{
  "abstract": "When searching for target objects among distractors, observers exploit information contained in the attentional template, a memory representation of target features that supports both search guidance and verification for target selection or distractor rejection. The good-enough framework proposes that, because guidance and verification serve distinct computational goals, the precision of the template varies within a given feature dimension: guidance relies on a coarser, relational code that optimizes the reduction of the search space to candidate target locations, whereas verification relies on a more precise, optimal code for target-identity decisions. However, in searches involving real-world, multidimensional stimuli, it remains unclear whether the representation in the template also varies along the depth of the visual hierarchy, from low-level visual features to more abstract information related to object identity. Here, we used a deep convolutional neural network (DCNN) to estimate target–distractor similarity at different levels of the visual hierarchy. We then manipulated this similarity in a target-present versus target-absent two-alternative forcedchoice (2AFC) task while recording eye movements. Guidance, operationalized as which of the two stimuli was fixated first and modeled as a function of their relative similarity to the target, was predicted primarily by low-level similarity, with a smaller contribution of high-level similarity. Verification, operationalized as dwell time on each distractor and modeled as a function of its similarity to the target, was also predicted by both low and high-level similarity, but showed a clear shift toward high-level similarity relative to guidance. Critically, the relative weight of high-level similarity nearly doubled from guidance to verification. Together, our findings reveal a shift in representational depth up the visual hierarchy. Beyond template precision, representational depth therefore constitutes a further axis along which template information is selectively exploited according to the computational demands of different epochs of visual search.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "Andrea Massironi"
    },
    {
      "name": "Emanuela Bricolo"
    },
    {
      "name": "Marco Alessandro Petilli"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/47c1d90s0taj5.bib",
  "cited_by": 0,
  "doi": "10.31234/osf.io/9ye3r_v1",
  "fulltext_url": null,
  "id": "47c1d90s0taj5",
  "issns": [],
  "landing_url": "https://osf.io/preprints/psyarxiv/9ye3r_v1/",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "bepress",
      "Attention"
    ],
    [
      "bepress",
      "Behavioral Neuroscience"
    ],
    [
      "bepress",
      "Cognitive Neuroscience"
    ],
    [
      "bepress",
      "Cognitive Psychology"
    ],
    [
      "bepress",
      "Computational Modeling"
    ],
    [
      "bepress",
      "Computational Neuroscience"
    ],
    [
      "bepress",
      "Memory"
    ],
    [
      "bepress",
      "Neuroscience"
    ],
    [
      "bepress",
      "Perception"
    ],
    [
      "bepress",
      "Quantitative Methods"
    ],
    [
      "bepress",
      "Social and Behavioral Sciences"
    ],
    [
      "bepress",
      "Vision"
    ],
    [
      "openalex",
      "2805|Visual perception and processing mechanisms"
    ]
  ],
  "openalex": "W7221142173",
  "pdf_url": "https://osf.io/9ye3r/download",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-09",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/47c1d90s0taj5.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:18Z",
      "native_id": "9ye3r",
      "source": "osf",
      "url": "https://osf.io/preprints/psyarxiv/9ye3r_v1/"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "bepress:Cognitive Neuroscience",
      "subfield": "neuroscience"
    }
  ],
  "title": "Visual search shifts weight up the visual hierarchy from attentional guidance to verification",
  "title_text": "Visual search shifts weight up the visual hierarchy from attentional guidance to verification",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:18Z",
  "url": "https://research.draconitic.com/paper/47c1d90s0taj5",
  "venue": "PsyArXiv",
  "versions": []
}