaged in computations that can be described in other terms. For example, the identified region might engage in texture analysis, with the variability in response magnitude related to the degree of successful processing. If the quality of texture analysis can influence the ability of the subject to identify objects, then we could observe a linear relationship between confidence report and neural activity. Similar arguments can be made for associated processes such as form analysis, semantic retrieval, etc. A perhaps more sophisticated view is that these putatively confounding mental operations actually constitute object identification (Tanaka 1993xTanaka, K. Science. 1993; 262: 685–688Crossref | PubMedSee all References, Malach et al. 1995xMalach, R., Reppas, J.B., Benson, R.R., Kwong, K.K., Jiang, H., Kennedy, W.A., Ledden, P.J., Brady, T.J., Rosen, B.R., and Tootell, R.B. Proc. Natl. Acad. Sci. USA. 1995; 92: 8135–8139Crossref | PubMed | Scopus (1099)See all References); i.e., it is fruitless to seek a naked object recognition process that is not itself composed of interactions between representations of shape, meaning, and function. If so, then the present study has identified for us a number of cortical regions that seem computationally important for object recognition. It will be the job of future studies to determine if and how these sites uniquely contribute to this behavior.Farah 1990xFarah, M.J. See all References, Farah Aguirre 1999xTrends Cog. Farah, M.J. and Aguirre, G.K. Sci. 1999; 3: 179–186See all References