Having More Choices Changes How Human Observers Weight Stable Sensory Evidence

Abstract

Decision-making becomes slower when more choices are available. Existing models attribute this slowing to poor sensory processing, to attenuated rates of sensory evidence accumulation, or to increases in the amount of evidence required before committing to a decision (a higher decision threshold). However, studies have not isolated the effects of having more choices on sensory and decision-related processes from changes in task difficulty and divided attention. Here, we controlled task difficulty while independently manipulating the distribution of attention and the number of choices available to male and female human observers. We used EEG to measure steady-state visually evoked potentials (SSVEPs) and a frontal late positive deflection (LPD), EEG markers of sensory and postsensory decision-related processes, respectively. We found that dividing attention decreased SSVEP and LPD amplitudes, consistent with dampened sensory responses and slower rates of evidence accumulation, respectively. In contrast, having more choices did not alter SSVEP amplitude and led to a larger LPD. These results suggest that having more options largely spares early sensory processing and slows down decision-making via a selective increase in decision thresholds. When more choices are available, decision-making becomes slower. We tested whether this phenomenon is due to poor sensory processing, to reduced rates of evidence accumulation, or to increases in the amount of evidence required before committing to a decision (a higher decision threshold). We measured choice modulations of sensory and decision-related neural responses using EEG. We also minimized potential confounds from changes in the distribution of attention and task difficulty, which often covary with having more choices. Dividing attention reduced the activity levels of both sensory and decision-related responses. However, having more choices did not change sensory processing and led to larger decision-related responses. These results suggest that having more choices spares sensory processing and selectively increases decision thresholds.

Stimulus visibility and uncertainty mediate the influence of attention on response bias and visual contrast appearance

“…Attention has been shown to change the neural CRFs measured in visual cortex in many different ways (Figure 7, left panels). These include (a) contrast gain by which attention shifts the horizontal position of neural CRFs, (b) response gain by which attention scales neural activity multiplicatively, (c) baseline input increases by which attention predominantly enhances the baseline input of the neural CRFs without mediating neural responses at high contrasts, and (d) additive baseline shifts by which attention increases the magnitude of sensory signals equally across all contrast levels (Buracas & Boynton, 2007; Di Russo, Spinelli, & Morrone, 2001; Hara & Gardner, 2014; Itthipuripat et al, 2017; Itthipuripat, Cha, Deering, Salazar, & Serences, 2018; Itthipuripat, Ester, Deering, & Serences, 2014; Itthipuripat, Garcia, et al, 2014; Itthipuripat et al, 2019; Kim et al, 2007; Lee & Maunsell, 2009; Li, Lu, Tjan, Dosher, & Chu, 2008; Murray, 2008; Pestilli et al, 2011; Pooresmaeili, Poort, Thiele, & Roelfsema, 2010; Reynolds & Heeger, 2009; Reynolds, Pasternak, & Desimone, 2000; Sprague, Itthipuripat, Vo, & Serences, 2018; Sundberg, Mitchell, & Reynolds, 2009; Treue & Martinez-Trujillo, 1999; Wang & Wade, 2011; Williford & Maunsell, 2006).…”

Functional MRI and EEG Index Complementary Attentional Modulations

“…Although there are many variants of the Naka-Rushton equation, we decided to use this version (Eq. 6) to make contact with the large number of past studies that have also used this Naka-Rushton equation to fit contrast response functions measured using a variety of measurement techniques (e.g., psychophysics, single-unit electrophysiology, EEG, and fMRI; Martínez-Trujillio andTreue, 2002, Kim et al, 2007;Herrmann et al, 2010;Pestilli et al, 2011;Carandini and Heeger, 2011;Itthipuripat et al, 2014aItthipuripat et al, ,b, 2017Itthipuripat et al, , 2018Reynolds and Heeger, 2009). Because the G r and G c parameters control the response and contrast gain of the function where the contrast axis ranges from zero to ϱ, the G r and G c parameters could in principle exceed the realistic range of stimulus contrast (0 -100% contrast).…”

“…Discrepancies between fMRI and EEG measures of early stimulus-evoked responses (SSVEP, P1, and LPD/P3) left us wondering whether we could find any similarity in attentional modulations measured with these two methods. We first examined the contralateral slow negative-going wave that emerged ϳ800 -2000 ms after stimulus onset (termed here as the CLN), which has been recently found to track the focus of spatial atten- tion (Itthipuripat et al, 2018;Hakim et al, 2019). The CLN component has a characteristic (e.g., temporal window, polarity, and electrode location) that resembles the contralateral delay activity, which is a marker of the active maintenance of attention during visual search and the maintenance of information in working memory (Woodman and Luck, 1999;Vogel and Machizawa, 2004;Vogel et al, 2005;Woodman et al, 2009;Carlisle et al, 2011;Kuo et al, 2012;Tsubomi et al, 2013).…”

“…Results from fMRI often support a mechanism whereby attention increases the evoked response to all stimuli equally, regardless of their contrast, called an "additive shift" (Buracas and Boynton, 2007;Murray, 2008; but see Li et al, 2008;Pestilli et al, 2011;Sprague et al, 2018b). In contrast, results from EEG and other electrophysiological measurements in visual cortex often support a contrast-dependent response modulation (either a horizontal shift of the CRF, called "contrast gain", or a multiplicative scaling of the CRF, called "response gain": Reynolds et al, 2000;Di Russo et al, 2001;Martínez-Trujillo and Treue, 2002; but see Williford and Maunsell, 2006;Kim et al, 2007;Lee and Maunsell, 2009;Lauritzen et al, 2010;Wang and Wade, 2011;Andersen et al, 2012;Itthipuripat et al, 2014aItthipuripat et al, ,b, 2017Itthipuripat et al, , 2018. Although these different types of attention effects could be because of differences in task designs, stimulus properties, recording sites, training duration, cognitive demand, and subjects' attentional strategy and expertise (Reynolds and Heeger, 2009;Herrmann et al, 2010;Itthipuripat et al, 2014aItthipuripat et al, , 2017Cohen, 2014, 2016;Zhang et al, 2016;Maniglia and Seitz, 2018;Ni et al, 2018), another reasonable source of divergence is the neural response properties to which each measurement is most sensitive (Boynton, 2011; Itthipuripat and Serences, 2016).…”

Similar network compositions, but distinct neural dynamics underlying belief updating in environments with and without explicit outcomes

“…First, they avoid a critical downside of binary decision tasks, which are biased by a confirmation/exclusion criterion, as the exclusion of one option inherently implies the only alternative available is the correct choice to make. Second, despite the significant increase in computational demands associated with 3-options tasks (Churchland et al, 2008; Churchland and Ditterich, 2012; Itthipuripat et al, 2018; Tajima et al, 2019), the tasks used for this study rely on a simple set of rules, easy-to-compute stochastic associations (80% for the most prevalent bead in a jar and for the most likely outcome value associated to a card) and only three types of discrete evidence (i.e. the three feedback values or the three bead colors), reducing the effect of second-order uncertainty (Fleming and Dolan, 2012; Fleming and Daw, 2017).…”