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Differential effects of intrinsic properties of natural scenes and interference mechanisms on recognition processes in long-term visual memory

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Abstract

Humans display remarkable long-term visual memory (LTVM) processes. Even though images may be intrinsically memorable, the fidelity of their visual representations, and consequently the likelihood of successfully retrieving them, hinges on their similarity when concurrently held in LTVM. In this debate, it is still unclear whether intrinsic features of images (perceptual and semantic) may be mediated by mechanisms of interference generated at encoding, or during retrieval, and how these factors impinge on recognition processes. In the current study, participants (32) studied a stream of 120 natural scenes from 8 semantic categories, which varied in frequencies (4, 8, 16 or 32 exemplars per category) to generate different levels of category interference, in preparation for a recognition test. Then they were asked to indicate which of two images, presented side by side (i.e. two-alternative forced-choice), they remembered. The two images belonged to the same semantic category but varied in their perceptual similarity (similar or dissimilar). Participants also expressed their confidence (sure/not sure) about their recognition response, enabling us to tap into their metacognitive efficacy (meta-d’). Additionally, we extracted the activation of perceptual and semantic features in images (i.e. their informational richness) through deep neural network modelling and examined their impact on recognition processes. Corroborating previous literature, we found that category interference and perceptual similarity negatively impact recognition processes, as well as response times and metacognitive efficacy. Moreover, images semantically rich were less likely remembered, an effect that trumped a positive memorability boost coming from perceptual information. Critically, we did not observe any significant interaction between intrinsic features of images and interference generated either at encoding or during retrieval. All in all, our study calls for a more integrative understanding of the representational dynamics during encoding and recognition enabling us to form, maintain and access visual information.

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Data and code availability

The data and R script to replicate all results presented in this study are deposited in the Open Science Framework (https://osf.io/b3snj/).

Notes

  1. We adopted the terminology, exemplar condition, to be faithful with the study by Konkle, et al. 2010b, where this manipulation was first introduced; and theoretically, this is what we refer to as category interference.

  2. Differently from Huebner and Gegenfurtner (2012), we maintained the same level of conceptual/category similarity (i.e. two kitchen scenes) and only manipulated perceptual similarity.

  3. Note, some studies refer to all DNN features as perceptual regardless the layer they are extracted from (e.g. Hovhannisyan et al. 2021; Heinen et al. 2023). Here instead, we operationally distinguish between early convolutional layers, more closely represent purely low-level perceptual features, and late convolutional layers that are deeper in the network and nearer to the output so reflecting better grouping properties typical of higher semantic features.

  4. Here we use the terms exemplar condition to describe the manipulation and category interference to describe the theoretical mechanism behind the manipulation. Previously, we used the term semantic interference to describe this manipulation (Mikhailova et al. 2021), but in the context of other manipulations (perceptual similarity), we opted to “exemplar condition”, which is in line with the terminology of the original study (Konkle et al. 2010b).

  5. In Appendix 4, we report corroborating results where exemplar condition was modelled as a categorical rather than continuous variable corresponding to the initial experiment manipulation.

  6. We also evaluated the relationship of perceptual and semantic image features with perceptual similarity effect at recognition, which has shown no interaction of perceptual similarity manipulation with perceptual features (z =  − 0.05, p = 0.96) and semantic features (z =  − 1.45, p = 0.15). Considering that adding perceptual similarity factor into the main analysis would lead to uninterpretable 4-way interactions and different memory phase that this manipulation is conducted at, we did not report this findings in the present paper.

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Acknowledgements

We additionally thank Wilma A. Bainbridge's lab at the University of Chicago for providing A.M. with the skills and expertise to execute image feature modelling presented in this work.

Funding

This work was funded by Fundação para a Ciência e Tecnologia with a PhD scholarship to A.M. (SFRH/BD/144453/2019) and a grant awarded to M.I.C. (PTDC/PSI-ESP/30958/2017).

Author information

Authors and Affiliations

  1. Institute for Systems and Robotics, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal

    Anastasiia Mikhailova & José Santos-Victor

  2. School of Psychology, University of East London, London, UK

    Sophie Lightfoot

  3. Sapienza, University of Rome, Rome, Italy

    Moreno I. Coco

  4. I.R.C.C.S. Santa Lucia, Fondazione Santa Lucia, Roma, Italy

    Moreno I. Coco

Authors
  1. Anastasiia Mikhailova
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  2. Sophie Lightfoot
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  3. José Santos-Victor
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  4. Moreno I. Coco
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Correspondence to Anastasiia Mikhailova or Moreno I. Coco.

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The authors have no financial or non-financial interests that are relevant to this article.

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All participants included in the study signed the informed consent before data collection.

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The study was conducted following the 1964 Declaration of Helsinki, the British Psychological Society’s Code of Ethics and Conduct (2018) and the UEL Code of Practice for Research Ethics (2015–16) and approved by the Psychology Ethics Committee of the University of East London proceeding data collection.

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Appendices

Appendix 1

Since the experiment was executed online, there is a stronger necessity for the quality control of the data obtained. Besides enforcing full-screen mode in the experiment to avoid participants switching between the tabs and ensuring that the experimental session duration of all participants falls under the same time frame (31 ± 6.36 min), we also evaluated the participants’ performance as a way to measure their attention to the task. The results of the binomial test of recognition accuracy are reported in the main text under the section Participants since according to its results one participant was excluded from further analysis. Here we additionally report the participants’ performance on the Flanker task. All of the participants performed above chance (overall, there were 98% of correct responses with a maximum rate of 10% incorrect responses by only one of the participants). Based on such results, we did not exclude any of the participants from the analysis.

Appendix 2

The sustained attention task (SAT, Robertson et al. 1997) and Flanker task (Eriksen and Eriksen 1974, implemented after Ridderinkhof et al. 1999) were utilised to fill the consolidation time between the memorisation and recognition phase and assess whether scores at these tasks, which tap into attentional capacity and inhibitory control mechanisms, would speak about effects of perceptual similarity of target and foil images and exemplar conditions on recognition memory.

In SAT trials (N = 10), participants were presented with a stream of 19 letters and were told to press the spacebar every time they saw the letter X (3 instances in each trial). At the end of each trial, they received written feedback about their performance. The average accuracy across participants in this task was 43% (SD = 5%), which did not significantly correlate with the recognition accuracy (r = 0.09, p = 0.64) and the slope of exemplar condition (r =  − 0.03, p = 0.85) at the LTVM task.

In Flanker trials (N = 60), participants were presented with five arrows in a horizontal line either pointing left or right and instructed to indicate the direction of the central arrow using arrow keys. Participants received a practice round of 4 trials and were feedback regarding their overall performance score at the end of the task. For the reaction time (RT) analysis of the Flanker task, we excluded all incorrect trials (about 1 trial per participant out of 60 trials). As expected, incongruent trials lead to significantly slower RT than congruent trials (t = 6.02, p > 0.001). The difference between congruent and incongruent trials RT was positively correlated with exemplar condition slope (r = 0.4, p = 0.02) but did not correlate with memory recognition performance (r = 0.25, p = 0.16).

Appendix 3

In this appendix, we provide a linear regression analysis of d’ as a function of continuous exemplar and perceptual similarity (PS) conditions (Fig. 

Fig. 6
figure 6

Regression plot of the d’ plotted as a function of continuous exemplar condition and perceptual similarity of target and foil images. Each point represents the average across participants and trials for each level of exemplar condition, lines indicate the estimates of a linear model fit to the data and the shaded bands represent the 95% confidence intervals

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6 and Table 3). We corroborate the significant main effects of PS, such that similar target and foil images were associated with a lower d’, and the significant main effect of exemplar condition, such that it decreases for increasing levels of category interference, and no significant interaction between these two factors.

Table 3 Linear model of d prime (a continuous variable) as a function of the perceptual similarity of target and foil images—PS (similar/dissimilar; with dissimilar as the reference level) and exemplar condition (a continuous variable)
Full size table

Appendix 4

We repeated all our analyses for replication purposes but now with exemplar condition expressed as categorical (i.e. 4, 8, 16, 32, with 4 as the reference level) rather than a continuous variable (see the original study by Konkle et al. 2010b). All analyses mostly replicate the patterns reported in the main text (Fig. 

Fig. 7
figure 7

Boxplot of A recognition accuracy and B memory response time for correct trials. Plotted as functions of exemplar condition and perceptual similarity of foil and target images. The boxplot body reflects the data within the first and the third quartile with the median represented as a boxplot’s horizontal bar and the mean represented as a triangle

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7 and Table 4), even though we now do not observe significant differences across all possible exemplar condition contrasts, which reinforces the methodological soundness of our approach to treating category interference as a continuous rather than categorical predictor.

Table 4 On the left: generalised linear mixed model of recognition accuracy (binomial variable; 0 = incorrect, 1 = correct). On the right: the linear mixed model of memory response time (continuous variable). These variables are represented as a function of the perceptual similarity of target and foil images—PS (similar/dissimilar; dissimilar–reference) and exemplar condition (levels 4, 8, 16 and 32; level 4–reference)
Full size table

Appendix 5

See Table 5 for descriptive statistics of recognition accuracy and memory reaction time with meta-d' values across perceptual similarity and exemplar conditions.

Table 5 The descriptive statistics (mean and standard deviation) of recognition accuracy and memory reaction time (RT in seconds) and the values of meta-d’ are presented by the perceptual similarity condition and by exemplar condition
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Mikhailova, A., Lightfoot, S., Santos-Victor, J. et al. Differential effects of intrinsic properties of natural scenes and interference mechanisms on recognition processes in long-term visual memory. Cogn Process 25, 173–187 (2024). https://doi.org/10.1007/s10339-023-01164-y

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