On average, we move our eyes three times per second. Where we decide to look next is one of the most frequent choices that our brains make, but how does the brain know where to look? Previous work has established that we tend to look towards elements that ‘pop out’ visually, are relevant for our goals, or places that have previously provided us with information. However, a lot of eye movement behavior cannot be explained by these factors.

Koevoet, Strauch et al. wanted to better understand how our brains decide where to look next. The researchers hypothesized that the ‘cost’ of eye movements (the brain has to compute the visual consequences of the eyes moving in a certain way) might influence where we look. To save resources, more ‘affordable’ eye movements may be preferred over costly ones.

To test this hypothesis, Koevoet, Strauch et al. measured the effort of eye movements by looking at the pupil size before an eye movement is made. A larger pupil size indicates more effort is required to prepare the movement. The measurements showed that the cost of eye movements depended on the direction of the eye movement, and that affordable ones were chosen over more costly ones. These findings indicate that effort drives where eyes move, supporting the more general idea that the brain is highly efficient with its resources.

Koevoet, Strauch et al.’s experiments show that cost predicts where the eyes will move. Future studies should explore where and how the brain stores and uses this information to make decisions, and how cost is integrated with other established factors that drive where we look.

Humans make fast, ballistic eye movements, called saccades, to explore the rich visual world (Findlay and Gilchrist, 2003). Saccades are executed approximately three to four times per second (Henderson, 2003; Henderson and Hollingworth, 1998). Where to saccade is, therefore, one of the most frequent decisions the brain is faced with (Bargary et al., 2017).

It is well established that the physical properties of the environment (bottom-up information) (Kümmerer et al., 2022; Kümmerer et al., 2016; Itti et al., 1998; Itti and Koch, 2001; Theeuwes, 1994), the goals of the observer (top-down information) (Posner, 1980; Petersen and Posner, 2012; Posner and Petersen, 1990; Desimone and Duncan, 1995), and prior knowledge about a scene (selection history) (Awh et al., 2012; Theeuwes et al., 2022) drive where the eyes are moved. However, even when these factors are kept constant, there are many systematic biases in eye-movement behavior, such as a bias for cardinal compared with oblique saccade directions (Foulsham and Kingstone, 2010; Gilchrist and Harvey, 2006; Bays and Husain, 2012; Anderson et al., 2008; Engbert and Kliegl, 2003; Tatler and Vincent, 2009). The presence of these biases suggests that additional factors must contribute to the decision of where to saccade, here referred to as ‘saccade selection.’ Recent evidence suggests that the effort involved with planning and executing (eye) movements may be one crucial factor in driving action selection (Shadmehr and Ahmed, 2020; Thomas et al., 2022; Hagura et al., 2017; Kadner et al., 2022; Cos et al., 2014; Cos et al., 2011; Cos et al., 2012; Todorov and Jordan, 2002). Effort is thought to be minimized whenever possible (Hull, 1943; Tsai, 1932), likely because it is costly to spend inherently limited cognitive resources (Friston, 2010; Shadmehr and Ahmed, 2020). We here use the term ’saccade cost’ to describe the intrinsic effort associated with planning and executing saccades. Although saccades are relatively affordable [i.e. not very costly; Findlay and Gilchrist, 2003; Theeuwes, 2012], they are executed very often (Henderson, 2003; Henderson and Hollingworth, 1998) and, therefore, even small costs should add up over time (Shadmehr and Ahmed, 2020; Koevoet et al., 2023). We here hypothesized that affordable saccades are preferred over costly saccades. This would be in line with recent evidence from computational models suggesting that saccade costs predict saccade behavior [e.g. Hoppe and Rothkopf, 2016; Hoppe and Rothkopf, 2019; Kadner et al., 2022; Thomas et al., 2022]. These studies either assumed saccade costs or indirectly inferred them from gaze behavior itself. However, no study has been able to quantify saccade costs (neuro-)physiologically, and therefore been able to directly test this hypothesis until recently.

We recently demonstrated that the effort of saccade planning can be measured with pupil size, which allows for a physiological quantification of saccade costs as long as low-level visual factors are controlled for (Koevoet et al., 2023). Pupil size is an established marker of effort (Kahneman, 1973; Bumke, 1911; Laeng et al., 2012; Mathôt, 2018; Strauch et al., 2022; Loewenfeld, 1993; Sirois and Brisson, 2014; van der Wel and van Steenbergen, 2018; Beatty, 1982). For instance, loading more in working memory or tracking more objects results in stronger pupil dilation (Beatty, 1982; Koevoet et al., 2024; Robison and Unsworth, 2019; Alnæs et al., 2014; Kahneman and Beatty, 1966; Unsworth and Miller, 2021; Unsworth and Robison, 2015; Ahern and Beatty, 1979; Hess and Polt, 1964). Pupil size not only reflects cognitive (or mental) effort but also the effort of planning and executing movements (Naber and Murphy, 2020; Richer and Beatty, 1985; Bumke, 1911). We leveraged this to demonstrate that saccade costs can be captured with pupil size, and are higher for oblique compared with cardinal directions (Koevoet et al., 2023). Here, we addressed whether saccade costs predict where to saccade.

We hypothesized that participants would prefer affordable over costly saccades to minimize effort expenditure. To test this, we first mapped out saccade costs across directions by measuring pupil size during saccade planning. To assess saccade preferences across the same directions, a subsequent free choice saccade task was employed. Previewing our results, saccade costs indeed predicted saccade preferences, as affordable directions were preferred over costly alternatives. Strikingly, this general principle even held when participants searched for targets in natural scenes in two additional experiments: saccade cost remained a fundamental driver of saccade selection. If saccades and other cognitive operations consume the same resources, this should reflect in adaptively changing saccade preferences in light of altering cognitive demands. We tested this idea experimentally by comparing saccade preferences with and without an auditory dual-task. As hypothesized, participants made fewer saccades overall under increased cognitive demand and especially cut the most costly directions. This provides convergent evidence that saccades are costly and rely at least in part on the same cognitive resources as other cognitively demanding operations.

We here investigated whether effort determines saccade preferences. We first measured pupil size prior to saccade execution across directions as a physiological marker of effort and thus saccade costs. Next, saccade preferences were assessed in the same participants and directions. We observed that affordable saccades were preferred over costly ones. This is especially remarkable given that the delayed saccades in the planning task likely differ in their oculomotor program from the immediate saccades in the preference task in some regard. Furthermore, when two possible saccade directions differed more in saccade cost, we found higher oculomotor conflict as indexed by stronger saccade trajectory deviations away from the non-selected option and increased onset latencies. In two additional experiments, we demonstrated the link between saccade costs and saccade preferences to be robust even when participants made unconstrained eye movements during natural viewing. Lastly, saccade directions were flexibly adjusted based on cost as cognitive demand increased. Together, this demonstrates that saccade costs fundamentally underlie saccade selection, even when physical salience, the goals of the observer, and selection history affect where the eyes are moved.

What contributes to intrinsic saccade costs? We speculate that at least three processes contribute to the total cost of a saccade (Koevoet et al., 2023): the complexity of oculomotor programming (Jainta et al., 2011; Wang and Munoz, 2021; Shadmehr and Ahmed, 2020), shifting of presaccadic attention (Rizzolatti et al., 1987; Deubel and Schneider, 1996), and predictive/spatial remapping (Rolfs, 2015; Melcher, 2007; Bays and Husain, 2007; Fabius et al., 2019). The complexity of an oculomotor program is arguably shaped by its neural underpinnings. For example, oblique but not cardinal saccades require communication between pontine and midbrain circuits (Sparks, 2002; King and Fuchs, 1979; Curthoys et al., 1984). Such differences in neural complexity may underlie the additional costs of oblique compared with cardinal saccades. Besides saccade direction, other properties of the ensuing saccade such as its speed, distance, curvature, and accuracy may contribute to a saccade’s total cost (Shadmehr and Ahmed, 2020; Naber and Murphy, 2020; Wang et al., 2017; Koevoet et al., 2023; Wang et al., 2021) but this remains to be investigated directly. Furthermore, presaccadic attention is shifted prior to each saccade to prepare the brain for the abrupt changes in retinal input resulting from saccades through spatial/predictive remapping (Melcher and Colby, 2008; Rolfs, 2015; Melcher, 2007; Bays and Husain, 2007; Rizzolatti et al., 1987; Deubel and Schneider, 1996; Van der Stigchel and Hollingworth, 2018). This preparation for upcoming changes in retinal input consumes neurocognitive resources and, therefore, likely also contributes to saccade costs (Koevoet et al., 2023; Shadmehr and Ahmed, 2020). To better understand saccade selection more generally, future work should elucidate which processes contribute to saccade costs, and how costs shape different (aspects of) saccades.

The observed differences in saccade costs across directions could be linked to established anisotropies in perception (Wexler et al., 2022; Barbot et al., 2021; Baldwin et al., 2012; Greenwood et al., 2017; Chakravarthi et al., 2022; Abrams et al., 2012; Ezzo et al., 2023), attention (Hanning et al., 2024; Hanning et al., 2022; Schütz, 2014; Carrasco et al., 2001; Mackeben, 1999; Ohl et al., 2024), saccade characteristics (Hanning et al., 2024; Greenwood et al., 2017; Hanning et al., 2022; Ohl et al., 2024), and (early) visual cortex (Himmelberg et al., 2022; Van Essen et al., 1984; Benson et al., 2021; Himmelberg et al., 2021; Silva et al., 2018; also see Himmelberg et al., 2023). For example, downward saccades are more costly than upward saccades, which mimics a similar asymmetry in early visual areas wherein the upper visual field is relatively underrepresented (Himmelberg et al., 2022; Van Essen et al., 1984; Benson et al., 2021; Himmelberg et al., 2021; Silva et al., 2018); similarly stronger presaccadic benefits are found for down- compared with upward saccades (Hanning et al., 2024, Hanning et al., 2022). Moreover, upward saccades are more precise than downward saccades (Greenwood et al., 2017). Future work should elucidate where saccade cost or the aforementioned anisotropies originate from and how they are related - something that pupil size alone cannot address.

We here measured cost as the degree of effort-linked pupil dilation. In addition to pupil size, other markers may also indicate saccade costs. For example, saccade latency has been proposed to index oculomotor effort (Shadmehr et al., 2019), whereby saccades with longer latencies are associated with more oculomotor effort. This makes saccade latency a possible complementary marker of saccade costs (also see Appendix). Although relatively sluggish, pupil size is a valuable measure of attentional costs for (at least) two reasons. First, pupil size is a highly established marker of effort, and is sensitive to effort more broadly than only in the context of saccades (Sirois and Brisson, 2014; Strauch et al., 2022; Kahneman, 1973; Kahneman and Beatty, 1966; Beatty, 1982; Laeng et al., 2012; Bumke, 1911; van der Wel and van Steenbergen, 2018; Koevoet et al., 2024; Loewenfeld, 1993; Mathôt, 2018). Pupil size, therefore, allows to capture not only the costs of saccades, but also of covert attentional shifts (Koevoet et al., 2023), or shifts with other effectors such as head or arm movements (Richer and Beatty, 1985; Voudouris et al., 2023). Second, as we have demonstrated, pupil size can measure saccade costs even when searching in natural scenes (Figure 4). During natural viewing, it is difficult to disentangle fixation duration from saccade latencies, complicating the use of saccade latency as a measure of saccade cost. Together, pupil size, saccade latency, and potential other markers of saccade cost could fulfill complementary roles in studying the role of cost in saccade selection.

Our findings are in line with established effort-based models that assume costs to be weighed against rewards during decision-making (Kool et al., 2010; Kurzban et al., 2013; Shenhav et al., 2021; Shenhav et al., 2013; Shenhav et al., 2017; Westbrook and Braver, 2015). In such studies, reward and cognitive/physical effort are often parametrically manipulated to assess how much effort participants are willing to exert to acquire a given (monetary) reward (e.g. Bustamante et al., 2023, Müller et al., 2022). Whereas this line of work manipulated the extrinsic costs and/or rewards of decision options (e.g. perceptual consequences of saccades [Lisi et al., 2019; Sedaghat-Nejad and Shadmehr, 2021] or consequences associated with decision options), we here focus on the intrinsic costs of the movement itself (in terms of cognitive and physical effort). Relatedly, the intrinsic costs of arm movements are also considered during decision-making: biomechanically affordable movements are generally preferred over more costly ones (Cos et al., 2011; Cos et al., 2014; Cos et al., 2012). We here extend these findings in two important ways. First, until now, the intrinsic costs of saccades and other movements have been inferred from gaze behavior itself or by using computational modeling (Thomas et al., 2022; Kadner et al., 2022; Hoppe and Rothkopf, 2016; Hoppe and Rothkopf, 2019; Cos et al., 2011; Cos et al., 2014; Cos et al., 2012; Petitet et al., 2021). In contrast, we directly measured cost physiologically using pupil size. Second, we show that physiologically measured saccade costs predict where saccades are directed in a controlled binary preference task, and even during natural viewing. Our findings could unite state-of-the-art computational models [e.g. Kadner et al., 2022; Thomas et al., 2022; Hoppe and Rothkopf, 2016; Hoppe and Rothkopf, 2019; Tatler et al., 2017] with physiological data, to directly test the role of saccade costs and ultimately further our understanding of saccade selection.

Throughout this paper, we have used cost in the limited context of saccades. However, cost-based decision-making may be a more general property of the brain (Friston, 2010; Kahneman, 1973; Attneave, 1959; Hasenstaub et al., 2010; Jamadar et al., 2024). Every action, be it physical or cognitive, is associated with an intrinsic cost, and pupil size is likely a general marker of this (Beatty, 1982). Note, however, that pupil dilation does not always reflect cost, as the pupil dilates in response to many sensory and cognitive factors which should be controlled for, or at least considered, when interpreting pupillometric data [e.g., see Mathôt and Vilotijević, 2023; Strauch et al., 2022; Mathôt, 2018; Sirois and Brisson, 2014]. Effort-linked pupil dilations are thought to be, at least in part, driven by activity in the brainstem locus coeruleus (LC) (Joshi et al., 2016; Joshi and Gold, 2020; Aston-Jones and Cohen, 2005b; Strauch et al., 2022) [but other neurotransmitters also affect pupil size, e.g. Grujic et al., 2024, Mazancieux et al., 2023]. Activity in LC with its widespread connections throughout the brain (Schwarz and Luo, 2015; Szabadi, 2013; Aston-Jones and Cohen, 2005b; Berridge and Waterhouse, 2003; Aston-Jones and Waterhouse, 2016; Aston-Jones and Cohen, 2005a) is considered to be crucial for the communication within and between neural populations and modulates global neural gain (Poe et al., 2020; Wainstein et al., 2022; Dahl et al., 2022; Corbetta et al., 2008; Posner et al., 2006). Neural firing is costly (Shadmehr and Ahmed, 2020; Attwell and Laughlin, 2001), and therefore LC activity and pupil size are (neuro)physiologically plausible markers of cost (Strauch et al., 2022). Tentative evidence even suggests that continued exertion of effort (accompanied by altered pupil dilation) is linked to the accumulation of glutamate in the lateral prefrontal cortex (Wiehler et al., 2022), which may be a metabolic marker of cost [also see Castrillon et al., 2023; Wiehler et al., 2022; Jamadar et al., 2024].

Besides the costs of increased neural activity when exerting more effort, effort should be considered costly for a second reason: Cognitive resources are limited. Therefore, any unnecessary resource expenditure reduces cognitive and behavioral flexibility (Friston, 2010; Shadmehr and Ahmed, 2020; Kahneman, 1973; Jamadar et al., 2024). As a result, the brain needs to distribute resources between cognitive operations and the oculomotor system. We found evidence for the idea that such resource distribution is adaptive to the general level of cognitive demand and available resources: Increasing cognitive demand through an additional primary auditory dual task led to a lower saccade frequency, and especially costly saccades were cut. In this case, it is important to consider that the auditory task was the primary task, which should cause participants to distribute resources from the oculomotor system to the counting task. In other situations, more resources could be distributed to the oculomotor system instead, for example, to discover new sources of reward (Shadmehr and Ahmed, 2020; Gottlieb, 2012). Adaptive resource allocation from, and to the oculomotor system parsimoniously explains a number of empirical observations. For example, higher cognitive demand is accompanied by smooth pursuits deviating more from to-be tracked targets (Kosch et al., 2018), reduced (micro)saccade frequencies (Figure 4; Siegenthaler et al., 2014; May et al., 1990; Walter and Bex, 2021; Cui and Herrmann, 2023), and slower peak saccade velocities (Di Stasi et al., 2011; App and Debus, 1998; Sylvestre and Cullen, 1999). Relatedly, more precise saccades are accompanied with worse performance in a crowding task (Greenwood et al., 2017). Furthermore, it has been proposed that saccade costs are weighed against other cognitive operations such as using working memory (Somai et al., 2020; Van der Stigchel, 2020; Hoogerbrugge et al., 2023; Koevoet et al., 2023; Ballard et al., 1995). How would the resources between the oculomotor system and cognitive tasks (like the auditory counting task) be related? One possibility is that both consume from limited working memory resources (Luck and Vogel, 2013; Ma et al., 2014). Saccades are thought to encode target objects in a mandatory fashion into (visual) working memory (Van der Stigchel and Hollingworth, 2018), and the counting task requires participants to keep track of the auditory stream and maintain the count of the instructed digit in working memory. However, the exact nature of which resources overlap between tasks remain open for future investigation (also see Nozari and Martin, 2024). Together, we propose that cognitive resources are flexibly (dis)allocated to and from the oculomotor system based on the current demands to establish an optimal balance between performance and cost minimization.

How does the brain keep track of saccade costs, and which areas use it during saccade selection? Although our data do not allow direct inferences about the precise neural circuitry underlying the computations of oculomotor selection, oculomotor control is generally thought to be steered by a network encompassing the frontal eye field (Paus, 1996; Bruce et al., 1985), the supplementary eye field (Schlag and Schlag-Rey, 1987; Amiez and Petrides, 2009; Sharika et al., 2013), the anterior cingulate cortex (Gaymard et al., 1998; Ruehl et al., 2021; Pouget, 2015; Conti and Irish, 2021), the superior colliculus (Gandhi and Katnani, 2011; Sparks, 1999; Sparks, 2002; Glimcher and Sparks, 1992; Wurtz and Albano, 1980; Basso and May, 2017; Strauch et al., 2022), and the cerebellum (Voogd and Barmack, 2006; Tanaka et al., 2021; Takagi et al., 1998; Van der Stigchel et al., 2006). These areas are not just associated with oculomotor control, but are all also thought to be crucial for decision-making processes (Zhang et al., 2021; Wang et al., 2020; Basso and May, 2017; Jun et al., 2021; Crapse et al., 2018; Deverett et al., 2018; Basso et al., 2021; So and Stuphorn, 2016; Yang and Heinen, 2014; Chudasama et al., 2013; Baumann et al., 2015; Schall, 2001). It is plausible that the weighing of saccade costs during saccade selection is performed by this oculomotor-decision making network, but other areas such as orbitofrontal cortex may also play a role (Padoa-Schioppa and Conen, 2017; Wallis, 2007).

We report a combination of correlational and causal findings. Despite the correlational nature of some of our results, they consistently support the hypothesis that saccade costs predict saccade selection [which we predicted previously, Koevoet et al., 2023]. Causal evidence was provided by the dual-task experiment as saccade frequencies - and especially costly saccades were reduced under additional cognitive demand. Only a cost account predicts (1) a link between pupil size and saccade preferences, (2) a cardinal saccade bias, (3) reduced saccade frequency under additional cognitive demand, and (4) disproportional cutting of especially those directions associated with more pupil dilation. Together, our findings converge upon the conclusion that effort drives saccade selection.

To conclude, we have demonstrated that saccade costs can be measured using pupil size and that these costs robustly predict saccade selection. We propose that saccade selection is driven by physical properties of the environment, the observer’s goals, selection history, and another fundamental factor: effort.

Data and analyses scripts to reproduce the results are available via the Open Science Framework: https://osf.io/n3ktm/. The original data from Mathôt et al., 2015 are available at https://github.com/smathot/materials_for_P0010.5.

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Author details

1. Damian Koevoet

   Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, Netherlands

   Contribution

   Conceptualization, Resources, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   Contributed equally with

   Christoph Strauch

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9395-6524

2. Laura Van Zantwijk

   Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, Netherlands

   Contribution

   Data curation, Formal analysis, Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

3. Marnix Naber

   Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, Netherlands

   Contribution

   Supervision, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

4. Sebastiaan Mathôt

   Department of Psychology, University of Groningen, Groningen, Netherlands

   Contribution

   Resources, Data curation, Software, Writing – review and editing

   Competing interests

   No competing interests declared

5. Stefan van der Stigchel

   Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, Netherlands

   Contribution

   Resources, Supervision, Funding acquisition, Project administration, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5918-3521

6. Christoph Strauch

   Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, Netherlands

   Contribution

   Conceptualization, Resources, Formal analysis, Supervision, Validation, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

   Contributed equally with

   Damian Koevoet

   For correspondence

   c.strauch@uu.nl

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-6380-8635

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