Persistent states in vision break universality and time invariance

Studies of perception usually emphasize processes that are largely universal across observers and—except for short-term fluctuations—stationary over time. Here we test the universality and stationarity assumptions with two families of ambiguous visual stimuli. Each stimulus can be perceived in two different ways, parameterized by two opposite directions from a continuous circular variable. A large-sample study showed that almost all observers have preferred directions or biases, with directions lying within 90 degrees of the bias direction nearly always perceived and opposite directions almost never perceived. The biases differ dramatically from one observer to the next, and although nearly every bias direction occurs in the population, the population distributions of the biases are nonuniform, featuring asymmetric peaks in the cardinal directions. The biases for the two families of stimuli are independent and have distinct population distributions. Following external perturbations and spontaneous fluctuations, the biases decay over tens of seconds toward their initial values. Persistent changes in the biases are found on time scales of several minutes to 1 hour. On scales of days to months, the biases undergo a variety of dynamical processes such as drifts, jumps, and oscillations. The global statistics of a majority of these long-term time series are well modeled as random walk processes. The measurable fluctuations of these hitherto unknown degrees of freedom show that the assumptions of universality and stationarity in perception may be unwarranted and that models of perception must include both directly observable variables as well as covert, persistent states.The neural networks underlying visual perception are complex systems and, as such, undoubtedly have internal states. The formal notion of “state” can be defined as the minimal set of variables that, together with the input to a system and the fixed processing mechanisms, allows one to predict the system’s output (1). If perception is a function of both the sensory input and internal states, then—because states can vary both across individual observers and over time—the presence of an internal state would manifest itself as potentially large individual differences in the perception of the same stimulus and in coherent temporal variations of perception of the same stimulus over time in a single observer. It is known that visual functions can be modulated (2) on brief time scales by priming (3–6), aftereffects (7–9), and sequence effects (10–13) [and sometimes on larger time scales as well (14)]; can undergo visible short-term fluctuations in the presence of multistable stimuli (15–20); and can undergo long-term or permanent changes in their structure through learning (21–24). Despite these examples, little is known about internal states of the visual system. In terms of underlying mechanisms, the internal state is represented naturally in recurrent but not in feed-forward neural networks (25, 26).Here we measure patterns of biases in two families of visual stimuli and show that, contrary to most known cases, these patterns can vary radically from one observer to the next, leading to stimuli that are often perceived in opposite ways by different observers. We show that these bias patterns can be predicted to a large extent by simple variables. We therefore call these parameters “state variables.” They constitute a form of long-term but dynamic memory in perception and are related to but distinct from the phenomenon of short-term coherence in the perception of multistable stimuli known as sensory memory (15–20), as we discuss below.The stimuli in the first family, which we call structure–from–motion (SFM), consist of moving dots simulating planes rotating in depth (Fig. 1A). [Examples of stimuli and response procedures for the two families of stimuli may be seen at lab-perception.org/demo/p/sfm and lab-perception.org/demo/p/tfm. Following the 16-trial session, the web page displays the participant’s bias.] Each stimulus can be perceived as having one of two tilts (27–29), separated by 180 degrees (Fig. 1 B and I). The stimuli in the second family, transparency–from–motion (TFM), consist of two sets of dots moving in opposite directions (Fig. 1C). The two sets of dots are usually perceived as transparent layers, one of which is seen as being closer or more salient (30–32), also giving rise to a 180 degree ambiguity (Fig. 1D). Observers reported which of the two tilts they perceived for SFM stimuli and which of the two motion directions they perceived closer for TFM stimuli. Successive stimuli had different orientations (tilts or motion directions), sampled in random order from the entire 360 degree range—in contrast to procedures used to study spontaneous fluctuations (15–20), in which only one stimulus is presented. To reduce spontaneous fluctuations, stimuli were brief (0.5 s).Open in a separate windowFig. 1.The two families of ambiguous stimuli and summary of data analysis. (A and B) SFM stimuli. Optic flow such as in A is ambiguous because it can be generated by the two configurations shown in B, corresponding to surfaces with tilts that differ by 180 degrees (see I for definition) and opposite directions of rotation. (Stimuli used in the study had 45 degrees between tilts and rotation axes, rather than 0 degrees, as shown here.) (C and D) TFM stimuli. Two sets of dots moving in opposite directions (C), with no explicit depth cues, are usually perceived as segregated by motion direction into two transparent layers, with one of the layers seen as closer or more salient than the other; (D) the motion direction of the front or salient layer is ambiguous by 180 degrees. (E) We represent one stimulus by two opposite-facing arrows, corresponding to the two possible tilts (SFM, shown here) or front-layer motion directions (TFM) that can be perceived. We will represent the tilt or motion direction that was reported by the darker arrow. (F) A typical pattern of responses obtained when we present a series of tilts or motion directions. Actual experiments had more values of tilts or directions. (G) To calculate the bias vector, we take the sum of the unit vectors corresponding to the perceived tilts or motion directions. (H) The direction of the bias vector yields the preferred tilt or motion direction, whereas its length (normalized by its maximum value) is a measure of bias strength. (I) Illustration of slant and tilt, the variables we use to parametrize surface orientations in SFM stimuli. Tilt is the orientation in the image plane of the projection of the surface normal vector.Each twofold ambiguity at a different stimulus orientation could have been resolved as an independent stochastic decision, which would have yielded an isotropic pattern of perceptual decisions across stimulus orientations. As we shall show, however, perceptions at different orientations are far from isotropic, following a pattern that is both highly stereotypical and idiosyncratic. The individual differences are governed by state variables—bias parameters that exhibit coherence over time, while also undergoing cumulative changes—whose dynamics we explore below.