Our data add to the evidence that the temporal decomposition of neural activity into transient and sustained patterns, or a continuum of them (4 ), may be a fundamental principle of deci- phering auditory information. The mechanisms of upstream propagation of differential neural activity have only been partially unraveled. At the cortical level, the transformation into differ- ent temporal response types could be achieved by separate synaptic networks (27 ). The gener- al rules, however, need to be viewed in light of the auditory network at large. In the thalamo- cortical circuitry, for instance, neural signals undergo radical reconstruction, with some properties preserving fidelity and others being transformed or generated anew in the auditory cortex (28). Temporal signal transformation ap- pears to be a fundamental principle in the au- ditory system and could be related to different hierarchical levels of sound characterization. This hypothesis becomes particularly perspicu- ous considering the serial properties of auditory information. References and Notes 1. J. C. Middlebrooks, A. E. Clock, L. Xu, D. M. Green, Science 264, 842 (1994). 2. J. P. Rauschecker, B. Tian, M. Hauser, Science 268,111 (1995). 3. S. S. Nagarajan et al., J. Neurophysiol. 87, 1723 (2002). 4. G. H. Recanzone, Hear. Res. 150, 104 (2000). 5. J. J. Eggermont, J. Neurophysiol. 87, 305 (2002). 6. G. Ehret, R. Romand, Eds., The Central Auditory Sys- tem (Oxford Univ. Press, New York, 1997). 7. M. P. Kilgard, M. M. Merzenich, Nature Neurosci. 1, 727 (1998). 8. T. Lu, L. Liang, X. Wang, Nature Neurosci. 4, 1131 (2001). 9. See supporting material on Science Online. 10. C. Pantev et al., Proc. Natl. Acad. Sci. U.S.A. 88, 8996 (1991). 11. A. L. Giraud et al., J. Neurophysiol. 84, 1588 (2000). 12. F. Hennel, N. Bolo, I. Namer, J. F. Nedelec, J. P. Macher, MAGMA 6 (suppl.), 87 (1998). 13. A. J. Bell, T. J. Sejnowski, Neural. 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U.S.A. 97, 11800 (2000). 27. M. Atzori et al., Nature Neurosci. 4, 1230 (2001). 28. L. M. Miller, M. A. Escabi, H. L. Read, C. E. Schreiner, Neuron 32, 151 (2001). 29. S. Makeig et al., Science 295, 690 (2002). 30. J. R. Duann et al., Neuroimage 15, 823 (2002). 31. K. Friston et al., Hum. Brain Mapp. 2, 189 (1995). 32. We are grateful to S. Makeig, G. H. Recanzone, A. Lu ¨thi, J.-R. Duann, and B. Feige for helpful comments on the manuscript. Supported by grants from the Swiss (63-58040) and the American National Science Foundations (9905266). Supporting Online Material www.sciencemag.org/cgi/content/full/297/5587/1706/ DC1 Materials and Methods SOM Text Fig. S1 References 24 May 2002; accepted 26 July 2002 Representation of the Quantity of Visual Items in the Primate Prefrontal Cortex Andreas Nieder,* David J. Freedman, Earl K. Miller Deriving the quantity of items is an abstract form of categorization. To explore it, monkeys were trained to judge whether successive visual displays contained the same quantity of items. Many neurons in the lateral prefrontal cortex were tunedforquantityirrespectiveoftheexactphysicalappearanceofthedisplays. Their tuning curves formed overlapping filters, which may explain why behav- ioral discrimination improves with increasing numerical distance and why dis- crimination of two quantities with equal numerical distance worsens as their numerical size increases. A mechanism that extracts the quantity of visual field items could contribute to general numerical ability. The ability to judge the relative quantity of items in the visual field is highly adaptive. Social animals such as primates can make de- cisions to fight or flee by judging the relative number of friends versus foes (1–3); in forag- ing, choosing a larger alternative can contribute to survival (4 ). These behaviors depend on the capacity to abstract information from sensory inputs and to retain it in memory, neural corre- lates of which are found in the prefrontal cortex (PFC) (5, 6 ). To investigate the role of PFC neurons in representing visual quantity, we trained monkeys to judge whether two succes- sive displays contained the same small number of items (Fig. 1A). Picower Center for Learning and Memory, RIKEN-MIT Neuroscience Research Center, and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. *To whom correspondence should be addressed: E- mail: nieder@mit.edu Fig. 3. (Top) The rela- tive contribution map of transient and sus- tained BOLD signal sources across all sub- jects and trials with the corresponding signals as identified by tempo- ral ICA. (Bottom) Sig- nals represent intrain- dividual averages of the five trials used as predictors within a group multiple regres- sion analysis (31). The functional map is pro- jected on the recon- structed cortical surface of the temporal lobes of a standard brain tem- plate. Color coding indicates the relative contribution of the two predictor classes and suggests a spatial continuum between the temporal response patterns. The contribution of the sustained response type becomes less predominant as one moves from the core to the belt areas. There was no notable hemispheric difference in the extension of the predominantly transient and sustained responses. R EPORTS 6 SEPTEMBER 2002 VOL 297 SCIENCE www.sciencemag.org 1708