VISUAL ATTENTION 201 202 DESIMONE DUNCAN ing case is bias to novelty.As shown in Figures 4c and d,for example,it is while monkeys performed delayed matching-to-sample(DMS)tasks with much easier to find an inverted (novel)target among upright (familiar)non- either novel or familiar stimuli.In DMS.a sample stimulus is followed by targets (Figure 4c)than the reverse (Figure 4d)(Reicher et al 1976).In fact. one or more test stimuli,and the animal signals when a test stimulus matches the time it takes to find an inverted character may be independent of the number the sample.For up to a third of the cells in this region,responses to novel of upright ones in a display (Wang et al 1992).which implies that multiple sample stimuli become suppressed as the animal acquires familiarity with objects have parallel access to memory and that familiarity is a type of object them (Fahy et al 1993,Li et al 1993,Miller et al 1991.Riches et al 1991). feature that can be used to bias attentional competition.A second consideration The cells are not novelty detectors.in that they do not respond to any nove ned imp tance.In a busy om,attention can be attracted by stimulus.Rather,they remain stimulus selective both before and after the b 1959).Similarly visual e with rd t n the In fact this shrinkage in the population of activated neurons as stimuli eider 1977).Thus,the top- fam the down selection bias of a current task can sometimes be overturned by infor for those stim ams the ures hew stim mation of long-term or general significance acting in a bottom-up fashion.In fashion drop out of the next sections we consider both bottom-up and top-down mechanisms for et al 1993).leaving those that are most selective.There is also direct evidence resolving competition. that some IT cells selective for faces become more tuned to a familiar face following expcrience (Rolls et al 1989). Bottom-Up Neural Mechanisms for Object Selection An effect akin to the novelty effect is also found for familiar stimuli that The first neural mechanisms for resolving competition we consider are those have been seen recently.When a test stimulus matches the previously seen that derive from the intrinsic or learned biases of the perceptual systems sample in the DMS trial,responses to that stimulus tend to be suppressed towards certain types of stimuli.We describe them bere as bottom-up pro (Miller et al 1991,1993:also see Baylis Rolls 1987,Eskandar et al 1992. cesses,not because they do not involve feedback pathways in visual Fahy et al 1993.Riches ct al 1991).Although it was originally roposed that （hey may well do so)but be se th utomatic processes this suppressive effect was dependent on that e hey appede sample,recent work has shown it to be an automatic outcome of any stimulus uli that stand out f the e pro sed preferentially a repetition (Miller&Desimone )For many cells.this uppression occurs near of the of ma en if the muli differ in size or etinal locations otherwis re ppea optimal stim y c receptiv al 1994).Thus,the detec ion ovelty and ency apparently may b letely are within a l arge sur at a high ev of s repres rounding region (for reviews see Allman et al 1985,Desimone et al 1985). together, at bo The greater the density of stimuli in the surround,the greater the suppression ve no been recently seen vill have a larger neural (Knierim Van Essen 1992).In the middle temporal area (MT),for example, giving them a competitive a vantage in ga ing con a cell that normally responds to vertically moving stimuli within its receptive orienting systems.This would explain the bi as towards novelty in the H feld may be unresponsive if the same stimuli are part of a larger moving behavioral data described above.The longer the organism attends to the obje pattern coverine the receptive field and surround (allman et al 1985 tanaka the more knowledge about the object is incorporated into the structure of the et al 1986).These mechanisms almost certainly contribute to the pop-out cortex;this reduces the visual signal.It will also reduce the drive on the effects of targets in visual search orienting system so that the organism is free to orient to the next new object As indicated above,the visual system also seems to be biased towards new (Li et al 1993,Desimone et al 1994).This view is compatible with Adaptive objects or objects that have not h n.Thus the t Resonance Theory (Carpenter Grossberg 1987),in which novel stimuli of a stimulu s m activate attentional systems that allow new lone-term memories to he formed. he e stim li n as the Consistent with these neurophysiological results in animals.a reduction in nd,or cor which t neural activation with stimulus epetition in human subjects has been seen in King exam cn ter both event-related p ve ocen entials of the temporal cortex(Begleiter et al 1993)and in brain-im oing studies (Squire et al 1992). VISUAL ATTENTION 201 ing case is bias to novelty. As shown in Figures 4c and d, for example, it is much easier to find an inverted (novel) target among upright (familiar) targets (Figure 4c) than the reverse (Figure 4d) (Reicher et al 1976). In the time it takes to find an inverted character may be independent of the number of upright ones in a display (Wang et al 1992), which implies that multiple objects have parallel access to memory and that familiarity is a type of object feature that can be used to bias attentional competition. A second consideration is long-term learned importance. In a busy room, attention can be attracted by the sound of one’s own name spoken nearby (Moray 1959). Similarly, long practice with one set of visual targets makes them hard to ignore when they are subsequently made irrelevant (Shiffrin & Schneider 1977). Thus, the top￾down selection bias of a current task can sometimes be overturned by infor￾mation of long-term or general significance acting in a bottom-up fashion. In the next sections we consider both bottom-up and top-down mechanisms for resolving competition. Bottom-Up Neural Mechanisms for Object Selection The first neural mechanisms for resolving competition we consider are those that derive from the intrinsic or learned biases of the perceptual systems towards certain types of stimuli. We describe them here as bottom-up pro￾cesses, not because they do not involve feedback pathways in visual cortex (they may well do so) but because they appear to be largely automatic processes that are not dependent on cognition or task demands. Stimuli that stand out from their background are processed preferentially at nearly all levels of the visual system. In visual cortex, the responses of many cells to an otherwise optimal stimulus within their classically defined receptive field may be completely suppressed if similar stimuli are within a large sur￾rounding region (for reviews see Allman et al 1985, Desimone et al 1985). The greater the density of stimuli in the surround, the greater the suppression (Knierim & Van Essen 1992). In the middle temporal area (MT), for example, a cell that normally responds to vertically moving stimuli within its receptive field may be unresponsive if the same stimuli are part of a larger moving pattern covering the receptive field and surround (Allman et al 1985, Tanaka et al 1986). These mechanisms almost certainly contribute to the pop-out effects of targets in visual search. As indicated above, the visual system also seems to be biased towards new objects or objects that have not been recently seen. Thus, the temporal context of a stimulus may contribute as much to its saliency as its spatial context. In the temporal domain, stimuli stored in memory may function as the temporal surround, or context, against which the present stimulus is compared. Striking examples of such temporal interactions have been found in the anteroventral portion of IT cortex. Most studies in this region recorded cells www.annualreviews.org/aronline Annual Reviews Annu. Rev. Neurosci. 1995.18:193-222. Downloaded from arjournals.annualreviews.org by University of California - San Diego on 01/05/07. For personal use only. 202 DESIMONE & DUNCAN while monkeys performed delayed matching-to-sample (DMS) tasks with either novel or familiar stimuli. In DMS, a sample stimulus is followed by one or more test stimuli, and the animal signals when a test stimulus matches the sample. For up to a third of the cells in this region, responses to novel sample stimuli become suppressed as the animal acquires familiarity with them (Fahy et al 1993, Li et al 1993, Miller et al 1991, Riches et al 1991). The cells are not novelty detectors, in that they do not respond to any novel stimulus. Rather, they remain stimulus selective both before and after the visual experience. In fact, this shrinkage in the population of activated neurons as stimuli become familiar may increase the selectivity of the overall neuronal population for those stimuli. As one learns the critical features of a new stimulus, cells activated in a nonspecific fashion drop out of the activated pool of cells (Li et al 1993), leaving those that are most selective. There is also direct evidence that some IT cells selective for faces become more tuned to a familiar face following experience (Rolls et al 1989). An effect akin to the novelty effect is also found for familiar stimuli that have been seen recently. When a test stimulus matches the previously seen sample in the DMS trial, responses to that stimulus tend to be suppressed (Miller et al 1991, 1993; also see Baylis & Rolls 1987, Eskandar et al 1992, Fahy et al 1993, Riches et al 1991). Although it was originally proposed that this suppressive effect was dependent on active working memory for the sample, recent work has shown it to be an automatic outcome of any stimulus repetition (Miller & Desimone 1994). For many cells, this suppression occurs even if the repeated stimuli differ in size or appear in different retinal locations (Lueschow et al 1994). Thus, the detection of novelty and recency apparently occurs at a high level of stimulus representation. Taken together, the results indicate that both novel stimuli and stimuli that have not been recently seen will have a larger neural signal in the visual cortex, giving them a competitive advantage in gaining control over attentional and orienting systems. This would explain the bias towards novelty in the human behavioral data described above. The longer the organism attends to the object, the more knowledge about the object is incorporated into the structure of the cortex; this reduces the visual signal. It will also reduce the drive on the orienting system so that the organism is free to orient to the next new object (Li et al 1993, Desimone et al 1994). This view is compatible with Adaptive Resonance Theory (Carpenter & Grossberg 1987), in which novel stimuli activate attentional systems that allow new long-term memories to be formed. Consistent with these neurophysiological results in animals, a reduction in neural activation with stimulus repetition in human subjects has been seen in both event-related potentials of the temporal cortex (Begleiter et al 1993) and in brain-imaging studies (Squire et al 1992). www.annualreviews.org/aronline Annual Reviews Annu. Rev. Neurosci. 1995.18:193-222. Downloaded from arjournals.annualreviews.org by University of California - San Diego on 01/05/07. For personal use only