What Do Contrast Threshold Equivalent Noise Studies Actually Measure? Noise vs. Nonlinearity in Different Masking Paradigms
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{"title"=>"What do contrast threshold equivalent noise studies actually measure? Noise vs. nonlinearity in different masking paradigms", "type"=>"journal", "authors"=>[{"first_name"=>"Alex S.", "last_name"=>"Baldwin", "scopus_author_id"=>"55505908200"}, {"first_name"=>"Daniel H.", "last_name"=>"Baker", "scopus_author_id"=>"15057725600"}, {"first_name"=>"Robert F.", "last_name"=>"Hess", "scopus_author_id"=>"7402478826"}], "year"=>2016, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "scopus"=>"2-s2.0-84961233516", "pui"=>"609034799", "doi"=>"10.1371/journal.pone.0150942", "sgr"=>"84961233516", "pmid"=>"26953796"}, "id"=>"52be49c1-b20f-3070-b0c8-a9914462d412", "abstract"=>"The internal noise present in a linear system can be quantified by the equivalent noise method. By measuring the effect that applying external noise to the system's input has on its output one can estimate the variance of this internal noise. By applying this simple \"linear amplifier\" model to the human visual system, one can entirely explain an observer's detection performance by a combination of the internal noise variance and their efficiency relative to an ideal observer. Studies using this method rely on two crucial factors: firstly that the external noise in their stimuli behaves like the visual system's internal noise in the dimension of interest, and secondly that the assumptions underlying their model are correct (e.g. linearity). Here we explore the effects of these two factors while applying the equivalent noise method to investigate the contrast sensitivity function (CSF). We compare the results at 0.5 and 6 c/deg from the equivalent noise method against those we would expect based on pedestal masking data collected from the same observers. We find that the loss of sensitivity with increasing spatial frequency results from changes in the saturation constant of the gain control nonlinearity, and that this only masquerades as a change in internal noise under the equivalent noise method. Part of the effect we find can be attributed to the optical transfer function of the eye. The remainder can be explained by either changes in effective input gain, divisive suppression, or a combination of the two. Given these effects the efficiency of our observers approaches the ideal level. We show the importance of considering these factors in equivalent noise studies.", "link"=>"http://www.mendeley.com/research/contrast-threshold-equivalent-noise-studies-actually-measure-noise-vs-nonlinearity-different-masking", "reader_count"=>21, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>1, "Researcher"=>6, "Student > Ph. D. Student"=>7, "Student > Postgraduate"=>2, "Student > Master"=>2, "Student > Bachelor"=>2, "Professor"=>1}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>1, "Researcher"=>6, "Student > Ph. D. Student"=>7, "Student > Postgraduate"=>2, "Student > Master"=>2, "Student > Bachelor"=>2, "Professor"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>2, "Engineering"=>1, "Agricultural and Biological Sciences"=>4, "Neuroscience"=>4, "Psychology"=>10}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Neuroscience"=>{"Neuroscience"=>4}, "Psychology"=>{"Psychology"=>10}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>4}, "Unspecified"=>{"Unspecified"=>2}}, "reader_count_by_country"=>{"United States"=>1, "United Kingdom"=>1, "France"=>1}, "group_count"=>2}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/4826422"], "description"=>"<p>Standard errors provided are calculated from the bootstrap distributions. Briefly, differences in <i>z</i> indicate changes in contrast gain whereas <i>σ</i> is the standard deviation of the internal noise. The predictions based on these parameters are shown in Figs <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0150942#pone.0150942.g003\" target=\"_blank\">3</a> and <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0150942#pone.0150942.g004\" target=\"_blank\">4</a>. The mean across observers is reported with its standard error.</p>", "links"=>[], "tags"=>["contrast sensitivity function", "equivalent noise method", "frequency results", "CSF", "transfer function", "input gain", "noise variance", "Contrast Threshold Equivalent Noise Studies", "gain control nonlinearity", "Different Masking Paradigms", "equivalent noise studies", "observers approaches", "Noise vs"], "article_id"=>3104896, "categories"=>["Neuroscience", "Biotechnology", "Biological Sciences not elsewhere classified", "Mathematical Sciences not elsewhere classified", "Science Policy"], "users"=>["Alex S. Baldwin", "Daniel H. Baker", "Robert F. Hess"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0150942.t002", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Fitted_model_parameters_in_dB_for_each_observer_with_the_log_likelihoods_of_the_fits_and_the_deviances_relative_to_the_saturated_model_/3104896", "title"=>"Fitted model parameters (in dB) for each observer, with the log-likelihoods of the fits and the deviances relative to the saturated model.", "pos_in_sequence"=>11, "defined_type"=>3, "published_date"=>"2016-03-08 08:25:01"}
  • {"files"=>["https://ndownloader.figshare.com/files/4826269"], "description"=>"<p>These values differ slightly from those in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0150942#pone.0150942.t002\" target=\"_blank\">Table 2</a> as those were obtained by fitting to the empirical data.</p>", "links"=>[], "tags"=>["contrast sensitivity function", "equivalent noise method", "frequency results", "CSF", "transfer function", "input gain", "noise variance", "Contrast Threshold Equivalent Noise Studies", "gain control nonlinearity", "Different Masking Paradigms", "equivalent noise studies", "observers approaches", "Noise vs"], "article_id"=>3104800, "categories"=>["Neuroscience", "Biotechnology", "Biological Sciences not elsewhere classified", "Mathematical Sciences not elsewhere classified", "Science Policy"], "users"=>["Alex S. Baldwin", "Daniel H. Baker", "Robert F. Hess"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0150942.g005", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Median_parameter_estimates_obtained_by_fitting_to_the_bootstrapped_pedestal_masking_data_with_95_confidence_intervals_/3104800", "title"=>"Median parameter estimates obtained by fitting to the bootstrapped pedestal masking data, with 95% confidence intervals.", "pos_in_sequence"=>5, "defined_type"=>1, "published_date"=>"2016-03-08 08:25:01"}
  • {"files"=>["https://ndownloader.figshare.com/files/4826341"], "description"=>"<p>Each data point is labelled with the number of trials tested at that contrast level. The Weibull fit is made just to the data presented in this figure, whereas the CRF fit is made to the data from all mask levels simultaneously.</p>", "links"=>[], "tags"=>["contrast sensitivity function", "equivalent noise method", "frequency results", "CSF", "transfer function", "input gain", "noise variance", "Contrast Threshold Equivalent Noise Studies", "gain control nonlinearity", "Different Masking Paradigms", "equivalent noise studies", "observers approaches", "Noise vs"], "article_id"=>3104839, "categories"=>["Neuroscience", "Biotechnology", "Biological Sciences not elsewhere classified", "Mathematical Sciences not elsewhere classified", "Science Policy"], "users"=>["Alex S. Baldwin", "Daniel H. Baker", "Robert F. Hess"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0150942.g008", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Example_psychometric_function_unmasked_detection_threshold_for_observer_DB_with_binomial_error_bars_showing_the_fits_from_both_the_Weibull_psychometric_function_and_the_CRF_model_/3104839", "title"=>"Example psychometric function (unmasked detection threshold for observer DB with binomial error bars) showing the fits from both the Weibull psychometric function and the CRF model.", "pos_in_sequence"=>8, "defined_type"=>1, "published_date"=>"2016-03-08 08:25:01"}
  • {"files"=>["https://ndownloader.figshare.com/files/4826404"], "description"=>"<p>The mean across observers is reported with its standard error.</p>", "links"=>[], "tags"=>["contrast sensitivity function", "equivalent noise method", "frequency results", "CSF", "transfer function", "input gain", "noise variance", "Contrast Threshold Equivalent Noise Studies", "gain control nonlinearity", "Different Masking Paradigms", "equivalent noise studies", "observers approaches", "Noise vs"], "article_id"=>3104872, "categories"=>["Neuroscience", "Biotechnology", "Biological Sciences not elsewhere classified", "Mathematical Sciences not elsewhere classified", "Science Policy"], "users"=>["Alex S. Baldwin", "Daniel H. Baker", "Robert F. Hess"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0150942.t001", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Magnitudes_of_the_dips_differences_between_the_unmasked_and_lowest_masked_thresholds_and_slopes_of_the_handles_straight_line_fits_to_the_data_where_the_pedestal_level_was_3_dB_above_that_which_gave_the_lowest_threshold_for_the_data_shown_in_Fig_3_/3104872", "title"=>"Magnitudes of the dips (differences between the unmasked and lowest masked thresholds) and slopes of the handles (straight line fits to the data where the pedestal level was >3 dB above that which gave the lowest threshold) for the data shown in Fig 3.", "pos_in_sequence"=>10, "defined_type"=>3, "published_date"=>"2016-03-08 08:25:01"}
  • {"files"=>["https://ndownloader.figshare.com/files/4826218"], "description"=>"<p>Median thresholds obtained from bootstrapping are plotted against pedestal mask contrast, with error bars indicating the 95% confidence intervals. The curves show predictions from <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0150942#pone.0150942.e021\" target=\"_blank\">Eq (11)</a> fitted to the raw data. The parameters for these fits are provided in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0150942#pone.0150942.t002\" target=\"_blank\">Table 2</a>.</p>", "links"=>[], "tags"=>["contrast sensitivity function", "equivalent noise method", "frequency results", "CSF", "transfer function", "input gain", "noise variance", "Contrast Threshold Equivalent Noise Studies", "gain control nonlinearity", "Different Masking Paradigms", "equivalent noise studies", "observers approaches", "Noise vs"], "article_id"=>3104746, "categories"=>["Neuroscience", "Biotechnology", "Biological Sciences not elsewhere classified", "Mathematical Sciences not elsewhere classified", "Science Policy"], "users"=>["Alex S. Baldwin", "Daniel H. Baker", "Robert F. Hess"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0150942.g003", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Pedestal_masking_dipper_functions_for_our_five_observers_/3104746", "title"=>"Pedestal masking (“dipper”) functions for our five observers.", "pos_in_sequence"=>3, "defined_type"=>1, "published_date"=>"2016-03-08 08:25:01"}
  • {"files"=>["https://ndownloader.figshare.com/files/4826242"], "description"=>"<p>The error bars show 95% confidence intervals. As in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0150942#pone.0150942.g003\" target=\"_blank\">Fig 3</a>, the curves show the predictions from <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0150942#pone.0150942.e021\" target=\"_blank\">Eq (11)</a>.</p>", "links"=>[], "tags"=>["contrast sensitivity function", "equivalent noise method", "frequency results", "CSF", "transfer function", "input gain", "noise variance", "Contrast Threshold Equivalent Noise Studies", "gain control nonlinearity", "Different Masking Paradigms", "equivalent noise studies", "observers approaches", "Noise vs"], "article_id"=>3104779, "categories"=>["Neuroscience", "Biotechnology", "Biological Sciences not elsewhere classified", "Mathematical Sciences not elsewhere classified", "Science Policy"], "users"=>["Alex S. Baldwin", "Daniel H. Baker", "Robert F. Hess"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0150942.g004", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Median_psychometric_slopes_obtained_from_bootstrapping_plotted_as_a_function_of_pedestal_mask_contrast_for_each_observer_/3104779", "title"=>"Median psychometric slopes obtained from bootstrapping plotted as a function of pedestal mask contrast for each observer.", "pos_in_sequence"=>4, "defined_type"=>1, "published_date"=>"2016-03-08 08:25:01"}
  • {"files"=>["https://ndownloader.figshare.com/files/4826299"], "description"=>"<p>Median thresholds obtained from bootstrapping are plotted against the standard deviation of the masking noise, with error bars indicating the 95% confidence intervals. The curves show predictions from the LAM and NLM fitted to the median thresholds. The parameters for these fits are provided in Tables <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0150942#pone.0150942.t003\" target=\"_blank\">3</a> and <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0150942#pone.0150942.t004\" target=\"_blank\">4</a>.</p>", "links"=>[], "tags"=>["contrast sensitivity function", "equivalent noise method", "frequency results", "CSF", "transfer function", "input gain", "noise variance", "Contrast Threshold Equivalent Noise Studies", "gain control nonlinearity", "Different Masking Paradigms", "equivalent noise studies", "observers approaches", "Noise vs"], "article_id"=>3104812, "categories"=>["Neuroscience", "Biotechnology", "Biological Sciences not elsewhere classified", "Mathematical Sciences not elsewhere classified", "Science Policy"], "users"=>["Alex S. Baldwin", "Daniel H. Baker", "Robert F. Hess"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0150942.g006", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Noise_masking_functions_for_our_five_observers_/3104812", "title"=>"Noise masking functions for our five observers.", "pos_in_sequence"=>6, "defined_type"=>1, "published_date"=>"2016-03-08 08:25:01"}
  • {"files"=>["https://ndownloader.figshare.com/files/4826473"], "description"=>"<p>Standard errors provided are calculated from the bootstrap distributions. Predictions for these parameters are shown by the solid curves in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0150942#pone.0150942.g006\" target=\"_blank\">Fig 6</a>. The mean across observers is reported with the standard error.</p>", "links"=>[], "tags"=>["contrast sensitivity function", "equivalent noise method", "frequency results", "CSF", "transfer function", "input gain", "noise variance", "Contrast Threshold Equivalent Noise Studies", "gain control nonlinearity", "Different Masking Paradigms", "equivalent noise studies", "observers approaches", "Noise vs"], "article_id"=>3104941, "categories"=>["Neuroscience", "Biotechnology", "Biological Sciences not elsewhere classified", "Mathematical Sciences not elsewhere classified", "Science Policy"], "users"=>["Alex S. Baldwin", "Daniel H. Baker", "Robert F. Hess"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0150942.t004", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Parameters_in_dB_obtained_by_fitting_the_LAM_model_to_simulated_nonlinear_model_NLM_data_that_were_generated_based_on_each_observer_s_dipper_function_/3104941", "title"=>"Parameters (in dB) obtained by fitting the LAM model to simulated nonlinear model (NLM) data, that were generated based on each observer’s dipper function.", "pos_in_sequence"=>13, "defined_type"=>3, "published_date"=>"2016-03-08 08:25:01"}
  • {"files"=>["https://ndownloader.figshare.com/files/4826320"], "description"=>"<p>Parameters from LAM fits to simulated data from the nonlinear model (using the parameter populations presented in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0150942#pone.0150942.g005\" target=\"_blank\">Fig 5</a>) compared against the parameters (medians with 95% confidence intervals) obtained by fitting the bootstrapped empirical thresholds.</p>", "links"=>[], "tags"=>["contrast sensitivity function", "equivalent noise method", "frequency results", "CSF", "transfer function", "input gain", "noise variance", "Contrast Threshold Equivalent Noise Studies", "gain control nonlinearity", "Different Masking Paradigms", "equivalent noise studies", "observers approaches", "Noise vs"], "article_id"=>3104824, "categories"=>["Neuroscience", "Biotechnology", "Biological Sciences not elsewhere classified", "Mathematical Sciences not elsewhere classified", "Science Policy"], "users"=>["Alex S. Baldwin", "Daniel H. Baker", "Robert F. Hess"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0150942.g007", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Parameters_from_LAM_fits_to_simulated_data_from_the_nonlinear_model_using_the_parameter_populations_presented_in_Fig_5_compared_against_the_parameters_medians_with_95_confidence_intervals_obtained_by_fitting_the_bootstrapped_empirical_thresholds_/3104824", "title"=>"Parameters from LAM fits to simulated data from the nonlinear model (using the parameter populations presented in Fig 5) compared against the parameters (medians with 95% confidence intervals) obtained by fitting the bootstrapped empirical thresholds.", "pos_in_sequence"=>7, "defined_type"=>1, "published_date"=>"2016-03-08 08:25:01"}
  • {"files"=>["https://ndownloader.figshare.com/files/4826446"], "description"=>"<p>Standard errors are calculated from the bootstrap distributions. Predictions for these parameters are shown by the dashed curves in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0150942#pone.0150942.g006\" target=\"_blank\">Fig 6</a>. The mean across observers is reported with the standard error.</p>", "links"=>[], "tags"=>["contrast sensitivity function", "equivalent noise method", "frequency results", "CSF", "transfer function", "input gain", "noise variance", "Contrast Threshold Equivalent Noise Studies", "gain control nonlinearity", "Different Masking Paradigms", "equivalent noise studies", "observers approaches", "Noise vs"], "article_id"=>3104914, "categories"=>["Neuroscience", "Biotechnology", "Biological Sciences not elsewhere classified", "Mathematical Sciences not elsewhere classified", "Science Policy"], "users"=>["Alex S. Baldwin", "Daniel H. Baker", "Robert F. Hess"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0150942.t003", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Parameters_obtained_by_fitting_the_LAM_model_to_the_data_for_each_observer_with_the_RMS_error_of_the_fit_/3104914", "title"=>"Parameters obtained by fitting the LAM model to the data for each observer, with the RMS error of the fit.", "pos_in_sequence"=>12, "defined_type"=>3, "published_date"=>"2016-03-08 08:25:01"}
  • {"files"=>["https://ndownloader.figshare.com/files/4826368"], "description"=>"<p>We obtained by simulating the detection of various levels of signal (-42 to 36 dB in 3 dB steps) by independently noisy channels with different standard deviations (6 to 18 dB in 3 dB steps). We simulated 5,000 trials per combination of signal and noise level, both for a system where the outputs were summed (ideal) before comparison between the two intervals and for a system where the max() was taken. The data from this simulation were fit by a psychometric function (see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0150942#sec006\" target=\"_blank\">Methods</a>), and then the average efficiency of the max() observer was calculated relative to the ideal.</p>", "links"=>[], "tags"=>["contrast sensitivity function", "equivalent noise method", "frequency results", "CSF", "transfer function", "input gain", "noise variance", "Contrast Threshold Equivalent Noise Studies", "gain control nonlinearity", "Different Masking Paradigms", "equivalent noise studies", "observers approaches", "Noise vs"], "article_id"=>3104854, "categories"=>["Neuroscience", "Biotechnology", "Biological Sciences not elsewhere classified", "Mathematical Sciences not elsewhere classified", "Science Policy"], "users"=>["Alex S. Baldwin", "Daniel H. Baker", "Robert F. Hess"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0150942.g009", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Efficiency_of_an_observer_who_combines_signals_over_multiple_channels_by_picking_the_maximum_expressed_relative_to_that_of_a_linear_ideal_observer_/3104854", "title"=>"Efficiency of an observer who combines signals over multiple channels by picking the maximum, expressed relative to that of a linear ideal observer.", "pos_in_sequence"=>9, "defined_type"=>1, "published_date"=>"2016-03-08 08:25:01"}
  • {"files"=>["https://ndownloader.figshare.com/files/4826167"], "description"=>"<p>The stimulus is shown within the fixation ring used to reduce uncertainty.</p>", "links"=>[], "tags"=>["contrast sensitivity function", "equivalent noise method", "frequency results", "CSF", "transfer function", "input gain", "noise variance", "Contrast Threshold Equivalent Noise Studies", "gain control nonlinearity", "Different Masking Paradigms", "equivalent noise studies", "observers approaches", "Noise vs"], "article_id"=>3104722, "categories"=>["Neuroscience", "Biotechnology", "Biological Sciences not elsewhere classified", "Mathematical Sciences not elsewhere classified", "Science Policy"], "users"=>["Alex S. Baldwin", "Daniel H. Baker", "Robert F. Hess"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0150942.g002", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/An_example_of_the_log_Gabor_stimuli_used_in_these_experiments_/3104722", "title"=>"An example of the log-Gabor stimuli used in these experiments.", "pos_in_sequence"=>2, "defined_type"=>1, "published_date"=>"2016-03-08 08:25:01"}
  • {"files"=>["https://ndownloader.figshare.com/files/4826140"], "description"=>"<p>The pathway for a single mechanism in a single interval is shown in full. Other mechanisms and intervals are implied by the dashed arrows. The tuned response to the target (<i>c</i>) and any external noise falling within the mechanism’s passband (<i>N</i><sub>ext</sub>) undergoes a nonlinear transformation (the mutual suppression pathways between the mechanisms in this stage have been omitted for clarity). Each mechanism is then affected by internal noise (<i>N</i><sub>int</sub>) and then some integration over their outputs is performed (with its behaviour resulting in a characteristic efficiency <i>η</i>). The observer then makes a decision based on which of the two intervals has the greater response.</p>", "links"=>[], "tags"=>["contrast sensitivity function", "equivalent noise method", "frequency results", "CSF", "transfer function", "input gain", "noise variance", "Contrast Threshold Equivalent Noise Studies", "gain control nonlinearity", "Different Masking Paradigms", "equivalent noise studies", "observers approaches", "Noise vs"], "article_id"=>3104698, "categories"=>["Neuroscience", "Biotechnology", "Biological Sciences not elsewhere classified", "Mathematical Sciences not elsewhere classified", "Science Policy"], "users"=>["Alex S. Baldwin", "Daniel H. Baker", "Robert F. Hess"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0150942.g001", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Diagram_of_an_example_model_showing_how_contrast_may_be_processed_by_the_visual_system_/3104698", "title"=>"Diagram of an example model showing how contrast may be processed by the visual system.", "pos_in_sequence"=>1, "defined_type"=>1, "published_date"=>"2016-03-08 08:25:01"}

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{"start_date"=>"2016-01-01T00:00:00Z", "end_date"=>"2016-12-31T00:00:00Z", "subject_areas"=>[]}
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