Distinguishing bias from sensitivity effects in multialternative detection tasks

Abstract

Studies investigating the neural bases of cognitive phenomena increasingly employ multialternative detection tasks that seek to measure the ability to detect a target stimulus or changes in some target feature (e.g., orientation or direction of motion) that could occur at one of many locations. In such tasks, it is essential to distinguish the behavioral and neural correlates of enhanced perceptual sensitivity from those of increased bias for a particular location or choice (choice bias). However, making such a distinction is not possible with established approaches. We present a new signal detection model that decouples the behavioral effects of choice bias from those of perceptual sensitivity in multialternative (change) detection tasks. By formulating the perceptual decision in a multidimensional decision space, our model quantifies the respective contributions of bias and sensitivity to multialternative behavioral choices. With a combination of analytical and numerical approaches, we demonstrate an optimal, one-to-one mapping between model parameters and choice probabilities even for tasks involving arbitrarily large numbers of alternatives. We validated the model with published data from two ternary choice experiments: a target-detection experiment and a length-discrimination experiment. The results of this validation provided novel insights into perceptual processes (sensory noise and competitive interactions) that can accurately and parsimoniously account for observers' behavior in each task. The model will find important application in identifying and interpreting the effects of behavioral manipulations (e.g., cueing attention) or neural perturbations (e.g., stimulation or inactivation) in a variety of multialternative tasks of perception, attention, and decision-making.

Changes in perceptual sensitivity related to spatial cues depends on subcortical activity

“…Luo and Maunsell (23) adopted a classic signal detection framework (24) and showed that attention-related modulation in visual cortical area V4 was related to changes in perceptual sensitivity, but not to changes in selection criterion; in their discussion, they suggest the complementary possibility that criterion shifts are mediated by subcortical structures such as the SC. This suggestion is consistent with other physiological results implicating the SC in criterion changes (25) and stimulus selection (26), and with recent models of attention that draw distinctions between changes in perceptual sensitivity and changes in choice bias (27). If the SC were associated with criterion shifts and not perceptual sensitivity, then when activity in the SC is suppressed by reversible inactivation animals should still show changes in perceptual sensitivity, because this aspect of their behavior would be mediated by cortical circuits left intact during the SC inactivation.…”

“…SDT-based models are often used to describe both detection and two-alternative forced-choice tasks (24). Extensions of this framework to multiple alternatives have been described in detail previously (27,35). Our model is derived in a manner similar to that of Sridharan et al (27) and we therefore superficially recapitulate its derivation.…”

“…The model makes no assumptions about the specific mechanisms that give rise to these evidence distributions except for the usual assumption that these distributions are Gaussian. Similarly, response bias is reflected by the decision criterion, but instead of a single scalar value as in the SDT model, criteria are conceptualized as planes separating the different regions of the decision space (27).…”

“…The total expected loss for option i is the sum over all conditional losses: R ij = P j≠i L ij pðjjψÞ. As Sridharan et al (27) observed, all decision regions share at least a point as a boundary within the decision space, meaning that planes of equal risk, or risk "isosurfaces," separate each decision region from every other decision region. Within each region, the conditional loss for the corresponding choice is less than that of each of the alternatives.…”

Visual attention: Linking prefrontal sources to neuronal and behavioral correlates

“…We will 30 first address the attention-driven changes in visual processing and 31 how they could benefit behavior (Section 1), then turn to some 32 methodological concerns regarding measuring attention at a 33 behavioral level (Section 2), review the evidence for several areas 34 as sources of attentional modulation (Section 3), and finally 35 examine experiments seeking to link attention in these source 36 areas to the signatures previously discussed (Section 4). (Mitchell et al, 2007;Cohen and Maunsell, 2009), 69 enhanced contrast sensitivity (Reynolds et al, 2000), increased 70 synaptic efficacy (Briggs et al, 2013), changes in noise correlations 71 between neurons (Mitchell et al, 2009;Ruff and Cohen, 2014; 72 Cohen and Maunsell, 2009), decreases in low-frequency LFP power 73 and coherence (Fries et al, 2001(Fries et al, , 2008Mitchell et al, 2009), 74 increases in gamma-band LFP power (Fries et An enhanced neuronal representation of the target stimulus 86 could underlie the behavioral benefits of attention, including 87 greater sensitivity (Sridharan et al, 2014), greater spatial resolu-88 tion (Carrasco, 2011), and faster response times (Posner, 1980). 89 Here we summarize the potential representational benefits of the 90 reported signatures of attention.…”