Martin Lages

Spatial frequency discrimination: Visual long-term memory or criterion-setting?

A long-term sensory memory is believed to account for spatial frequency discrimination when reference and test stimuli are separated by long intervals. We test an alternative proposal: that discrimination is determined by the range of test stimuli, through their entrainment of criterion-setting processes. Experiments 1 and 2 show that the 50% point of the psychometric function is largely determined by the midpoint of the stimulus range, not by the reference stimulus. Experiment 3 shows that discrimination of spatial frequencies is similarly affected by orthogonal contextual stimuli and parallel contextual stimuli and that these effects can be explained by criterion-setting processes. These findings support the hypothesis that discrimination over long intervals is explained by the operation of criterion-setting processes rather than by long-term sensory retention of a neural representation of the stimulus.

Bayesian models of binocular 3-D motion perception

Psychophysical studies on three-dimensional (3-D) motion perception have shown that perceived trajectory angles of a small target traveling in depth are systematically biased. Here, predictions from Bayesian models, which extend existing models of motion-first and stereo-first processing, are investigated. These statistical models are based on stochastic representations of monocular velocity and binocular disparity input in a binocular viewing geometry. The assumption of noise in these inputs together with a plausible prior for 3-D motion leads to testable predictions of perceived trajectory angle and velocity. Results from two experiments are reported, suggesting that disparity rather than motion processing introduces perceptual bias.

How Predictable are “Spontaneous Decisions” and “Hidden Intentions”? Comparing Classification Results Based on Previous Responses with Multivariate Pattern Analysis of fMRI BOLD Signals

In two replication studies we examined response bias and dependencies in voluntary decisions. We trained a linear classifier to predict “spontaneous decisions” and in the second study “hidden intentions” from responses in preceding trials and achieved comparable prediction accuracies as reported for multivariate pattern classification based on voxel activities in frontopolar cortex. We discuss implications of our findings and suggest ways to improve classification analyses of fMRI BOLD signals that may help to reduce effects of response dependencies between trials.

Screening and sampling in studies of binocular vision

Binocular deficits are relatively common within a typical sample of observers. This has implications for research on binocular vision, as a variety of stereo deficits can affect performance. Despite this, there is no agreed standard for testing stereo capabilities in observers and many studies do not report visual abilities at all. Within the stereo literature, failure to report screening and sampling has the potential to undermine the results of otherwise strictly controlled research. We reviewed research articles on binocular vision published in three journals between 2000 and 2008 to illustrate how screening for binocular deficits and sampling of participants is approached. Our results reveal that 44% of the studies do not mention screening for stereo deficits and 91% do not report selection of participants. The percentage of participants excluded from studies that report stereo screening amounts to 3.9% and 0.7% for studies that do not report stereo screening. These low numbers contrast with the exclusion of 17.6% of participants in studies that report screening for binocular deficits as well as selection of participants. We discuss various options for stereo testing and the need for stereo-motion testing with reference to recent research on binocular perception.

Spatial and temporal tuning of motion in depth.

We used the Pulfrich effect to investigate perception of motion in depth. Independent manipulation of spatial and temporal frequency content in stereoscopic motion stimuli revealed the tuning characteristics of motion-in-depth perception. Sensitivity to interocular phase difference between sinusoidally oscillating sine-wave gratings was measured in four observers who judged direction of motion in depth. Discrimination thresholds in terms of interocular phase difference were determined to investigate spatial and temporal tuning characteristics of a system that is based on interocular phase difference, interocular delay, binocular disparity and velocity difference. Temporal frequency tuning of interocular phase difference thresholds was band pass and relatively dependent on spatial frequency variation. These results together with evidence from two control experiments support the idea that sensitivity to direction of motion in depth is limited by a stereo-motion system that monitors binocular horizontal disparity and motion rather than interocular phase difference, interocular delay, or interocular velocity difference.

Motion-aftereffect-induced blindness

Motion-induced blindness (MIB) describes the occasional disappearance of salient visual objects in the presence of moving features (Y. S. Bonneh, A. Cooperman, & D. Sagi, 2001). Here we test whether motion adaptation and the ensuing motion aftereffect (MAE) are sufficient to trigger disappearance of salient targets. In three experiments, observers adapted to either rotating or static stimuli. Immediately afterwards, a static test pattern was presented consisting of a mask with texture elements and three superimposed target dots in a triangular arrangement. Observers reported dot disappearance and reappearance. The results clearly show that illusory motion in a static test pattern, following motion adaptation, promotes the disappearance of target dots. Furthermore, disappearance is modulated by the depth relationship between test pattern and targets, increasing for targets placed stereoscopically behind the test pattern. We conclude that MIB is influenced by perceived relative motion between depth-segregated features.

A Criterion Setting Theory of Discrimination Learning that Accounts for Anisotropies and Context Effects

We can discriminate departures from the vertical or horizontal more accurately than from other orientations. This may reflect perceptual learning, but the mechanisms behind such learning are not well understood. Here we derive a theory of discrimination learning based on criterion setting theory (CST; Treisman and Williams, 1984), an extension of signal detection theory in which judgment of the current stimulus is partly determined by previous discriminations and context. The CST-based theory of discrimination learning (CST-DL) describes mechanisms which use information from previous acts of discrimination to improve current decision making. CST-DL distinguishes between types of decision criteria and provides an account of anisotropies and context effects affecting discrimination. Predictions from this model are tested in experiments on anisotropies in orientation and depth perception. The results obtained support CST-DL. They also support the conclusion that the account of the retention of sensory information in delayed discrimination provided by CST is superior to the traditional belief that information retention relies on a fixed memory trace or representation of the stimulus.

On the inverse problem of binocular 3D motion perception

It is shown that existing processing schemes of 3D motion perception such as interocular velocity difference, changing disparity over time, as well as joint encoding of motion and disparity, do not offer a general solution to the inverse optics problem of local binocular 3D motion. Instead we suggest that local velocity constraints in combination with binocular disparity and other depth cues provide a more flexible framework for the solution of the inverse problem. In the context of the aperture problem we derive predictions from two plausible default strategies: (1) the vector normal prefers slow motion in 3D whereas (2) the cyclopean average is based on slow motion in 2D. Predicting perceived motion directions for ambiguous line motion provides an opportunity to distinguish between these strategies of 3D motion processing. Our theoretical results suggest that velocity constraints and disparity from feature tracking are needed to solve the inverse problem of 3D motion perception. It seems plausible that motion and disparity input is processed in parallel and integrated late in the visual processing hierarchy.

Flipping a coin in your head without monitoring outcomes? Comments on predicting free choices and a demo program

In a recent study Soon et al. (2013) predicted abstract intentions from fMRI BOLD activities in localized areas of the brain. Activities in a spherical cluster of voxels served as input to a multivariate pattern classifier (linear SVM). The accuracy for predicting the intention to add or subtract two numbers was determined for clusters centered on different voxels. A prediction accuracy of 60% averaged across participants and based on 10-fold cross-validation was achieved for patterns of voxel activities in the medial frontopolar cortex and precuneus up to 4 s before participants reported being consciously aware of their decision. The prediction accuracy in this study was similar to studies on predicting spontaneous left or right motor decisions (Soon et al., 2008; Bode et al., 2011). Since the task demands placed on the participants create similar methodological issues as in previous studies (Lages and Jaworska, 2012), it seems possible that the multivariate classifier picked up sequential information processing between trials (Bode et al., 2012). Although the average prediction accuracy of 60% returned to chance level for patterns of voxel activity in the two brain areas shortly after the onset of a new trial and remained at 50% between trials, this observation is neither necessary nor sufficient for the absence of sequential information processing. In order to investigate sequential dependencies the outcome of at least one preceding trial and the current trial needs to be taken into account. Depending on task and response, sequential information processing between trials may emerge in distributed form within the default mode network (DMN) at variable time points (Guggisberg and Mottaz, 2013). In the following we illustrate the issue and suggest how the data may be analyzed. To illustrate sequential effects consider the following inconspicuous sequence of ten responses (S, A, A, A, S, A, S, A, S, S) where A and S stand for freely choosing addition and subtraction, respectively. There are five As and five Ss suggesting a binomial process with rate parameter p = 0.5. However, if we consider the nine subsequent pairs of responses {(S,A), (A,A), …, (S,S)} then we obtain unequal transition probabilities. A trained classifier that predicts the next response from the preceding response would be 3 out of 5 times or 60% correct if the preceding response is A and 3 out of 4 times or 75% correct if the response is S. Starting with a random guess in the first trial, this gives an average prediction accuracy of 65%. In two behavioral studies replicating two different choice tasks (Haynes et al., 2007; Soon et al., 2008) we used subsequent response pairs to train a linear classifier (SVM) and obtained an average prediction accuracy of 61.6 and 64.1%, respectively (Lages and Jaworska, 2012). When asked to generate a random sequence, people typically alternate between binary responses with a probability of about 0.6 (Lopes, 1982). This response pattern appears to be relevant in Soon et al.s study (2013) since the only behavioral evidence for memory-less choice in the 17 (out of 34) selected participants is a histogram plotting average frequencies for different lengths of response sequences fitted by an exponential distribution (Figure S1). The authors take the excellent fit as evidence for random performance. Recently Allefeld et al. (2013) released a detailed account of the behavioral data but there are no further details how the data were compiled and fit. Nevertheless, it is discernable from their Figure S1 that the fit represents an exponential function with two parameters rather than an exponential distribution with a single parameter and that observed frequencies do not add up to probability 1.0. In addition, an exponential distribution would only provide a meaningful approximation of the geometrically distributed phase lengths if choosing addition and subtraction were equally probable [p = (1−p) = 0.5]. However, even with a best-fitting rate parameter of 1-exp(−0.826) ≈0.56 the exponential distribution underestimates the relative frequency of alternations (A,S) and (S,A) with phase length 1 as well as repetitions (A,A,S) and (S,S,A) with phase length 2 (see Figure ​Figure1).1). Increased frequencies for short phase lengths are a hallmark of non-random human choice behavior (Wagenaar, 1972; Lopes, 1982; Treisman and Faulkner, 1987; Falk and Konold, 1997) and these characteristics are not only present in the behavioral data of Soon et al. (2013) but also in Soon et al. (2008); Bode et al. (2011), and Haynes et al. (2007) suggesting that free or spontaneous choice tasks result in non-random behavior. Figure 1 Histogram for length of response sequences (phase length or runs) re-plotted as relative frequencies (adapted from Figure S1 in Soon et al., 2013). The data points are fitted by an exponential function with two parameters (red curve) and an exponential ... A related concern arises from the searchlight analyses. If patterns of voxel activities are analyzed within a moving spherical cluster to predict behavioral responses then pre-processing of the data and definition of the searchlight are important (Etzel et al., 2013; Todd et al., 2013). The implementation of regions of interest, temporal constraints (hemodynamic delay), pre-processing (covariates), and data selection can invalidate the results of a searchlight analysis (Kriegeskorte et al., 2009; Lindquist et al., 2009). In Soon et al. (2013) trials were selected (undersampled) in order to balance the mean response rate. It is therefore possible that the searchlight found a cluster of voxels that was predictive of the next response in the context of the preceding response, simply because transitions between successive responses remained unbalanced. A repeated choice task with self-monitoring of the decision process invites sequential dependencies because the observer has to remember goals, constraints, and execution of the task. If, for example, participants shift a decision criterion following each response (Lages and Treisman, 1998, 2010; Lages and Paul, 2006; Treisman and Lages, 2010) or engage in metacognition by recalling the last response before making the next then neural correlates of these response-dependent processes introduce a confound that would be picked up by a searchlight analysis as soon as transitions between response categories are unbalanced. We recommend that rather than postulating a 50% chance level, prediction accuracy should be tested with a permutation test (Stelzer et al., 2013) and/or separate multivariate classification analyses conditional on the previous response. Only if individual prediction accuracies reliably exceed observable benchmarks such as response bias and transition probabilities can we rest assured that results are not confounded. The interested reader is invited to test predictability of their own free choice behavior by downloading the demo program in the Appendix.

Motion and disparity processing informs Bayesian 3D motion estimation

Welchman et al. (1) propose a Bayesian model that combines a velocity prior for slow motion (,2, ,3) with approximations of lateral velocity Vx and velocity in depth Vz to model biased perception of 3D motion trajectories in the x–z plane (3, ,4). Although decomposing a motion …