Quantifying the effect of intertrial dependence on perceptual decisions

Abstract

In the perceptual sciences, experimenters study the causal mechanisms of perceptual systems by probing observers with carefully constructed stimuli. It has long been known, however, that perceptual decisions are not only determined by the stimulus, but also by internal factors. Internal factors could lead to a statistical influence of previous stimuli and responses on the current trial, resulting in serial dependencies, which complicate the causal inference between stimulus and response. However, the majority of studies do not take serial dependencies into account, and it has been unclear how strongly they influence perceptual decisions. We hypothesize that one reason for this neglect is that there has been no reliable tool to quantify them and to correct for their effects. Here we develop a statistical method to detect, estimate, and correct for serial dependencies in behavioral data. We show that even trained psychophysical observers suffer from strong history dependence. A substantial fraction of the decision variance on difficult stimuli was independent of the stimulus but dependent on experimental history.We discuss the strong dependence of perceptual decisions on internal factors and its implications for correct data interpretation.

Adaptable history biases in human perceptual decisions

“…Choice-history biases have been observed when subjects had to make judgments about physical weights (9), auditory stimuli (12,16,18), or visual stimuli (4,8,10,11,14,18). As in our experiment, subjects show a diversity of biases ranging from switching strategies (4,9,12,14,16), to staying strategies (10,11,18), to success-stay/fail-switch strategies (5, 6, 8, 18). These biases are typically limited to the preceding one trial and the magnitude of the choice history biases is inversely proportional to the strength of sensory stimulus, such that weaker sensory stimulus elicits stronger choice history biases (13,15,18).…”

“…As in our experiment, subjects show a diversity of biases ranging from switching strategies (4,9,12,14,16), to staying strategies (10,11,18), to success-stay/fail-switch strategies (5, 6, 8, 18). These biases are typically limited to the preceding one trial and the magnitude of the choice history biases is inversely proportional to the strength of sensory stimulus, such that weaker sensory stimulus elicits stronger choice history biases (13,15,18). These effects are readily captured by our simple choice history model (Fig.…”

“…These biases generally manifest as switch-after-failure A B or as stay-after-success strategies (8,10,11,18). Both types of biases were disadvantageous in that they reduced sensitivity for most of the subjects.…”

“…However, under such conditions, human observers exhibit nonsensory biases (9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20) For instance, in the classical two-alternative forced choice task, a subject is asked to make a perceptual decision based solely on the present sensory evidence (21) and to use success or failure feedback only to optimize their use of such sensory information (22). However, both mice (14) and humans (15,17,18) make choices that are biased by their recent history of successes and failures and are thus suboptimal. Such biases, where prior beliefs rather than evidence guide behavior, are a form of fallacy, similar to the gambler's fallacy or hot-hand fallacy (23, cf.…”

The use of computational modeling to link sensory processing with behavior in Drosophila

“…behavioral states). While the standard GLM framework can still accommodate these cases (Pillow et al 2008;Fründ et al 2014), more complex types of non-stationarities affecting the stimulus response mapping can be modelled by combining LNs with hidden Markov models (HMM), in which the HMM models the switching between states and one LN model per state captures state-specific input-output transformations ( (Escola et al 2011), see (Wiltschko et al 2015) for a related approach).…”