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Chapter 5 Chapter 5 Learning to attend in primary visual cortex Liviu Stănişor, Chris van der Togt and Pieter R. Roelfsema 1 Chapter 5 ABSTRACT The attentional selection of relevant visual objects at the expense of irrelevant ones is associated with the modulation of neuronal activity in early visual areas. It is unknown if these attentional selection signals are immediately present if a new task is learned. In this study we investigated the effects of learning on neuronal activity in area V1 of monkeys with an icon-selection task. In a two-choice paradigm, the monkeys had to learn to differentiate between two small images (icons) and choose the relevant one by making a saccade toward a target connected to the icon by a curve. Neural activity was recorded from neurons with receptive fields on the curves so that the receptive field stimulation was constant across trials. At the start of learning, the monkeys performed at chance level and the representation of the selected curve was enhanced over the representation of the non-selective curve at an early point during the trial. Performance improved with learning, and this behavioral improvement was associated with in increased delay in the appearance of the attentional selection signal. Learning the identity of the target icon caused a paradoxical suppression of the curve in the vicinity of the icon, which caused a delay in the attentional selection of the curve. These results provide a new and unexpected insight in processes responsible for the learning of multi-step tasks. INTRODUCTION The primary visual cortex is the first cortical stage of visual information processing. Neurons in area V1 contribute to the detection of elementary visual features like color or orientation, but their activity is also modulated by the relevance of visual stimuli (Roelfsema et al. 1998; Poort and Roelfsema 2009). During tasks that consist of several mental operations, neurons in area V1 that code image elements relevant for current operation increase their activity so that it is possible to monitor time-course of a sequence of cognitive steps in this area (Moro et al. 2010; Roelfsema et al 2003). One example of this is the search-then-trace task that we recently devised. In this task, subjects first carry out a visual search for a specific marker and they then have to trace a curve that starts at that marker. In previous work, we recorded 2 Chapter 5 from area V1 in this task and we found that neurons that coded the marker that had to be searched increase their activity at an early point in time during the trial whereas neurons that represented the curve to be traced enhanced their activity after a delay. Thus it is possible to monitor the precise time-course of a sequence of cognitive operations in area V1. The modulation of neuronal activity in early visual areas during curve-tracing and visual search is presumably responsible for the shifts of visual attention that subjects make during these tasks (Kim and Cave 1995; Houtkamp et al. 2003). In the previous chapter of this thesis, we investigated the neuronal activity in area FEF in a variant of the search-then-trace task where monkeys had to learn the identity of the icon that denoted the start of a target curve that had to be traced. In the early phase of the learning session, the monkeys did not yet know the identity of the target icon and they therefore had to make decisions with uncertain evidence. We found that the activity of neurons in FEF that coded for one of the possible decisions quickly ramped up and the monkeys made fast but random decisions. When the monkeys started to learn the meaning of the icons, we found that the increase in FEF activity towards a saccade was delayed as if a time-consuming visual search operation was interspersed before the monkeys reached their decision. Visual search and curve-tracing presumably rely on interactions between the frontal and visual cortex. Models of visual search propose that a feature-selective signal is fed from frontal areas back to the visual cortex to enhance the activity of neurons with a receptive field on the target of search (van der Velde and de Kamps 2001; Hamker 2005). Similarly, the neuronal correlates of curve-tracing emerge in visual and frontal cortex at approximately the same time after the presentation of the stimulus (Khayat et al 2009), which suggests that curve-tracing also relies on the reciprocal interactions between these cortical regions. As a direct test of the coupling between frontal and visual cortex a few studies increased the activity of neurons in the frontal cortex with microstimulation (Moore and Armstrong 2003, Ekstrom et al 2008) or local injection of drugs (Noudoost and Moore 2001) and observed an increase in the activity of neurons in visual cortex that code the same location in space. We therefore considered the possibility that the change in the activity in area FEF in the icon-learning task would be associated with changes in activity in visual cortex. 3 Chapter 5 Previous studies on the effects of learning on activity in visual cortex usually investigated the effects of extensive training protocols where animals were trained across many days (Kobatake et al. 1998; Schoups 2001; Schwartz et al 2002; Lee et al 2002; Furmanski et al 2004; Sigman and Gilbert 2005; Kourtzi et al 2005; Li et al 2008). To our knowledge, it has not yet been investigated if and how neuronal responses in the visual cortex change as a result of learning within a single recording session. During a single learning session, neurons in frontal cortex change their responses (Chen and Wise 1996; Asaad et al 1998; Yotsumoto et al 2008 and 2009). Are these learning effects in frontal cortex associated with altered attentional response modulation in visual cortex? In the present study we recorded from area V1 in the learning version of the searchthen-trace task to investigate a number of questions. First, in previous work the modulation of the neuronal activity by task relevance in early visual areas was investigated in welltrained tasks. It is therefore unknown if random decisions are reflected by a modulation of neuronal responses in early visual cortex. Second, one previous study demonstrated that the attentional response modulation required several days to develop in area V1 in a task where the animal had to learn to integrate a path of collinear contour elements (Li et al 2008). However, in that task the performance of the animal also improved gradually over several days. Will the attentional modulation of neuronal activity in primary visual appear within a single learning session of the icon-selection task? Third, we wanted to investigate the source of the increased delay in target selection in the icon-selection task where learning caused longer response times and later response modulations in frontal cortex. Alterations in the attentional modulation might give insight in the transition from the early phase with fast and random decisions to the later phase where decisions are informed by a successful visual search. MATERIALS AND METHODS Two monkeys (A and G) participated in this study. In a first operation, a head holder was implanted and a gold ring was inserted under the conjunctiva of one eye for the measurement of eye position. In a separate operation, arrays of 4x5 or 5x5 electrodes (Blackrock Microsystems) were chronically implanted in area V1. The surgical procedures 4 Chapter 5 were performed under aseptic conditions and general anesthesia. Details of the surgical procedures and the postoperative care have been described elsewhere (Roelfsema et al 1998, 2007). All procedures complied with the US National Institutes of Health Guidelines for the Care and Use of Laboratory Animals and were approved by the institutional animal care and use committee of the Royal Netherlands Academy of Arts and Sciences of the Netherlands. The stimuli were presented on a monitor with a diagonal of 35.5 cm (14 inch), a resolution of 1024 by 768 pixels, a refresh rate of 100 Hz, and positioned at a distance of 75 cm from the monkey's eyes. The objects that appeared on the screen were colorful figures that were approximately 0.8° in diameter; we will refer to these as ‘icons’ (Figure 1). The saccade targets were red discs with a radius of 0.8° connected to the icons by a curve that was two pixels wide (0.05°). The eccentricity of the saccade targets was 3.5° for monkey A and 4° for monkey G. Behavioral task The aim of the icon selection task was to induce learning by presenting a new set of stimuli each day. A trial started as soon as the monkey’s eye position was within a 1.2°×1.2° square window centered on a red fixation point (FP in Fig. 1a). After an interval of 300 ms, the stimulus appeared on the screen. It consisted of two curves starting at two different small icons. The monkey was presented with a new pair of icons each day, one of which was relevant and the other was to be ignored. The curve segment in the receptive fields of the recorded neurons was always the same so that differences in activity between conditions were caused by effects on the neuronal firing rates from outside the receptive field. There were three different types of trials: easy, intermediate and difficult (Fig. 1b). In the case of the easy stimuli, the relevant icon appeared on top of the fixation point whereas the other object appeared at a distance from the fixation point. For the stimuli of intermediate difficulty, neither icon overlapped with the fixation point, but the relevant one was closer to the fixation point than the irrelevant one. The difficult stimuli had both icons equally spaced from the fixation point so that the monkey could not use positional cues and 5 Chapter 5 had to rely on the identity of the relevant and irrelevant icon. Great care was taken to use different cues (combination of shapes, contour and filling colors etc.) so that the relevant icon was distinct from the irrelevant one and the pairs of icons differed across days. In order to promote learning, the stimuli of the three difficulty levels were interleaved so that the animals could use the information from trials with an easy and intermediate difficulty while they increased their performance for the difficult ones. After the stimuli were presented, a delay of 500 ms followed, after which the fixation point disappeared (go cue) and the monkeys could indicate their choice by making a saccade to one of the saccade targets and they then progressed to the next trial. We used this paradigm in order to investigate (1) differences in neuronal activity between levels of difficulty and (2) the changes in neuronal activity induced by learning. We considered the learning session successful if the accuracy for the difficult stimuli was 75% or better at the end of each recording session. An average number of 281 trials per recording session were recorded (difficult stimuli) for monkey A and 136 trials for monkey G. We recorded behavioral data across 22 sets for monkey A as well as monkey G (44 sets in total) and neuronal data across 19 sets for monkey A and 22 sets for monkey G (41 sets in total). Recording of multiunit activity Spiking activity was recorded from the chronically implanted multi-electrode arrays (Cyberkinetics Neurotechnology Systems Inc.) with TDT (Tucker Davis Technologies) multichannel recording equipment. As in previous studies (Logothetis et al. 2001 Supèr and Roelfsema 2005; Xing et al. 2009; Yeh et al 2009; Pooresmaeili et al. 2010;), the MUA signal was amplified, band-pass filtered (300-9000Hz), full-wave rectified, low-pass filtered (<200Hz) and sampled at a rate of 763Hz. MUA recorded in this manner gives an instantaneous measure of spiking activity of neurons around the electrode tip. Previous studies compared MUA to single-unit data in a curve tracing task and found that it yields a reliable estimate of the average single-unit response (Supèr et al 2005, Cohen and Maunsell 2009). For every recording site we calculated the signal-to-noise ratio by dividing the maximum of the average evoked response to the standard deviation of the spontaneous 6 Chapter 5 activity in a 200 ms window before stimulus onset. We included recording sites with a signal to-noise ratio between 0.28 and 11.11, with a mean of 3.91. The receptive field dimensions of the neurons at the recording sites were estimated using the onset and offset of the visual response to a slowly moving white bar on a black background, in each of eight possible directions (Supèr & Roelfsema, 2005). The median area of the receptive fields was 0.52 deg2 (ranging from 0.1 deg2 to 2.4 deg2). Receptive field eccentricity ranged from 1.21° to 5° with an average of 3°. The stimulus configuration was devised in such a way that all the RFs fell onto one of the curves and they never overlapped with the discs or with the icons. Analysis of neuronal responses We recorded neuronal data across 19 sets for monkey A and 22 sets for monkey G (a total of 41 sets). We computed peri-stimulus time histograms (PSTHs) in a time window ranging from 100 ms before stimulus onset and up to 800 ms afterwards and normalized MUA activity to the peak response after subtraction of the spontaneous activity (average activity in the last 100 ms before stimulus onset) and smoothing with a Gaussian kernel (standard deviation 10 ms). The peak response was determined as the difference between the highest and lowest activity in an interval between 0 and 150 ms after the stimulus onset. When analyzing differences in modulation between conditions, we used a 200 ms computational time window starting at 200 ms after stimulus onset unless otherwise specified. Latency analysis Attentional response modulation reflects differences in neuronal firing rate between relevant and irrelevant stimuli. Methods to quantify the onset of the modulation of activity start by taking the difference between activity evoked by target and distractor stimuli. To compute the latency of attentional modulation we used a method based on the cumulative 7 Chapter 5 sum of this response difference. This sum is a function of time and represents the integral y of modulation from stimulus onset (t=0) up to each point in time. τ =t y (t ) = ∑ x(τ ) τ =0 where x(τ ) is the response modulation at time τ . One advantage of this method is that it averages out noise which leads to smooth graphs (Bellman and Roth 1969; McGee and Carleton 1970). The slope of the function y(t) reflects the average modulation in the time period of interest and changes in this angle can be used to determine the onset of modulation. We used piecewise linear fitting to estimate average modulation magnitude in different epochs and used the joints between segments to estimate the change points. We fitted two lines; the first had a zero slope, and the second a positive slope using the shape prescriptive modeling toolbox implemented in MATLAB by John D’Ericco (Mathworks 2009). When computing the cumulative sum of the modulation, we considered the neuronal activity until the average reaction time. Channels with poor fits (which produced latencies smaller than 50 ms after stimulus onset) were excluded from the analysis (1 site in monkey G and 2 in monkey A). RESULTS We trained two monkeys to select one of two small icons using the icon-selection task with three levels of difficulty. The monkey started a trial by direction gaze to a fixation point (FP in Fig. 1a). After a delay of 300 ms, we presented two curves, two discs and two icons one of which was the target icon. When monkey had identified the target icon, he had to trace the curve connected to this icon to localize the target disc as the goal of a saccade. After an additional delay of 500 ms, the FP disappeared which was the go cue for the monkeys to make the saccade. Saccades to the disc connected to the target icon were rewarded with a drop of apple juice. 8 Chapter 5 Every day we presented a new pair of icons that had not been seen by the monkey, and the animal had to learn the identity of the target icon by trial and error. We facilitated learning with positional cues (Fig. 1b) because the target icon was closer to the fixation point than the distractor icon in two of the three difficulty levels. In the easy condition the target icon was placed on top of the fixation point, in the intermediate condition it was closer to the fixation point than the distracter icon, and in the difficult condition both icons were at an equal distance from the fixation point. Therefore, the monkey could solve the task by selecting the icon closest to the fixation point at two of the difficulty levels. Either curve could be target or distractor at each of the three difficulty levels so that there were a total of six stimuli that were randomly interleaved. Behavioral measures of learning We obtained behavioral data from a total of 44 recording sessions with both monkey A and monkey G. Figure 2 shows how their accuracy and the reaction time changed during the behavioral task. The monkeys hardly made any errors for the easy and intermediate stimuli and we therefore did not observe changes in accuracy in these conditions. In the difficult condition, the accuracy was initially close to 50% (chance level) and it reached a maximum higher than 90% for monkey A and close to 100% for monkey G. The learning curve of monkey G was somewhat steeper than the learning curve of monkey A. To investigate if the learning was significant, we compared the accuracy between early trials (trials 1-30) and late trials (121-150 in Monkey A; 61-90 in Monkey G). The increases in accuracy in the difficult condition were highly significant in both animals (Monkey A:χ2=95.3, df=1, P<10-6; G: χ2=60.6, df=1, P<10-6, Chi-Square test). The monkeys had to maintain fixation for 500ms after stimulus onset and were then cued to make a saccade. In spite of this fixation requirement we found that the reaction times differed between difficulty levels and changed with learning. We analyzed the average reaction times with a two-way ANOVA with factors ‘difficulty’ and ‘epoch’. For the factor epoch we chose three intervals that differed between the two monkeys because they differed in learning speed: early (trials 1-30), intermediate (61-90 in Monkey A, 31-60 in Monkey G) and late (121- 150 in Monkey A, 61-90 in Monkey G) (grey bars in Figure 2). We 9 Chapter 5 observed a significant main effect of difficulty on reaction time because difficult trials were associated with longer reaction times in both monkeys (Monkey A, F2,261=34.7, p<10-5; Monkey G, F2,261=1272, p<10-5). The effect of learning on the reaction time differed between monkeys, with reaction time decreasing in monkey G (F2,261=20.5, p<10-5) and slightly increasing in monkey A (F2,261=3,1, p=0.046). However, the increase in reaction time in monkey A occurred only in the easy and intermediate condition, as the reaction times decreased in the difficult condition (592±44ms in the early trials vs. 575±36ms in the late trials, t-test: p>0.05), just as was observed in monkey G (833±156ms vs. 767±138ms, t-test: p>0.05). In both animals the response times for the difficult stimuli on error trials were significantly longer than those on correct trials (both monkeys, p<10-5, t-test). Neuronal activity in area V1 in the icon-selection task The stimuli were always configured in such a way that the receptive fields (RFs) of the MUA recording sites fell either on the target curve or on the distracter curve (Fig. 1a) but not on the icons. Figure 1c shows the neuronal response of neurons at one example V1 recording site when the RF fell on the target curve (red response) and when it fell on the distracter curve (blue response), for easy, intermediate and difficult stimuli. The responses were obtained by averaging across all correct trials and across all recording sessions (N=19) with this recording site. It can be seen that the initial visual response triggered by the appearance of the curve in the receptive field was identical for the two conditions. However, after a delay of 150-200 ms neuronal responses evoked by the target curve became stronger than the activity evoked by the distracter curve. Previous studies attributed this response modulation to the shift of visual attention to the target curve (Roelfsema et al. 1998; Poort and Roelfsema 2009). It can also be seen that the onset of the response modulation in the difficult condition is later than in the easy condition. 10 Chapter 5 Effects of task difficulty on neuronal activity in area V1 Figure 3 shows the population response for each monkey (monkey A: upper panels, N=26 recording sites; monkey G: lower panels, N=7 recording sites), averaged across all recording sessions (monkey A 19 sessions; monkey G 22 sessions). The lower panels show the response modulation computed as the difference between the activity evoked by the target and distracter curve. It can be seen that the profile of the population response was similar to that of the example recording site in Figure 1c. The initial responses did not discriminate between the target and the distractor curve, and the attentional response modulation occurred after a delay. The temporal profile of the attentional modulation differed between the difficulty conditions. In the easy and intermediate conditions, the modulation started relatively abruptly, whereas the increase in modulation was more gradual in the difficult condition. Figure 4 illustrates the modulation (upper panels) and the cumulative sum of the modulation (lower panels, see Methods). It can be seen that the modulation is weaker in the difficult condition than in the other two conditions, and that the latency of modulation is longer. We analyzed the modulation in the difficult stimuli across recording sites and we found it significant in case of monkey A (between 200 and 400 ms after stimulus onset, N = 26 sites, p<0.01, paired Wilcoxon signed rank test) as well as monkey G (between 400 and 600 ms, N = 7 sites, p=0.01, paired Wilcoxon signed rank test). Interestingly, we found a slight inverse modulation in the difficult condition in monkey G that later reversed and then gradually built up until the onset of the saccade. This reversed modulation might be a consequence of the visual search. Previous studies demonstrated that search is associated with an enhanced representation of the icon in visual cortex (Chelazzi et al 2001; Moro et al. 2010) and our result suggest that this selection process is associated with the suppression of the representation of the adjacent curve. When the monkey starts to trace, however, this suppression reverses and V1’s representation of the target curve is enhanced. To investigate the reliability of the differences in the early response modulation between conditions, we quantified the attentional effect a window from 100-300 ms after stimulus onset. An ANOVA revealed a significant effect of task difficulty on the magnitude of the modulation in both monkeys (one-way ANOVA, monkey A: p <10-5, F2,83 = 44.6; monkey 11 Chapter 5 G: p <10-5, F2,20 = 37.8). Early modulation in the difficult condition was significantly smaller than in the easy and intermediate condition (p < 0.01, Tukey’s HSD test). We did not observe a significant difference in modulation strength between the easy and intermediate condition in either monkey (p> 0.05, Tukey’s HSD test). We measured the latency of the response modulation with a piecewise linear fitting method on the cumulative sums of modulatory activity (Fig. 4b). Latency is estimated as the time of the first deflection point in the fit (see Methods for details). Across the individual recording sites in monkey A, the average latency of the modulation was 115±6 ms (mean ± s.e.m.) in the easy condition, 126±3 ms in the intermediate condition and 165±11 ms in the difficult condition. In monkey G these latencies were 100±19 ms, 119±20 ms and 240±42 ms, respectively. We tested the effect of difficulty on modulation latency with a one-way ANOVA and found a significant effect in both monkeys (monkey A: F2,83=93, p<10-6; monkey G: F2,20=44, p<10-6). The latency of the response modulation did not differ significantly between the easy and intermediate condition in either monkey (p>0.01, post-hoc test) but the latency was longer in the difficult condition in both animals (difficult vs. intermediate and difficult vs. easy both ps<0.01, Tukey’s HSD). The effects of learning on neuronal activity in area V1 In order to investigate the effect of learning on the modulation of V1 activity, we investigated neuronal activity in trials of the three epochs during the progress of learning (see grey regions in Figure 2 for the selection of trials); an early epoch (random performance), an intermediate epoch and a late epoch (maximal performance). For both monkeys we selected 30 trials per condition (15 trials with the target curve in the RF and 15 with the distractor, in each learning phase). In this analysis we first calculated the cumulative sum of (target–distracter) modulation (Fig. 5a) using the neuronal activity first averaged across recording days and then across recording sites. We did not observe consistent effects of learning on the cumulative sum of modulation in the easy and intermediate conditions. For example, the cumulative modulation in the easy condition in monkey A rose less steeply in the intermediate epoch than in the first set of trials (early 12 Chapter 5 phase), but it rose more steeply in the last epoch. In monkey G the cumulative modulation tended to rise more steeply with learning in the easy and intermediate condition. In contrast, the changes observed in the difficult condition were greater and highly consistent across animals. The right panels of Figure 5a show the cumulative sum plots of the response modulation averaged across all V1 recording sites. Learning induced a suppression of activity evoked by the target curve (reverse modulation), and this effect became maximal in the last epoch when the animals reached their maximal accuracy. These results indicate that learning to select the target icon causes a suppression of the adjacent curve in area V1, and this suppression changes into a response enhancement when the monkey traces the target curve. To further characterize these effects, we estimated the latency of (positive) modulation of individual recording sites during the early (red symbols in Fig. 5a) and late epoch (blue symbols) in the difficult task. The latency of the modulation in both monkeys increased with learning (Fig. 5b). The median latency for monkey A (n = 26 recording sites) was 220±14 ms for the first epoch (trials 1-30) and it increased to 290±9 ms in the late epoch (trials 121-150), a difference that was significant (p < 0.01, paired t-test). V1 neurons in monkey G showed the same effect with a median latency of 270±66 ms for the first epoch and 570±45 ms in the late epoch (p < 0.01, paired t-test). This increase in latency with learning was associated with an increased suppression of the activity evoked by the target curve during the initial phase of the trials (reversed modulation). To measure the suppression, we calculated the y-offset of the best fitting line used to estimate the latency of the modulation (arrows on the y-axis in Fig. 5a, right panel) across individual recording sites. Figure 5c illustrates the significant increase in suppression with learning (both monkeys: p < 0.01, paired t-test). We next investigated if the effect of learning on the latency on the response modulation was specific for the difficult condition where learning was most pronounced. We therefore repeated the same analysis in the easy and intermediate condition. In contrast to the effects in the difficult condition, there was even a small decrease in the latency of modulation between early and late epoch. This result indicates that the increase in modulation latency was specific to the difficult condition where the monkey’s performance increased the most. 13 Chapter 5 Error trials If the initial suppression of the target curve adjacent to the relevant icon is caused by the attentional selection of one of the icons, then the neuronal responses should invert on erroneous trials in the difficult condition where the monkey selects the wrong icon. Figure 6 compares the attentional modulation of the V1 representation of the curve and its cumulative sum between correct trials (red traces) and trials when the monkey made an erroneous saccade to the distracter (blue traces), in the difficult condition. On correct trials, the representation of the target curve was initially suppressed relative to the distractor curve and enhanced at a later point in time. On error trials, the response modulation has a vertically mirrored profile, suggesting that the distractor curve’s representation adjacent to the erroneously selected distractor icon is initially suppressed but that the distractor curve’s representation is enhanced during the subsequent curve-tracing process. The difference in the response modulation between erroneous and correct trials was significant in monkey A (t-test, p<0.01 in a window from 200-400ms) as well as in monkey G (t-test, p<0.01 in a window from 400-600ms). DISCUSSION In the present study we investigated the influence of learning on V1 activity in task where monkeys had to learn to select a target icon with unknown identity. Initially, the animals had to make their decisions without knowing the sensory-motor mapping but with time they learned to select the appropriate response. In our previous study we recorded from area FEF during this task to study the integration of neuronal activity for the optimal selection of eye-movements and the influence of learning on this selection process. We found that the FEF neurons initially reached fast but random decisions that while learning proceeded changed into more gradual and accurate decisions. The delay in the buildup of FEF activity that occurred with learning suggested that one of the curves was initially selected at random, whereas learning the identity of the target icon caused an additional 14 Chapter 5 delay, as if an additional time-consuming visual search operation was added to the mental routine. The present results demonstrate that the initial fast process that randomly selects one of the two curves is also associated with attentional modulation in area V1, so that the selected curve is represented with stronger activity than the non-selected curve. The implication is that attentional modulation of neuronal activity in primary visual cortex does not only occur in well-trained tasks, but even during the initial stages of learning where the animals explore the stimulus-response mappings. Our results also provide insight into the source of the delay in the response modulation in area FEF that occurs during the progression of learning. Learning to select the target icon is associated with a paradoxical suppression of the representation of the adjacent target curve. Visual search is known to boost the representation of the target icon in visual cortex (Chelazzi et al 2001) and we have now observed that it also suppresses the representation of the adjacent target curve. These results therefore suggest that the neuronal selection process during search has a Mexican hat profile, with a central attended region surrounded by a ring of suppression, as has also been observed in other tasks and with different techniques (Serences et al 2004, Hopf et al 2006). The strength of the suppression became stronger with learning and it thereby caused an increasing delay in the propagation of enhanced activity along the target curve. We suggest that this suppression process might also be responsible for the delay in the response modulation that we observed in our previous FEF study. Previous V1 learning studies The present study differs from previous studies that monitored the consequences of learning on activity in visual cortex (e.g. Kobatake et al., 1998, Schoups 2001; Lee et al 2002; Schwartz et al 2002; Furmanski et al 2004; Kourtzi et al 2005; Sigman and Gilbert 2005; Li et al 2008). In these previous studies, the subjects were trained in a difficult detection or classification tasks and it was found that learning increases the neuronal sensitivity to a specific feature or visual pattern while behavior improved over several days. In the present study we monitored the V1 representation of the two curves that were held constant and 15 Chapter 5 the changes in activity therefore reflected the changes in the efficiency and timing of the attentional selection process rather than altered sensitivity to specific visual features. Comparison between easy and difficult trials Activity in area V1 provides insight into sequential cognitive tasks because the response enhancements index the component processes, like visual search and tracing (Roelfsema et al., 2003; Moro et al., 2010). In the easy and intermediate conditions of the present icon selection task, the monkeys could solve the task by tracing the curve that started nearest to the fixation point. Under these conditions, the activity evoked by the target curve became stronger than the activity evoked by the distractor at a relatively early point in time (between 100 and 130 ms). In the difficult condition, the monkeys had to first carry out a visual search and the insertion of this visual search operation caused a delay in the enhancement of the representation of the target curve in accordance with previous studies (Roelfsema et al. 2003; Moro et al. 2010). These previous studies did not find a suppression of the adjacent target curve, as was observed in the present icon-selection task. One possible difference between the present study and these previous studies was that the monkeys had to learn the identity of the target icon during whereas the animals carried out a highly familiar search for a specific color in the earlier studies. The attentional modulation in the difficult condition was also weaker than that in the easy and intermediate conditions, in accordance with a study that varied task difficulty in the curve-tracing task (Roelfsema & Spekreijse 2001). Thus, the neuronal correlates of attentional selection in area V1 are strongest if the selection of the target curve is easy. One might therefore have also have expected that learning would lead to enhanced attentional modulation in area V1. This is not what we observed, but the absence of an increase in attentional modulation with learning in the difficult condition was presumably caused by the inhibition caused by visual search, as was discussed above. Source of the attentional response modulation in area V1 16 Chapter 5 It is unlikely that the primary visual cortex solves the icon-selection task by itself and the response modulation during search and tracing presumable depends on the interaction between area V1 and higher visual areas. Models of visual search propose that a representation of the search target in frontal cortex provides feedback to visual areas to enhance the activity of neurons that code matching features in the visual image (van der Velde and de Kamps 2001; Hamker 2005; Zhou and Desimone 2011). This resulting focus of enhanced activity in retinotopic areas could then be used as a starting point for the tracing process that selects all contour elements of the target curve and eventually highlights one of circles at the end of the relevant curve as target for the saccade. Models of tracing have proposed that neurons in visual cortex propagate increased activity along the representation of the target curve until it is entirely labeled with enhanced activity (Grossberg and Raizada 200; Roelfsema 2006). We previously compared the neuronal correlates of curve-tracing between areas V1 and FEF and found that the attentional selection of the relevant curve occurs at approximately the same time in both areas (Khayat et al. 2009), suggesting that curve tracing also requires reciprocal interactions within a network of areas that includes areas of visual and frontal cortex. It is unknown where the decision is taken to select one of the icons and to then trace the curve attached to this icon. Areas in frontal cortex like the FEF and in parietal cortex like area LIP have been implicated in eye movement decisions (Kim and Shadlen 1999; Platt and Glimcher 1999; Shadlen and Newsome 2001; Gold and Shadlen 2007). However, it is also conceivable that there are other, perhaps even unexplored regions that contribute to icon selection and decision making. It is even possible that these decisions depend on an interaction between multiple areas that together act as an attractor network that converges into one of a number of attractor states. Studies that change neuronal activity in frontal cortex observed effects in visual cortex, thereby providing insight in how these areas interact with each other (Moore and Armstrong 2003; Ekstrom et al 2008). The finding that V1 also reflects the outcome of the decision process is compatible with an active involvement of V1 in such an attractor network, but it is equally compatible with more passive role where V1 receives feedback from higher visual areas that take the decision. 17 Chapter 5 Conclusion We conclude that processing in the icon-selection task proceeds from an initial stage where the monkey takes fast and random decisions that is followed by a stage where the decision become more accurate. The curve-tracing process becomes delayed with learning because the monkeys insert a visual search process before tracing, which causes an initial suppression of the target curve and it therefore takes more time before the target curve’s representation is enhanced and can be selected as the target of an eye movement in brain regions that select saccades, including the frontal eye fields. 18 Chapter 5 Figure 1. The icon-selection task and activity at an example V1 recording site. a, The monkeys were required to fixate a central fixation point (FP) for 500 ms, after which two 19 Chapter 5 icons appeared that were both connected to a disc by a curve. The stimuli were configured so that either the target curve (T) or the distractor curve (D) fell in the receptive field (RF) of a group of neurons in area V1. After a second delay of 500 ms, the monkeys had to indicate their choice by making a saccade to the disc connected to the relevant target icon (blue arrow). b, Example stimuli in the easy, intermediate and difficult condition. In the easy and intermediate conditions, the relevant icon was superimposed on the fixation point (easy condition) or closer to the fixation point than the irrelevant icon (intermediate condition), and these spatial cues allowed the monkeys to solve the task even if they had not yet learned the identity of the target icon. c, Activity of an example V1 recording site in the three conditions. Note that the activity evoked by the target curve is stronger than that evoked by the distractor curve at each of the difficulty levels. 20 Chapter 5 Figure 2. Accuracy and reaction times of the two monkeys in the icon-selection task. a, Accuracy (percentage correct) as a function of trial number for trials of the easy (blue), intermediate (green) and difficult condition (red). The gray bars illustrate bins of 30 trials (early, intermediate and late during sessions) that were selected for further analysis. b, Reaction times (measured from stimulus appearance) for trials of the three difficulty levels. The cyan traces represent reaction times for the error trials. 21 Chapter 5 Figure 3. Averaged activity evoked by the target and distractor curve and attentional response modulation at three levels of difficulty. Red and blue traces show the population response averaged over all recording sites and all trials of the easy, intermediate and difficult stimuli for both monkeys separately. Lower panels show the attentional response modulation (target minus distracter activity). Note the different scale in the lower right panel for monkey G. 22 Chapter 5 Figure 4. Average profile and timing of attentional response modulation. a. Modulation for easy (blue traces), intermediate (green traces) and difficult trials (red traces). b. Cumulative sums of modulation. The numbers denote the onset time of the attentional response modulation as estimated with a piecewise linear fitting procedure. 23 Chapter 5 Figure 5. Cumulative sum analysis of modulation during the learning process. a, Time course of the cumulative sum of modulation for both monkeys. The different shades of grey denote bins of 30 trials representing early, intermediate and late periods during sessions. 24 Chapter 5 Cyan and red symbols in the right panels show the estimated modulation latencies for individual recording sites in the first and last epoch during learning, respectively. The dashed lines show the best piecewise linear fits that were used to estimate the latency of the attentional response modulation at the population level for the first (magenta) and last epoch (cyan). b, Latency of modulation across individual recording sites in the first (abscissa) and last epoch of trials (ordinate) in the difficult condition. c, Degree of response suppression across individual recording sites in the first (abscissa) and last epoch of trials (ordinate) in the difficult condition. Note the increase in latency and suppression in the last trials. Figure 6. Comparison of V1 activity on correct and erroneous trials in the difficult condition. a, Attentional response modulation on correct (red traces) and erroneous trials (blue traces) for both monkeys. 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