Neuronal responses to one-dimensional orientations are combined to represent two-dimensional composite patterns, which plays a key role in intermediate-level vision such as texture segmentation. However, where and how the visual cortex starts to represent composite patterns, such as a plaid consisting of two superimposing gratings of different orientations, remains neurophysiologically elusive. Psychophysical and modeling evidence has suggested the existence of early neural mechanisms specialized in plaid detection [1-6], but the responses of V1 neurons to an optimally orientated grating are actually suppressed by a superimposing grating of different orientation (i.e., cross-orientation inhibition) [7, 8]. Would some other V1 neurons be plaid detectors? Here we used two-photon calcium imaging  to compare the responses of V1 superficial-layer neurons to gratings and plaids in awake macaques. We found that many non-orientation-tuned neurons responded weakly to gratings, but strongly to plaids, often with plaid orientation selectivity and cross-angle selectivity. In comparison, most (~94%) orientation-tuned neurons showed more or less cross-orientation inhibition, regardless of the relative stimulus contrasts. Only a small portion (~8%) of them showed plaid facilitation at off-peak orientations. These results suggest separate subpopulations of plaid and grating responding neurons. Because most plaid neurons (~95%) were insensitive to motion direction, they were plaid pattern detectors, not plaid motion detectors.
Perceptual learning, which improves stimulus discrimination, typically results from training with a single stimulus condition. Two major learning mechanisms, early cortical neural plasticity and response reweighting, have been proposed. Here we report a new format of perceptual learning that by design may have bypassed these mechanisms. Instead it is more likely based on abstracted stimulus evidence from multiple stimulus conditions. Specifically, we had observers practice orientation discrimination with Gabors or symmetric dot patterns at up to 47 random or rotating location´orientation conditions. Although each condition received sparse trials (16 trials/session), the practice produced significant orientation learning. Learning also transferred to a Gabor at a single untrained condition with 2-3 time lower orientation thresholds. Moreover, practicing a single stimulus condition with matched trial frequency (16 trials/session) failed to produce significant learning. These results suggested that learning with multiple stimulus conditions may not come from early cortical plasticity or response reweighting with each particular condition. Rather, it may materialize through a new format of perceptual learning, in which orientation evidence invariant to particular orientations and locations is first abstracted from multiple stimulus conditions, and then reweighted by later learning mechanisms. The coarse-to-fine transfer of orientation learning from multiple Gabors or symmetric-dot-patterns to a single Gabor also suggested the involvement of orientation concept learning by the learning mechanisms.
A person’s ability to discriminate fine differences in tone frequency is vital for everyday hearing such as listening to speech and music. This ability can be improved through training (i.e., tone frequency learning). Depending on stimulus configurations and training procedures, tone frequency learning can either transfer to new frequencies, which would suggest learning of a general task structure, or show significant frequency specificity, which would suggest either changes in neural representations of trained frequencies, or reweighting of frequency-specific neural responses. Here we tested the hypothesis that frequency specificity in tone frequency learning can be abolished with a double-training procedure. Specifically, participants practiced tone frequency discrimination at 1 or 6 kHz, presumably encoded by different temporal or place coding mechanisms, respectively. The stimuli were brief tone pips known to produce significant specificity. Tone frequency learning was indeed initially highly frequency specific (Experiment 1). However, with additional exposure to the other untrained frequency via an irrelevant temporal interval discrimination task, or even background play during a visual task, learning transferred completely (1-to-6 kHz or 6-to-1 kHz) (Experiments 2-4). These results support general task structure learning, or concept learning in our term, in tone frequency learning despite initial frequency specificity. They also suggest strategies to design efficient auditory training in practical settings.