Decision making is the cognitive process of choosing an action among a set of alternatives. Decision making is often studied in experiments, composed of trials, each associated with a single decision. While a decision in a trial is primarily determined by the relevant features of the alternatives in that trial, biases are commonly observed 1 .Of specific relevance to this work are participant-specific tendencies to prefer one alternative over the other(s). Such biases, which we term idiosyncratic choice biases (ICBs) have been described as early as half a century ago in perceptual discrimination [2][3][4] and operant learning tasks [5][6][7] .In discrimination tasks, the ICBs interfere with the estimate of perceptual noise. In

Idiosyncratic tendency to choose one alternative over others in the absence of an identified reason is a common observation in two-alternative forced-choice experiments. It is tempting to account for it as resulting from the (unknown) participant-specific history and thus treat it as a measurement noise. Here we quantify idiosyncratic choice biases in a perceptual discrimination task and a motor task. We report substantial and significant biases in both cases that cannot be accounted for by the experimental context. Then, we present theoretical evidence that even in idealized experiments, in which the settings are symmetric, idiosyncratic choice bias is expected to emerge from the dynamics of competing neuronal networks. We thus argue that idiosyncratic choice bias reflects the microscopic dynamics of choice and therefore is virtually inevitable in any comparison or decision task.

In natural settings, many stimuli impinge on our sensory organs simultaneously. Parsing these sensory stimuli into perceptual objects is a fundamental task faced by all sensory systems. Similar to other sensory modalities, increased odor backgrounds decrease the detectability of target odors by the olfactory system. The mechanisms by which background odors interfere with the detection and identification of target odors are unknown. Here we utilized the framework of the Drift Diffusion Model (DDM) to consider possible interference mechanisms in an odor detection task. We first considered pure effects of background odors on either signal or noise in the decision-making dynamics and showed that these produce different predictions about decision accuracy and speed. To test these predictions, we trained mice to detect target odors that are embedded in random background mixtures in a two-alternative choice task. In this task, the inter-trial interval was independent of behavioral reaction times to avoid motivating rapid responses. We found that increased backgrounds reduce mouse performance but paradoxically also decrease reaction times, suggesting that noise in the decision making process is increased by backgrounds. We further assessed the contributions of background effects on both noise and signal by fitting the DDM to the behavioral data. The models showed that background odors affect both the signal and the noise, but that the paradoxical relationship between trial difficulty and reaction time is caused by the added noise.

In natural settings, many stimuli impinge on our sensory organs simultaneously. Parsing these sensory stimuli into perceptual objects is a fundamental task faced by all sensory systems. Similar to other sensory modalities, increased odor backgrounds decrease the detectability of target odors by the olfactory system. The mechanisms by which background odors interfere with the detection and identification of target odors are unknown. Here we utilized the framework of the Drift Diffusion Model (DDM) to consider possible interference mechanisms in an odor detection task. We consider effects of background odors on both signal and noise in the decision-making dynamics, and show that these produce different predictions about decision accuracy and speed. To test these predictions, we trained mice to detect target odors that are embedded in random background mixtures in a two-alternative choice task. We found that the behavioral data is most consistent with background odors acting by adding noise to the decision-making dynamics. The added noise decreases the correct rate, but also decreases decision times, thereby creating a paradoxical relationship between speed and accuracy of target detection, where mice make faster and less accurate decisions in the presence of background odors.

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