Stimulus Context and Reward Contingency Induce Behavioral Adaptation in a Rodent Tactile Detection Task

Abstract

Behavioral adaptation is a prerequisite for survival in a constantly changing sensory environment, but the underlying strategies and relevant variables driving adaptive behavior are not well understood. Many learning models and neural theories consider probabilistic computations as an efficient way to solve a variety of tasks, especially if uncertainty is involved. Although this suggests a possible role for probabilistic inference and expectation in adaptive behaviors, there is little if any evidence of this relationship experimentally. Here, we investigated adaptive behavior in the rat model by using a well controlled behavioral paradigm within a psychophysical framework to predict and quantify changes in performance of animals trained on a simple whisker-based detection task. The sensory environment of the task was changed by transforming the probabilistic distribution of whisker deflection amplitudes systematically while measuring the animal's detection performance and corresponding rate of accumulated reward. We show that the psychometric function deviates significantly and reversibly depending on the probabilistic distribution of stimuli. This change in performance relates to accumulating a constant reward count across trials, yet it is exempt from changes in reward volume. Our simple model of reward accumulation captures the observed change in psychometric sensitivity and predicts a strategy seeking to maintain reward expectation across trials in the face of the changing stimulus distribution. We conclude that rats are able maintain a constant payoff under changing sensory conditions by flexibly adjusting their behavioral strategy. Our findings suggest the existence of an internal probabilistic model that facilitates behavioral adaptation when sensory demands change.The strategy animals use to deal with a complex and ever-changing world is a key to understanding natural behavior. This study provides evidence that rodent behavioral performance is highly flexible in the face of a changing stimulus distribution, consistent with a strategy to maintain a desired accumulation of reward.

Emerging experience-dependent dynamics in primary somatosensory cortex reflect behavioral adaptation

“…Those animals had successfully acquired the basic training and first time adaptation to changing stimulus statistics. The psychophysical techniques were based on a behavioral paradigm we recently developed in the rat 16 . Figure 3a To compare the modulated S1 activity with the adapted behavior, we used classical signal detection theory 31,32 .…”

“…To test this hypothesis, we designed a series of psychophysical experiments evaluating behavioral performance and neuronal activity at different training stages. The training stages include gradual learning of a basic detection task and an advanced stage with changing sensory contingencies 16 . To repeatedly measure signals of large neuronal pools across training stages, we performed chronic wide-field imaging of S1 activity with the genetically encoded voltage indicator (GEVI) ‘ArcLight’ 17,18 in behaving mice.…”

“…However, the largest amplitude of the high range (16°) is not part of the low range, and vice versa, the smallest amplitude of the low range (1°) is not part of the high range.Figure 3bdepicts typical psychometric curves from an example mouse, performing the task first under the high range (magenta) and under the low range condition (green). There is an obvious and consistent shift of the psychometric curve in response to the changing stimulus statistics, which we refer to here as "adaptive behavior".In response to a shift in stimulus distribution, we consider two extreme hypotheses as established previously by a simple reward expectation model16 . The null hypothesis (H0) asserts that the animal does not adjust its behavior, and thus operates from the same psychometric function (black dotted curve on top of magenta curve).…”

Dynamics of history-dependent perceptual judgment

“…Although it can be appealing to conceive of sensory-perceptual systems as ideal observers that reliably map physical inputs onto the appropriate responses, experimental data frequently reveal variability in perceiving or acting upon separate presentations of an identical stimulus. Performance of a perceptual task appears to involve less a rigid stimulus-to-response mapping than a flexible adjustment to the full experimental context, including the history of rewards, choices and stimuli 14,[32][33][34][35] . By sorting the sequences of trials according to each of these factors, it becomes possible to disentangle their contributions.…”

“…This lack of constancy can be studied in the laboratory, where the identical physical input can yield different perceptual judgments across the extended sequence of trials typical of a controlled experiment. Paralleling the variability in subjective estimation and decision making, variability in the face of an unchanging physical input is also found in neuronal responses at all levels of the sensory pathway and at all levels of resolution, from single neurons to EEG 1-5 . Some studies have been able to link the variability in the judgment of physical input to the sequence of preceding trialsthe trial history [6][7][8][9][10][11][12][13][14][15] . But trial history is itself complex and multifactorial, for it consists of previous stimuli, previous choices (expressed as actions), and previous outcomes (rewards, collected or lost).…”