Diagnostic Performance of an Eye-Tracking Assisted Visual Inference Language Test in the Assessment of Cognitive Decline due to Alzheimer’s Disease

Abstract

Background:

The assessment of language changes associated with visual search impairment can be an important diagnostic tool in the Alzheimer’s disease (AD) continuum.

Objective:

Investigate the performance of an eye-tracking assisted visual inference language task in differentiating subjects with mild cognitive impairment (MCI) or AD dementia from cognitively unimpaired older adults (controls).

Methods:

We assessed a group of 95 older adults (49 MCI, 18 mild dementia due to AD, and 28 controls). The subjects performed the same task under multiple experimental conditions which generate correlated responses that need to be taken into account. Thus, we performed a non-parametric repeated measures ANOVA model for verbal answers, and a linear mixed model (LMM) or its generalized version for the analysis of eye tracking variables.

Results:

Significant differences were found in verbal answers across all diagnostic groups independently of type of inference, i.e., logic or pragmatic. Also, eye-tracking parameters were able to discriminate AD from MCI and controls. AD patients did more visits to challenge stimulus (Control-AD, –0.622, SE = 0.190, p = 0.004; MCI-AD, –0.514, SE = 0.173, p = 0.011), more visits to the correct response stimulus (Control-AD, –1.363, SE = 0.383, p = 0.002; MCI-AD, –0.946, SE = 0.349, p = 0.022), more fixations on distractors (Control-AD, –4.580, SE = 1.172, p = 0.001; MCI-AD, –2.940, SE = 1.070, p = 0.020), and a longer time to first fixation on the correct response stimulus (Control-AD, –0.622, SE = 0.190, p = 0.004; MCI-AD, –0.514, SE = 0.173, p = 0.011).

Conclusion:

The analysis of oculomotor behavior along with language assessment protocols may increase the sensitivity for detection of subtle deficits in the MCI-AD continuum, representing an important diagnostic tool.

Keywords

Alzheimer’s disease cognitive impairment eye movement eye-tracking visual inferences visual search impairment

INTRODUCTION

Visual search impairment has been reported in patients with mild cognitive impairment (MCI) and Alzheimer’s disease (AD) [1 –5]. In addition, specific language processes can be altered in subjects with cognitive decline due to AD [6 –9]. The assessment of such linguistic processes would help distinguish normal aging from cognitive impairment even at early stages [10]. Combining visual inference tests with eye movement analysis can be an efficient manner for distinguishing individuals with cognitive impairment from healthy individuals.

An inference is a mental representation that derives a new idea from the combination of a pre-established concept with the one provided by a certain stimulus [11, 12]. In a more simplified way, one can define inference as a conclusion or judgment of a fact, a sound, a picture, or a whole scene based on what is already known by the subject and what is currently being presented to him. It is permeated by language and other cognitive functions, among which is visual processing.

Although individuals make inferences daily, studies using visual inferences in cognitive research are still scarce. Furthermore, there are no defined protocols to investigate the impact of cognitive decline on the inferential process. Therefore, in the current study, we aimed to investigate the performance of an eye-tracking assisted visual inference language task in differentiating subjects with MCI or AD dementia from cognitively unimpaired olderadults.

METHODS

Population and sample

Participants were recruited from the outpatient service of a memory clinic in a tertiary hospital in Sao Paulo, Brazil. The convenience sample comprised older adults diagnosed with normal cognition, MCI, and mild dementia due to AD. We initially screened 350 older adults with cognitive complaints from June 2019 until May 2020. After initial triage, 268 clinical and cognitive assessments were performed. Then, after exclusion criteria were applied, the final sample comprised 95 older adults with varying degrees of cognitive impairment. These were 18 with mild dementia due to AD (AD group), 49 with mild cognitive impairment (MCI group), and 28 cognitively unimpaired (control group). The study was approved by the local Ethics Committee, and all participants signed informed consent.

Inclusion and exclusion criteria

Participants with or without cognitive complaints who were sixty years or older, with normal or corrected-to-normal vision, were included. Laboratory tests (blood count, biochemistry, serum levels of glucose, thyroid hormones, folic acid, vitamin B12, lipid profile, and immune tests for syphilis) and neuroimaging scans such as computerized tomography (CT) or magnetic resonance imaging (MRI) were obtained for all participants for the diagnostic procedure and to exclude medical conditions that could be responsible for the cognitive symptoms. For the group with AD dementia, we included only patients with mild dementia, established according to the Clinical Dementia Rating scale (CDR) (CDR = 1) [13]. Patients with moderate or severe dementia due to AD (CDR = 2 or CDR = 3), non-AD dementia, visual or oculomotor disabilities, monocular vision, illiterates, and those with other neurological or psychiatric comorbidities were excluded from the study.

Cognitive screening, neuropsychological, language, and functional assessment

A physician (either a neurologist or a geriatric psychiatrist) performed a cognitive screening protocol at baseline, which included the Brazilian versions of the Montreal Cognitive Assessment (MoCA) [14], the Geriatric Depression Scale (GDS) [15], and the Scale of Instrumental Activities of Daily Living (IADL) [16]. A comprehensive neuropsychological examination was then performed for all participants which included the Rivermead Behavioral Memory Test [17, 18]; the Trail Making Test [19, 20]; the Digit Span test [21, 22]; the FAS-COWA test [19]; the Rey Auditory Verbal Learning Test (RAVLT) [23, 24], the Stroop test [19, 20], and the Rey-Osterrieth Complex Figure test [25, 26]. Additionally, the subjects underwent a language assessment that covered all linguistic domains (phonology, semantics, syntax, and pragmatics): An Object and Action Naming Battery (OANB) [27]; Test for Reception of Grammar –2 (TROG-2) [28], The Token Test [29]; The Northwestern Anagram Test (NAT) [30]; La Gestion De L'Implicite (The Implicit Management Test – IMT) [31]; The Cookie Theft Picture [32]; and the Semantics Association Test [33].

Diagnostic assessment

Participants were evaluated following the assessment protocol consisting of cognitive screening, clinical, neuropsychological, and evaluation by a language pathologist. A multidisciplinary consensus was held to establish diagnoses considering all available clinical information. The team included psychiatrists and neurologists, neuropsychologists, and language pathologists. The diagnosis of MCI was defined using the Mayo Clinic guidelines [34], and the diagnosis of probable AD was made according to the National Institute on Aging – Alzheimer’s Association (NIA-AA) [35].

Apparatus and calibration

We used a Tobii TX300 eye tracker to record eye movements. The apparatus for this study was used in other projects by our group [4, 5], where additional information can be found. In brief, the system sampling capacity was 300 Hz, with a processing latency of 1–3.3 ms. The participants were seated 65 cm from the 23” screen presenting the stimuli (total visual angle of 10.1 deg.). Head movements were not restrained: participants’ head movement could vary freely by±37 cm along the horizontal, and by±17 cm vertically. We conducted an eye movement register and the following data filtering using the Tobii Studio Software. Previously existing images were presented in black and white on a white background and were formatted to fit the computer screen. To guarantee accuracy in registering participants’ visual search, we have performed a calibration of the eye-tracking system before the task presentation. A nine-point (3x3 greed) calibration procedure was accomplished by having the participant fixate nine points at specific locations on the screen by following a moving marker (red circle) that appeared across the nine points. The calibration occurred until the equipment accurately mapped the participant’s gaze on the screen. We excluded participants to whom the equipment could not be calibrated (under 85% of corresponding visual search map). This same procedure was done for previous studies from our group [4, 5].

Testing and scoring procedures

The ability to perform visual inferences was assessed using a computational version of the instrument “300 exercices de compréhension d’inférences logique et pragmatique et de chaînes causales” (“300 Exercises of Comprehension of Logical and Pragmatic Inferences and Causal Chains”) [36]. For this study, only the pictorial stimuli were employed, consisting of 13 black-and-white pictures with tracing and textures that were homogeneous. Two pictures were used only as an example, and the other 11 were used as the experimental tasks. All stages of the test required inferential abilities at different levels of complexity.

Task description

The participant should observe pictures (a total of 13 pictures) presented separately on the first screen (challenge stimulus), e.g., a scene in which a boy crying with a toothache approaches his mother (Fig. 1, top, screen A). At this moment, all eye-tracking parameters were recorded. The participant should then decide which would be the best outcome for the previous scene, as portrayed in pictures 1, 2, or 3 (Fig. 1, bottom, screen B). All eye-tracking parameters were again recorded. To minimize memory bias, the challenge stimulus from screen A is presented again on screen B. In this example, the correct answer corresponds to the scene that shows the mother taking the boy to the dentist (Fig. 1, bottom, screen B, picture 2). The total duration of the test was recorded for each participant. All pictures were presented consecutively, during one session.

Fig. 1

Example of an inference process test. The participant should observe a scene: a boy crying with a toothache approaches his mother (screen A, top); the participant should then decide which was the best outcome for the story (screen B, bottom); the correct answer is one of the three pictures presented (the scene showing the mother taking the boy to the dentist) (screen B, picture 2).

The task required two types of inferential processing: a) logical inferences, i.e., to find the logical cause or consequence of a given situation, and b) pragmatic inferences, i.e., to determine which situation, among the alternatives, is most likely to occur. The stimuli allowed for only one correct answer and no time limit was imposed for performing the task. Scoring was done as follows: 0 – failed inference realization comprehension; and 1 – correct inference realization. The maximum score was 6 for logical inferences, 5 for pragmatic inferences, and 11 for total (logical and pragmatic) inferences. Concurrently with the assessment of inferences based on pictorial stimuli, eye movement behavior was collected.

Subsequently, for data analysis, areas of interest were manually defined as the spatial delimitation in which fixations and ocular visits occurred on each screen. These areas corresponded to the perception of the image as a whole and the subcomponents considered crucial for decision-making and inference realization. Thus, the following areas were defined (Fig. 2):

Fig. 2

Interest areas were manually defined as the spatial delimitation in which fixations and ocular visits occurred. These areas corresponded to the perception of the image as a whole and the subcomponents considered crucial for decision-making and understanding of inferences.

In screen A: challenge stimulus A – the whole image presented in screen A; target A – part of the image presented in screen A defined as crucial for inference comprehension.

In screen B: challenge stimulus B – the same image presented in screen A, but in the answer’s screen; correct target B – part of the image from the correct answer presented in screen B defined as crucial for inference comprehension; correct response stimulus B – the whole image of the correct answer presented in screen B; distractor stimulus B – the whole incorrect pictures presented in screen B.

The following eye-tracking parameters were captured: time to first fixation, number of fixations, number of visits, and total fixation duration. Together these variables comprise the visual search map. These parameters are defined elsewhere [5].

Statistical analyses

Numerical variables were described as mean and standard deviation, whereas categorical ones as absolute and relative frequencies. Importantly, in the present study, the same subjects performed the same task (inferential task) under multiple experimental conditions (different pictures) which generated correlated responses that needed to be taken into account. Thus, we performed a non-parametric repeated measures ANOVA model to verify the verbal answers of each group for each inference type, i.e., logic inference or pragmatic inference [37]. Additionally, eye tracking variables were assessed with linear mixed models (LMM) or their generalized version with gamma and inverse normal families. Each parameter was assessed with a distinct model. The outcome of each model was the parameter of interest (e.g., number of fixations) and the predictor was the group. Pairwise comparisons of groups on variables with strong evidence of difference were computed with estimated marginal means, and Tukey’s method was used to deal with the multiple inference problem. We performed the pairwise comparison of group means to estimate the means of each group (control, MCI, and AD) for a given parameter within its model, followed by a comparison of those means. Eye-tracking variables were then used as predictors of correct verbal answers in a generalized linear mixed model for the binomial family. Noteworthy, non-parametric repeated measures ANOVA and LMM tests deal with the problem of correlated observations in which longitudinal data fall into. However, this was a cross-sectional study with repeated measures over multiple experimental conditions. Models were set as group comparisons including a random intercept for the subject that accounts for correlated observations within each participant; this is equivalent to setting a compound symmetry correlation structure for each subject. The different experimental conditions are not of interest, hence they are being averaged out and the groups compared over all the conditions for each parameter of interest.

Finally, we applied Spearman’s rank correlation test to verify the presence of correlations between the visual search map parameters and the neuropsychological assessment parameters.

Residual and random effects normality were assessed with QQ plots. A plot of fitted values against the squared root of absolute values of standardized residuals was constructed to evaluate homoscedasticity. When any of those assumptions were not met, alternatives in the generalized linear mixed model were tried. Only when those failed the non parametric approach was used. All analyses were conducted on statistical package R 4.2.1 (http://www.r-project.org/).

RESULTS

Overview

A total of 95 individuals were included in the study and distributed in three diagnostic groups, i.e., cognitively healthy subjects or controls (n = 28), MCI (n = 49), and mild dementia due to AD (n = 18). All participants completed the test.

Clinical and sociodemographic data

Table 1 summarizes the clinical and sociodemographic data of the sample. There were no statistically significant differences in mean age across diagnostic groups (p = 0.094), nor in the number of males and females. Individuals with AD dementia had fewer years of education than controls and MCI subjects (p < 0.001). As expected, AD subjects had lower scores on the cognitive screening test (MoCA), and MCI subjects had intermediate results. As for the GDS, controls scored lower than subjects with MCI and AD. Subjects with AD also had higher scores in functional assessment (Lawton & Brody), indicating, as expected, a worse performance as compared to MCI and controlgroups.

Table 1

Clinical and sociodemographic characteristics of the sample

	Control	MCI	AD	p	Post-hoc
	(n = 28)	(n = 49)	(n = 18)		(p < 0.05)
Age	70.7 (8.0)	69.3 (6.4)	73.8 (5.4)	0.094	NA
Education	14.6 (3.1)	13.3 (4.2)	8.8 (4.6)	<0.001	C, MCI > AD
Sex W n (%)	16 (57.1)	26 (56.1)	9 (50.0)	0.950	NA
MoCA	27.8 (1.4)	24.3 (3.2)	18.1 (3,7)	<0.001	C > MCI > AD
GDS	3.8 (4.9)	8.1 (6.2)	8.2 (3.6)	0.002	C < MCI, AD
Lawton &Brody	26.5 (1.4)	26.0 (2.5)	20.9 (4.2)	<0.001	C, MCI > AD

All data are presented as means and standard deviation unless otherwise noted. NA, not applicable; MCI, mild cognitive impairment; AD, Alzheimer’s disease; MoCA, Montreal Cognitive Assessment; GDS, Geriatric Depression Scale; Statistical test: Kruskal-Wallis with post-test Dunn or Chi-Square when needed. Significance set at 0.05.

Verbal answers for inference type between diagnostic groups

First, we investigated if there was a difference between logic inference and pragmatic inference across diagnostic groups. No evidence of interaction or main effect of inference were found, only main effect of group (Inference, statistic = 1.64, df = 1, p = 0.200; Group:inference, statistic = 2.37, df = 1, p = 0.094; Group, statistic = 26.04, df = 1, p < 0.001; non-parametric repeated measures ANOVA) (Fig. 3). Differences in verbal answers were statistically significant across all diagnostic groups (Control-MCI, logic: mean = 0.952, 95% CI = 0.887–0.983 versus mean = 0.918, 95% CI = 0.868–0.950; pragmatic: mean = 0.936, 95% CI = 0.859–0.974 versus mean = 0.872, 95% CI = 0.821–0.908, p = 0.008; Control-AD, logic: mean = 0.952, 95% CI = 0.887–0.983 versus mean = 0.694, 95% CI = 0.591–0.773; pragmatic: mean = 0.936, 95% CI = 0.859–0.974 versus mean = 0.744, 95% CI = 0.645–0.829, p < 0.001; MCI-AD, logic: mean = 0.918, 95% CI = 0.868–0.950 versus mean = 0.694, 95% CI = 0.591–0.773; pragmatic: mean = 0.872, 95% CI = 0.821–0.908 versus mean = 0.744, 95% CI = 0.645–0.829, p < 0.001) (Fig. 3).

Fig. 3

Verbal answers depending on inference type between diagnostic groups. No evidence of interaction or main effect of inference were found, only main effect of group (top graph; Inference, statistic = 1.64, df = 1, p = 0.200; Group:inference, statistic = 2.37, df = 1, p = 0.094; Group, statistic = 26.04, df = 1, p < 0.001, respectively; non-parametric repeated measures ANOVA). Differences in verbal answers were significant across all diagnostic groups (bottom table; Control-MCI p = 0.008; Control-AD p < 0.001; MCI-AD p < 0.001).

Visual search map across diagnostic groups

There were statistically significant differences between groups regarding duration of the test. The total duration of the test was higher in the AD group, followed by the MCI, and control groups (Table 2).

Table 2

Duration of the test for all groups

	mean (SD)	min-max	p	Post-hoc Bonferroni
Control (n = 28)	197.1 (34.5)	[152.3–311.2]
MCI (n = 49)	210.3 (50.8)	[150.7–405.1]		Control < AD (p < 0.0001)
AD (n = 18)	262.9 (63.9)	[172.5–430.7]	<0.0001	MCI < AD (p = 0.001)

ANOVA test. MCI, mild cognitive impairment; AD, Alzheimer’s disease; SD, standard deviation; min-max, minimum and maximum values. Values in seconds. Significance set at 0.05.

Comparing the visual search map across diagnostic groups, there were statistically significant group differences only for the answer screen (screen B). The following parameters: number of visits to the challenge stimulus, number of visits to the correct response stimulus, number of fixations on distractors, and time to first fixation on the correct response stimulus (Table 3) were capable of distinguishing diagnostic groups. Pairwise post-hoc analyses revealed that AD subjects had more visits to the challenge stimulus (Control-MCI, –0.108, SE = 0.149, p = 0.748; Control-AD, –0.622, SE = 0.190, p = 0.004; MCI-AD, –0.514, SE = 0.173, p = 0.011), more visits to the correct response stimulus (Control-MCI, –0.417, SE = 0.300, p = 0.350; Control-AD, –1.363, SE = 0.383, p = 0.002; MCI-AD, –0.946, SE = 0.349, p = 0.022), more fixations on distractors (Control-MCI, –1.640, SE = 0.919, p = 0.180; Control-AD, –4.580, SE = 1.172, p = 0.001; MCI-AD, –2.940, SE = 1.070, p = 0.020), and a longer time to first fixation on the correct response stimulus (Control-MCI, –0.108, SE = 0.149, p = 0.748; Control-AD, –0.622, SE = 0.190, p = 0.004; MCI-AD, –0.514, SE = 0.173, p = 0.011) than controls and MCI subjects. No differences in these parameters were found between controls and subjects with MCI.

Table 3

Visual search map parameters between groups

Parameter	Group	Mean	95% CI	p
Time to First fixation (target A)*	Control	0.50	0.42–0.60	0.252
	MCI	0.55	0.48–0.64
	AD	0.65	0.54–0.82
Number of fixations (target A)**	Control	7.22	6.31–8.47	0.294
	MCI	6.36	5.87–6.81
	AD	6.28	5.51–7.12
Number of visits (target A)**	Control	3.15	2.59–4.24	0.478
	MCI	2.69	2.52–2.85
	AD	2.67	2.40–3.01
Fixation duration (target A)**	Control	2.67	2.37–2.92	0.595
	MCI	2.45	2.25–2.64
	AD	2.55	2.26–2.85
Number of visits (challenge stimulus B)**	Control	2.73	2.27–3.67	0.007
	MCI	2.84	2.59–3.14
	AD	4.34	3.54–5.66
Number of visits (correct target B)*	Control	2.82	2.55–3.19	0.101
	MCI	3.01	2.74–3.34
	AD	3.93	3.46–4.64
Number of visits (correct response stimulus B)**	Control	2.94	2.65–3.34	0.002
	MCI	3.36	3.11–3.65
	AD	4.30	2.81–5.10
Number of fixations (correct target B)*	Control	4.51	4.03–5.14	0.168
	MCI	5.12	4.60–5.77
	AD	6.75	5.78–8.10
Number of fixations (correct response stimulus B)*	Control	8.05	7.15–9.33	0.063
	MCI	9.91	8.96–11.10
	AD	12.20	10.60–14–50
Number of fixations (distractor stimulus B)**	Control	4.76	4.04–5.85	< 0.001
	MCI	6.40	5.60–7.64
	AD	9.34	7.59–11.80
Time to first fixation (correct target B)***	Control	1.59	1.42–1.84	0.444
	MCI	1.77	1.59–2.02
	AD	2.37	1.95–2.98
Time to first fixation (correct response stimulus B)**	Control	1.21	1.08–1.38	0.003
	MCI	1.32	1.20–1.47
	AD	1.83	1.51–2.32

p-values refer to overall differences between groups. AD, Alzheimer’s disease; MCI, mild cognitive impairment; SD, Standard deviation; CI, confidence interval. *Generalized linear mixed models (gamma). **Linear mixed models. ***Generalized linear mixed models (inverse). Significance set at 0.05.

We applied generalized linear mixed models for the binomial family to predict the odds of a correct answer based on the visual search map. Table 4 portrays the results from these models. The odds ratio (OR) expresses how much an increase of one unit of each parameter changes the odds of correct response. We observed that the parameters time to first fixation, number of visits, and fixation duration on target stimulus in screen A (target A) significantly contributed to the odds of correct answers in the inference comprehension task (screen B). Each second in the time to first fixation on the target (target A) increased the odds of a correct answer by 2.17 times. Each visit to the target (target A) increased the odds of a correct answer by 38%. Each second of the fixation duration increased the odds of a correct answer by 56% (Table 4). The number of fixations on the correct response stimulus on screen B increased the odds of making correct answers in the respective task (each fixation on the stimulus increases the odds of a correct answer by 20%), while the number of fixations on the distractor (distractor stimulus B) reduced the odds of a correct answer (each fixation on the distractor reduces the odds of a correct answer by 15%) (Table 4).

Table 4

Analysis of the impact of eye-tracking parameters (visual search map) on the inference task

Parameter	OR	95% CI	p
(Intercept)	7.80	[2.35–25.94]	< 0.001
Time to first fixation (target A)*	2.17	[0.99–4.74]	0.052
Number of fixations (target A)**	0.87	[0.72–1.05]	0.159
Number of visits (target A)**	1.38	[1.02–1.86]	0.036
Fixation duration (target A)**	1.56	[1.06–2.30]	0.023
Number of visits (challenge stimulus B)**	0.96	[0.81–1.14]	0.614
Number of visits (correct target B)*	0.73	[0.46–1.16]	0.180
Number of visits (correct response stimulus B)**	0.94	[0.69–1.29]	0.717
Number of fixations (correct target B)*	0.97	[0.76–1.24]	0.816
Number of fixations (correct response stimulus B)*	1.20	[1.05–1.36]	0.006
Number of fixations (distractor stimulus B)**	0.85	[0.80–0.91]	< 0.001
Time to first fixation (correct target B)***	0.82	[0.59–1.14]	0.235
Time to first fixation (correct response stimulus B)**	0.86	[0.55–1.34]	0.506

*Generalized linear mixed models (gamma). **Linear mixed models. ***Generalized linear mixed models (inverse). OR expresses how much an increase of one unit of each parameter changes the odds of correct response. OD, Odds ratio; CI, confidence interval. Significance set at 0.05. Pearson’s product-moment correlation 0.370, p < 0.001.

Correlations between oculomotor parameters and neuropsychological assessment

All visual search map parameters except for the number of visits and the number of fixations to target A had a significant correlation with one or more cognitive screening and neuropsychological tests (Table 5).

Table 5

Correlations between oculomotor parameters and cognitive assessment

Oculomotor parameters	Cognitive	Control	MCI	AD	rho
	assessment	mean (SD)	mean (SD)	mean (SD)
Number of visits (challenge stimulus B)	MoCA	27.8 (1.4)	24.3 (3.2)	18.1 (3.8)	–0.414
	OANB object	154.8 (4.7)	146.4 (16.0)	135 (17.8)	–0.437
	TMTa time	43.7 (15.8)	57.2 (31.3)	86.9 (44.6)	0.475
	TMTb time	103.3 (105.7)	138.1 (93.0)	275 (119.5)	0.451
	Stroop III time	29.5 (9.1)	37.2 (13.2)	54.9 (20.1)	0.412
Number of visits (correct target B)	TMTa time	43.7 (15.8)	57.2 (31.3)	86.9 (44.6)	0.421
Number of visits (correct response stimulus B)	MoCA	27.8 (1.4)	24.3 (3.2)	18.1 (3.8)	–0.464
	OANB object	154.8 (4.7)	146.4 (16.0)	135 (17.8)	–0.423
	OANB verbs	87.4 (4.7)	80.2 (11.0)	70.3 (10.9)	–0.410
	IMT	25.3 (2.3)	22.1 (5.6)	18 (6.1)	–0.410
	TMTa time	43.7 (15.8)	57.2 (31.3)	86.9 (44.6)	0.441
Number fixations (correct response stimulus B)	MoCA	27.8 (1.4)	24.3 (3.2)	18.1 (3.8)	–0.418
	OANB object	154.8 (4.7)	146.4 (16.0)	135 (17.8)	–0.418
	IMT	25.3 (2.3)	22.1 (5.6)	18 (6.1)	–0.434
	TMTa time	43.7 (15.8)	57.2 (31.3)	86.9 (44.6)	0.420
Number of fixations (distractor stimulus B)	MoCA	27.8 (1.4)	24.3 (3.2)	18.1 (3.8)	–0.461
	OANB object	154.8 (4.7)	146.4 (16.0)	135 (17.8)	–0.447
	OANB verbs	87.4 (4.7)	80.2 (11.0)	70.3 (10.9)	–0.439
	IMT	25.3 (2.3)	22.1 (5.6)	18 (6.1)	–0.402
	TMTa time	43.7 (15.8)	57.2 (31.3)	86.9 (44.6)	0.443
	TMTb time	103.3 (105.7)	138.1 (93.0)	275 (119.5)	0.412

SD, Standard deviation; MCI, mild cognitive impairment; AD, Alzheimer’s disease; MoCA, Montreal Cognitive Assessment; OANB, Object and Action Naming Battery; TMT, Trail Making Test; IMT, Implicit Management Test. Significance set at 0.05.

DISCUSSION

In this study, we investigated the diagnostic performance of a visual inference language test using an eye-tracking device in differentiating subjects with cognitive impairment from cognitively unimpaired older adults. Results revealed: i) significant group differences in verbal answers across all diagnostic groups; ii) several oculomotor parameters and corresponding areas of interest, defined as the visual search map required for task completion, were capable of distinguishing subjects with mild dementia (AD) from the other diagnostic groups, i.e., number of visits to the challenge stimulus, number of visits to the correct response stimulus, number of fixations on distractors, and time to first fixation on the correct response stimulus. Subjects with AD did more visits to the challenge stimulus, more visits to the correct response stimulus, more fixations on distractors, and had a longer time to first fixation on the correct response stimulus than controls and MCI subjects. To the best of our knowledge, this is the first study to report findings of a visual inference language test using eye-tracking measures, further investigating the impact of cognitive decline on the ability of a subject to understand visual inferences.

Type of inference and verbal answer

The understanding of inferences is considered a high-demanding task that recruits multiple cognitive functions such as language, memory, attention, and executive function. Therefore, it could be affected even in the early stages of MCI. In this study, the determinant of differences in verbal answers was the diagnostic group. Controls had a higher number of correct answers followed by MCI and AD subjects, subsequently. Different from expected, the type of inference, i.e., logic or pragmatic, did not discriminate diagnostic groups as per verbal answers. The cognitive demand in pragmatic inferences is considered higher than that of logical inferences. Our findings suggest that the level of cognitive decline seen in MCI subjects is sufficient to affect even the simpler type of visual inference.

Studies on inferences and cognitive decline focus mainly on textual inferences, with a lack of studies evaluating the inferential process through images. Schmitter-Edgecombe and Creamer verified that subjects with amnestic MCI produced fewer inferences in a story comprehension task than controls and had more difficulties explaining story events and using preliminary text information to support inference generation [38]. Similarly, Gaudreau et al. found that MCI participants were impaired in identifying ironic or sincere stories that required mental inference capacities, compared to control subjects [39]. Two studies evaluated a different cohort of MCI individuals in a textual inference task and also found that MCI patients had difficulty understanding inferences compared to controls [40, 41]. The results presented in our study are analogous to the findings found in the literature focused on text inferences [38 –41].

Visual search map

As previously stated, the sum of the oculomotor parameters is defined as the visual search map. Although the parameters analyzed in screen A are significantly relevant for the correct understanding of inferences, the main differences found in this study were related to the visual search map on the “answers screen”, or screen B. The number of visits to the challenge stimulus, number of visits to the correct response stimulus, number of fixations on distractors, and time to first fixation on the correct response stimulus were particularly affected by group differences. Ultimately, the visual search map allowed the identification of the main visual aspects related to a better performance in the pictoric inference comprehension task. These findings could have both diagnostic and therapeutic implications.

In our study, screen A was composed only of a single stimulus, minimizing the odds for errors related to the subject’s distraction and inattention. Additionally, the subjects had no additional task but to scrutinize the image in screen A, which probably contributed to the absence of significant differences in the visual search map among the diagnostic groups. A previous study from our group found that the analyzed parameters were not able to discriminate between diagnostic groups in the familiarization phase when the subjects had only to look at the image presented on the screen [5]. Conversely, screen B presented the target stimulus and two distracting stimuli in addition to the challenge stimulus. Together, these findings suggest that increasing cognitive demand in a given task may account for differences in performance in subjects with different degrees and types of cognitive impairment.

It is known that the performance of an inference comprehension task consists of at least 4 steps: 1) attention to clues; 2) selection of relevant clues; 3) integration of relevant clues and finally; 4) association of explicit cues with previous experience. The inferential process follows an organized and stepwise order that includes different cognitive domains initiating with a semantic encoding, then the creation or recovery of mental models [42], defined by implicit and explicit stimulus information culturally and socially determined, ultimately leading to the comprehension of the visual stimuli [43]. This complex process involves diverse cognitive skills such as attention, sustained attention, memory, working memory, and theory of mind [44]. The maintenance of the attentional level is a basic condition for all cognitive tasks and a failure at this point can influence all later stages in a sequence of cognitive integration [45]. From a clinical point of view, our findings of an increase in target fixation time, an increase in the number of visits to the target, as well as an increase in the fixation duration have led us to hypothesize that patients whose attentional processing is adequate are in advantage in understanding visual inferences when compared to patients with attentional impairment. Furthermore, these results suggest that the differences among diagnostic groups are related to impairment in late and more complex processes of inferential processing, i.e., integration of relevant cues and association of explicit cues with previous experience (which is performed on screen B, where more than one stimulus is presented simultaneously, requiring that individuals divide their attention while selecting the appropriate answer). Camargo et al. found a similar pattern in their study, i.e., subjects with AD made longer fixations and more visits on distractors, more frequent fixations, and more visits to the target areas than other groups during the response phase [5].

Neuropsychological assessment

Most visual search map parameters were correlated with one or more cognitive screening and neuropsychological tests. The MoCA test showed a negative correlation with the following ET parameters, all from screen B tasks: number of visits to the challenge stimulus, number of visits to the correct response stimulus, and number of fixations on the correct response stimulus and the distracting stimuli. The pattern found for these parameters indicates that subjects expended a greater cognitive effort to perform the task. This pattern would lead to a lower score on the cognitive screening test. Tadokoro et al. used one of the ET parameters (fixation duration on predefined areas of interest) to assess tasks of visual working memory, attention, calculation, and visuospatial function in a group of control patients and patients with MCI and AD [46]. The authors found that the total eye tracking score significantly decreased in the MCI group and that this result correlated positively with the Mini-Mental State Examination (MMSE) score.

The parameters number of visits to the challenge stimulus, number of visits to the correct response stimulus, number of fixations on the correct response stimulus, and number of fixations on the distracting stimuli (all on screen B) also negatively correlated with the OANB, a test of oral naming of nouns and verbs involving visual processing. According to Spezzano et al., the naming task requires the recovery of phonological and semantic information, organized in a memory system that is accessed given the particularities of a given stimulus and involves the identification of the represented object [47]. All these steps are necessary for understanding inferences through visual stimuli.

The trail-making test (TMT) is a test that assesses attention, sequencing, mental flexibility, visual searching, and motor function. Likewise, the Stroop test, in addition to assessing such skills, also investigates executive function and inhibitory control. The main variable of interest, in both tests, is the total time to complete the test. Thus, we hypothesized that a worse TMT performance would relate to an increase in the number of fixations and visits not only in the correct target stimulus but also in the whole visual stimulus. A previous report from our group [4] aimed to determine whether older adults with MCI or incipient dementia due to AD displayed subtle signs of impairment in executive functioning as compared to healthy controls, through the analysis of eye movement. The results revealed that subjects with AD and MCI had a greater impact on the frequency and latency of eye movement metrics, showing a greater executive decline than controls. Additionally, the results were correlated with cognitive tests that evaluated inhibitory control, attention, working memory, and self-monitoring, including the TMT and Stroop.

Finally, as previously mentioned, the IMT evaluates the ability to comprehend inferences during reading activities and is designed to assess adult individuals with cognitive and/or communication complaints [31]. As expected, we found a correlation between the results of both tests that require the involvement of the same cognitive abilities, visual search map and attention, in addition to language. Importantly, although ET parameters and traditional cognitive assessments have previously shown positive correlations, there is a lack of studies further exploring these associations and the potential role of ET as an alternative to more traditional neuropsychological testing.

Underlying neuropathology

Both cerebral hemispheres are involved on some level in the inferential process. However, it has been reported that the right hemisphere is related to the ability to use context to generate inferences, thus being predominantly responsible for the inferential ability [48, 49]. Studies with individuals with right hemisphere damage show conflicting results with some revealing loss of ability to generate inferences and to predict upcoming events [50] while others maintain that these patients are still able to generate inferences [51]. These conflicting results may be interpreted as each hemisphere contributing differently to the generation of inferences depending on the context and background needed for the process, i.e., if familiar or unfamiliar situations, for instance [49]. Aspects related to the visual stimulus, e.g., its complexity also plays an important role in determining the brain regions involved in the inferential process [52]. Moreover, bilateral large-scale cognitive networks, including areas of visual processing, have also been implicated in inferential processing [53].

Limitations

Our study has some limitations. One aspect not investigated by our protocol is the role of top-down mediation on oculomotor parameters during a task. Oculomotor behavior during different visual tasks is known to be influenced by top-down factors that direct fixations and visits toward task-relevant areas of interest [54]. This aspect was investigated by Castelhano et al. using instructions– searching for a particular aspect or remembering characteristics of an image– that influenced specific oculomotor parameters, such as fixation duration and saccade amplitudes [55]. These findings were similar to those described by Mills et al., with spatial (e.g., amplitude) and temporal characteristics (e.g., duration) being found to be influenced by the task instructions [56]. In our study, the same instruction was given to all participants across the whole experiment. However, the top-down mediation could be a confounding factor in the study of cognitive decline in different stages of the AD continuum. There is evidence that top-down attentional control is impaired in individuals with MCI [57].

In general, one main difficulty faced when studying visual inferences is the ability to discriminate the cognitive demand involved (e.g., inferential, visuoperceptual, attentional, or working memory). These aspects have a non-neglectable effect on the interpretation of results. We partially addressed this matter by trying to isolate the inferential process domain when we added the challenge stimulus image to screen B, presented initially on screen A, reducing the subject’s need to demand working memory capacity for the task.

Although the number of subjects included was sufficient for reliable results, further studies with larger sample sizes are needed before these findings can be generalized. Our main focus was to determine the validity of the test developed by our team. The relatively reduced sample size also impacted our choice to not distinguish MCI subtypes (amnestic and non-amnestic), albeit recognizing that the visual search impairment present in amnestic MCI has been implicated as an early diagnostic marker in the AD continuum [3]. Also, non-AD MCI patients may have distinct visual search patterns, representing a bias in our results. However, there is a lack of studies investigating oculomotor behavior in MCI of all types, with most of them showing conflicting findings related to saccadic movements [58 –61]. We believe that our present findings will contribute to the literature in the field.

Furthermore, cultural and academic backgrounds together with schooling play an important role in an individual’s ability to process images presented in an inference task [52, 62]. This interference cannot be neglected. A previous study from our group revealed that lower-educated subjects performed worse in an inferential task than higher-educated ones for both visually simple and complex pictures [63]. In our study, subjects with AD dementia had fewer years of education than controls and subjects with MCI. To avoid at least in part the effect of schooling on the ability to make inferences, we used only visually simple and inferentially simple images, a concept defined previously [63].

FUTURE PERSPECTIVES

Several studies have shown that visuospatial deficits can be present earlier than typical semantic and episodic memory manifestations associated with MCI and AD dementia [64 –66], hence placing visual search assessments as promising markers of cognitive impairment in the AD continuum. Noteworthy, the visual inference task performed with an eye-tracker measuring oculomotor parameters provides information on predictors of cognitive decline other than the outcome, i.e., correct, or incorrect inference (verbal answers). The analyzed parameters (visual search map) convey important information on oculomotor behavior associated with cognitive impairment. Since the first study from our group [67], we have been able to characterize a series of visual search changes present in cognitively impaired patients. Also, other aspects such as saccadic movements portray a comprehensive picture of the oculomotor behavior in the AD continuum [4]. Currently, cerebrospinal fluid and neuroimaging-based biomarkers are the validated markers of AD neurodegeneration. The validation of eye-tracking parameters in biomarker-confirmed AD pathology would significantly improve the applicability of these methods. Moreover, with the possibility of more reliable results with the assessment of oculomotor behavior, this tool could represent an invaluable substitute for more time-consuming neuropsychological assessments.

The number of studies using eye tracking to identify cognitively impaired subjects has been growing over the years. However, it is still challenging to precisely identify which ocular parameters best distinguish different diagnoses. Machine learning-based methods have been recently applied to eye-tracking-derived data with encouraging results. We have demonstrated that an automatic classification model, a machine learning strategy, was able to allocate individuals in each respective group with an accuracy of 74% [68]. However, further studies must be conducted with a larger sample to validate the accuracy of the mentioned model.

CONCLUSION

Visual search monitoring is a useful tool in differentiating subjects with cognitive impairment from those with normal cognition. In addition, similarities between the performances of subjects with MCI and those with AD dementia in some tests reinforce the importance of eye-tracking parameters in diagnosing cognitive decline. Furthermore, subjects with MCI seem to already present oculomotor patterns that differentiate them from controls, ultimately placing them closer to the performance of subjects with AD dementia. Ultimately, the combination of eye tracking measures with other markers of cognitive decline would represent an important tool in improving the diagnostic workup of patients in the AD continuum.

Footnotes

ACKNOWLEDGMENTS

The authors have no acknowledgments to report.

FUNDING

This study was supported by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) (project 88887.136408/2017-00 – 465412/2014-9 – Instituto Nacional de Biomarcadores em Neuropsiquiatria (INBioN). The Laboratory of Neuroscience (LIM-27) receives financial support from Fundacão de Amparo à Pesquisa do Estado de São Paulo (FAPESP, Project 2014/20913-3) and Associação Beneficente Alzira Denise Hertzog da Silva (ABADHS).

CONFLICT OF INTEREST

The authors have no conflict of interest to report.

DATA AVAILABILITY

All data from this study are available upon request.

References

Perry

, Hodges

(1999) Attention and executive deficits in Alzheimer’s disease: A critical review. Brain 122 (Pt 3), 383–404.

Foldi

, Lobosco

, Schaefer

(2002) The effect of attentional dysfunction in Alzheimer’s disease: Theoretical and practical implications. Semin Speech Lang 23, 139–150.

Molitor

, Ko

, Ally

(2015) Eye movements in Alzheimer’s disease. J Alzheimers Dis 44, 1–12.

Pereira

MLGF

, Camargo

MVZA

, Bellan

AFR

, Tahira

, Dos Santos

, Machado-Lima

, Nunes

FLS

, Forlenza

(2020) Visual search efficiency in mild cognitive impairment and Alzheimer’s disease: An eye movement study. J Alzheimers Dis 75, 261–275.

Camargo

MVZA

, Pais

, Bellan

AFR

, Tahira

, Dos Santos

, Sant’Ana

LCFG

, Radanovic

, Forlenza

(2022) Impact of cognitive demand on eye movement pattern in patients with Alzheimer’s disease. J Alzheimers Dis 90, 85–95.

Ahmed

, Haigh

, de Jager

, Garrard

(2013) Connected speech as a marker of disease progression in autopsy-proven Alzheimer’s disease. Brain 136, 3727–3737.

Berisha

, Wang

, LaCross

, Liss

(2015) Tracking discourse complexity preceding Alzheimer’s disease diagnosis: A case study comparing the press conferences of Presidents Ronald Reagan and George Herbert Walker Bush. J Alzheimers Dis 45, 959–963.

Garrard

, Maloney

, Hodges

, Patterson

(2005) The effects of very early Alzheimer’s disease on the characteristics of writing by a renowned author. Brain 128, 250–260.

Mueller

, Hermann

, Mecollari

, Turkstra

(2018) Connected speech and language in mild cognitive impairment and Alzheimer’s disease: A review of picture description tasks. J Clin Exp Neuropsychol 40, 917–939.

10.

Taler

, Phillips

(2008) Language performance in Alzheimer’s disease and mild cognitive impairment: A comparative review. J Clin Exp Neuropsychol 30, 501–56.

11.

Alonso

AOL

(2004) Information and inference as combined cognitive processes. Interdisciplinaria 21(Suppl), 23–34.

12.

Mason

(2007) Review of pragmatics: A multidisciplinary perspective, Louise Cummings. Edinburgh University Press (2005), 336 pp, ISBN: 0-7486-1682-9 [Book Review]. Cogn Syst Res 8, 48–52 .

13.

Morris

(1993) The Clinical Dementia Rating (CDR): Current version and scoring rules. Neurology 43, 2412–2414.

14.

Sarmento

ALR

(2009) Apresentação e aplicabilidade da versão brasileira da MoCA (Montreal Cognitive Assessment) para rastreio de comprometimento cognitivo leve. Thesis. Escola Paulista de Medicina, Universidade Federal de Sao Paulo (UNIFESP), Sao Paulo.

15.

Almeida

, Almeida

(1999) Confiabilidade da versão brasileira da Escala de Depressão Geriátrica (GDS) versão reduzida. Arq Neuropsiquiatr 57, 421–426.

16.

Lawton

, Brody

(1969) Assessment of older people: Self-maintaining and instrumental activities of daily living. Gerontologist 9, 179–186.

17.

Wilson

, Cockburn

, Baddeley

, Hiorns

(1989) The development and validation of a test battery for detecting and monitoring everyday memory problems. J Clin Exp Neuropsychol 11, 855–870.

18.

Yassuda

, Flaks

, Viola

, Pereira

, Memória

, Nunes

, Forlenza

(2010) Psychometric characteristics of the Rivermead behavioral memory test (RBMT) as an early detection instrument for dementia and mild cognitive impairment in Brazil. Int Psychogeriatr 22, 1003–1011.

19.

Spreen

, Strauss

(1998) A Compendium of Neuropsychological Tests. Administration, Norms, and Commentary, 2nd Ed., Oxford University Press.

20.

Campanholo

, Romão

, Machado

MAR

, Serrao

, Coutinho

DGC

, Benute

GRG

, de Lucia

MCS

(2014) Performance of an adult Brazilian sample on the trail making test and Stroop test. Dement Neuropsychol 8, 26–31.

21.

Wechsler D (1997) Wechsler Adult Intelligence Scale-III. San Antonio, TX, The Psychological Corporation. Nascimento E (2004) “Adaptation, validation and normatization of the WAIS-III in a Brazilian sample [Adaptação, validação e normatização do WAIS-III para uma amostra brasileira],” in WAIS-III: Manual para administração e avaliação, ed D. Wechsler (São Paulo: Casa do Psicólogo), pp. 161–192.

22.

Nascimento

(2004) Adaptation, validation and normatization of the WAIS-III in a Brazilian sample [Adaptação, validação e normatização do WAIS-III para uma amostra brasileira]. In WAIS-III: Manual para administração e avaliação, Wechsler

, ed. Casa do Psicólogo, São Paulo, pp. 161–192.

23.

Rey

(1964) The Clinical Examination in Psychology [L’examen clinique en psychologie], Paris, Presses Universitaires.

24.

Malloy-Diniz

, Lasmar

VAP

, Gazinelli

LSR

, Fuentes

, Salgado

(2007) The Rey auditory-verbal learning test: Applicability for the Brazilian elderly population. Braz J Psychiatry 29, 324–329.

25.

Osterrieth

(1944) Test of copying a complex figure; contribution to the study of perception and memory [Le test de copie d’une figure complexe; contribution à l’étude de la perception et de la mémoire]. Arch Psychol 30, 206–356.

26.

Oliveira

, Rigoni

, Andretta

, Moraes

(2004) Validação do Teste Figuras Complexas de Rey na população brasileira [Validation of the Rey complex figures test in the Brazilian population]. Aval Psicol 3, 33–38.

27.

Drucks

, Masterson

(2000) An object and action naming battery, Psychology Press, Philadelphia.

28.

Bishop

(2003) Test for Reception of Grammar version 2 (TROG-2): Manual, Pearson, London.

29.

De Renzi

, Vignolo

(1962) The Token Test: A sensitive test to detect receptive disturbances in aphasics. Brain 85, 665–678.

30.

Weintraub

, Mesulam

, Wieneke

, Rademaker

, Rogalski

, Thompson

(2009) The northwestern anagram test: Measuring sentence production in primary progressive aphasia. Am J Alzheimers Dis Other Demen 24, 408–416.

31.

Duchene May-Carle

(2000) Gestion de l’ Implicite, Ortho Edition, France.

32.

Goodglass

(2005) Evaluación de la Afasia y de Trastornos Relacionados Editorial Médica Panamericana, S. A., Madrid.

33.

Thompson C, Weintraub S (2010) Worksheet for semantic associations test. FTLD Workgroup of the ADC Program (David Knopman, MD, Chair) and the National Alzheimer’s Coordinating Center (Walter A. Kukull, Ph.D., Director), University of Washington.

34.

Petersen

(2004) Mild cognitive impairment as a diagnostic entity. J Intern Med 256, 183–194.

35.

McKhann

, Knopman

, Chertkow

, Hyman

, Jack

Jr , Kawas

, Klunk

, Koroshetz

, Manly

, Mayeux

, Mohs

, Morris

, Rossor

, Scheltens

, Carrillo

, Thies

, Weintraub

, Phelps

(2011) The diagnosis of dementia due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s Dement 7, 263–269.

36.

Boutard

, Brouard

(2003) 300 exercises de compréhension d’ inferences logique et pragmatique et de chaînes causales [300 exercises of understanding logical and pragmatic inferences and causal chains], Ortho Edition, France.

37.

Brunner

, Domhof

, Langer

(2002) Nonparametric Analysis of Longitudinal Data in Factorial Experiments, Wiley, New York.

38.

Schmitter-Edgecombe

, Creamer

(2010) Assessment of strategic processing during narrative comprehension in individuals with mild cognitive impairment. J Int Neuropsychol Soc 16, 661–671.

39.

Gaudreau

, Monetta

, Macoir

, Poulin

, Laforce

Jr , Hudon

(2015) Mental state inferences abilities contribution to verbal irony comprehension in older adults with mild cognitive impairment. Behav Neurol 2015, 685613.

40.

Silagi

, Romero

, de Oliveira

, Trés

, Brucki

SMD

, Radanovic

, Mansur

(2020) Inference comprehension from reading in individuals with mild cognitive impairment. Acta Neurol Belg 121, 879–887.

41.

Maziero

, Belan

AFR

, Camargo

MVZA

, Silagi

, Forlenza

, Radanovic

(2021) Textual inference comprehension in mild cognitive impairment: The influence of semantic processing and verbal episodic memory. Front Aging Neurosci 4, 13.

42.

Johnson-Laird

(1983) Mental models. Harvard University Press, Cambridge.

43.

Sternberg

(2000) Psicologia cognitiva, Artmed, Porto Alegre.

44.

Saldert

, Ahlsén

(2007) Inference in right hemisphere damaged individuals’ comprehension: The role of sustained attention. Clin Linguist Phon 21, 637–55.

45.

Engelhard

, Rozenthal

, Laks

(1996) Neuropsicologia VIII – Atenção: Aspectos neuropsicológicos. Rev Bras Neurol 32, 101–106.

46.

Tadokoro

, Yamashita

, Fukui

, Nomura

, Ohta

, Ueno

, Nishina

, Tsunoda

, Wakutani

, Takao

, Miyoshi

, Higashi

, Osakada

, Sasaki

, Matsumoto

, Kawahara

, Omote

, Takemoto

, Hishikawa

, Morihara

, Abe

(2021) Early detection of cognitive decline in mild cognitive impairment and Alzheimer’s disease with a novel eye tracking test. J Neurol Sci 15, 427.

47.

Spezzano

, Mansur

, Radanovic

(2013) Applicability of the “An Object and Action Naming Battery” in Brazilian Portuguese. Codas 25, 437–443.

48.

Virtue

, Haberman

, Clancy

, Parrish

, Jung Beeman

(2006) Neural activity of inferences during story comprehension. Brain Res 1084, 104–114.

49.

Sundermeier

, Virtue

, Marsolek

, van den Broek

(2005) Evidence for dissociable neural mechanisms underlying inference generation in familiar and less-familiar scenarios. Brain Lang 95, 402–413.

50.

Ribeiro

, Mansur

, Radanovic

(2015) Impairment of inferential abilities based on pictorial stimuli in patients with right-hemisphere damage. Appl Neuropsychol Adult 22, 161–169.

51.

Blake

(2009) Inferencing processes after right hemisphere brain damage: Effects of contextual bias. J Speech Lang Hear Res 52, 373–84.

52.

Myers

, Brookshire

(1996) Effect of visual and inferential variables on scene descriptions by right-hemisphere-damaged and non-brain-damaged adults. J Speech Hear Res 39, 870–880.

53.

Kuperberg

, Lakshaman

, Caplan

, Holcomb

(2006) Making sense of discourse: An fMRI study of causal inference across sentences. Neuroimage 1, 343–361.

54.

Kardan

, Henderson

, Yourganov

, Berman

(2016) Observers’ cognitive states modulate how visual inputs relate to gaze control. J Exp Psychol Hum Percept Perform 42, 1429–1442.

55.

Castelhano

, Pollatsek

, Rayner

(2009) Integration of multiple views of scenes. Atten Percept Psychophys 71, 490–502.

56.

Mills

, Hollingworth

, Van der Stigchel

, Hoffman

, Dodd

(2011) Examining the influence of task set on eye movements and fixations. J Vis 11, 17.

57.

Perry

, Hodges

(2003) Dissociation between top-down attention control and the time course of visual attention as measured by attentional dwell time in patients with mild cognitive impairment. Eur J Neurosci 18, 221–226.

58.

Yang

, Wang

, Su

, Liu

, Xiao

, Kapoula

(2011) Long latency and high variability in accuracy speed of prosaccades in Alzheimer’s disease at mild to moderate stage. Dement Geriatr Cogn Dis Extra 1, 318–329.

59.

Yang

, Wang

, Su

, Xiao

, Kapoula

(2012) Specific saccade deficits in patients with Alzheimer’s disease at mild to the moderate stage and in patients with amnestic mild cognitive impairment. Age 35, 1287–1298.

60.

Heuer

, Mirsky

, Kong

, Dickerson

, Miller

, Kramer

, Boxer

(2013) Antisaccade task reflects cortical involvement in mild cognitive impairment. Neurology 81, 1235–1243.

61.

Alichniewicz

, Brunner

, Klünemann

, Greenlee

(2013) Neural correlates of saccadic inhibition in healthy elderly and patients with amnestic mild cognitive impairment. Front Psychol 4, 467.

62.

Silveira JRC (2005) The image: Interpretation and communication. Linguagem em Discurso. 5 [A imagem: Interpretação ecomunicação]. Available online at: http://www3.unisul.br/paginas/ensino/pos/linguagem/0503/05.htm. Accessed in October, 2022.

63.

Ribeiro

, Freitas

MID

, Radanovic

, Mansur

(2010) The generation of visual inferences in normal elderly: Influence of schooling and visual complexity. Dement Neuropsychol 4, 194–201.

64.

Tales

, Haworth

, Nelson

, Snowden

, Wilcock

(2005) Abnormal visual search in mild cognitive impairment and Alzheimer’s disease. Neurocase 11, 80–84.

65.

Rozzini

, Chilovi

, Conti

, Bertoletti

, Delrio

, Trabucchi

, Padovani

(2007) Conversion of amnestic mild cognitive impairment to dementia of Alzheimer type is independent to memory deterioration. Int J Geriatr Psychiatry 22, 1217–1222.

66.

Johnson

, Storandt

, Morris

, Galvin

(2009) Longitudinal study of the transition from healthy aging to Alzheimer’s disease. Arch Neurol 66, 1254–1259.

67.

Pereira

MLGF

, Villa

, Koh

, Camargo

MVZA

, Belan

AFR

, Radanovic

, Pomplun

, Forlenza

(2020) Saccadic eye movements associated with executive function decline in mild cognitive impairment and Alzheimer’s disease. Alzheimers Dement 16, e040036.

68.

Belan

AFR

, Camargo

MVZA

, Pereira

MLGF

, Assis Filho

, Santos

, Vieira

, Rêgo

, Forlenza

(2019) Monitoring oculomotor behavior during a visual inference language test: Contribution of eye tracking technology. Poster presented at Convencao Nacional dos Departamentos Cientificos da Academia Brasileira de Neurologia (NEURODC), Campinas, Sao Paulo, Brazil.