the mask array helped the listener re-direct attention away from the mask and greatly aided detection. This would not apply to the broadband noise used here and Green [1].

Cues tones presented very close to the signal are not only informative but also interfere with detection [26,27] even when the cue is valid (i.e., has the same frequency as the signal). In the present work, valid cues were presented 82 ms before the signal, when interference is small, about 2 dB in both *const* and *var* [28].

Previous Studies: Energy Detection

The experimental literature contains several previous studies of the role of attention in detection, starting with Green [1], and I analyze these in the next section. (I provide new results from three listeners in the *var*/*const* × *valid cue*/*no cue* experiment.) My analyses show that the widely assumed 'energy detection' model of auditory threshold provides a rather poor approximation of the data.

Green [1] measured proportion correct detection (Pc) in two-alternative forced choice (2AFC) trials, for 800, 1250, 2250, and 3200 Hz signal tones, which were constant in each block of trials (*const*), and for 100, 300, 500, 1000, or 3500 Hz signal tones, randomized across trials (*var*). Cues were never provided. Tones were 100 ms in duration (and ramped on and off to avoid clicks, as is standard). Detection was measured over an 11 dB range of signal levels. Tones were presented in wide-band noise whose amplitude was constant at a 40 dB spectrum level. Pc's from all frequencies taken together are plotted in Figure 2 of his article.

Sound level in decibel (dB) units equals 10log₁₀[(P/P₀)²], where P₀ is a reference level, either 0.0002 dynes/cm² in the case of dB SPL (sound pressure level) or the threshold in the case of dB SL (sensation level). Since thresholds in dB SPL varied with frequency, Green [1] plotted 2AFC accuracy (Pc) against signal dB SL, where at every frequency, 5 dB SL was defined to correspond to Pc = 75% (chance being 50%). Given the small numbers of recorded observations at each signal level, from 3 to 6, median (rather than mean) Pc's were read from Figure 2 in his work and are given below in Table 1 under the heads Pc *const* and Pc *var*.

<table><caption>**Table 1.** Data from Green [1], Figure 2. Pc *const* and Pc *var* are medians at each signal dB level. Amp = 10dB/20 is given for reference. Noise level was constant. Pcon and Pvar (converted to d'con and d'var) are the Pc's corrected for 2% lapsing.</caption><thead><tr><th>Amp</th><th>dB</th><th>Pccon</th><th>Pcvar</th><th>Pcon</th><th>Pvar</th><th>d'con</th><th>d'var</th><th>log d'con</th><th>log d'var</th></tr></thead><tbody><tr><td>1.12</td><td>1</td><td>0.54</td><td>0.55</td><td>0.541</td><td>0.551</td><td>0.145</td><td>0.181</td><td>-0.839</td><td>-0.741</td></tr><tr><td>1.26</td><td>2</td><td>0.55</td><td>0.58</td><td>0.551</td><td>0.582</td><td>0.181</td><td>0.291</td><td>-0.741</td><td>-0.535</td></tr><tr><td>1.41</td><td>3</td><td>0.64</td><td>0.66</td><td>0.643</td><td>0.663</td><td>0.518</td><td>0.596</td><td>-0.286</td><td>-0.225</td></tr><tr><td>1.58</td><td>4</td><td>0.66</td><td>0.68</td><td>0.663</td><td>0.684</td><td>0.596</td><td>0.676</td><td>-0.225</td><td>-0.170</td></tr><tr><td>1.78</td><td>5</td><td>0.78</td><td>0.76</td><td>0.786</td><td>0.765</td><td>1.120</td><td>1.023</td><td>0.049</td><td>0.010</td></tr><tr><td>2.00</td><td>6</td><td>0.87</td><td>0.82</td><td>0.878</td><td>0.827</td><td>1.644</td><td>1.330</td><td>0.216</td><td>0.124</td></tr><tr><td>2.24</td><td>7</td><td>0.91</td><td>0.91</td><td>0.918</td><td>0.918</td><td>1.972</td><td>1.972</td><td>0.295</td><td>0.295</td></tr><tr><td>2.51</td><td>8</td><td>0.97</td><td>0.93</td><td>0.980</td><td>0.939</td><td>2.893</td><td>2.184</td><td>0.461</td><td>0.339</td></tr><tr><td>2.82</td><td>9</td><td>0.98</td><td>0.97</td><td>0.990</td><td>0.980</td><td>3.279</td><td>2.893</td><td>0.516</td><td>0.461</td></tr><tr><td>3.16</td><td>10</td><td>0.98</td><td>0.95</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>3.55</td><td>11</td><td>0.98</td><td>0.98</td><td></td><td></td><td>linear</td><td>slope</td><td>0.177</td><td>0.146</td></tr></tbody></table>

For signals above 9 dB SL, the mean error rate (E) did not depend on level, so it is likely that the few remaining errors, which averaged 2%, were due to lapses in attention [29,30]. Corrected for lapsing with E = 2%, the detection rates (Pc - 0.5E)/(1 - E) are listed in Table 1 under the headings Pconst and Pvar. Plotting these against signal dB SL yields slopes of 0.056 in *var* (r = 0.98) and 0.062 in *const* (r = 0.99), close to the slopes of 5% per dB reported in the auditory detection literature [5,6,31]. Table 1 gives d'const = √2z(Pconst) and d'var = √2z(Pvar), where z(P) is the standard Normal z-score of proportion P. The final two columns give these d's in log₁₀ units. (Note: log(d') exists as all Ps exceeded 50%, or d' = 0.)
According to Green and Swets [32], a detector of sinusoidal signal tones obeys

$$d' = k(S/No) \qquad (1)$$

where S is the signal level, No is the noise level in the channel that detects the signal, and k is a constant of proportionality. They took No to equal the external noise provided by the experimenter, because there was no good evidence for internal auditory noise, and Brownian motion in air ensures No > 0 and so prevents division by zero in the quiet. For a peak–trough detector, S and No are in units of amplitude or sound pressure, P/Po. For an energy detector, S and No are in units of (P/Po)². Converting Equation (1) to dBs by canceling Po in the ratio S/No, and writing S<sub>dB</sub> and No<sub>dB</sub> for the Signal and Noise in dBs,

$$\log_{10}(d') = \log_{10}(k) + b(S_{\text{dB}} - No_{\text{dB}}) \qquad (2)$$

where the slope, b, is 1/20 for a peak–trough or amplitude detector and 1/10 for an energy detector [32].

The regression of log₁₀(d') on S<sub>dB</sub> from Table 1 gave slopes (b) of 0.146 in var (r = 0.98) and 0.177 in const (r = 0.98). These slopes clearly reject the peak–trough detector (b = 1/20) but are also on average 16% steeper than the energy detector. Green [1] stated that the slope was 1/10 in both var and const, but he may have been misled, both because log(d') puts undue weight on near-zero d's, and because d' becomes unstable at high Pc's [30]. However, Green's claim has entered the literature and it is often taken for granted that his data showed that the ear is an energy detector.

Green, Birdsall and Tanner [33] had previously used uncued 1000 Hz signals in const and again reported slopes consistent with energy detection in wide-band noise for four listeners and three stimulus durations. However, interpolating the S<sub>dB</sub> levels at d' = 0.5 and d' = 2.0 in all 12 of their plots, the average of the resulting log(d') versus S<sub>dB</sub> slopes is again 14% steeper than the energy detector. Dai [31] also reported slopes of 0.14 in const and 0.15 in var, not 0.10, using an uncued 'profile' task in which listeners discriminated an array of 21 well-spaced tones from the same array plus a signal tone. It thus appears that for uncued tones of constant frequency, the log-log psychometric slopes are consistently around 14% too steep for pure energy detection.

As stated above, Green [1] concluded from his data that the listeners are uncertain about signal frequency, not only in var but also in const, because the thresholds in var and const were only 3 dB apart, not the 10 dB estimated from noise exclusion. The weakness of the uncertainty effect is too dramatic to be an artefact of the somewhat different frequency ranges Green employed in const and var. However, to obtain a steepening of 14% in const requires the listener to be uncertain about which of at least 32 channels contains the signal [29,32]. Such high channel uncertainty seems unlikely, given that the signal in const was fixed in every auditory parameter (lateralization, duration, onset time, and frequency). Alternatively, the noise level in var might be determined by the maximal noise in each CB, not the summed noise across CBs, an idea which correctly predicts a 3 to 4 dB uncertainty effect [32]. However, attention to all the possible signals in var implies attending to the noise in each of the possible signal CBs, rendering the max operator unrealistic. Scharf, Reeves, and Giovanetti [34] offered yet another explanation of the weak uncertainty effect. Attention can be focused on an unexpected signal frequency in less than 52 ms [24,28], and as Green [1] and Dai [31] used 100 ms tones, much of the noise from non-signal CBs in var could be excluded by shifting attention to the signal before it terminated. That is, rather than assuming uncertainty in const, Scharf et al. [34] assumed more certainty in var. They [34] estimated the true uncertainty effect as 9 dB in an overshoot experiment in which attentional focusing was completely disrupted by the onset of broadband noise, close to the predicted 10 dB. Thus, the conclusion that the normal listener excludes noise from all non-signal CBs when focusing may be correct after all.

The uncertainty effect in the current experiment was expected to be 3 dB, as the const/var method was used, rather than the overshoot procedure. It is the slopes that are
of concern here. Given the earlier results [1,31,33], it seemed likely that with no cue, the present results would also show a steeper psychometric slope than the ideal energy detector. The question at issue was whether pre-cuing could help listeners focus attention and bring the slope closer to 1/10, the energy detector. If so, the claim can be made that attention to known sounds permits the ear to operate as an ideal receiver of signal energy.

## 2. Methods

The terms *var* and *const* will continue to be used for conditions in which frequency was varied unpredictably over trials or was constant for a block of trials. The *var* and *const* conditions were like Green's [1], except that tone duration was 40 ms, not 100 ms. The same frequencies were used in both conditions, since the uncertainty effect decreases with frequency [25]. Signals were preceded 82 ms earlier by a valid cue, also 40 ms in duration, or were, like Green's, uncued.

*Participants.* One male (MA) and two female (TA and NA) Northeastern University undergraduates, aged 19, 20, and 22, served as listeners. All three had normal audiograms and detection thresholds. Hour-long sessions were run over several weeks to obtain data. None reported using drugs (prescribed or otherwise) during the course of the study. The study was authorized by the human subjects committee of Northeastern University. Listeners gave informed consent. They were paid USD 10 per hour and were told they could leave the study at any time without loss of payment. They were informed that the study was undertaken to facilitate audiometry, but not that it was a study of focusing.

*Apparatus.* Listeners sat in a sound-attenuated booth (Eckel Industries) and heard sounds generated by a Tucker-Davis (Alachua, FL, USA) TDT System III signal processor (RP2.1) sampled at a rate of 48.83 kHz. A microcomputer (Dell Optiplex GX270; Dell Computers, Round Rock, TX, USA) programmed in Pascal controlled the processor and collected data via a response box (TDT BBOX). Sounds were sent through a headphone driver (TDT HB7) to Sony MDR-V6 cushioned headphones (Sony Corp, Tokyo, Japan). Waveforms, frequency content, and distortion were checked with a wave-analyzer and an oscilloscope. Digital filters were used to generate new wide-band 50 dB SPL noise on every trial, which resembled an analogue bi-quad bandpass filter flat from 200 to 6000 Hz.

*Stimuli.* Trials began with a warning signal appearing on a visual display screen. Half a second later, a 40 ms cue tone appeared in 'cued' trial blocks. In 'no-cue' blocks, the cue was set to zero amplitude to maintain timing by the program. The cue or silent cue interval was followed after 82 ms by a sinusoidal tone of 40 ms duration in half the trials, or no signal in the remaining trials. The 40 ms cues and signals were gated by cosine ramps (5.6 ms rise and 6.4 ms fall ms times), so each was 52 ms *in toto*.

Tone duration was 40 ms to reduce the chance that, in *var*, the listener could shift attention during the signal. An even briefer tone might reduce this chance even further, but at a cost; the attention band matches the critical band (the CB) for long duration tones but is considerably wider for 5 ms tones [17] and still somewhat wider even for the 40 ms tones used here [15]. Thus, to keep the attention band within reasonable limits, signal duration was not reduced to below 40 ms. The spectrum level of the 50 dB SPL broadband noise, namely 12.44 dB from 570 to 3400 Hz, was also chosen to be low since the attention band, unlike the CB, widens at higher levels [13].

*Procedure.* Signal and no-signal trials were intermixed at random, and listeners reported whether the signal was present or not (a single interval 'Yes/No' task). Blocks comprised 110 trials each. The cue condition was alternated after every trial block. A single-interval method was used rather than 2IFC to ensure that the cue signal interval was the same on every cued trial. Unless voluntarily delayed by the listener, the next trial began 500 ms after the response. In *const*, the same frequency tone was presented in every one of 55 signal trials. In *var*, the signal tone was selected at random from the same list of 11 frequencies as were employed in *const*, with each frequency appearing 5 times. These frequencies were spaced at least one CB apart [9].
*Thresholds.* To accommodate slight variations in sensitivity across sessions, the level of the middle (1266 Hz) tone was adjusted in 1 dB increments to reach 89% correct at the start of each session. All other signal tones were adjusted by adding the same amount to each listener's no-cue thresholds. These were measured in 3 initial sessions, for the 50 dB noise, at each of the 11 frequencies from 632 to 3140 Hz, using an adaptive procedure that converged on 79% correct. Table 2 lists the 11 frequencies and mean thresholds in dB SPL for each listener. (The expected increase in threshold from 632 to 3140 Hz, from the ratio of their ERBs, is 5.9 dB; actual increases for NA, TA and MA were 4.1, 7.3, and 3.3 dB.) The mean threshold at 1082 Hz was 35 dB, close to the 39 dB found by Baer, Moore and Glasburg [35] for 40 ms, 1000 Hz tones heard in background noise that was 3 dB higher than that used here. When presented, cues were 12 dB above the levels in Table 2. Experimental blocks were run after the levels were adjusted. At the start of each block, five additional (unrecorded) trials were run to notify the listener of the current condition.

<table><caption>**Table 2.** Thresholds of 40 ms tones in 50 dB SPL noise for each listener and frequency.</caption><thead><tr><th>Hz</th><th>632</th><th>767</th><th>917</th><th>1082</th><th>1266</th><th>1481</th><th>1720</th><th>1994</th><th>2318</th><th>2693</th><th>3140</th></tr></thead><tbody><tr><th>NA</th><td>36.2</td><td>36.3</td><td>36.3</td><td>36.3</td><td>36.1</td><td>36.0</td><td>36.8</td><td>37.6</td><td>38.4</td><td>39.2</td><td>40.3</td></tr><tr><th>TA</th><td>31.6</td><td>32.4</td><td>33.5</td><td>33.2</td><td>33.4</td><td>35.5</td><td>36.7</td><td>36.1</td><td>37.5</td><td>37.3</td><td>38.9</td></tr><tr><th>MA</th><td>36.2</td><td>36.6</td><td>36.9</td><td>37.2</td><td>37.6</td><td>38.0</td><td>38.3</td><td>38.6</td><td>38.9</td><td>39.2</td><td>39.5</td></tr></tbody></table>

*Design.* The four conditions obtained by crossing const versus var with no cue versus pre-cue were run on each of the 11 frequencies, at five signal levels ($S_{dB}$) spaced 2 dB apart. The order of signal levels, and of frequencies in *var*, was randomized within blocks. The order of conditions was randomized across blocks. Each listener ran in hour-long sessions over several weeks for a total of 60 blocks or 6600 experimental trials. The first week was devoted to obtaining no-cue signal thresholds. There followed two weeks of practice, during which the listeners became familiar with the Yes/No task, and with both cue and no-cue conditions in both *const* and *var*, before the experiment was run.

### 3. Results

Hit and false alarm rates in each trial block were converted to $d' = z(Phit) - z(Pfa)$. In *const*, hit and false alarm rates were recorded for each frequency in each trial block. In *var*, the common false alarm rate was applied to each frequency; only the hit rates were frequency-specific. Detectabilities based on this assumption correlated well ($r = 0.98$) in pilot work with 2AFC detectabilities over the range of frequencies used, when measured with no cue, implying that listeners adopted the same criteria independent of frequency. This is not surprising since in *var*, the upcoming frequency was unknown. Note that 2AFC trials are normally preferred but cannot be used with a cue without unbalancing the two intervals.

The values of $d'$ were tabulated for each signal level for each listener, signal frequency, and condition. Individual d's were between 0.03 and 4.0 (as logs, between -1.52 and +0.60), so errant floor and ceiling effects are possible. The d's and signal levels were therefore averaged into frequency groups, low (632-917 Hz), middle (1082-1720 Hz), and high (1994-3140 Hz), no consistent variation with frequency being apparent within each group. The rows in Table 3 give $log_{10}(d')$, dB SPL, and dB SL, for each listener and frequency group (low fr, mid fr, and hi fr). The condition is specified by column headings from left to right, Cue *var*, Cue *const*, No cue *var*, and No cue *const*. Signal level in dB SL was obtained by subtracting from dB SPL the shifts given in Col. 1 below each listener. Thus, for TA low fr. in data row 1, dB SPL in cue var (row 1, col 2) was 38.90 and the shift (row 2, col 1) was 34.08, so dB SL (row 1, col 4) was 38.90 - 34.08 = 4.82. For TA low fr. cue const, dB SPL (row 1, col 5) was 39.0 and the shift (row 3, col 1) was 32.11, so dB SL (row 1, col 7) was 39.0 - 32.11 = 6.89, and so forth.
<table><caption>**Table 3.** Column 1 identifies the listener and frequency group. Successive columns identify dB SPL, log(d'), and signal dB SL, under the headings for the condition (cue var, cue const, no cue var, and no cue const). d's were obtained at 5 or 6 signal levels with the cue, and 4 or 5 signal levels with no cue.</caption><thead><tr><th rowspan="2">Listener</th><th colspan="2">Cue var</th><th colspan="2">Cue const</th><th colspan="2">No Cue var</th><th colspan="2">No Cue const</th></tr><tr><th>Fr.group</th><th>dB SPL</th><th>log(d)</th><th>dB SL</th><th>dB SPL</th><th>log(d)</th><th>dB SL</th><th>dB SPL</th><th>log(d')</th><th>dB SL</th></tr></thead><tbody><tr><td>TA Low</td><td>38.90</td><td>0.34</td><td>4.82</td><td>39.0</td><td>0.51</td><td>6.89</td><td>38.00</td><td>0.41</td><td>3.90</td><td>35.00</td><td>0.58</td><td>4.21</td></tr><tr><td>34.08</td><td>36.90</td><td>0.28</td><td>2.82</td><td>37.0</td><td>0.37</td><td>4.89</td><td>34.90</td><td>-0.08</td><td>0.80</td><td>33.00</td><td>0.55</td><td>2.21</td></tr><tr><td>32.11</td><td>34.90</td><td>0.18</td><td>0.82</td><td>35.0</td><td>0.27</td><td>2.89</td><td>32.90</td><td>-0.01</td><td>-1.20</td><td>31.00</td><td>0.29</td><td>0.21</td></tr><tr><td>34.10</td><td>32.90</td><td>-0.21</td><td>-1.18</td><td>33.0</td><td>0.08</td><td>0.89</td><td>29.90</td><td>-0.72</td><td>-3.20</td><td>29.00</td><td>-0.54</td><td>-1.79</td></tr><tr><td>30.79</td><td>30.90</td><td>-0.25</td><td>-3.18</td><td>31.0</td><td>-0.12</td><td>-1.11</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>TA Med</td><td>43.80</td><td>0.43</td><td>6.80</td><td>42.0</td><td>0.54</td><td>5.47</td><td>39.80</td><td>0.54</td><td>7.31</td><td>39.00</td><td>0.53</td><td>4.72</td></tr><tr><td>36.99</td><td>41.80</td><td>0.41</td><td>4.80</td><td>40.0</td><td>0.36</td><td>3.47</td><td>37.80</td><td>0.32</td><td>5.31</td><td>37.00</td><td>0.48</td><td>2.72</td></tr><tr><td>36.53</td><td>39.80</td><td>0.32</td><td>2.80</td><td>39.0</td><td>0.32</td><td>2.47</td><td>35.80</td><td>0.26</td><td>3.31</td><td>35.00</td><td>0.43</td><td>0.72</td></tr><tr><td>32.49</td><td>37.80</td><td>0.12</td><td>0.80</td><td>38.0</td><td>0.22</td><td>1.47</td><td>33.80</td><td>-0.52</td><td>1.31</td><td>33.00</td><td>-0.09</td><td>-1.28</td></tr><tr><td>34.28</td><td>35.80</td><td>0.05</td><td>-1.20</td><td>37.0</td><td>-0.04</td><td>0.47</td><td></td><td></td><td></td><td>31.00</td><td>-0.77</td><td>-3.28</td></tr><tr><td></td><td>33.80</td><td>-0.45</td><td>-3.20</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>TA High</td><td>47.96</td><td>0.46</td><td>8.43</td><td>45.0</td><td>0.47</td><td>3.59</td><td>43.96</td><td>0.58</td><td>9.51</td><td>45.00</td><td>0.58</td><td>4.73</td></tr><tr><td>39.53</td><td>45.96</td><td>0.41</td><td>6.43</td><td>43.0</td><td>0.26</td><td>1.59</td><td>41.96</td><td>0.44</td><td>7.51</td><td>43.00</td><td>0.54</td><td>2.73</td></tr><tr><td>41.41</td><td>43.96</td><td>0.38</td><td>4.43</td><td>41.0</td><td>0.07</td><td>-0.41</td><td>39.96</td><td>0.34</td><td>5.51</td><td>41.00</td><td>0.38</td><td>0.73</td></tr><tr><td>34.45</td><td>41.96</td><td>0.17</td><td>2.43</td><td>39.0</td><td>-0.45</td><td>-2.41</td><td>37.96</td><td>0.19</td><td>3.51</td><td>39.00</td><td>-0.40</td><td>-1.27</td></tr><tr><td>40.27</td><td>39.96</td><td>0.06</td><td>0.43</td><td>37.0</td><td>-1.02</td><td>-4.41</td><td></td><td></td><td></td><td>37.00</td><td>-0.52</td><td>-3.27</td></tr><tr><td></td><td>37.96</td><td>-0.18</td><td>-1.57</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>NA Low</td><td>42.28</td><td>0.41</td><td>4.56</td><td>44.0</td><td>0.55</td><td>6.02</td><td>38.28</td><td>0.54</td><td>2.01</td><td>37.00</td><td>0.38</td><td>2.39</td></tr><tr><td>37.72</td><td>40.28</td><td>0.31</td><td>2.56</td><td>42.0</td><td>0.50</td><td>4.02</td><td>36.28</td><td>0.24</td><td>0.01</td><td>35.00</td><td>0.23</td><td>0.39</td></tr><tr><td>37.98</td><td>38.28</td><td>0.11</td><td>0.56</td><td>40.0</td><td>0.24</td><td>2.02</td><td>34.28</td><td>-0.77</td><td>-1.99</td><td>33.00</td><td>-0.33</td><td>-1.61</td></tr><tr><td>36.27</td><td>36.28</td><td>-0.44</td><td>-1.44</td><td>38.0</td><td>0.05</td><td>0.02</td><td>32.28</td><td>-1.50</td><td>-3.99</td><td>31.00</td><td>-0.77</td><td>-3.61</td></tr><tr><td>34.61</td><td>34.28</td><td>-0.12</td><td>-3.44</td><td>36.0</td><td>-0.28</td><td>-1.98</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td>32.28</td><td>-0.50</td><td>-5.44</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>NA Med</td><td>42.29</td><td>0.38</td><td>3.42</td><td>42.0</td><td>0.32</td><td>5.53</td><td>38.29</td><td>0.39</td><td>1.78</td><td>39.00</td><td>0.41</td><td>3.54</td></tr><tr><td>38.86</td><td>40.29</td><td>0.18</td><td>1.42</td><td>41.0</td><td>0.20</td><td>4.53</td><td>36.29</td><td>0.20</td><td>-0.22</td><td>37.00</td><td>0.33</td><td>1.54</td></tr><tr><td>36.47</td><td>38.29</td><td>0.03</td><td>-0.58</td><td>39.0</td><td>0.20</td><td>2.53</td><td>34.29</td><td>-0.78</td><td>-2.22</td><td>35.00</td><td>-0.19</td><td>-0.46</td></tr><tr><td>36.50</td><td>36.29</td><td>-0.24</td><td>-2.58</td><td>37.0</td><td>0.05</td><td>0.53</td><td>32.29</td><td>-1.22</td><td>-4.22</td><td>33.00</td><td>-0.27</td><td>-2.46</td></tr><tr><td>35.46</td><td>34.29</td><td>-0.88</td><td>-4.58</td><td>35.0</td><td>-0.12</td><td>-1.47</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td>32.29</td><td>-0.58</td><td>-6.58</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>NA High</td><td>44.84</td><td>0.42</td><td>4.65</td><td>44.0</td><td>0.34</td><td>3.14</td><td>40.84</td><td>0.44</td><td>1.54</td><td>39.00</td><td>0.33</td><td>1.30</td></tr><tr><td>40.19</td><td>42.84</td><td>0.31</td><td>2.65</td><td>42.0</td><td>0.07</td><td>1.14</td><td>38.84</td><td>0.04</td><td>-0.46</td><td>37.00</td><td>-0.17</td><td>-0.70</td></tr><tr><td>40.86</td><td>40.84</td><td>0.09</td><td>0.65</td><td>40.0</td><td>0.00</td><td>-0.86</td><td>36.84</td><td>-0.97</td><td>-2.46</td><td>35.00</td><td>-0.70</td><td>-2.70</td></tr><tr><td>39.31</td><td>38.84</td><td>-0.21</td><td>-1.35</td><td>38.0</td><td>-0.41</td><td>-2.86</td><td>34.84</td><td>-1.45</td><td>-4.46</td><td>33.00</td><td>-1.40</td><td>-4.70</td></tr><tr><td>37.70</td><td>36.84</td><td>-0.28</td><td>-3.35</td><td>36.0</td><td>-0.45</td><td>-4.86</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>MA Low</td><td>38.57</td><td>0.44</td><td>3.47</td><td>41.0</td><td>0.44</td><td>8.23</td><td>40.57</td><td>0.55</td><td>7.06</td><td>39.00</td><td>0.55</td><td>7.00</td></tr><tr><td>35.09</td><td>36.57</td><td>0.41</td><td>1.47</td><td>39.0</td><td>0.39</td><td>6.23</td><td>38.57</td><td>0.38</td><td>5.06</td><td>37.00</td><td>0.52</td><td>5.00</td></tr><tr><td>32.77</td><td>34.57</td><td>0.15</td><td>-0.53</td><td>37.0</td><td>0.32</td><td>4.23</td><td>36.57</td><td>0.38</td><td>3.06</td><td>35.00</td><td>0.47</td><td>3.00</td></tr><tr><td>33.50</td><td>32.57</td><td>-0.59</td><td>-2.53</td><td>35.0</td><td>0.22</td><td>2.23</td><td>34.57</td><td>0.01</td><td>1.06</td><td>33.00</td><td>0.32</td><td>1.00</td></tr><tr><td>32.00</td><td>30.57</td><td>-0.90</td><td>-4.53</td><td>33.0</td><td>-0.08</td><td>0.23</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>MA Med</td><td>39.79</td><td>0.48</td><td>4.82</td><td>41.0</td><td>0.51</td><td>5.08</td><td>41.79</td><td>0.53</td><td>4.84</td><td>39.00</td><td>0.58</td><td>3.55</td></tr><tr><td>34.97</td><td>37.79</td><td>0.39</td><td>2.82</td><td>39.0</td><td>0.44</td><td>3.08</td><td>39.79</td><td>0.44</td><td>2.84</td><td>37.00</td><td>0.46</td><td>1.55</td></tr><tr><td>35.92</td><td>35.79</td><td>0.15</td><td>0.82</td><td>37.0</td><td>0.24</td><td>1.08</td><td>37.79</td><td>0.36</td><td>0.84</td><td>35.00</td><td>0.22</td><td>-0.45</td></tr><tr><td>36.95</td><td>33.79</td><td>-0.26</td><td>-1.18</td><td>35.0</td><td>-0.02</td><td>-0.92</td><td>35.79</td><td>-0.34</td><td>-1.16</td><td>33.00</td><td>-0.78</td><td>-2.45</td></tr><tr><td>35.45</td><td>31.79</td><td>-0.30</td><td>-3.18</td><td>33.0</td><td>-0.50</td><td>-2.92</td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>
<table><caption>**Table 3.** *Cont.*</caption><thead><tr><th rowspan="2">Listener</th><th colspan="2">Cue var</th><th colspan="2">Cue const</th><th colspan="2">No Cue var</th><th colspan="2">No Cue const</th></tr><tr><th>Fr.group</th><th>dB SPL</th><th>log(d)</th><th>dB SL</th><th>dB SPL</th><th>log(d)</th><th>dB SL</th><th>dB SPL</th><th>log(d')</th><th>dB SL</th></tr></thead><tbody><tr><td>MA High</td><td>41.03</td><td>0.42</td><td>3.83</td><td>41.0</td><td>0.38</td><td>3.05</td><td>43.03</td><td>0.47</td><td>5.93</td><td>43.00</td><td>0.60</td><td>5.88</td></tr><tr><td>37.20</td><td>39.03</td><td>0.20</td><td>1.83</td><td>39.0</td><td>0.23</td><td>1.05</td><td>41.03</td><td>0.37</td><td>3.93</td><td>41.00</td><td>0.51</td><td>3.88</td></tr><tr><td>37.95</td><td>37.03</td><td>-0.03</td><td>-0.17</td><td>37.0</td><td>-0.11</td><td>-0.95</td><td>39.03</td><td>0.14</td><td>1.93</td><td>39.00</td><td>0.25</td><td>1.88</td></tr><tr><td>37.10</td><td>35.03</td><td>-0.30</td><td>-2.17</td><td>35.0</td><td>-0.54</td><td>-2.95</td><td>37.03</td><td>-0.41</td><td>-0.07</td><td>37.00</td><td>0.07</td><td>-0.12</td></tr><tr><td>37.12</td><td>33.03</td><td>-0.39</td><td>-4.17</td><td>33.0</td><td>-0.65</td><td>-4.95</td><td></td><td></td><td></td><td>35.00</td><td>-0.34</td><td>-2.12</td></tr></tbody></table>

Regressions of $log_{10}(d')$ against $S_{dB}$, as in Equation (2), were conducted separately for each frequency group, to determine if the slopes varied systematically with frequency. They did not, in agreement with Green [1], as shown by the averaged slopes plotted in Figure 1. Critically, the mean slope without a cue was 0.16 in *const* and 0.19 in *var*, close to the 0.16 slope found in Green [1] and the 0.14 slope in Dai [31], whereas the slopes with a cue averaged 0.107 in *const* and 0.102 in *var*, both close to 0.10. These data confirm Green's suggestion that the listener is uncertain about frequency in *const*, when—as in his experiment—there is no cue to guide attention. The new result is that *with* a cue, the listener is very close to an ideal detector of signal energy (slope: 0.10).

<figure><img src="image_18.png" /><figcaption>**Figure 1.** Mean slopes of $log(d')$ versus dB in each condition: frequency uncertain ('var') or certain ('const') with no cue or validly cued 82 ms prior to the signal. Frequencies were in the low, middle, or high ranges (see text). Bars show $\pm$ SE of the mean.</figcaption></figure>

This paper could stop here, given that the data—with a cue—conformed to Equation (1). However, data were also pooled across frequencies using the method of Green [1] with data shifted horizontally so $d' = 1$ ($log_{10} d' = 0$) at 0 dB SL. (Without shifting, the dependence of dB SPL on threshold scatters the data.) Shifts (given in Table 3, col 1) were obtained by dividing the intercepts by the slopes of the linear fits to the $log_{10}(d')$ versus dB SPL data, separately for each listener, frequency group, and condition. Plots of Green's type
are shown in Figures 2–4 for listeners TA (top), NA (middle) and MA (bottom). Left panels show $log_{10}$ (d') against dB SL in var—right panels, in const. There was no cue (upper panels for each listener) or a cue (lower panels). Solid lines show linear regressions following Equation (2). Data were fit with quadratic regressions (dotted lines), for which the proportionality predicted by Equation (1) is not quite correct, despite the high linear $r^2$ (see Table 4, col 5). The $r^2$ s in the last two columns of Table 4 are lowered by the additional variability from shifting, but still show that the quadratic $r^2$ exceeds the linear $r^2$ by up to 12%. It is unlikely that the quadratic fits were random as all the bows faced downwards and had the same general form.

<figure><img src="image_19.png" /><figcaption>**Figure 2.** $Log_{10}(d')$ for all frequencies, plotted against dB SL. Listener TA. Frequency was uncertain in var (**left panels**) or certain in const (**right panels**). There was no cue (**upper panels**) or a cue (**lower panels**). Solid lines show linear regressions; mild bows were fit to the quadratic regressions shown by dotted lines.</figcaption></figure>
<figure><img src="image_20.png" /><figcaption>Figure 3. As in Figure 2, for listener NA.</figcaption></figure>

<table><caption>**Table 4.** First three data columns: slopes, intercepts, and r² for linear regressions of log(d') on signal dB SL for each listener and condition, averaged over the three frequency groups. Last two columns: linear and quadratic r² for the shifted data shown in Figures 2–4.</caption><thead><tr><th rowspan="2">Listener</th><th rowspan="2">Condition</th><th colspan="4">Means over Frequencies</th><th>Shift</th><th>Shift</th></tr><tr><th>slope</th><th>inter cpt</th><th>r<sup>2</sup> lin</th><th>r<sup>2</sup> lin</th><th>r<sup>2</sup> quad</th><th>r<sup>2</sup> quad</th></tr></thead><tbody><tr><td rowspan="4">TA</td><td>var No Cue</td><td>0.073</td><td>-2.46</td><td>0.88</td><td>0.86</td><td>0.87</td><td>0.87</td></tr><tr><td>const NoC</td><td>0.165</td><td>-5.77</td><td>0.82</td><td>0.83</td><td>0.92</td><td>0.92</td></tr><tr><td>var Cue</td><td>0.084</td><td>-3.08</td><td>0.89</td><td>0.86</td><td>0.98</td><td>0.98</td></tr><tr><td>const Cue</td><td>0.110</td><td>-4.13</td><td>0.94</td><td>0.85</td><td>0.94</td><td>0.94</td></tr><tr><td rowspan="4">NA</td><td>var No Cue</td><td>0.340</td><td>-12.76</td><td>0.90</td><td>0.91</td><td>0.99</td><td>0.99</td></tr><tr><td>const NoC</td><td>0.195</td><td>-7.05</td><td>0.95</td><td>0.89</td><td>0.93</td><td>0.93</td></tr><tr><td>var Cue</td><td>0.102</td><td>-3.96</td><td>0.87</td><td>0.85</td><td>0.85</td><td>0.85</td></tr><tr><td>const Cue</td><td>0.089</td><td>-3.43</td><td>0.94</td><td>0.89</td><td>0.90</td><td>0.90</td></tr></tbody></table>
<table><caption>**Table 4.** *Cont.*</caption><thead><tr><th rowspan="2">Listener</th><th rowspan="2">Condition</th><th colspan="4">Means over Frequencies</th><th rowspan="2">Shift</th><th rowspan="2">Shift</th></tr><tr><th>slope</th><th>inter cpt</th><th>r<sup>2</sup> lin</th><th>r<sup>2</sup> lin</th><th>r<sup>2</sup> quad</th></tr></thead><tbody><tr><td rowspan="4">MA</td><td>var No Cue</td><td>0.099</td><td>-3.57</td><td>0.85</td><td>0.69</td><td>0.81</td></tr><tr><td>const NoC</td><td>0.125</td><td>-4.35</td><td>0.88</td><td>0.71</td><td>0.92</td></tr><tr><td>var Cue</td><td>0.134</td><td>-4.76</td><td>0.94</td><td>0.87</td><td>0.89</td></tr><tr><td>const Cue</td><td>0.109</td><td>-3.94</td><td>0.91</td><td>0.83</td><td>0.94</td></tr><tr><td>Mean</td><td>var No Cue</td><td>0.171</td><td>-6.26</td><td>0.88</td><td>0.82</td><td>0.89</td></tr></tbody></table>

<figure><img src="image_21.png" /><figcaption>**Figure 4.** As in Figure 2, for listener MA.</figcaption></figure>

A modification of Equation (1) to include a hard threshold, So, helps linearize these bows. Signals below the hard threshold are assumed to be inaudible. The *effective* signal is now defined as the signal level above So, so $d' = 0$ if $S < So$ and

$$d' = k(S - So)/No, \text{ for } S > So. \qquad (3)$$

The effective signal in dB, namely $20\log_{10}(S - So)$, is shifted further to the left at low than at high dBs, straightening out the bows. Figure 5 shows the quadratic fit to Green's [1]
const data (with lapsing accounted for) on the left, and the linearized curve applied to the same data with So = 6 on the right.

<figure><img src="image_22.png" /><figcaption>**Figure 5.** *Const* data from Green [1], taken from Table 1 above, plotted as log (d') versus signal dB SL (**left**), and after applying a hard threshold of 6 dB (**right**).</figcaption></figure>

The same approach was taken for the present data (Figures 2–4). A value of So = 2.5, or 8 dB SPL, straightened out the quadratic bows and provided the best-fit linear regressions when applied to all three listeners and conditions. Note that So would be smaller, approaching 0 dB SPL, for the detection of long duration tones at absolute threshold. As Green and Swets [32] point out, ideally signal detection theory presumes that there is no sensory threshold. However, there is no obvious reason for a quadratic function, and a hard threshold may be more realistic.

**4. Discussion**

Listeners differ in the extent to which they benefit from cuing the frequency of the sig-
nal and knowing the frequency in advance. One explanation for the individual differences,
as presented by Green [1], is that listeners vary in the degree to which they can voluntarily
focus on a known frequency. However, there are no independent measurements here of
focusing efficiency, so this remains speculative. A follow-up study with more listeners
would be helpful in this respect, if the ability to focus could be independently assessed,
perhaps using the probe-signal method of Greenberg and Larkin [12] or the overshoot
method of Scharf et al. [34] on the same listeners. Here, one can only conclude that for this
small sample, pre-cuing 82 ms beforehand *with a valid cue* let these listeners detect signals
based on signal energy.

Green [1] compared known frequency to unknown as one way of assessing the effect
of knowing the signal exactly by contrasting *const* with *var*. This procedure, also adopted
here, may confound exogenous attention, controlled by the stimulus, with endogenous
attention, controlled by the expectation or knowledge of the listener. In *const*, the same tone
is repeated trial after trial, which can lead to two opposite effects; the priming of one tone
on the subsequent one of the same frequency, which can increase stimulus salience, and
inhibition or fatigue due to repeated stimulation, which can reduce the salience of weak
stimuli [24]. These exogenous effects do not occur in *var*. Thus, the comparison of *const*
with *var* may not be a simple comparison of known frequency versus unknown. Separating
these two sources of attentional control would be useful.

The assumption that attention is paid equally to the signal as to the external noise
in the signal critical band (the CB) in const is plausible for long-duration tones when the
attention band has the same width as the CB. For the brief tones used here, the situation is
more complex. Reeves [15] showed that the attention band for 40 ms tones is wider than
the signal CB due to additional noise from adjacent CBs, which is progressively removed
as signal duration increases. Unexcluded noise will have reduced the uncertainty effect.
Scharf et al. [16] showed that, for 350 ms tones, the width of the attention band is equal to
the CB whether the signal is cued or not, but no such evidence exists for 40 ms tones. A
further issue is that the attention effect inferred here is untethered to other experimental
methods of controlling attention. A final problem is that the temporal evolution of the cue
and signal was ignored here, yet small differences in timing between the cue and the signal
have large effects on signal detection [26]. Indeed, listeners can pick up temporal coherence
in complex auditory streams even when paying attention elsewhere [36,37]. Knowing the
temporal dynamics of focusing may aid understanding how attention suppresses noise
and possibly enhances the signal.

**5. Conclusions**

In conclusion, uncued signals do not follow energy detection but generate steeper
slopes, which may be partially accounted for by uncertainty even when stimulus frequency
is known. Reducing uncertainty by pre-cuing, so that listeners can focus on the signal
frequency and avoid including noise from irrelevant critical bands, demonstrates that the
energy model assumed by Green [1] is correct after all. This finding comports well with
the fundamental role of attention summarized by Grossberg [7], in which resonance with
known information in long-term memory (here, pitch), followed by a successful match,
aids perception.

**Funding:** AFOSR grant FA9550-04-1-0244 to Reeves and Scharf.

**Data Availability Statement:** Data are tabulated in the paper.

**Acknowledgments:** Zhenlan Jin programmed the experiments and Jennifer Olyjarchek helped run
subjects and tabulate data. The late Bertram Scharf conceptualized the research program but did not
plan these experiments or write the paper; any errors or misinterpretations are the sole responsibility
of A.R.

**Conflicts of Interest:** The author and lab members have no conflict of interest.

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Article

# Accentuation as a Mechanism of Visual Illusions: Insights from Adaptive Resonance Theory (ART)

Baingio Pinna <fnref n="1" />*, Jurģis Šķilters <fnref n="2" /> and Daniele Porcheddu <fnref n="3" />

**Abstract:** This study introduces and examines the principle of accentuation as a novel mechanism in perceptual organization, analyzing its effects through the framework of Grossberg's Adaptive Resonance Theory (ART). We demonstrate that localized accentuators, manifesting as minimal dissimilarities or discontinuities, can significantly modulate global perceptions, inducing illusions of geometric distortion, orientation shifts, and apparent motion. Through a series of phenomenological experiments, we establish that accentuation can supersede classical Gestalt principles, influencing figure-ground segregation, shape perception, and lexical processing. Our findings suggest that accentuation functions as an autonomous organizing principle, leveraging salience-driven attentional capture to generate perceptual effects. We then apply the ART model to elucidate these phenomena, focusing on its core constructs of complementary computing, boundary-surface interactions, and resonant states. Specifically, we show how accentuation-induced asymmetries in boundary signals within the boundary contour system (BCS) can propagate through laminar cortical circuits, biasing figure-ground assignments and shape representations. The interaction between these biased signals and top-down expectations, as modeled by ART's resonance mechanisms, provides a neurally plausible account for the observed illusions. This integration of accentuation effects with ART offers novel insights into the neural substrates of visual perception and presents a unifying theoretical framework for a diverse array of perceptual phenomena, bridging low-level feature processing with high-level cognitive representations.

**Keywords:** visual perception; perceptual organization; dissimilarity; adaptive resonance theory; accentuation principle

## 1. Introduction

Kurt Koffka, one of the most influential scientists in Gestalt psychology, posed a fundamental question in perceptual science: “Why do things look as they do?” [1]. This inquiry encapsulates the essence of Gestalt psychology, which seeks to understand not just what we perceive, but the underlying mechanisms that shape our perception of the world. The principles of perceptual organization, first introduced by Max Wertheimer [2–5] (for comparative overviews and updates, see also [6,7]), now recognized as the foundational Gestalt principles, address this question by explaining how the brain organizes visual information into coherent structures. These principles have become central to vision science, offering phenomenological insights into our tendency to perceive elements within the visual field as parts of a unified whole, rather than as isolated entities.

<figure><img src="image_23.png" /></figure>

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Academic Editors: Birgitta Dresp-Langley and Luiz Pessoa

Received: 1 October 2024

Revised: 13 February 2025

Accepted: 21 February 2025

Published: 25 February 2025

**Citation:** Pinna, B.; Šķilters, J.; Porcheddu, D. Accentuation as a Mechanism of Visual Illusions: Insights from Adaptive Resonance Theory (ART). *Information 2025*, *16*, 172. https://doi.org/10.3390/info16030172

**Copyright:** © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

<footnote n="1">¹ Department of Biomedical Science, University of Sassari, 07100 Sassari, Italy</footnote>

<footnote n="2">² Laboratory for Perceptual and Cognitive Systems, Faculty of Science and Technology, University of Latvia, LV-1586 Riga, Latvia; jurgis.skilters@lu.lv</footnote>

<footnote n="3">³ Department of Economics and Business, University of Sassari, 07100 Sassari, Italy; daniele@uniss.it</footnote>

<footnote>* Correspondence: baingio@uniss.it</footnote>
Koffka's question arose from the specific inquiries addressed in Wertheimer's original work: What principles determine the appearance of objects? What governs the grouping of discrete visual elements into larger wholes? How do individual components organize to create holistic objects? What are the structured wholes that we perceive as objects? Thus, Koffka's question, "Why do things look as they do?" not only marks the advent of Gestalt psychology but also finds specific answers within the principles of perceptual organization.

Wertheimer identified several key principles: proximity, similarity, continuity, closure, symmetry, convexity, Prägnanz (simplicity), past experience, common fate, and parallelism. For instance, the proximity principle posits that, all else being equal, elements close together are perceived as part of a unified group. This principle can be illustrated by Figure 1a, where the proximity principle organizes dots along vertical and horizontal axes, resulting in a matrix of rows and columns. The equal proximities between the rows and columns facilitate a reversible perception, alternating between rows and columns, creating a strong sense of uniformity and vertical–horizontal stability. The high degree of homogeneity in the dot matrix generates a perception of a dotted surface on a gray background, despite the visibility of individual dots. At a foundational phenomenological level, the individual dots are perceived as a matrix, and at a more advanced level, as a dotted square surface. This demonstrates the different levels of phenomenological organization, from individual elements to their grouping based on proximity, culminating in the perception of a dotted square surface, thereby supporting the role of grouping principles.

<figure><img src="image_24.png" /><figcaption>**Figure 1.** Proximity and similarity principles: all else being equal, the closest and most similar elements are grouped together creating columns (a,b), rows (a,c), and oblique arrangements (d).</figcaption></figure>

Alternative groupings, such as zigzag patterns, can be formed through selective attention, although this is less likely because of the strong influence of the proximity principle. Even with intentional effort, perceiving diagonal groupings is difficult, as attention tends to focus on elements that are close together. The emphasis on rows and columns can be adjusted by altering the distances between them, thereby creating a variation in proximity (see [2–5]).

To modify the stability of Figure 1 while preserving the proximity principle, it is sufficient to introduce another type of dissimilarity between the rows and columns. In Figure 1b,c, the elements are grouped differently by reversing contrast polarity, using black and white elements on a gray background. Similar effects can be achieved by altering color, shape, or other perceptual features, although contrast polarity produces the most significant dissimilarity and change in perceptual organization due to the stark differences in luminance [8].

Subjects generally perceive alternating columns or rows consisting of white and black dots, respectively. As demonstrated in Figure 1d, the diagonal grouping of the dots can be emphasized by manipulating contrast polarity. In this case, the newly established grouping mechanism operates independently of, and even contrary to, the proximity principle, as the distances between elements along the diagonals are greater than those along the horizontal and vertical axes. According to Wertheimer, these last three groupings are governed by the similarity principle, which posits that, all else being equal, elements that are most similar
are perceived as a cohesive group. This is evident in the perceptual formation of columns, rows, and diagonal structures composed of black and white dots in Figure 1b-d.

At first glance, Wertheimer's explanation for human descriptions seems straightforward, highly plausible, and consistent with empirical observations. It is no coincidence that the principle of similarity has rarely been contested (though see [9], for a notable exception). Some researchers have explored how varying levels of similarity and discontinuity facilitate the grouping and segmentation of regions [10–16].

Building on previous phenomenological descriptions, an initial observation reveals that introducing dissimilarities through reversed contrast effectively disrupts the uniform proximities among columns and rows, thereby favoring one arrangement over another. Simultaneously, the similarity in contrast polarity among the dots helps integrate them into distinct perceptual wholes. Dissimilarities assist in the segregation and differentiation of adjacent groups, while similarities enhance the internal cohesion of dots within each group.

Under these conditions, dissimilarities and similarities work together to enhance perceptual grouping by emphasizing the differences between groups and reinforcing the homogeneity within them. Therefore, a comprehensive understanding of a perceptual whole must consider at least two complementary dynamics: dissimilarities acting as divisive boundaries that highlight discontinuities within the visual field, and similarities reinforcing internal cohesion, contributing to the prominence of the segregated object. Essentially, dissimilarities define and separate the boundaries of emergent objects, while similarities enhance the perceived uniformity of their internal structures. The segregation caused by dissimilarity introduces discontinuities within the visual field, generating new information by reducing uncertainty, which is further supported by the principle of similarity. As a result, attention is naturally drawn to this emergent information, making alternative groupings that deviate from this perceptual organization highly unlikely. In short, the discontinuities introduced by dissimilarity serve as strong attractors for attention, functioning as salient features that capture and direct its focus. Dissimilarity restructures the prior perceptual organization. Dissimilarity also explains perceptual dynamics—although perception operates according to the canonical grouping principles, it is just a part of the overall dynamics of perceptual organization—and dissimilarity provides a new and stronger structure changing and constraining visual field.

In Figure 1, the synergistic interaction between similarity and dissimilarity, as predicted by Wertheimer, leads to the perception of rows, columns, or diagonals. But there is more. Returning to Figure 1b,c, by focusing attention on 3 × 3 sub-matrices within each global matrix, engaging in non-immediate perceptual processes, and comparing them, something not immediately apparent can be observed. This is an illusory phenomenon of elongation and widening, respectively, following the direction emerging from the grouping as reported by human subjects. Based on human descriptions, perceptual organization, contrary to Wertheimer's claim, does not end with grouping but can lead to other related phenomena.

To better visualize the illusions, observe the geometrically square patterns in Figure 2, which appear more clearly elongated or widened in the direction of the grouping. In these cases, groupings eliciting these illusions are due to both the principle of similarity and the principle of proximity. These groupings define the overall shape of the object, which in Figure 1a appears as a perfectly square matrix, and in Figure 1b, appears as a slightly vertically elongated rectangular matrix composed of alternating columns of black and white dots, and in Figure 1c as a horizontally oriented rectangular matrix composed of alternating rows of black and white dots.
<figure><img src="image_25.png" /><figcaption>**Figure 2.** Despite the shape of each pattern has a geometrical square shape, it appears as a vertical or as a horizontal rectangle following the directions of the grouping.</figcaption></figure>

These findings indicate that, in addition to grouping, which, as Wertheimer described, is undoubtedly perceptually significant, there are other forms of organization that are not necessarily linked to grouping alone. In Figure 2, the connection to grouping is evident, just as it is in the conditions illustrated in Figure 3, where the synergistic relationship between similarity and dissimilarity not only influences grouping but also other shape attributes (cf. [17–19]).

<figure><img src="image_26.png" /><figcaption>**Figure 3.** Proximity and similarity principles: all else being equal, the closest and most similar elements are grouped together, creating oblique (a–c) and vertical arrangements (d).</figcaption></figure>

In addition to the deformations along the direction of grouping, there is another phenomenon, not clearly visible in Figure 1, more evidently noticeable in Figure 3, which is obtained by rotating the dot matrices of Figure 1 by 45 degrees.

Specifically, in Figure 3a,d, the matrices appear globally not as rotated squares but as diamonds, unlike the conditions illustrated in Figure 3b,c, which instead seem like squares rotated 45 degrees, the first rotated counterclockwise and the second clockwise. In the case of the diamonds, however, no orientation is perceived other than the canonical one, which belongs to the diamond, which is not a rotated square. Nevertheless, there is a substantial difference between Figure 3a,d regarding the grouping of the dots. In Figure 3a, they appear grouped according to the principle of proximity, while in Figure 3d, both black and white dots follow the larger distances along the diagonals. Thus, the appearance of Figure 3d as a diamond is significantly more pronounced compared to Figure 3a.

A similar effect, although much more pronounced, is illustrated in Figure 4, where the rotated square-diamond effect involves not only the overall matrix of elements, in this case composed of small squares, but also the individual elements themselves. More specifically, in Figure 4 (top row), the square elements are perceived as squares (left), and the diamond elements are perceived as diamonds (right). All these elements are embedded within a diamond-shaped whole, which is in both conditions perceived as a large diamond. This result is expected, as the direction of similarity emphasizes the sides of the squares on the
left and the angles of the diamonds on the right, both for the individual elements and the
overall shapes.

<figure><img src="image_27.png" /><figcaption>**Figure 4.** Squares perceived as squares (1st); diamond as diamonds (2nd); squares as diamonds (3rd) and diamonds as rotated squares (4th).</figcaption></figure>

In Figure 4 (bottom row), the squares are perceived as diamonds (left), and the di-
amonds are perceived as rotated squares (right). Similarly, the global diamond shapes
in both conditions are perceived as large rotated squares. In both cases, the direction of
grouping highlights the attribute that contrasts with the geometrical shape, angles instead
of sides, and sides instead of angles.

These preliminary results demonstrate that Wertheimer's principles, not only group
elements, but through such groupings, they determine the directions that can influence
dimensions, such as elongating and widening, as well as changing the apparent shape of
both individual elements and wholes that encompass them. More simply, we could say that
the resulting groupings accentuate directions and orientations that give rise to the shapes
of objects. Even more briefly, the groupings seem to act as direction accentuators.

The limitations of Gestalt psychology mentioned here, which will become even more
evident in the subsequent sections, are part of a broader context of the theory's shortcom-
ings. Gestalt theory is often criticized for being too descriptive, focusing on how perceptual
organization occurs (e.g., through principles like proximity, similarity, and closure) without
providing mechanistic explanations for why these phenomena happen.

The theory is seen as lacking parsimony, as it introduces multiple principles without a unifying framework to explain their interplay or underlying mechanisms. Moreover, Gestalt theory does not clearly differentiate between perception and thought, leading to ambiguity in how higher-level cognitive processes influence perceptual organization.

The theory's focus on the holistic nature of perception sometimes overlooks the role of
context and individual differences, which can significantly shape perceptual experiences.
The theory does not address the neural and computational underpinnings of perception,
which modern cognitive neuroscience has shown to be critical for understanding percep-
tual processes.

Despite these limitations, Gestalt theory's emphasis on experimental phenomenology
and the qualitative aspects of perception remains influential and valuable.

In the following sections, we will explore what has been defined as the principle
of accentuation, which, although it appears in these cases to be inextricably linked to or
entirely identified with Wertheimer's principles, is in fact largely independent of them.
Moreover, we will also examine all the illusory phenomena that can be generated from
accentuation, much beyond the perceptual grouping studied by the Gestaltists. Finally, we
will propose an explanatory theory capable of accounting for all the observed phenomena
and explaining the effectiveness of this principle.

In summary, the primary aim of this study is to investigate the principle of accentuation as an innovative mechanism in perceptual organization, exploring its effects within the framework of Grossberg's Adaptive Resonance Theory (ART).

**2. General Methods**

2.1. *Participants*

Unless otherwise noted, each experiment detailed in the following sections involved different groups of 12 undergraduate students from fields such as linguistics, literature, human sciences, architecture, and design. These participants had limited knowledge of Gestalt psychology and were unaware of the specific phenomena being investigated or the objectives of the experiments. The groups consisted of both male and female students, all with normal or corrected-to-normal vision.

## 2.2. Stimuli

The stimuli were presented on a 21" color CRT monitor (Sony GDM-F520,
1600 × 1200 pixels, 100 Hz refresh rate) (Sony Corporation, Tokyo, Japan) controlled by
a MacBook computer. The testing environment was illuminated with an Osram Daylight
fluorescent light (250 lx, 5600 K) (OSRAM Licht AG, Munich, Germany). All stimuli were
displayed on a frontoparallel plane positioned 50 cm from the observer. Participants' head
positions were stabilized using a chin rest, and stimuli were viewed binocularly.

The luminance of the white elements was 122.3 cd m⁻². Black components had a luminance value of 2.6 cd m⁻². The gray background luminance was 62.5 cd m⁻², about halfway between the white and black line components.

The stimuli were generated using a robust yet straightforward graphic design tool
like Adobe Illustrator (v. 28.4.1), making them easily replicable. The figure dimensions,
approximately 10° of visual angle, can be freely adjusted with minimal impact on their
phenomenological clarity. The core methodology for stimulus creation relied on the simple
but effective principle of introducing discontinuities or variations in specific regions near
or along an object's contour, or by forming perceptual groupings that highlight different
directional features of the stimulus or alter the symmetry of the object.

## 2.3. *Procedure*

The primary method used was a phenomenological free-report approach, where
participants were asked, “What do you see?” Separate groups of 12 observers each described
a single stimulus from the group of stimuli shown in the subsequent sections to prevent
cross-stimulus interaction and contamination. The descriptions included in the paper are
based on the spontaneous reports provided by at least 9 out of 12 participants in each group,
with edits for conciseness and representativeness. To ensure unbiased representation and
avoid interpretive bias, three independent graduate students in linguistics, who were
unaware of the hypotheses, evaluated the descriptions [17,20–22]. These descriptions were then incorporated into the text to support the development of the arguments. All reports were spontaneous, and the presentation concluded once participants completed their descriptions. Participants viewed the stimuli during their reports, and the observation time was not restricted (see also [1,23–27]).

In future studies, we plan to incorporate specific quantitative methods, such as eye-
tracking, reaction time measurements, or psychophysical tasks, to further enhance the rigor
and robustness of our approach. These methodologies will allow for a more comprehensive
analysis and provide additional insights into the phenomena under investigation.

During the experiment, participants were encouraged to reflect while observing and to perceive the stimuli in various ways. The experimenter also posed additional questions to prompt closer and deeper observation. Any potential variations that emerged during free exploration were recorded by the experimenter and are documented in the following sections. This documentation is essential for determining the optimal conditions for the emergence of the perceptions under investigation.

**3. The Principle of Accentuation**

3.1. *Towards a Single Dot Accent*

By modifying Figure 3 as shown in Figure 5, through a gradual reduction in perceptual
groupings until reaching extreme conditions, such as those where the number of white
dots is reduced to two, positioned at opposite corners of the dot matrix, or even to a single
dot, as seen in the first and second stimuli of the second row in Figure 5, the change in
the global shape from a diamond to a rotated square can be easily observed, similar to the
effects perceived in Figure 3.

<figure><img src="image_28.png" /><figcaption>**Figure 5.** Rotated squares and diamonds from accentuation.</figcaption></figure>

It is noteworthy that these effects persist even when a white dot of varying size is
added outside the complete matrix or when one of the dots in the matrix is removed.
Consequently, if the dissimilarity appears on one side of the matrix, a rotated square is
perceived; if it appears at a corner or adjacent to it, a diamond is perceived. It seems as
though the dissimilarities accentuate either the sides or the corners, thus emphasizing the
flattening effect (sidedness) due to the phenomenological characteristics of the sides or the
sharpness (pointedness) inherent to the corners and vertices of the matrix [17–19].
Note also that the grouping of the dots follows the accentuated directionality rather than proximity or similarity, as suggested by Wertheimer. Regarding similarity, the presence of a single dot or an external dot, even if of opposite polar contrast, should not fall under the principle of similarity. On the contrary, that single dot acts as a dissimilar element entirely independent of similarity (cf. [17–19]).

The rotated square-diamond phenomenon can also be accentuated as illustrated in Figure 6. In this case as well, the grouping of the individual dots follows the accentuated property and direction, that is, the rotated square or the diamond. This entails that it is the accentuation that determines the grouping of the individual dots, independently of and against Wertheimer's principles [19]. Accentuation provides the configuration of elements with an entirely new structure.

<figure><img src="image_29.png" /><figcaption>**Figure 6.** Rotated squares and diamonds from accentuation.</figcaption></figure>

It is important to clarify the difference between a square rotated 45 degrees and a diamond. Geometrically, they are the same, but phenomenologically, they are entirely different, possessing different figural meanings. The former highlights and brings to the forefront one of its characteristics, namely being a square, and thus flattened along its side, while the latter accentuates the angles, the points, and the vertexes. A square is, in fact, composed of sides and angles that can be alternately emphasized through the methods we have discussed so far, as well as many others that we will explore later. It is as if the square contains within it two possible forms, one of which, independent of the other, can be brought out and emphasized through the principle of accentuation. We will return to this point in the next section with more complex figures involving small squares and dots.

Geometric squares can be easily switched in diamonds when their diagonals are grouped based on similarity (see Figure 7). The same elements can thus appear as squares or diamonds depending on the direction of the perceptual grouping based on similarity. The effect is even more evident in Figures 8 and 9.

<figure><img src="image_30.png" /><figcaption>**Figure 7.** The same checks within the same whole perceived as squares or diamonds.</figcaption></figure>
<figure><img src="image_31.png" /><figcaption>**Figure 8.** Diamonds perceived as rotated squares and squares perceived as diamonds.</figcaption></figure>

<figure><img src="image_32.png" /><figcaption>**Figure 9.** Squares perceived as diamonds and diamonds perceived as rotated squares.</figcaption></figure>

In Figure 8, the stimuli on the left half, despite containing geometrically identical elements, can appear as rotated squares in the upper part and as diamonds in the lower part. The opposite occurs in the stimuli illustrated on the right half, where, despite being identical to each other, they appear as diamonds in the upper part and squares in the lower part. It is worth noting the secondary effects of phenomenal transparency that arise due to the grouping of the contrast belonging to the individual elements, effects that are nonetheless unrelated to accentuation, which is the sole cause of the shape change [28].

A square-diamond switch, similar to those just described, is shown in Figure 9, where the spheres and their elements are identical in pairs along the vertical axis.

As with the dot matrices previously discussed, it is possible to achieve extreme conditions that are as independent as possible from the principle of similarity in the case of geometric squares, as illustrated in Figures 10–12.
<figure><img src="image_33.png" /><figcaption>**Figure 10.** Diamonds and rotated squares from accentuation.</figcaption></figure>

<figure><img src="image_34.png" /><figcaption>**Figure 11.** By reducing the vertical and oblique arrangements to only two checks placed at the antipodes, diamonds and rotated squares from accentuation.</figcaption></figure>
<figure><img src="image_35.png" /><figcaption>**Figure 12.** The white circles accentuate sides or angles within the checks and the large matrices thus switching their appearance as diamonds or rotated squares.</figcaption></figure>

Figure 13 demonstrates the same effects, even in grids formed only by outlines, which can appear either crossed or parallel depending on the direction accentuated by similarity [28].

<figure><img src="image_36.png" /><figcaption>**Figure 13.** Crossed or parallel lines induced on a lattice by dissimilarity.</figcaption></figure>

These changes in shape and orientation can cause noticeable distortions within the same homogeneous structure, especially when different directions along the sides or diagonals are accentuated, as demonstrated by Figure 14. Although the two matrices are identical and perfectly regular, they appear divided into two halves, with one part distorted along its diagonal while the other is oriented according to the vertical and horizontal directions of the space. The overall deformation is very evident. The deformation of individual elements and the global matrix in the two cases illustrated in Figure 15 is equally strong. The diamonds on the left appear wider and less tilted than those on the right. The same occurs with the two matrices as a whole, which are deformed differently.

So far, we have worked with groups of elements arranged in a matrix form. This implies a connection that is not entirely severed from Wertheimer's grouping principles, although the reduction to the extreme cases illustrated has demonstrated the possibility of making accentuation completely independent, allowing it to become a fully-fledged principle in its own right.
<figure><img src="image_37.png" /><figcaption>**Figure 14.** The same checks perceived as squares or diamonds.</figcaption></figure>

<figure><img src="image_38.png" /><figcaption>**Figure 15.** The rhombi on the left are perceived to be bigger and less tilted and distorted than those on the right. Even the two matrices as a whole appear distorted in different ways.</figcaption></figure>

To achieve a complete detachment, it is necessary to work on a single element rather than on groups. To this end, consider the conditions illustrated in Figure 16, where the introduction of dissimilar elements inside (black rectangles) or outside (dots) geometric squares results in the accentuation of direction or position relative to the geometric shapes along the sides or angles, giving rise to the rotated square-diamond effect.

<figure><img src="image_39.png" /><figcaption>**Figure 16.** Accentuation of diamond (first and last rows) and rotated squares shapes (second and third rows) by introducing dissimilarity.</figcaption></figure>
If we now replace the geometric squares with geometric diamonds or rhombuses (Figure 17), the accentuation leads to the perception of diamonds (first row) and parallelograms (second row), phenomenologically very different shapes despite being geometrically identical. The same effect is achieved if, instead of internal rectangles, external dots are used (not illustrated).

<figure><img src="image_40.png" /><figcaption>**Figure 17.** The rhombi of the two rows cannot be perceived as geometrically equal. The shapes of the second row are more clearly perceived as parallelograms.</figcaption></figure>

The different degrees of distortion due to the presence of dots in different positions, which accentuate the different directions of the same figure, can be observed in Figure 18, where the overall deformation of the parallelogram is more intense in the first condition and partially compensated in the second.

<figure><img src="image_41.png" /><figcaption>**Figure 18.** Different degrees of deformation of the same parallelogram.</figcaption></figure>

Before proceeding, it is worth introducing some reflections. From a more theoretical perspective, accentuation is indeed a principle of organization, grouping, and form and structure generator that operates independently of Wertheimer's classic grouping principles. Moreover, it relies on dissimilarity, discontinuity, and the presence of changes. As dissimilarities, accents will be more effective on the elements and properties of both elementary and global objects to which they are applied, the more dissimilar they are. Dissimilarity implies phenomenal salience, and therefore greater power in attracting attention to a region or direction of the visual field where the processes of shape formation and object grouping are particularly decisive [19,28].

Finally, it is worth listing the effects obtained so far from the use of accentuators. Through the previous results, we observed elongations and widenings, shape switches between square-diamond or rhombus-parallelogram, deformations, and the segregations of portions within homogeneous wholes. These are not the only possible variations; we will explore others in the next section.
## 3.2. Illusions from Accents

In Figure 2, we observed how the accentuated direction suggested by grouping induces the global deformation of the set of elements. This result appears to be contrary to what is observed in the Helmholtz square illusion (Figure 19; see also [29]), where a square composed of horizontal segments appears taller and slimmer compared to the same square composed of vertical segments, which appears wider, like a sort of horizontal rectangle. In short, a square segmented horizontally appears as a kind of vertical rectangle, while one segmented vertically appears as a horizontal rectangle. If we now reconsider the conditions illustrated in Figure 2, we can notice an apparently opposite effect, where vertical groupings create a vertical rectangle, while horizontal groupings elicit an illusory horizontal rectangle. Seemingly, there is an unacceptable contradiction between these two conditions.

<figure><img src="image_42.png" /><figcaption>**Figure 19.** Helmholtz square illusion.</figcaption></figure>

However, as suggested, the contradiction is only apparent. In fact, it is not just the horizontal or vertical segments of the Helmholtz square that determine the perceived shape, but the arrangement or grouping of elements that holistically defines the form. Whether they are segments, dots, squares, or diamonds, the result remains the same. Thus, since the arrangement of the horizontal segments in the Helmholtz square is vertical, the result will be a vertical rectangle. The opposite occurs in the case of vertical segments arranged horizontally. This is equivalent to the conditions illustrated in Figure 2 [18].

If all this is true, then we could easily achieve an even more intense effect of elongation or widening than those shown both in Figure 2 and in the Helmholtz square. It is simply a matter of multiplying the accentuated directions resulting from the grouping of elements, as illustrated in Figure 20 [18,19].

More specifically, the first row of Figure 20 presents two conditions where the horizontal and vertical directions and arrangements have been duplicated, respectively. In the top-left condition, two horizontal directions have been accentuated due to the arrangement of the individual vertical segments and the two horizontal bands composed of black and white vertical segments. In the top-right condition, the vertical directions have been accentuated instead. The result is a phenomenon of illusory widening and slimming that is much more intense than what is observed in the Helmholtz square. This effect can be further accentuated and amplified by adding elements (the red rectangle and the red dots).
that further reinforces the directionality accentuated by the previous figural conditions (second and third row of Figure 20).

<figure><img src="image_43.png" /><figcaption>**Figure 20.** Improved Helmholtz square illusion.</figcaption></figure>

Accents can therefore modify the shape to which they are connected, to which they belong, along the direction suggested, or the component of the figure that shows a discontinuity, dissimilarity, and, more generally, a change. This is the case with the paired conditions shown in Figure 21. Here, the individual conditions have been misaligned to prevent alignments from counteracting the illusory effect induced by discontinuity and dissimilarity or from activating visual analytical processes different from those associated with accentuation, which, after capturing attention, seems to generalize the information by spreading it globally, as we will see in the following figures. Nevertheless, even when the shapes are aligned, the effect persists unchanged.

In the first two conditions of Figure 21, the absence or cancellation of a portion of contour within the two opposite sides of the squares slims and elongates the overall square shape, in the first case, and shortens and widens the square, in the second. Similar effects are visible in the two subsequent rows, where different types of dissimilarity are present.

Similarly, we can extend the illusory phenomenon shown in the second row of Figure 20, which we can refer to as the ‘tie effect’, to other conditions. The presence of a vertical or horizontal strip (modal or amodal rectangle) causes a deformation of the square in the direction defined by the strip (Figure 22). Notice the strength of the induced deformation by analytically comparing the geometrically identical squares.
<figure><img src="image_44.png" /><figcaption>**Figure 21.** Vertical and horizontal rectangle following the directions of the accentuation.</figcaption></figure>

<figure><img src="image_45.png" /><figcaption>**Figure 22.** Vertical and horizontal rectangle following the directions of the accentuation.</figcaption></figure>
Based on these results, it is possible to suggest that the use of ties in men's clothing, as well as necklaces, pendants, and necklines in women's fashion, may serve a similar function, namely elongating and slimming, thereby making the body and posture more elegant. Moreover, it is often mistakenly believed that shirts with horizontal stripes widen the body, making it appear heavier, while vertical lines elongate it. In fact, it is exactly the opposite, as demonstrated by the Helmholtz square. However, it is possible to make the desired effects much more intense than those of the Helmholtz square by using the variations suggested in Figure 20 [19].

Other conditions that appear very different at first glance, such as the emoji faces illustrated in Figure 23, show that a gradual disappearance of the eyes and mouth along the vertical or horizontal axis causes a significant illusory change in the dimensions of the face, which in all cases consists of an identical circumference. Comparing the first and last faces in both rows, one can immediately notice the different widths of the circle, which becomes increasingly deformed vertically in the first row and horizontally in the second.

<figure><img src="image_46.png" /><figcaption>**Figure 23.** Different widths of the circle, which becomes increasingly deformed vertically in the first row and horizontally in the second.</figcaption></figure>

These last conditions suggest another illusory phenomenon of misalignment and variation in relative distance, as shown in Figure 24. Here, there are three rows of small circles, each containing a dot inside. In the first row, which serves as a control figure, the dot is placed at the center of each circle. In the second row, the dots are positioned differently, either higher or lower, but always within the circles. In the third row, the horizontal distance between the dots varies. The perceptual results are as follows: a perfect alignment of dots and circles in the first row, an apparent misalignment of the circles in the second row, and, finally, a perceived horizontal convergence or divergence of the circles in pairs in the third row, thereby altering the relative distance between them.

<figure><img src="image_47.png" /><figcaption>**Figure 24.** Illusory misalignment and variation in the relative distance of the circles.</figcaption></figure>
More generally, it is as if the enclosing elements, whether circles or squares, are pushed in the relative direction defined by the dot. If this is true, it becomes possible to achieve additional illusory phenomena, such as those in Figure 25, where the presence of a dot determines the direction of the apparent motion tendency of the ball under static conditions. It is as if the ball is following the dot and not the other way around. In the first row, the ball seems to roll to the left in the first case and to the right in the second. In the second row, it appears to move upward and downward, respectively. In the third row, it seems to rise upward or fall downward, following the dot.

<figure><img src="image_48.png" /><figcaption>**Figure 25.** The ball is following the dot.</figcaption></figure>

Thus, this implies that the accent can also determine the direction of pointing, even against the Configural Orientation Effect studied by Attneave [30] and Palmer [31] (see also [17,19]), who demonstrated that the arrangement of three equilateral triangles determines their pointing direction according to the overall configuration. In a sense, this could be considered an effect of accentuation due to their grouping, although, based on the previously presented phenomena, accentuation appears to be more of a local rather than a global phenomenon, dependent on a broader reference system. This is easily observable and therefore demonstrable in the conditions illustrated in Figure 26, where, despite the configural orientation effect pointing to the right, the equilateral triangles point upward in the first row and downward in the second.

<figure><img src="image_49.png" /><figcaption>**Figure 26.** Illusory pointing.</figcaption></figure>
There is more. If this is true, then we can make the phenomenon even more interesting, for example, by replacing the equilateral triangles with isosceles triangles synergistically oriented according to the configural orientation effect. If the accentuation of each triangle in a different direction is sufficiently noticeable, then we would expect that the shape of the isosceles triangles would also change or deform, transforming them into scalene triangles. This result is clearly demonstrated in Figure 27. Therefore, in addition to the pointing effect, there is also a shape change analogous to the switch between a rotated square and a diamond, as previously described.

<figure><img src="image_50.png" /><figcaption>**Figure 27.** Illusory pointing and the scalene effect.</figcaption></figure>

As previously mentioned, these accentuation-induced phenomena do not necessarily have to operate in each element of a configuration. It is sufficient for them to be present in a single component for the effect to propagate to all the others. This is the diffusion, filling-in or the generalization of an accentuated element that captures attention. The effect observed in this single element spreads to all the others, even across a very broad area (Figure 28). The effect can be compared to social conditions characterized by prejudice, where a single case is generalized to an entire population. Probably, the underlying processes in social contexts are analogous to those described by the principle of accentuation.

In the previous figures, we have seen how a single dot can not only modify a shape through deformation or a switch of its shape attributes, such as sides or angles, but also change the pointing direction, alignment, relative distance, and even alter the direction of apparent tendency to move despite being in a static condition. We will now explore how possible effects due to accentuation apply to different shapes beyond the square, circle, and triangle that we have worked with so far. We ask ourselves the following questions: If a square contains within it two potential forms that can be accentuated by a simple dot, what happens to an irregular quadrilateral with four different sides and four different angles? How many inner shapes can we accentuate given the diversity of its shape attributes? Based on our results, we can assume that all the present diversities or dissimilarities can give rise to different shapes, resulting in a total of eight possible forms. In Figure 29, for simplicity, three of the eight possible forms are shown, aligned in a way that contrasts with the configural orientation effect.

As can be easily observed, the elements in the first row appear as trapezoids, in the second as rhomboids, and in the third as shapes that are different from the previous ones. It is as if accentuation creates a sort of camouflage, making each shape different and almost unrecognizable compared to the actual geometric form present.
<figure><img src="image_51.png" /><figcaption>**Figure 28.** The accentuation effect of a single element spreads to all the others.</figcaption></figure>
<figure><img src="image_52.png" /><figcaption>**Figure 29.** Trapezoids, rhomboids, and other shape within the same geometrical quadrilateral.</figcaption></figure>

The phenomenon becomes even more evident when we move from a quadrilateral to a more complex irregular shape, like the first one illustrated in Figure 30, which at first glance appears as an irregularly undulating amoeboid shape, without any part being particularly prominent or more salient than others, except maybe for the left side, which is wider than the rest. However, at first sight, nothing seems to stand out significantly, with attention jumping from one part to another without lingering too long on any component. If we now add a dot, the accentuated region emerges in the foreground, characterizing all the other parts based on it (Figure 30, second row). Thanks to the dot, the irregular shape becomes a sort of organism moving in the direction of the dot, as seen in Figure 25. At the same time, all other components of each organism adapt to the properties of the accentuated part, making the illustrated shapes appear different from each other in many different attributes, not just expressively.

<figure><img src="image_53.png" /><figcaption>**Figure 30.** Different organic shapes from accentuation.</figcaption></figure>

We might ask ourselves how many possible shapes can be accentuated. Certainly, many more than those of the square and the irregular quadrilateral. One might also wonder if the dot acts as a sort of eye, thereby attributing the characteristics of a head to the region where it is applied, from which the body follows. It is likely that this attribution is indeed the case, but not because it resembles an eye, as can be demonstrated by the examples
illustrated in Figure 31, where the accents do not resemble eyes but still act similarly to the dot.

<figure><img src="image_54.png" /><figcaption>**Figure 31.** Different organic shapes from accentuation.</figcaption></figure>

The use of accents cannot only render an irregular shape unrecognizable, as illustrated in Figure 32, where the four shapes correspond to the same one rotated progressively more, but also make it easily recognizable as the same rotated shape, as demonstrated in Figure 33 [19].

<figure><img src="image_55.png" /><figcaption>**Figure 32.** Rotated irregular shapes unrecognizable from accentuation.</figcaption></figure>
<figure><img src="image_56.png" /><figcaption>**Figure 33.** Rotated irregular shapes recognizable from accentuation.</figcaption></figure>

In the same way that ties, necklaces, necklines, and other pendants elongate and slim the human body, it is highly likely that natural accentuators, known as ocelli, present in the markings of many organisms, starting with butterflies, operate to camouflage or modify the shape of the organism, helping to avoid predators while also deceiving prey, thereby improving adaptive fitness (see Figure 34) [17,19].

<figure><img src="image_57.png" /><figcaption>**Figure 34.** Accentuation in human fashion and animal markings (all pictures are sourced from publicly available sources).</figcaption></figure>

Accentuation through single points can thus produce various illusory effects since it can be assumed that they highlight the figurality of the region where they are present, along with all the phenomenal attributes associated with that region. This means that the principle of accentuation, in addition to determining Wertheimer's perceptual grouping, as shown, may also be responsible for figure-ground segregation [32]. In Figure 35, several conditions demonstrate accentuation as a principle of figure-ground segregation to be added to the well-known principles studied by Rubin [33].
<figure><img src="image_58.png" /><figcaption>**Figure 35.** The role of accentuation in figure-ground segregation.</figcaption></figure>

It is worth noting that accentuation with dots is just one of the many possible ways to accentuate the intrinsic properties of shapes. Figure 36 shows other methods to achieve similar effects to those previously discussed. What unites and makes accentuation effective, and what underlies the phenomenological notion of accentuation, is a simpler characteristic that, in harmony with Gestalt theory, has been termed dissimilarity. This corresponds to a discontinuity or the presence of a completely different element in proximity to a region carrying phenomenal attributes. Figure 36 contains discontinuities and dissimilarities of a different nature compared to those previously demonstrated.

In the first row, we once again observe the rotated square-diamond switch due to the removal of a corner and part of the upper side. In the second row, different vertices of the same pattern composed of isosceles triangles have been removed, which seem to point both locally and globally in a direction opposite to the geometrically privileged direction, that is, along the two equal sides of each small triangle and the overall composed triangle. As already observed in the case of Figure 27, this results in the isosceles triangles appearing both locally and globally as scalene triangles. In the third row, three identical octagons with the same orientation, due to the accentuation of vertices and sides in the second and third conditions, appear as two octagons rotated relative to each other, the first with vertices in the foreground and the second with sides emphasized, making them seem differently oriented.
<figure><img src="image_59.png" /><figcaption>**Figure 36.** Different kinds of accentuation.</figcaption></figure>

This last condition allows us to immediately take the next step, which addresses the question: what is the resulting effect if we accentuate different sides of the same square? The answer is demonstrated in Figure 37 [19,34]. In this case, white squares have one side accentuated in black. This is the dissimilar side and therefore responsible for the accentuation, which causes both a deformation of the squares, appearing as small rectangles oriented either vertically or horizontally, and an alternation in their orientation, up-down in the first case, and left-right in the second. In other words, while the elements of the first matrix appear alternately oriented upward and downward, as if moving in the direction suggested by the accent, those in the second matrix seem oriented and moving horizontally to the left and right. One can also notice the apparent misalignment of the small squares, as well as their deformation, as if they were small rectangles oriented vertically or horizontally.

<figure><img src="image_60.png" /><figcaption>**Figure 37.** Apparent up and down and left and right motion from accentuation.</figcaption></figure>
When shifting the gaze from one small square to another, an apparent movement is also observed, not just a tendency, but a kind of real movement or quick shift, as if the entire grid of alternating rows and columns were unstable and trying to reach an alignment that is impossible to achieve. This effect becomes even more evident if, while following the tip of a pen as it slowly moves up and down, left or right, within each matrix, one observes in peripheral vision what happens to the small squares around it. The previously described instability appears as a real movement of adjustment [19,34].

The phenomena just described can be amplified by accentuating the small squares in different ways, as shown in Figure 38 [19,34], where the accentuation involves two sides and therefore a different vertex, alternately from one small square to the adjacent one. First of all, one can notice the alternating deformation along the vertices of the individual elements, as if each were being pulled by an opposite vertex, creating a sort of rhomboid shape. Observing the figure as previously described, by focusing on the tip of a pen that slowly moves across the image, a kind of illusory movement can be noticed where each component seems to slide and partially rotate, just as if it were a real movement [19,34].

<figure><img src="image_61.png" /><figcaption>**Figure 38.** Shape deformations of the checks and apparent motion.</figcaption></figure>

The apparent movement becomes even more evident when the conditions of similarity between the small squares allow it to be transmitted from one element to another, as illustrated in Figure 39. Observing the figure using the previously described technique results in a very noticeable horizontal motion when the pen is moved vertically, and a vertical motion when it is moved horizontally. Additionally, due to the accentuation of opposite vertices in the upper half of the pattern of small squares compared to those in the lower half, a loss of geometric alignment parallelism occurs, resulting in alternating columns that form concave and convex patterns in pairs [19,34].

It is not necessary to accentuate the entire vertex to achieve a similar effect, even if it is less pronounced than the previous one (Figures 40 and 41). A single dot is sufficient to observe the apparent motion using the previously described observation technique.

If the small squares are rotated by 45 degrees and two dots are placed on opposite sides, both within each individual square and between those that compose a column and the next, other interesting effects are observed (Figure 42). First, the apparent motion
persists in a very noticeable way. Additionally, each small square appears elongated in the accentuated direction, as expected. Therefore, since they appear as small rectangles, if one were to try to visually align the elements of one column with another, perfect overlap would be impossible [19,28,34].

<figure><img src="image_62.png" /><figcaption>**Figure 39.** Global convex/concave effects and apparent motion.</figcaption></figure>

<figure><img src="image_63.png" /><figcaption>**Figure 40.** Apparent motion under more simple accentuation.</figcaption></figure>
<figure><img src="image_64.png" /><figcaption>Figure 41. Apparent motion under simple accentuation.</figcaption></figure>

<figure><img src="image_65.png" /><figcaption>Figure 42. Apparent motion and shape deformations under simple accentuation.</figcaption></figure>

Accentuation, in all its forms, proves to be particularly effective as a generator of illusions. But that is not all. Accentuation can also be useful in explaining other illusions that, at first glance, seem to be caused by phenomena and variables unrelated to accentuation. This is the case with the well-known Shepard's table [35], illustrated in Figure 43, where the tabletop on the left appears very different from the one on the right. The explanations provided so far focus on the different perspective orientations. However, reducing the stimulus
to an extreme case, as shown in the lower part of Figure 43, suggests that the accentuation
caused by pairs of dots is more than sufficient to generate the phenomenon. Figure 43a
shows the exact relative distortion of Shepard's table, a distortion that appears inverted in
Figure 43b when the accentuators are arranged in the opposite way. In Figure 43c,d, the
two parallelograms appear almost identical, with the accents balancing each other out [19].

<figure><img src="image_66.png" /><figcaption>**Figure 43.** Shepard's table from accentuation.</figcaption></figure>

One final effect, but no less important, is that accents can either facilitate or hinder the
process of reading a text. In Figure 44, several conditions are shown where, by accentuating
entire words or certain letters in different positions through polar contrast, whether or not
spaces between words are present, the reading process can be made easier or more difficult,
affecting the reading time or increasing errors. Similar results are obtained using different
colors or placing dots above individual letters. The reader is invited to try reading the text
in all the combinations presented here to confirm this effect.
<figure><img src="image_67.png" /><figcaption>**Figure 44.** Accents can either facilitate or hinder the process of reading a text.</figcaption></figure>

Given these visual illusions from accentuation, in the next section, we will explain both of them and the concept of accentuation itself in light of the Adaptive Resonance Theory (ART).
# 4. Accentuation in Light of the Adaptive Resonance Theory (ART)

## 4.1. Introduction

Stephen Grossberg's theory of visual perception stands as a cornerstone in our understanding of how the brain processes visual information, learns to recognize patterns, and generates conscious percepts. Developed over several decades, this comprehensive framework integrates multiple aspects of neural processing and has been instrumental in elucidating various phenomena in vision and cognition.

At the heart of Grossberg's theory lies the principle of complementary computing. This concept posits that the brain is organized into pairs of complementary subsystems, each specializing in different, often opposing, aspects of information processing. In vision, this principle is exemplified by two primary subsystems: the Boundary Contour System (BCS) and the Feature Contour System (FCS). The BCS is responsible for detecting and completing edges and boundaries, operating independently of contrast polarity and completing boundaries inwardly. Conversely, the FCS fills in surface properties like color and brightness, being sensitive to contrast polarity and spreading activation outwardly until stopped by boundaries. These systems, primarily associated with the interblob and blob streams in visual cortical areas V1 and V2, respectively, interact to allow the perception of coherent objects with well-defined boundaries and surfaces.

Central to Grossberg's theory is the Adaptive Resonance Theory (ART), which explains how the brain learns to categorize and recognize patterns while maintaining stability in its learned memories. ART describes a process where sensory inputs activate potential categorical representations in higher cortical areas through fast, bottom-up processing. These activated categories then send feedback signals to lower areas, representing learned expectations about the input. When bottom-up and top-down signals match sufficiently, a resonant state occurs, leading to conscious perception and learning. This mechanism elegantly solves the stability-plasticity dilemma, allowing the brain to remain plastic (able to learn new information) while also remaining stable (not forgetting previously learned information).

Grossberg's theory provides a detailed account of how visual processing is implemented in the layered structure of the visual cortex. It describes how different cortical layers perform specific functions: layer 4 receives bottom-up input and is modulated by top-down attention, layers 2/3 are the site of long-range horizontal connections involved in perceptual grouping, layer 6 provides modulatory feedback to layer 4, and layer 5 is involved in transmitting signals to other brain regions. This laminar organization explains phenomena like perceptual grouping, attention, and figure-ground segregation.

The theory extends to 3D vision and figure-ground perception through the FACADE (Form-And-Color-And-Depth) model. This model proposes that the visual system represents information at different depth levels, with 3D boundaries and surfaces interacting to create coherent percepts. Surface properties are filled in within depth-specific boundary representations, and figure-ground separation emerges from the interactions between boundaries and surfaces at different depths.

Attentional modulation plays a crucial role in Grossberg's theory. It distinguishes between spatial attention, which enhances processing in the specific regions of visual space, and object attention, which focuses on features and properties of specific objects. The concept of an attentional shroud, a form-fitting distribution of spatial attention, is introduced to explain how attention aids in object learning and recognition.

The ARTSCAN model, a further extension of Grossberg's theory, elucidates how the brain coordinates spatial and object attention during the learning and recognition of object categories. It explains processes such as view-invariant category learning, eye
movement control during object exploration, and the formation and access of invariant
object representations.

A key concept in Grossberg’s theory is that conscious experiences are the emergent
properties of resonant states in the brain. These resonances can occur between bottom–up
features and learned categorical representations (feature-category resonance), between
filled-in surface representations and spatial attention (surface-shroud resonance), and even
between cognitive and emotional processes (cognitive-emotional resonance).

The theory incorporates several learning mechanisms, including self-organizing fea-
ture maps for learning the topographic representations of sensory inputs, adaptive filters
for mapping between different levels of representation, competitive learning for categoriza-
tion and pattern recognition, and match-based learning that occurs during resonant states
to refine and stabilize learned representations.

Grossberg’s framework also addresses the temporal aspects of visual processing,
distinguishing between rapid feedforward processing and slower feedback and resonance.
It explains phenomena like visual persistence and afterimages through the dynamics of
neural activation and reset.

Importantly, this theory extends beyond vision, providing frameworks for understand-
ing speech and language processing, cognitive–emotional interactions, decision making,
action selection, working memory, and sequence learning. This broad applicability under-
scores the theory’s power in providing a unified account of how the brain sees, learns, and
adapts to its environment.

In summary, Grossberg’s theory offers a comprehensive, biologically plausible frame-
work for understanding visual perception and cognition. Its strength lies in its integration
of multiple levels of analysis, from individual neurons to network dynamics to psychologi-
cal phenomena, making it a powerful tool for explaining a wide range of perceptual and
cognitive processes.

4.2. *Applying Grossberg's Theory to Accentuation Phenomena*

Grossberg’s theory provides a comprehensive explanation for the perceptual effects
observed in Figure 1. The alternating black and white dots create strong boundary signals
in the Boundary Contour System (BCS). According to the principle of complementary
computing, these boundaries interact with the Feature Contour System (FCS) to fill in
surface properties [36]. In conditions b and c, the contrast polarity differences between
adjacent columns or rows create stronger vertical or horizontal boundaries, respectively.
This asymmetry in boundary strength leads to a bias in the resonant state formed between
bottom-up sensory inputs and top-down expectations. The Adaptive Resonance The-
ory (ART) component of Grossberg’s model suggests that this resonance reinforces the
perception of elongation in the direction of stronger boundaries [11].

The attentional mechanisms described in ARTSCAN further enhance this effect [37].
The accentuated boundaries capture spatial attention, forming an attentional shroud that
conforms to the elongated percept. This attentional modulation strengthens the resonant
state, making the illusion of elongation more pronounced and stable.

The shape deformations and rotated square-diamond effects in these figures can be
explained through the interaction of boundary completion and surface filling-in processes
in Grossberg's model [38,39]. In Figure 2, the accentuation created by contrast polarity
differences induces asymmetric activation in the BCS. This asymmetry biases the boundary
completion process, leading to a perceptual elongation of the overall shape. The FCS then
fills in surface properties consistent with these altered boundaries, reinforcing the illusion.

For the rotated square-diamond effect (Figures 3 and 4), Grossberg's theory of 3D
vision and figure-ground separation becomes relevant [40]. The accents (dots or lines) create
strong, localized boundary signals that influence the global interpretation of shape. In ART terms, these salient features rapidly activate categorical representations (corresponding to different figural meanings) of either "diamond" or "rotated square". The activated category then sends top-down expectations that enhance consistent features, creating a resonant state that stabilizes the chosen percept. The ARTSCAN model further explains how attention is deployed differently in each case. For the diamond percept, attention is drawn to the corners, while for the rotated square, it focuses on the sides. This attentional deployment reinforces the respective interpretations through feedback loops in the laminar cortical circuits described by Grossberg [41].

Figures 5–9 demonstrate how even minimal accentuation can dramatically alter perception. Grossberg’s theory explains this through the concept of “symmetry-breaking” in neural networks [42]. A single accent creates an asymmetry in the BCS activation, which is then amplified through recurrent processing in the laminar cortical circuits. The ART component of the theory is crucial here [11,39]. The accent serves as a highly salient feature that rapidly activates a specific categorical representation (e.g., “diamond” or “rotated square”). Once activated, this category sends strong top-down signals that reshape the processing of the entire figure, even in regions far from the accent. The FACADE theory’s explanation of figure-ground separation is also relevant [40]. The accent influences the assignment of border ownership, affecting how the visual system interprets the object’s shape and its relation to the background.

Grossberg’s theory offers a rich explanation for these complex phenomena of Figures 37–42. The apparent motion and shape instability arise from the interaction between boundary formation, surface filling-in, and attentional processes [43,44]. The alternating accents create ambiguous boundary signals in the BCS. As attention shifts (modeled by ARTSCAN’s attentional shroud mechanism), these ambiguous signals interact with motion processing circuits. Grossberg’s model of motion perception suggests that the visual system attempts to resolve this ambiguity by generating illusory motion percepts [45]. The shape instability can be understood through the concept of multi-stable resonances in ART [11]. Different interpretations of the stimuli compete, with slight shifts in attention or neural noise causing switches between alternative resonant states. Each state corresponds to a different perception of shape or motion.

Figure 35 demonstrates how accentuation influences figure-ground segregation, a process well explained by Grossberg’s FACADE theory [40]. The accents create asymmetric boundary signals in the BCS, which bias the assignment of border ownership. This bias propagates through the laminar cortical circuits, influencing larger-scale figure-ground assignments. The ART component of the theory explains how these local accents can have global effects. The accented regions activate specific categorical representations, which then send top-down signals that reshape the interpretation of the entire image, reinforcing the figure-ground segregation initiated by the accent.

Grossberg’s theory of 3D vision and surface perception provides a more inclusive account of the effect of Figure 43 [40]. The accents influence the BCS’s interpretation of depth cues, biasing the perception of surface orientation. This bias is then reinforced through resonant interactions between boundary and surface representations in the FACADE model. The ARTSCAN model further explains how attention is deployed differently based on the accent placement, reinforcing either the “long” or “wide” interpretation of the table [37]. This attentional deployment creates a feedback loop that stabilizes the illusory percept.

Grossberg’s ARTWORD model, an extension of ART principles to word recognition, explains the effects of Figure 44 [46]. The accents modify the saliency map of the text, altering how spatial attention (modeled by ARTSCAN) is deployed during reading. This affects the speed and accuracy of word form activation in the ventral visual stream. The
theory suggests that accents falling on key letters or word boundaries may facilitate reading by rapidly activating appropriate word categories. Conversely, accents that disrupt normal word boundaries may interfere with the resonance formation necessary for fluent reading.

In short, Grossberg's comprehensive theory provides a unifying framework for understanding the illustrated accentuation effects. By considering how accentuation interacts with boundary formation, surface filling-in, attentional processes, and categorical learning, we can elucidate the neural mechanisms underlying these compelling visual phenomena. This synthesis not only explains existing illusions but also opens avenues for predicting and testing new perceptual effects based on the interplay between local accents and global percepts.

The comparison of Grossberg's theoretical explanations with other prominent theories in visual perception and cognitive neuroscience highlights the strengths and unique aspects of his approach.

While Gestalt theory provides intuitive explanations for many perceptual phenomena, Grossberg's approach offers a more mechanistic, neurally-grounded account [47]. For instance, in explaining the accentuation effects (Figures 1-4), this theory details how the similarity principle emerges from interactions between boundary and surface processing systems, providing a neural basis for Gestalt phenomena. It bridges the gap between phenomenological descriptions (like those in Gestalt theory) and underlying neural mechanisms.

While Feature Integration Theory [48-50] focuses on attentional processes in binding features, Grossberg's theory provides a more comprehensive account of how boundaries and surfaces interact to create object percepts [51]. For example, in explaining the rotated square-diamond effect (Figures 3 and 4), this theory addresses not just feature binding but also how categorical representations influence perception through top-down feedback. It offers a more integrated view of perception, linking low-level feature processing with high-level cognition and attention.

Predictive coding [52-54] suggests that the brain constantly generates predictions about sensory inputs and updates these predictions based on prediction errors. Grossberg's Adaptive Resonance Theory (ART) provides a more detailed account of how these expectations interact with bottom-up sensory information [11]. In explaining phenomena like the apparent motion illusions (Figures 37-42), this theory not only accounts for prediction but also describes how resonant states stabilize percepts, therefore providing a more comprehensive model of how learning, memory, and perception interact, and addressing issues like the stability-plasticity dilemma that predictive coding models often overlook.

While Bayesian models (see [55]) provide a mechanistic account of perceptual inference, Grossberg's theory offers a more biologically and evolutionary plausible implementation of how these inferences might occur in neural circuits [56]. For instance, in explaining figure-ground segregation (Figure 35), this theory details how laminar cortical circuits implement processes that could be described in Bayesian terms and bridges the gap between abstract computational principles and concrete neural mechanisms, providing testable predictions about brain function. In explaining the Shepard's table illusion (Figure 43), Grossberg's theory not only accounts for the effect but also explains how it arises from interactions between 3D boundary and surface processing systems [40]. It provides a bridge between the computational power of neural networks and the biological constraints and functional organization of the brain.

Grossberg's theory is particularly strong in terms of providing biologically plausible mechanisms for how the brain might implement complex perceptual and cognitive processes; integrating attention, learning, and memory with perceptual processes in a single coherent framework; offering detailed explanations for how complementary processes (like boundary formation and surface filling-in) interact to produce coherent percepts;
and addressing fundamental issues in neural computation, such as the stability-plasticity
dilemma [46].

In conclusion, Grossberg's theory offers a powerful, biologically grounded framework for understanding visual perception and related cognitive processes. Its strengths lie in its comprehensiveness, biological plausibility, and ability to integrate multiple aspects of perception and cognition. However, these strengths also contribute to its main challenges: complexity, difficulty in empirical validation, and potential limitations in generalizability or integration with other cognitive theories.

In short, Grossberg's theory, while comprehensive and biologically grounded, faces
several challenges. Its complexity, while a strength, can be a double-edged sword, making it
difficult to fully grasp and implement. This complexity also poses challenges for experimen-
tal testing, as designing studies to conclusively test all aspects of the theory simultaneously
can be problematic. The theory's unique mathematical formalization, though rigorous,
is less widely adopted than other approaches, potentially limiting its accessibility. Com-
putationally, implementing the full model can be resource-intensive, which may restrict
its practical applications in certain contexts. In its attempt to explain a broad range of
phenomena, the theory also lacks a strong evolutionary perspective, not always clearly
accounting for why particular neural mechanisms evolved.

The choice between Grossberg’s theory and other approaches often depends on the specific research question, the level of analysis required, and the balance between comprehensiveness and simplicity needed for a particular study or application. While Grossberg’s theory provides deep macro-level insights into the mechanisms of perception and cognition, researchers may sometimes opt for simpler or more domain-specific theories when addressing particular questions or phenomena.

The complexity of Grossberg’s theory makes it not particularly straightforward to apply and generalize, even in geometrically simple cases such as those illustrated in the present work. To better address these challenges and enhance the applicability of ART principles to the study of optical-geometric illusions, the following steps would be necessary:

Development of Simplified Models: Future research should focus on creating simplified ART-based models specifically designed for analyzing visual illusions. These models should prioritize key mechanisms such as boundary formation, surface filling-in, and perceptual grouping, which are essential for explaining how illusions arise. By reducing computational complexity while preserving the core principles of ART, these models would enable more straightforward empirical testing and wider adoption.

Establishment of Practical Guidelines: To help in applying ART to illusion studies, it would be necessary to provide clear, step-by-step guidelines. These guidelines should cover critical aspects such as experimental design, parameter selection, and interpretation of results within the ART framework.

Inclusion of Illustrative Examples: Our study represents a significant step forward in bridging the gap between theoretical principles and practical applications by incorporating concrete examples of how ART can explain specific illusions. These examples serve as clear, relatable illustrations of ART's explanatory power in the context of illusions, providing researchers with actionable insights into the mechanisms underlying perceptual phenomena. By grounding ART principles visual examples, our work not only enhances understanding but also paves the way for broader adoption and further exploration of ART's potential in visual perception research.

By implementing these strategies, ART could become more accessible and applicable to
researchers studying illusions, fostering a deeper understanding of perceptual phenomena.
These efforts would not only enhance the accessibility of ART-based approaches but also
encourage a broader exploration of their potential in the field of visual perception and
illusion research.

These limitations, however, should not overshadow the significant contributions of
Grossberg’s theory to our understanding of perception and cognition. Rather, they highlight
areas where further development, simplification, or integration with other approaches could
potentially enhance its already considerable explanatory power and practical applicability.

**Author Contributions:** Conceptualization and illusions: B.P.; Methodology, Data collection and analyses: B.P., J.Š. and D.P.; Writing and approving the final manuscript: B.P., J.Š. and D.P. All authors have read and agreed to the published version of the manuscript.

**Funding:** B.P. was supported by PRIN 2022 Missione 4—Componente C2—investimento 1.1, Fondo per il PRIN—del PNRR (Finanziamento dell’Unione Europea – NextGenerationEU) for the research project by the title: “Bayesian inference reconsidered in the light of new visual illusions” CUP J53D23007730006 and PRIN 2022 PNRR Missione 4 del PNRR—Componente C2—investimento 1.1 del PNRR for the project entitled: “Can the dynamics underlying face perception help prevent gender stereotypes” CUP J53D23016900001. B.P. and J.Š. were supported by Fundamental and Applied Research project, Republic of Latvia, “Development of an integrative approach to the assessment of cognitive abilities in patients with neurodegenerative diseases of the central nervous system” (project No. lzp-2022/1-0100).

**Institutional Review Board Statement:** In accordance with local laws and institutional policies, approval from an ethics committee was not required for this study as the experiments were limited to healthy individuals and did not involve invasive methods. The study adhered fully to local regulations and institutional standards and all procedures were carried out in line with the Declaration of Helsinki.

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study. Participants did not receive course credit or financial compensation.

**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.

**Acknowledgments:** B.P. greatly acknowledges Rīga Stradiņš University and the Vice-Rector Agrita Kiopa for the visiting professor program, which gave him the chance to start and complete this research.

**Conflicts of Interest:** The authors declare no conflicts of interest.

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Article

# An ART Tour de Force on Mental Imagery: Vividness, Individual Bias Differences, and Complementary Visual Processing Streams

Amedeo D'Angiulli *, Christy Laarakker and Derrick Matthew Buchanan

Department of Neuroscience, Carleton University, Ottawa, ON K1S 5B6, Canada;
christylaarakker@cmail.carleton.ca (C.L.); matthewbuchanan@cmail.carleton.ca (D.M.B.)

* Correspondence: amedeo.dangiulli@carleton.ca

**Abstract:** Grossberg's adaptive resonance theory (ART) provides a framework for understanding possible interactions between mental imagery and visual perception. Our purpose was to integrate, within ART, the phenomenological notion of mental image vividness and thus investigate the possible biasing effects of individual differences on visual processing. Using a Vernier acuity task, we tested whether indirect estimation of relative V1 size (small, medium, large) and self-reported vividness, in three subgroups of 53 observers, could predict significant effects of priming, interference, or more extreme Perky effects (negative and positive), which could be induced by imagery, impacting acuity performance. The results showed that small V1 was correlated with priming and/or negative Perky effects independently of vividness; medium V1 was related to interference at low vividness but priming at high vividness; and large V1 was related to positive Perky effects at high vividness but negative Perky effects at low vividness. Our interpretation of ART and related modeling based on ARTSCAN contributes to expanding Grossberg's comprehensive understanding of how and why individually experienced vividness may drive the differential use of the dorsal and ventral complementary visual processing pathways, resulting in the observed effects of imagery on concurrent perception.

**Keywords:** adaptive resonance theory; imagery; perception; vividness; visual cortex; visual priming; perky effect

## 1. Introduction: ART and Vividness of Visual Mental Imagery

Grossberg's adaptive resonance theory (ART) provides a robust framework for understanding how imagery-perception interactions occur in complementary dorsal and ventral cortical visual processing. The objective of this paper was to integrate within ART a phenomenological component based on the classic psychological notion of mental image vividness [1] to supplement the “how” explanation with a “why” explanation describing processing biases which are associated with individual anatomical differences.

The vividness of mental imagery has been studied for more than a century in psychology and is traditionally defined as a quasi-perceptual (predominantly *visual*) phenomenological experience [2–5]. In his comprehensive theory combining phenomenological and behavioral perspectives, Marks [3,6] posits vividness as a dynamic process guided by intentional, conscious, and voluntary executive and regulatory mechanisms embedded in generating and using visual mental imagery. This dynamic, processual view of vividness aligns with Grossberg’s view of an “emerging theory of imagery” in the neuroscience domain as “…part of a larger neural theory of visual seeing and thinking” ([7], p. 195).

The rapid advancement of the neuroscience of vision (often inspired by Grossberg himself) has led to many new findings on mental images and has led to increased interest in vividness. For most people, generating visual mental images depends on the same neural and functional mechanisms involved in seeing. Imagery and perception involve not only similar mechanisms, but also similar synchronizations across brain areas [8].

**Citation:** D'Angiulli, A.; Laarakker, C.; Buchanan, D.M. An ART Tour de Force on Mental Imagery: Vividness, Individual Bias Differences, and Complementary Visual Processing Streams. *Information* **2024**, *15*, 59. https://doi.org/10.3390/info15010059

Academic Editors: Luiz Pessoa and Birgitta Dresp-Langley

Received: 14 November 2023

Revised: 16 January 2024

Accepted: 16 January 2024

Published: 19 January 2024

<figure><img src="image_68.png" /></figure>

**Copyright:** © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Moreover, physical representations and imagined scenarios both influence the detection of target stimuli. Thus, studies have emphasized that early visual areas impact both the top-down generation of images during mental imagery and the vividness of images retrieved from memory [9]. While some findings suggest similar underlying mechanisms for visual imagery and visual perception, other findings suggest an overlap, and some research suggests equivalent mechanisms [9]. These overlaps in the top-down connectivity of neural mechanisms in perception and imagery—which are a necessary ingredient of ART—suggest a consistent relationship between mental imagery and the functional impact of conscious perception [10]. Using imagery, ambiguous stimuli can be formed into more precise and stable visual perceptions. As a paradigmatic example, vividness can induce bias, which determines the dominant perception of one of two binocularly different color patches [11]. Grossberg’s approach, however, is an important advancement. Not only does it describe the contextually dependent differential variants of top-down connectivity and the influence of bottom-up processing on imagery, but also, it seamlessly integrates phenomenological components as a causally relevant and non-epiphenomenal aspect of consciousness within the same unitary model. Thus, Grossberg’s idea of resonance in ART can be extended to create an implementable neural model of vividness of mental imagery. Following the ART rationale, vividness may depend on the complex functional links among the processes of consciousness, learning, expectation, attention, resonance, and synchrony (CLEARS) [12].

According to Grossberg [12], resonance occurs among different brain areas. The brain areas associated with perception and imagination communicate dynamically, resulting in the construction of mental images. In this process, resonance reflects the synchronization of neural activity between brain areas (particularly the ventral and dorsal streams), and increased resonance between such areas leads to stronger connections and the integration of more information [12]. Thus, the strength of resonance between areas involved in imagery and perception may be related to the vividness of the mental images. The top-down and bottom-up feedback recurrent connections utilizing the dorsal and ventral streams allow for the precision, refinement, and enhancement of the vividness of the image. Specifically, the high-level mechanisms that control the selective detection of change need a modulating factor that makes attention a phenomenologically vivid reality (as per William James’s famous dictum: “...the taking possession by the mind, in clear and vivid form...” https://psychclassics.yorku.ca/James/Principles/prin11.htm, accessed on 1 September 2023). Consistent with the assumptions of ART [12,13], we argue that the top-down voluntary processing related to the vividness of imagery is important for distributing attention and contributes to the categorization, recognition, and prediction of objects. Externally and internally directed attention determines our ability to learn and adjust neural patterns [14]. While externally directed attention determines the objects, features, and spatial locations of what we see in our visual field, the mechanisms of internally directed attention are less known, and their connections to imagery and vividness have not yet been clarified [14]. An exception is a study by Gjorgieva et al. [15], who reported a clear relationship between attention and image vividness, showing that the latency of parietal ERP signatures of internally directed attention was inversely related to image vividness, replicating an earlier behavioral finding of D’Angiulli and Reeves [16], namely, that vividness was associated with faster processing.

## 1.1. *Review of Related Imagery Research*

In most individuals, visual mental images do not impact the accuracy of visual perception (i.e., how well an individual resolves external visual stimuli). In other individuals, however, concurrent mental images do impact perception by either augmenting or decreasing baseline visual performance. These facilitation and interference phenomena have been widely recognized and studied in the field of perception, where they are traditionally known as priming and interference, or negative and positive Perky effects, respectively [17]. However, negative and positive Perky effects are generally considered absolute changes
in accuracy of approximately ±5% relative to the acuity baseline, while the correspond-
ing priming and interference effects are relative within-subjects changes that could be
empirically smaller but significant numerical deltas. Very recent findings from cognitive
neuroscience studies (generally involving functional magnetic resonance imaging (fMRI))
have confirmed and extended previous findings (generally involving electroencephalogra-
phy (EEG) or event-related potential (ERP) (see review in [18])) on the neural correlates of
the VVIQ and trial-by-trial vividness ratings based on instructions that emphasize sensory
strength (see reviews in [1,19]). Furthermore, other behavioral evidence has shown that
vividness can prime stimulus detection [18], and several reports have shown that image
vividness impacts visual sensitivity (see [17–21]).

Recent research has shown that the size of V1, the precision of visual acuity, and the vividness of mental imagery are related. The average or baseline accuracy of visual processing or visual precision can be assessed by tasks measuring accuracy in resolving detailed information in perceptual visual images, such as the Vernier acuity task, a standardized and validated paradigm [17]. In one version of this task, participants report the side of the visual hemifield (left or right) in which offset parallel line segments appear, either monocularly. By instructing participants to generate visual mental images while presenting Venier acuity targets, it is possible to empirically show whether imagery increases or decreases task performance (i.e., improves or hinders visual acuity, respectively) [16,17]. Importantly, Venier acuity is correlated with cortical magnification and the size of receptive fields in V1, making it a reliable behavioral proxy indicator of V1 size [22,23].

Bergmann et al. [21] showed that priming and interference depend on an individual’s accuracy (i.e., visual precision); however, importantly, individual differences in visual precision differ from individual differences in sensory strength and can be associated with differences in the size of V1. Namely, when asked to create a concurrent visual mental image, individuals with a typical (medium) V1 size had average visual precision and showed no change in visual precision relative to baseline. However, individuals with larger V1 areas demonstrated above-average visual precision and with concurrent imagery, showed priming effects. In contrast, individuals with relatively smaller V1 regions demonstrated average visual precision and with concurrent imagery, showed “sensory” interference.

Although Bergmann et al.’s [21] study was successful in elucidating the neural early vi-
sual correlates of individual differences associated with mental imagery, they only partially
clarified the neural substrate of vividness. Despite the finding that vividness was related
to the prefrontal cortex (PFC) volume, they did not clarify how individual differences in
vividness judgments reflect individual differences in V1 or how individual characteristics in
early visual processing are driven by changes in PFC activity. Kosslyn and colleagues [24]
suggested that during visual processing, the magnocellular pathway in the dorsal stream is
recruited for spatial imagery tasks (locating objects), while the parvocellular pathway in the
ventral stream is mainly recruited for object imagery tasks (identifying objects) [13]. The
dorsal stream is recruited more in individuals with stronger sensory interference, while the
ventral stream is recruited by individuals with stronger visual precision but weaker sensory
interference (as per Bergman et al.) [21]. Based on this evidence, individual differences
in the size of V1 might be correlated with recruitment of the dorsal and ventral path-
ways. D’Angiulli et al. [25] performed ERP and EEG studies, demonstrating hemispheric
asymmetry during mental image generation, which reflects the differential distribution of
activity in the dorsal and ventral pathways; magnocellular neurons associated with spatial
precision ([24]) are mainly active in the right dorsal electrode sites, whereas parvocellular
neurons associated with object shape ([24]) are mainly active in the left ventral electrode
sites. Importantly, ERP activity and polarity in prefrontal regions were inversely correlated
with those in the occipital regions. The latter finding cannot be explained according to EEG
operation, as the inversely correlated activity was selectively identified only between those
electrode sites and not within the other sites. The same electrode selectivity was also found
for anticorrelated synchronization and desynchronization of EEG power involving alpha,
beta, and theta band frequencies.
Kosslyn's work has been fundamental to the imagery research field. Nevertheless, it has also biased the field so that researchers now widely assume an underlying natural taxonomy of different mental representations matched with various neural systems or mechanisms. On this basis, individual differences are derived from the preferential mechanism or modules used and the characteristics of particular tasks that require images, which determines the alternate predominance of either the dorsal or ventral streams (for example, see [26]). In contrast, according to Grossberg [27], both the ventral and the dorsal streams always maintain complementary *parallel* cortical processes [27]. The dorsal stream includes the spatial and motor processes that control adaptation based on changing bodily parameters. Moreover, the dorsal stream involves spatial representations, whereas the ventral stream is specific and does not consider object views, positions, or sizes. Therefore, as Grossberg [27] described, the two streams operate in parallel to perform actions according to the specific object and scene, incorporating the processes of the ventral and dorsal streams accordingly. This corresponds to the key principle of complementary computation, according to which complementary cortical streams coordinate their activity reciprocally and dynamically to synergistically overcome their "weaknesses", namely, their limitations due to their modular rigidity and biomolecular boundedness in processing independent isolated aspects. He described the two coordination dynamics as *what-to-where* and *where-to-what* excitatory and inhibitory parallel interactive learning pathways. However, we suggest that image vividness may bias the weight of processing by recruiting one stream more than the other, acting as another complementary or competing process to the attention given to a visual object. That is, visual "images" are not the same as consciousness; they have a phenomenological gradient corresponding to the strength of resonance, which might reflect higher-level complementary dynamic processing from the top-down inputs to V1. Therefore, if the two streams are imbalanced, the view, position, or size of an object may be incorrect or unable to adapt to changing parameters; thus, the spatial context of the object may be unknown. The top-down interactions of these streams include object and spatial attentional processes that could influence whether imagery hinders or facilitates visual processes. Furthermore, the extent to which the dorsal and ventral streams influence the direction of internal attention may determine whether visual processes are hindered or facilitated by mental images [27].

To determine the nature of the vivid-is-fast relation, D'Angiulli and Reeves [28] modeled latencies of visual mental image generation under several visual angle (foveal and parafoveal) conditions to probe magnocellular (mainly dorsal) versus parvocellular (mainly ventral) pathways and measured concurrent trial-by-trial vividness ratings after instructing the observers to report sensory strength rather than details of the contents (see [5]). Based on the best available functional mapping of streams of information processing during image-related tasks [29], these data suggest that vividness ratings increase when they reflect fast judgments, presumably corresponding to the involvement of frontal areas based on input from ventral streams (ventralization), whereas ratings decline when the same observers make slower judgments (still presumably controlled by frontal areas) based on inputs from dorsal visual pathway streams (dorsalization) [28].

## 1.2. *The Present Study: Hypotheses and Predictions*

If, as the reviewed evidence suggests, individual differences in visual processing can bias how images are accessed and consciously self-reported, then more interference or priming could reflect disequilibrium between dorsal and ventral processes, which is linked with individual anatomical differences and levels of vividness. This is the central tenet we aim to investigate in the present study *from within* Grossberg's ART framework.

Following D'Angiulli and Reeves [27] and Kosslyn et al. [24], we hypothesize that a large V1 should correlate with top-down ventralization (top-down ventral dominance), whereas a small V1 should correlate with top-down dorsalization (top-down dorsal dominance). To test these hypotheses, we devised an experiment in which participants were asked to generate a visual mental image of their own choice (under strict image guidelines).
and project those images onto a screen where the observers had to simultaneously detect a Vernier acuity target (i.e., an offset line). This experimental design allowed us to investigate the degree to which imagery influences the perception of fine acuity stimuli. The predicted outcome scenarios derived from the hypotheses are presented in Figure 1.

<figure><img src="image_69.png" /><figcaption>**Figure 1.** Schematic representation of the predicted outcomes for the interaction between visual mental imagery and target perception as a function of V1 size and complementary processing streams. Red line indicates no effect of imagery over acuity referencing baseline equilibrium between dorsal and ventral streams.</figcaption></figure>

We expected that large, ventrally dominant V1 regions would direct attention internally within the ventral/what pathway (corresponding to Grossberg's "object shroud" [13]) in the case of high vividness, which should interfere with attentional resources directed externally to the position of the concurrently presented visual target (Grossberg's "spatial shroud" [13]), thereby disrupting the what-to-where stream. In this scenario, the what-to-where pathway should compete with where-to-what processing, resulting in reduced detection of the target spatial position (i.e., positive Perky effects). However, in the case of low vividness, the overlap in parvocellular processing between imagery and concurrent acuity would be minimal, and this competition among the pathways should be significantly reduced. In contrast, individuals with small, dorsally dominant V1 regions should perform mostly magnocellular processing and show minimal overlap between parvocellular processing and concurrent acuity, with this overlap further reduced in the case of low vividness. In this scenario, the what-to-where pathway should compete with where-to-what processing. Consequently, priming should occur at both vividness levels for small V1 processing, with more extreme priming (i.e., negative Perky effects) observed for low vividness. Finally, according to Bergman et al. [21], we predict that medium V1 regions should engage parvo- and magnocellular pathways approximately equally, balancing competition and cooperation among the two streams. Accordingly, in this case, vividness should bias attention by enhancing or reducing attention toward the target primarily through the what-to-where pathway; thus, we expect moderate priming effects for high vividness and moderate interference effects for low vividness, the opposite to what should occur for the case of large V1.
## 2. Materials and Methods

### 2.1. Participants

Initially, 60 first-year undergraduate psychology students aged 18 to 25 years (M = 22, SD = 1.75) were recruited to participate through Carleton University's Student Organization of North America (SONA) subject recruitment system. Inclusion in the study was conditional on signing a written informed consent form. The Institutional Behavioural Research Ethics Board of Carleton University approved this study (protocol code 111569 27 November 2019), which was conducted in conformity with the Declaration of Helsinki and Canada's Tri-Council Policy Statement.

Among the participants, 53% identified as male and 47% identified as female. In the preliminary phase of the study, the participants underwent personality and imagery screening using the VVIQ and the Big Five Inventory [30]. The VVIQ was the first questionnaire the participants completed. Participants practiced visualizing while completing this questionnaire. Following the standard procedure, the participants were presented with the 16 VVIQ descriptions and asked to rate the mental images that came to their minds on a scale of 1 (perfectly clear) to 7 (no image). Only data from participants who scored within the typical range (within 2 SD of the grand mean) in both questionnaires were included in the present study. Two candidates obtained the minimum possible score on the VVIQ, while five candidates scored below the norm on one of the items in the Big Five Inventory; therefore, their data were not included in the analysis. Finally, after screening, 53 participants were included; twenty-nine participants received a psychology course credit of 1.5%, whereas twenty-four participants were volunteers who did not receive any compensation. Preliminary inspection and analysis of the data did not reveal statistically significant differences between these two groups (multivariate test of the variance difference between the scores from the two samples in all eight no-imagery and imagery conditions yielded the following summary statistics: Hotelling's trace = 0.28 (df: 8, 51); p ~ 0.20).

### 2.2. Apparatus

The stimuli and background were projected onto a white screen mounted on a wall by a Sony Duocom LCD Data Projector (XGA VPL-CX1, Sony Electronics Inc., Park Ridge, NJ, USA). Projections were controlled by the experimenter using a keyboard connected to an LG Prosys computer, which also ran the computer program displayed by the projector and recorded the data for the visual acuity task. The stimuli were generated, displayed, and controlled using the Visual Basic program. The experimental setup is graphically represented in the right panel of Figure 2.

<figure><img src="image_70.png" /><figcaption>**Figure 2.** Schematic of the experimental setup. **Left Panel:** Actual screen appearance of the offset lines used for the acuity test. The particular offset in the shown trial is on the left visual hemifield (hence the correct response was “left”); note that the lines are not drawn to scale. **Right panel:** Participants (represented in (A)) were seated directly in front of the projected stimuli (represented in (B)) Stimuli were projected using a Sony Duocom LCD data projector (B). The stimuli were projected on a white screen mounted on a wall perpendicular to the participant's field of vision (represented in (C)).</figcaption></figure>
## 2.3. Stimuli and Procedure

Participants were tested individually and seated in front of a table, upon which the projector rested (see Figure 2, right panel). The experimenter sat on the left of the table. Participants were asked whether they knew what mental imagery was and if they could generate one as an example. In the initial familiarization and subsequent practice phases, participants were encouraged to verbalize and describe their images.

Participants started the experimental session by completing the VVIQ and the TSDVI. The experimenter verbally explained the task instructions (adapted from the Appendix of ([28]; pp. 544–546); the modification concerned the response modality, which was changed in the present procedure). Vividness was defined as “the extent to which the imagined object is lifelike or resembles real seeing” ([28]; p. 545). Examples of vivid imagery (e.g., your mother’s face) and nonvivid imagery (e.g., your kindergarten teacher’s face) were provided, and participants were asked to provide some examples. The terms dynamic image (“something moving”) and static image (“not moving” and “still”) were described, and examples were provided (e.g., a Canadian flag blowing in the wind, a face not talking or moving at all). The latter was adapted from a previous protocol ([30]; Experiment 2). Participants were asked to generate other examples, verbalize and describe their experiences, and share their self-rating of vividness. To self-rate the vividness of their images, participants were asked to use the same 7-point Likert scale as the one used for the VVIQ items (i.e., from 1 = perfectly clear to 7 = no image). Following completion, both questionnaires were removed, and the participants started the visual acuity task trials.

The next phase of the experimental procedure for the actual acuity task session was adapted from the standard protocol for ERP and EEG data collection used by D'Angiulli et al. [25] (in the present experiment, only behavioral data were collected). Although the main focus of the present study was to compare vivid and nonvivid static visual mental images, two classic issues in the formation of imagery are the level of voluntary control and the incorporation of dynamic images. Both aspects may involve moving images. Previous research has shown that static imagery is significantly more vivid than dynamic imagery of the same content [31,32] and might be more difficult to voluntarily control. This might be due to the involvement of key areas of the dorsal stream, such as the MT [32]. Thus, we instructed the participants to generate vivid and nonvivid moving images, as a secondary objective was to explore whether asking participants to generate moving images might bias dorsal recruitment. Therefore, the instructions were changed according to the block of trials so that each participant was asked to produce images under previously validated instructions [28,32], yielding types of four mental image: *static vivid (SV)*, *dynamic vivid (DV)*, *static nonvivid (SNV)*, or *dynamic nonvivid (DNV)*. A fifth condition involved performing the acuity task without generating any mental image (no image, NI). In the static and dynamic conditions, participants were instructed to start the experiment by generating a small set of 2–3 images with consistent vividness self-ratings between 2 and 3 to define the vivid images, while another set of images with vividness ratings between 6 and 7 were used to define nonvivid images. To assess V1 size through neuropsychological methods, the NI condition and its associated visual acuity score served as indirect measures of an individual's V1 size, given that visual acuity is associated with the size of V1 [22,23]. One type of mental image or NI was used in each block of trials, and the order of the image types was randomly determined before the experiment using a free, online random number generator (http://www.random.org/ (accessed on 17 January 2024). The NI and the four mental image types created 5 experimental conditions. There were 5 practice blocks (1 for each mental image type and 1 for the NI condition) of 5 trials each. The experimental trials consisted of 16 blocks of 10 trials each, with 8 blocks of mental images and 8 blocks of NI. (All executable programs for the experiment can be publicly viewed and downloaded from doi:10.5281/zenodo.10076451).

For the Vernier acuity task, the room was dimly lit at lighting settings approximating D'Angiulli et al. [25]. Participants were told that at any point, they could stop the experiment and take a break or have the lights turned on. A white screen measuring 1 ft × 1 ft
was mounted on the wall, 4 ft. away from participants. The surface was painted on a black background, and a pair of thin, vertical white lines 5 mm long was placed at the center, with the lines stacked and separated by approximately 2.3 in (5.84 cm). A third thin, white vertical line (approx. 5 cm long) between, and slightly offset (approx. 2 mm left or right), from the two shorter lines was displayed for 68 ms by the experimenter for each trial on cue. This stimulus line was the target (see Figure 2, left panel). The complete line displays a subtended 2.62° visual angle. The line was displayed on a black background using a standard CRT monitor (11 × 19.5 cm). The contrast and brightness were set to a minimum, and the color was set to grayscale. The task corresponded to photopic conditions, with an approximate luminance of 50 cd/m² and a Weber contrast of 21:1 (as in [26]).

Cues came from the participant, either by verbally saying “ok” or clicking a retractable pen to indicate that a mental image was being generated. During each image trial, participants were asked to look between the two short lines and to imagine a specific image (as indicated by the experimenter). When the generated mental image was sufficiently stable, participants were asked to cue the experimenter, who then clicked the mouse; the experimenter’s mouse click caused the long line (target) to flash for a duration of 67 ms, either to the left or right of the two short lines (presentation side was random). Participants, while holding onto their image, viewed the line and indicated which side they thought the line appeared on by saying “left” or “right” (see left panel of Figure 2). If uncertain, participants were instructed to give their best guess. *No image* (NI) trials proceeded similarly, but participants were instructed not to produce mental images during the acuity task. Participants took as much time as they needed between trials to ensure that their images were strong and reliable for each trial. If the patient was distracted or if an image was lost while completing a trial, the entire block was discarded, and the patient began a new trial.

Most participants had no difficulty understanding the different mental image types, as indicated by their own examples. Only three participants required extra practice. These participants were either given 5 extra practice trials for each image type or repeated the incomplete block. The experiment lasted approximately 1.5 h. Participants were debriefed following the visual detection task.

## 2.4. Analytical Approach

Participants' scores were recorded as a percentage of the total correct responses (accurately answering if the stimulus line was on the left or right side) in the visual detection task over each block of 10 trials. As the blocks for each mental image type were randomly selected to include two imagery blocks and two no-imagery blocks, the results yielded scores ≥20 for NI and >20 for each mental image type. These scores were then transformed into percentages of correct responses. The computer data acquisition program automatically rounded percent values to the closest integer real number without decimal places. The performance scores were calculated as NI % correct—image % correct. In other words, the image trial correct scores were subtracted from the NI trial correct scores to obtain a percentage, which was either negative or positive. Positive scores indicated interference, and negative scores indicated priming. Based on criteria similar to those used in the current literature [16], interference corresponding to a score difference of ≥5% was defined as a *positive Perky* effect, while priming corresponding to a score difference of ≤−5% was defined as a *negative Perky* effect.

In the first analysis, we described the overall distribution of all effects without parametric (i.e., normality) assumptions. Performance scores were calculated for each participant for each mental image type (including dynamic images). No result (0% or “null” effects), interference (including positive Perky effects), and priming (including negative Perky effects) cases were subdivided into 3 categories, and each effect score (NI imagery) for each mental image type was subjected to the Kruskal–Wallis test (nonparametric repeated-measures ANOVA). Subsequently, for each mental image type, interference and priming data were compared using two independent sample tests (Mann–Whitney test) to explore possible post hoc differences following the initial Kruskal–Wallis test.
In the subsequent analysis, following confirmatory parametric diagnostics showing tolerable deviation from normality, we focused primarily on static imagery, and we re-gressed the effect of imagery on concurrent acuity accuracy (as % difference scores) against baseline acuity performance. As a secondary analysis for completeness, we performed the same analysis for the dynamic imagery to provide a comparison for reference. We converted the r coefficients to Zr scores as a polynomial contrast to test for trends. Multiple t tests with Simes-Bonferroni adjustment were applied to determine significant differences between mental image types, including polynomial linear pattern tests. Simes-Bonferroni adjustment yielded a threshold of *p* < 0.03, corresponding to a critical t value of 2.021 (*df* = 51).

Finally, to investigate the relationship between interference, priming and Perky effects, and the size of V1, we scaled all the measured percentage difference scores using the average NI collapsed on all blocks subtracted from each condition and tested between-subject effects using a three-way repeated measures hierarchical mixed model ANOVA with the following factors: V1 size (3 levels: small, medium, large) × vividness (vivid vs. nonvivid) × image type (static vs. dynamic). We included subjects as a covariate to control for individual subject variance. The categories of relative V1 size were extracted from the empirically observed distribution of baseline visual acuity performance scores, under the assumption derived from previously reviewed literature that visual sensitivity is a proxy for V1 size. There exists no accepted absolute partition of V1 size based on this indirect measurement method; therefore, we divided the observed interval of scores arbitrarily in three ordered bins of same data density. Based on the intervals of baseline acuity performance in the NI condition, V1 was categorized into three groups as follows: small, corresponding to a baseline performance interval between 20% and 45%; medium, corresponding to a baseline performance interval between 46% and 55%; and large, corresponding to a baseline performance interval between 56% and 90%. These defined the three relative size group categories used to empirically test the predictions represented in Figure 1.

### 3. Results

#### 3.1. *Interference and Priming Distributions*

For all mental image types, effect scores by group were found to be significant (for SV: $\chi^2(2) = 35.02$, d = 2.79; for SNV: $\chi^2(2) = 36.41$, d = 2.96; for DV: $\chi^2(2) = 38.16$, d = 3.21; for DNV: $\chi^2(2) = 39.10$, d = 3.34; for all tests, N = 53 and *p* < 0.0001). The results showed that for all mental image types except DV, the proportion of participants who showed priming and/or negative Perky effects was greater than the proportion of participants who showed interference and/or positive Perky effects, and the distribution of the effect scores significantly differed, showing that the differences in the types of effects observed were not due to chance. The number of participants experiencing each effect, the proportion, and the mean rank for each image type are shown in Table 1.

<table><caption>**Table 1.** Proportions (and n) of participants experiencing interference/positive Perky effects, priming/negative Perky effects, or null effects for each type of imagery.</caption><thead><tr><th rowspan="3">Effect type</th><th colspan="4">Image Types</th></tr><tr><th colspan="2">Static</th><th colspan="2">Dynamic</th></tr><tr><th>Vivid</th><th>Nonvivid</th><th>Vivid</th><th>Nonvivid</th></tr></thead><tbody><tr><td>Interference</td><td>32.1 (17)</td><td>43.4 (23)</td><td>45.3 (24)</td><td>39.6 (21)</td></tr><tr><td>Priming</td><td>58.5 (31)</td><td>49.1 (26)</td><td>41.5 (22)</td><td>43.4(22)</td></tr><tr><td>Null</td><td>9.4 (5)</td><td>7.5 (4)</td><td>13.2 (7)</td><td>17 (9)</td></tr></tbody></table>

Note. N = 53. Effect Type refers to the resulting score after the NI trials. Negative scores were labeled as priming, including negative Perky effects; positive scores were labeled as interference, including positive Perky effects; and scores of 0 were considered null or no effect. All image types were found to be significant at the *p* = 0.0001 level. The numbers represent the proportions (%) of participants experiencing the effect, as calculated by N/53 × 100. All the numbers are rounded. The numbers in parentheses represent the number of participants out of N = 53 who experienced each effect for the SV image type (n indicates each image type).
Table 1 outlines the differences observed across mental image types. The largest difference between interference and priming within one image type was 14 participants, with more participants showing priming effects; this was observed for the SV image type (interference: *n* = 17, priming: *n* = 31). For SNV images, only 3 additional participants showed priming effects (interference: *n* = 23, priming: *n* = 26). For DV images, the difference was 2 participants, with more participants showing interference effects (interference: *n* = 24, priming: *n* = 22). For DNV images, 2 additional participants showed priming effects (interference: *n* = 21, priming: *n* = 23).

## 3.2. *Positive and Negative Perky Effects*

The Mann-Whitney U tests showed significant differences between the positive Perky and negative Perky effects for SV (Z = -5.66, *p* = 0.000, d = 2.47) and SNV (Z = -5.53, *p* = 0.000, d = 2.34) images. The results were similar for dynamic images (DV: Z = -5.58, *p* = 0.000, d = 2.39; DNV: Z = -5.43, *p* = 0.000, d = 2.24).

We then plotted all the Perky scores for each participant in the static vivid and nonvivid conditions to explain trends based on the image type. These data are presented in Figure 3.

<figure><img src="image_71.png" /><figcaption>**Figure 3.** Effect of concurrent static imagery on acuity as a function of the baseline Vernier acuity performance for all participants for vivid (A) and nonvivid (B) images. Gray band indicates 95% confidence interval.</figcaption></figure>
The correlation/regression coefficients for SV (r² = 0.60; r = 0.78) and SNV (r² = 0.44; r = 0.66) images showed significant differences between SV and SNV images (t = 2.52, d = 0.49), with a steeper slope for vivid images (i.e., higher interference and priming effects) than for nonvivid images. The same finding was observed for dynamic images: DV (r² = 0.38; r = 0.62) versus DNV (r² = 0.36; r = 0.60) (t = 3.62, d = 0.71). The graphs for this analysis are shown in Figure 4.

A

<figure><img src="image_72.png" /></figure>

B

<figure><img src="image_73.png" /><figcaption>**Figure 4.** Effect of concurrent dynamic imagery on acuity as a function of the baseline Vernier acuity performance for all participants for vivid (A) and nonvivid (B) images. Gray band indicates 95% confidence interval.</figcaption></figure>

Further pairwise *t* tests revealed significant differences between SV and DNV images (t = 6.03, d = 1.18) and between SNV and DNV images (t = 3.34, d = 0.66); however, nonsignificant differences were found between SV and DV images (t = 1.96, d = 3.84) and between SNV and DV images (t < 1). This reflects the trend SV > SNV = DV > DNV in terms of the r² value. To verify the significance of this pattern, the *r* coefficients were converted to Zr scores, which were subsequently subjected to polynomial contrast tests. The results confirmed that the trend SV > SNV = DV > DNV was reliable (t(51) = 3.38; *p* = 0.0014, d = 0.66). This finding suggested that there was a clear difference related to vividness, but
there was some overlap between the effects of static and dynamic imagery. To determine the overall pattern, we next examined the possible confounding role of the size of V1.

### 3.3. Priming/Negative Perky Effects vs. Interference/Positive Perky Effects as a Function of V1 Size

In this analysis, we rescaled the effect using the average NI score subtracted from each condition separately (ISV, IDV, ISNV, and IDNV) since we assumed that the baseline remained the same for each subject (we did not find repeated measures of significant changes in NI across the blocks). The three-way V1 size x vividness x static/dynamic ANOVA showed that the only significant interaction with V1 size was vividness (see results in Table 2). The interaction can be analyzed according to Figure 5, which graphically represents the ANOVA results. Individuals with medium V1 sizes experienced modest interference with nonvivid images but priming with vivid images. In comparison, individuals with large V1s experienced priming and negative Perky effects with nonvivid images and interference and positive Perky effects with vivid images, while individuals with small V1s experienced priming irrespective of the vividness of the image (Figure 5).

<table><caption>**Table 2.** ANOVA results, showing the effects of concurrent imagery on Vernier acuity as a function of vividness and V1 size.</caption><thead><tr><th>Source</th><th>Type III Sum of Squares</th><th>Df</th><th>Mean Square</th><th>F</th><th>p</th><th>Partial Eta Squared</th></tr></thead><tbody><tr><td>Corrected Model</td><td>4319.311 <sup>a</sup></td><td>12</td><td>359.943</td><td>2.405</td><td>0.006</td><td>0.127</td></tr><tr><td>Intercept</td><td>772.346</td><td>1</td><td>772.346</td><td>5.161</td><td>0.024</td><td>0.025</td></tr><tr><td>Vividness</td><td>419.980</td><td>1</td><td>419.980</td><td>2.806</td><td>0.095</td><td>0.014</td></tr><tr><td>Static/dynamic</td><td>4.871</td><td>1</td><td>4.871</td><td>0.033</td><td>0.857</td><td>0.000</td></tr><tr><td>Subject covariate</td><td>20.567</td><td>1</td><td>20.567</td><td>0.137</td><td>0.711</td><td>0.001</td></tr><tr><td>V1 size</td><td>2242.093</td><td>2</td><td>1121.047</td><td>7.491</td><td>&lt;0.001</td><td>0.070</td></tr><tr><td>Static/dynamic X Vividness</td><td>70.489</td><td>1</td><td>70.489</td><td>0.471</td><td>0.493</td><td>0.002</td></tr><tr><td>Static/dynamic X V1 size</td><td>230.724</td><td>2</td><td>115.362</td><td>0.771</td><td>0.464</td><td>0.008</td></tr><tr><td>Vividness X V1 size</td><td>1192.185</td><td>2</td><td>596.093</td><td>3.983</td><td>0.020</td><td>0.038</td></tr><tr><td>Static/dynamic X Vividness X V1 size</td><td>21.749</td><td>2</td><td>10.875</td><td>0.073</td><td>0.930</td><td>0.001</td></tr><tr><td>Error</td><td>29,780.807</td><td>199</td><td>149.652</td><td></td><td></td><td></td></tr><tr><td>Total</td><td>34,781.250</td><td>212</td><td></td><td></td><td></td><td></td></tr><tr><td>Corrected Total</td><td>34,100.118</td><td>211</td><td></td><td></td><td></td><td></td></tr></tbody></table>

<figure><img src="image_74.png" /><figcaption>**Figure 5.** Effect of concurrent imagery on baseline visual acuity performance as a function of vividness and V1 size. Error bars represent standard errors. The data shown are controlled for subject covariates. Dots and bars in blue and green colors highlight data and standard errors for vivid and nonvivid V1 size categories, respectively.</figcaption></figure>

<footnote><sup>a</sup> R<sup>2</sup> = 0.127.</footnote>
# 4. Discussion

## 4.1. *Summary of Findings and Interpretative Framework*

In summary, we found that visual imagery can have priming and interference effects on visual perception depending on differences in individual neuroanatomy (i.e., V1 size). These findings are consistent with our original hypothesis that individual variability in V1 size may influence acuity precision, as V1 biases the differential activation of dorsal and ventral visual streams by directing the streams toward complementary cooperative or competitive interactions. In addition to the influence of V1 size, image vividness modulated perceptual acuity precision. More specifically, our results demonstrated that participants experienced interference, priming, Perky, and null effects across all the image types. Moreover, the priming and interference effects changed as a function of vividness, regardless of the image content (static vs. dynamic). Image vividness acted as a predictor of acuity performance, as reflected by the fact that the four imagery conditions interfered with perception according to our original hypothesis (see Figure 1). Specifically, the more vivid the imagery is, the more interference in the perception. Overall, our results replicate the findings of Reeves et al. [17] and are consistent with the classic “dipper” effect.

The trend of the imagery effects is compatible with classic theories in the experimental and cognitive psychology literature that suggest a functional equivalence between imagery and perception [26,33]. Specifically, adding mental imagery to a visual acuity task reduces the sensory and cognitive resources available for perception, particularly because the process of generating a visual image is related to the process of perceiving a visual image [24,33]. In other words, different image types can influence the working memory load imposed on perception by top-down mental image processing during concurrent imagery-perception tasks. Thus, images affect individuals’ perception based on (1) sensory-based working memory capacity and (2) single processing pathways for imagery and perception. If perception and imagery share a common processing pathway and individuals are instructed to simultaneously use visual perception and visual imagery, the two processes must be balanced to achieve optimal performance. Furthermore, both tasks are governed by working memory and higher-order processes. Therefore, the extent to which perception and imagery processing are equilibrated, as well as the imposed working memory load distribution, largely determine the effect of concurrent imagery on perception. Thus, equilibrium between imagery and perception would be reflected by null effects, disequilibrium in favor of perception would be reflected by priming or negative Perky effects, and disequilibrium in favor of imagery would be reflected by interference or positive Perky effects. Thus, the vividness of mental imagery and the size of V1 can individually or jointly improve or reduce an individual’s processing ability by priming or interfering with the perception of visual stimuli.

Here, what we refer to as resource competition and equilibrium can be related to Grossberg’s ART logic. Namely, feature-category resonance involves the complementary integration of attended features and activated categories related to an object. Resources are reduced when, in the underlying neural networks, the weights of the connections are being learned during the dual task. Direct evidence and conceptualization of this process have been provided in a landmark study by Craver-Lemley and Reeves [34].

We clarify that Grossberg’s conceptualization of imagery does not implicate a necessary role of V1. According to Grossberg, imagery is a “volitionally-mediated shift” that “enables top-down expectations, in the absence of supportive bottom-up inputs, to cause conscious experiences of imagery and inner speech, and thereby to enable fantasy and planning activities to occur”. In addition, in certain conditions, tonically hyperexcited expectations can lead to conscious experiences in the absence of bottom-up inputs and volition. However, although “both bottom-up activation of visual percepts and top-down cognitively-activated and volitionally-modulated imagery are possible within the visual system” [12], visual representations underlying imagery are formed through hierarchical and interstream interactions in areas V2 to V4, and the final constructed and phenomenologically perceived visual representations are formed in V4 [12]. Imagery, conceived as
"top-down expectations plus attention", operates across this hierarchy, thereby reorganizing bottom-level properties [12].

Furthermore, Grossberg argued that, only in some instances imagery should “have effects that are equivalent to bottom-up activation by visual scenes”. This argument is supported with the example of imagery of bi-stable patterns (i.e., Necker cube) which should be already biased by top-down expectation and imagined according to one interpretation. We suggest that vividness influences the strength of resonance and individual differences, which, as shown by the literature, could involve resonance loops between the PFC and V1 and be linked to the bottom-up biasing of *spatial* attention (position of a target) without directly influencing bottom-up sensory or low-level effects. Thus, V1 involvement could be linked to the vividness of the level of consciousness or the level of resonance between complementary processes related to the mental image but not the content of the image (in the present context, static or dynamic), as has been recently shown for other features (i.e., object shape) in priming experiments [18]. Thus, activation of V1 during image-related tasks could be the result of top-down reentrant pathways such as indirect PCF feedback to A17 and A18 via A7 [35,36]. The resulting effects of these interactions could be observed in the what-to-where visual stream of processing, where “knowledge” of the features of an imagined object, i.e., the object shroud, influences the detection of a spatial bottom-up sensory target, as in our Vernier acuity task with concurrent imagery.

## 4.2. ARTSCAN Modeling of the Findings

In this section, we apply Grossberg's ART and, more specifically, its related ARTSCAN implementation [37,38] to explain the present findings with a descriptive graphical model. The outcomes of our task can be described in terms of the interactions between the what-to-where and where-to-what parallel processing streams, and these interactions can be examined by considering each of the cases corresponding to the V1 size (3) × vividness (2) factorial analysis (see Figure 5). The cases of vivid mental images with small and medium V1s, shown in Figure 6, can be considered together because the only critical difference is the extent to which parvocellular (P-cells) and magnocellular (M-cells) neuronal pools are recruited. In the small V1 case, more M-cells than P-cells are activated. In contrast, for the medium V1 case, the activation of both pools should be approximately equal. This is the "combo" scenario modeled in Figure 6.

<figure><img src="image_75.png" /><figcaption>**Figure 6.** An ARTSCAN interpretation of the processes underlying the interaction between concurrent imagery and Vernier acuity line offset detection in the scenario where the observer has small or medium V1 and vivid imagery.</figcaption></figure>
For the case of a small V1 with vivid imagery, the activation of M-cells largely exceeds that of P-cells. Thus, processing is biased toward the spatial pathway. This bias inhibits category reset according to the established attentional distribution. Because of the high vividness, however, the object characteristics are very and equally strong. The size of V1 is associated with the differential activity distribution of the M- and P-cell density. The cell density is directly related to the weight of excitation between and within the what and where/how streams. With a small or medium V1, due to the decreased density of P-cells, the focus is the object boundary of the mental image. Because of the perception of the image boundary and the contour in the periphery, in the small V1 case, or a weaker contour, in the medium V1 case, the object is perceived as fully transparent and/or in the background plane; therefore, the category or knowledge features of the imagined object receive more attention. With high vividness, the what stream is fully excited from bottom-up PCF recruitment, but it does not interfere with the where/how stream; in contrast, as the attentional distribution is biased toward the area of the possible target position, the image basically provides a background to better detect the gap between the offset lines. Thus, in the case of vivid images with small and medium V1s, spatial attention is strongly primed by two complementary excitatory influences from interacting systems in V3 and the frontal eye field (FEF). The M-cells allow for strong establishment of the mental image and inhibit the category reset mechanism in the posterior parietal cortex, shifting attention to the category features. The what stream focuses on the imagery present to "fill in" the gap in the spatial features. Thus, the target position is attended to. Without the category reset inhibitory action, the two systems remain complementary, and medium to strong priming (negative Perky effects) occurs.

The case of small V1 and nonvivid imagery, shown in the top panel of Figure 7, is similar to the case illustrated in Figure 6, except that the excitatory connections from the image are weaker than those in the case of high vividness. As a result, the imagined object is perceived as a faint background and receives less attention, which is firmly directed to the area containing the gap in the offset lines, leading to strong priming (negative Perky effects). In contrast, in the scenario depicted in the bottom panel of Figure 7, in which an individual has lower vividness but a large V1, the significant change is the predominant excitation of P-cell activity. This situation induces partial and weak activation of the inhibitory pathway linked with the category reset system since the object is relatively weak. Under these conditions, the object is in focus, and other images do not compete with the image through occluding surfaces. Therefore, in the low vividness condition, the brain focuses on the object boundary rather than the object surface. Imagery then facilitates the detection of the flashed line to "fill in" the boundary. The what stream is the dominant pathway due to P-cell excitation, and ultimately, this pathway is only weakly inhibited by the category reset mechanism. Although the attentional distribution should not be considerably altered, the extent of priming is much weaker than in the above cases, and priming may not occur.

In the large V1 condition with high vividness, as illustrated in the top panel of Figure 8, there is a greater density of P-cells than M-cells. With high image vividness, the brain focuses on the object rather than allowing interference from a competing perceived contour. This induces a discrepancy because more attention is removed from the possible target position and moved to the object, which triggers the where/how system to initiate the category reset mechanism and shift the spatial attention to stabilize perception. However, the boundary and surface of the mental image are both attended to since the object shroud is strong (due to high vividness). Phenomenologically, the image is no longer transparent due to competition between spatial attention and focusing on object category features; therefore, knowledge of its feature contents is similar to amodal perception, with a foreground surface occluding the area where the target and the offset gap appear. The latter results in strong interference in the perception and detection of the target (positive Perky effect). In the related scenario of medium-size V1 and nonvivid imagery, shown in the bottom panel of Figure 8, the density of P- and M-cells is the same, leading to equilibrium between the what and where/how streams. The size of V1 influences the density of P- and M-cells,
impacting the bias or equilibrium between the what and where/how streams. Since there is bias toward the where stream in the low vividness case, the category reset mechanism is activated. The equal density of the P- and M-cells inhibits spatial attention, and the visual mental image is associated with a weak object shroud. This situation induces partial and weak activation of the inhibitory pathway linked with the category reset system since the stability and knowledge of the object are undermined. Under these conditions, the object is in focus amodally, and no other images compete with it on an occluding opaque surface. This leads to interference in the detection of the target.

<figure><img src="image_76.png" /><figcaption>**Figure 7.** An ARTSCAN interpretation of the processes underlying the interaction between concurrent imagery and Vernier acuity line offset detection in two scenarios in which the observer has nonvivid imagery and either small V1 (**top** panel) or large V1 (**bottom** panel).</figcaption></figure>

In summary, the two key factors that influence imagery-perception interactions are the equilibrium between the strength of resonance in the what system, which determines the extent to which the imagined object is phenomenologically “present” as evident conscious knowledge, and the stable activation of attention toward a spatial target through the control system guided by the object category reset mechanism. Table 3 describes in detail the 7 features that determine the different results.
<figure><img src="image_77.png" /><figcaption>**Figure 8.** An ARTSCAN interpretation of the processes underlying the interaction between concurrent imagery and Vernier acuity line offset detection in two scenarios in which the observer has nonvivid imagery and either large V1 (top panel) or medium V1 (bottom panel).</figcaption></figure>

<table><caption>**Table 3.** Summary of the key aspects of the complementary where-to-what and what-to-where pathways in the present experiment.</caption><thead><tr><th>V1 Size × Vividness</th><th>Interacting Processes</th></tr></thead><tbody><tr><td>Small V1, Vivid(see Figure 6)</td><td><ul><li>M-cells > P-cells</li><li>Object shroud strong</li><li>Spatial shroud strong</li><li>Object category reset mechanism inhibited by the spatial shroud</li><li>Strong excitation at the target position</li><li>Visual phenomenology: object image boundary as background surface</li><li>Behavioral outcome: negative Perky (strong priming)</li></ul></td></tr><tr><td>Medium V1, Vivid(see Figure 6)</td><td><ul><li>M-cells = P-cells</li><li>Object shroud strong</li><li>Spatial shroud moderate</li><li>Object category reset mechanism inhibited by the spatial shroud</li><li>Strong what and moderate where excitation at the target position</li><li>Visual phenomenology: object image boundary as transparent or background, peripheral field</li><li>Behavioral outcome: variable priming</li></ul></td></tr></tbody></table>
<table><caption>**Table 3.** *Cont.*</caption><thead><tr><th>V1 Size × Vividness</th><th>Interacting Processes</th></tr></thead><tbody><tr><td>Large V1, Vivid (see Figure 8, Top Panel)</td><td><ul><li>M-cells &lt; P-cells</li><li>Object shroud strong</li><li>Spatial shroud weak</li><li>Object category reset mechanism inhibits what excitation to target position and spatial shroud</li><li>No excitation at the target position</li><li>Visual phenomenology: object image contour as occluding modal foreground surface</li><li>Behavioral outcome: positive Perky (strong interference)</li></ul></td></tr><tr><td>Small V1, Non-Vivid (see Figure 7, Top Panel)</td><td><ul><li>M-cells &gt; P-cells</li><li>Object shroud weak</li><li>Spatial shroud strong</li><li>Object category reset mechanism inhibited by the spatial shroud</li><li>Weak what and strong where excitation at the target position</li><li>Visual phenomenology: object boundary as amodal background</li><li>Behavioral outcome: variable priming</li></ul></td></tr><tr><td>Medium V1, Non-Vivid (see Figure 8, Bottom Panel)</td><td><ul><li>M-cells = P-cells</li><li>Object shroud weak</li><li>Spatial shroud moderate</li><li>Object category reset mechanism inhibited by the spatial shroud and partially by Object Shroud</li><li>Weak where excitation to target position</li><li>Visual phenomenology: Amodal perception a foreground surface</li><li>Behavioral outcome: variable interference</li></ul></td></tr><tr><td>Large V1, Non-Vivid (see Figure 7, Bottom Panel)</td><td><ul><li>M-cells &lt; P-cells</li><li>Object shroud weak</li><li>Spatial shroud weak</li><li>Object category reset mechanism partially inhibits what excitation to target position and spatial shroud</li><li>Weak what and where excitation at the target position</li><li>Visual phenomenology: object image boundary as amodal background</li><li>Behavioral outcome: priming or no effect</li></ul></td></tr></tbody></table>

## 4.3. *Contributions of Present Work*

### 4.3.1. The Primacy of Complementary Visual Cortical Streams

It is widely accepted that the dorsal visual processing stream is recruited for spatial perception (determining where an object is located), while the ventral stream is recruited for object perception (identifying what an object is) [39,40]. Since the dorsal nervous system determines the positions of visual stimuli in space, we suggest that this pathway is also involved in generating externally projected mental images (placing imagined images in particular spatial locations). Moreover, we theorize that the dual processing involved in detecting visual stimuli and externally projecting mental images may be responsible for creating interference (positive Perky effect) when detecting visual stimuli. Furthermore, given the role of the ventral visual system in object recognition, i.e., categorization, ventrally dominant individuals receive less interference from concurrent imagery, as ventral mediated imagery does not directly compete with the resources necessary for accurate perception of visual details in the external environment.

In agreement with Grossberg's theory, the "what" and "where/how" cortical streams seem to interact to determine the vividness and perception of visual objects. We observed that visual acuity scores were dependent on an individual's V1 size and vivid imagery ability. This influences the recruitment of the dorsal and ventral visual pathways. The involvement of the dorsal and ventral pathways determines whether the imagery is more sensory/category oriented or more detail oriented. Grossberg [25] described how visual cortical areas such as V1 enable our brains to consciously see. The ventral (what) and dorsal (where/how) streams interact through bottom-up, horizontal, and top-down interactions [25]. Top-down interactions use object and spatial attentional processes to engage consciousness. Top-down attentive matching creates synchronous resonant states that lead to bottom-up recognition of categories and top-down expectations [25]. Attentive matching may impact both mostly conscious (object shroud) and mostly unconscious (spatial shroud) seeing.