Science produces models that provide parsimonious descriptions of the world. In cognitive psychology, models regularly compete to explain a few phenomena. But models can survive experiment after experiment, both because of the difficulty of capturing participant’s nuanced behavior, and because models often make highly overlapping predictions. In these cases, a model succeeds through its relative parsimony.
In cognitive psychology, measures of information criteria, specifically Akaike’s and the Bayesian information criteria (Schwarz 1978; Akaike 1973), determine a winning model.
In last week’s post, I discussed how some experiments in cognitive psychology require researchers to pick a differently intense stimulus for each participant. In particular, I discussed a procedure for picking an intensity that elicits positive responses on approximately half of trials, the staircase procedure. In the staircase procedure, the researcher increases the intensity after every positive response and decreases the intensity after every negative response. If the participant completes another set of trials in which the intensity is fixed to the average of the intensities that were used during the staircase, the participant will provide positive responses approximately half of the time.
Performing any experiment on cognition requires deciding which stimuli to use. Some experiments require participants to make many errors, requiring the stimuli to be challenging. In other experiments, participants must respond accurately, requiring stimuli that are easy but not so easy that participants lose attention. Moreover, participants behave idiosyncratically, so to avoid wasting either the researchers’ or participants’ time the stimuli ought to be tailored to each participant. To decide which stimuli to use, researchers can rely on a psychometric function (Figure 1).
Perceptual decisions can be deconstructed with evidence accumulation models. These models formalize expectations about how participants behave, when that behavior involves repeatedly sampling information towards until surpassing a necessary threshold of information. At a cognitive level, the different models instantiate the components differently, but at a neural level the models rely on common mechanisms. To accumulate evidence the models assume two distinct populations of neurons. One population responds to available information.
One framework for understanding perception casts it as inference: just as a statistician uncovers noisy data to uncover patterns, an organism perceives when it converts sensations into guesses about its environment. The framework not concrete enough to be called a theory of perception, since it is not clear what data could falsify it1. But the framework can remind perceptual researchers about the many strategies available for modeling the world.
Cognitive experiments can require participants to complete hundreds of trials, but completing so many trials invariably alters participants’ behavior. Their behavior late in the experiment can depend on their behavior early in the experiment. Although such dependence can be an experimental confound, the dependence itself can provide clues about cognition. One simple kind of dependence occurs through learning; hundreds of trials provides participants ample practice. A more subtle dependence can emerge between sequential trials, an effect called serial dependence.
Perceiving the world requires representing the world in neural tissue. A neuron is tuned to perceivable information when different values of that information cause the neuron to fire at a different rate. For example, most visual neurons are tuned to spatial location. The spatial tuning could be measured by placing a recording electrode in a neuron in a macaque’s visual cortex while the macaque fixated on the center of a computer monitor and a picture moved across that monitor.
Objects in the visual environment move suddenly and erratically, and visual perception must be sensitive to the changes that are important. But each saccade and head tilt change the image imprinted on the retina, and to perceive every tremor ignores the stability of the visual environment; a desk will still look like a desk in a few seconds. The visual system must therefore balance the ability to detect subtle changes in the environment against the efficiency afforded by accurate predictions.